Matthieu Bernard
email: matthieubernard@outlook.com

Marcela Ovando-Tellez

Mathias Benedek
email: mathias.benedek@uni-graz.at

Emmanuelle Volle
email: emmavolle@gmail.com

Yoed N Kenett
email: yoedk@sas.upenn.edu

Sarah Bouanane

Joan Belo

Theophile Bieth

TABLE OF CONTENTS ………………………………………………………………….. Chapter 1: Theoretical background ………………………………………………………

Keywords: creativity, semantic networks, network science, associative thinking creativity, semantic network, brain networks, functional connectivity, cognition creativity, verbal fluency, polysemy, functional connectivity, semantic networks Abstract thinking, Categorization, Semantic Priming, EEG, N400, Semantic Memory Creativity, divergent thinking, associative thinking, cognitive control, prefrontal cortex, default mode network, frontoparietal control network, lesion study, disconnection, noninvasive brain stimulation, brain connectivity, neurology

The associative theory of creativity suggests that creative abilities rely on the organization of semantic associations in memory. Recent research has demonstrated that semantic network methods allow testing this hypothesis. The aim of the current study was to investigate the properties of semantic networks at the individual level, in relation to creative abilities. Semantic judgement ratings were used to estimate individual semantic networks, whose topological properties measured by several graph metrics were correlated with individual creativity scores.

We found a correlation between the theoretical semantic distance of our stimuli and the relatedness ratings given by the participants, demonstrating the validity of our approach.

Importantly, we found a close relationship between creative abilities assessed by an achievement questionnaire and divergent thinking tasks and individual semantic network metrics, replicating and extending previous similar findings.

First, I would like to thank Emmanuelle for having trusted me, for receiving me without hesitation in my first approach to the cognitive neuroscience field. Thank you for your guidance, unconditional support, and all the knowledge and inspiration you have given me.

Thanks to the jury members: Nathalie George, Mathieu Cassotti, Guillaume Dumas and Evangelia Chrysikou for accepting to review my work. Thanks to Evangelia Chrysikou and Mathieu Cassotti for reading my manuscript and for your valuable feedback. Special thanks to Mathieu Cassotti and Guillaume Dumas for accepting to be part of my thesis committee. Thank you for the fruitful discussions and your attentive feedback during these years.

I would like to thank Mathias Benedek and Yoed Kenett for all the support during my PhD. Thanks for the fascinating discussion about neuroscience, for your attentive feedback and for sharing with me your knowledge and inspiring me to my future work.

Thanks to the Lab members, Lara, Toni, Emmanuel, Bénédicte, and Richard for all the academic and personal support during these years.

Thanks to Beatrice for welcoming me to work with you and for guiding me. Thank you for helping me and taking care of my adaptation to French life and culture in my arrival.

Thanks to Michel Thiebaut for all your academic and personal support during my first years of PhD. Thank you for your kindness and for always being available.

Thanks to Mario Chavez, Daniel Margulies and Fabrizio DeVico for all your advice and valuable comments during my work. Thanks for your support and willingness to help.

I would like to thank to my initial PhD's team: Chris, Chloe and Clara. Thanks to all of you for the moments we shared. Chris, thanks for all the coffee breaks we had with insightful discussions about science and life. Thanks to Clara for your kindness and generosity. Thanks to Chloe for your support, for our conversations and for being there always available for helping. I hope all of us can meet again in some point of our lives.

Thanks to the CreaTeam, Alizée, Theo, Sara, Stella, Vincent, Victor. Thank you for all the support during my academic work, thank you for the interesting discussions and brainstorming sessions we had during these years. All of you are very inspiring people. Special thanks to Emmanuelle, Alizée, Theo and Stella for being an important support when the Covid-19 took us by surprise and changed our lives, thank you for the moments we shared to keep morale high. Special thanks to my PhD partner Theo, for all the valuable discussions, the exchange of knowledge and all the support during these years.

Thanks to all the students who passed through the lab throughout the years, to Arabella, Elsa, Martina, Valerie, Vincent, Yoan, Delphine, Patrick, Angelina, Anna. Thanks for the support and all the moments we shared. Thanks for all conversations and to make our work life happier. Thanks Elsa and Arabella for being the ones in charge of the social life in the FrontLab! Special thanks to the students that worked with me during the development of this project.

Thanks to Joan, Mathieu and Sarah. Thanks for all your valuable work, without you this project would not have been possible.

Thanks to the people of the different platforms at the ICM. Thanks to Benoit, Romain, Stephanie, Melanie, Frédéric and Karim. Thank you for your collaboration and support.

Thanks to Becas Chile for providing the funding which enabled me to do my PhD.

I would like to thank to my parents, for believing in me and for being a fundamental support in this process. Thank you for being so close despite the physical distance, for supporting me all the time and encouraging me every day.

Thanks to my sister for your constantly caring about me, for being my strong support and guide and confidant in life, thank you for always being there for me. Thanks to Christian for always being present and for your humor, which makes us always smile. Thank you both for making me happy with Elenita's arrival.

Thanks to my family, my grandfather, uncle, aunt, cousins, and nieces and nephew for giving me so much love. Thank you for your support and for always being present despite the distance. Thank you, grandmother, for always being present in my memory and life.

To my life friends, who have always been there to encourage me, for a talk at any time, in the good and bad moments, for the constant support and love at every moment. I cannot name all of you because I am so fortunate of having many of you. Thank you for being an important part of my life.

Thanks to the Latin family I met in France. You have been my company and joy during these years. All of you have become an essential part of my life. Thanks to Javiera, Pato and Lautarin, Clau and Seba, Estefi and Seba, Naty, Cata, Diego and Juli. ¡Los quiero! Thanks to Jorge and Simón for being my little 'Giffoise' family. Thank you for being by my side every day and for giving me so much love. I hope this is just the beginning of a great and long adventure together in our lives. By being with you, every difficulty becomes more bearable. Je vous aime! ¡Gracias Totales!

SUMMARY

Creativity is a high-level cognitive function at the basis of various domains of human activity. However, this human capacity, while essential to face the challenges of our society as well as our daily lives, is still poorly understood. Previous research indicates that individual differences in semantic memory structure and processes contribute to individual's creative abilities. In this thesis, we aim to better understand the relationships between semantic memory and creativity and the underlying brain correlates.

In the first part of this thesis, we performed two studies to explore how the properties of semantic memory and brain networks relate to creative behavior. First, in a behavioral study, we investigated the relationships between semantic memory organization and creative thinking.

By means of network-based methods, we built individual semantic networks as a proxy of the semantic memory structure and explored their properties in relation to creative abilities. We found that individual differences in semantic network properties correlated to divergent thinking and to creative behavior in real life. Then, in a second study, we replicated these findings and explored the brain functional connectivity underlying the semantic network properties predicting real-life creativity. We found that unique brain functional connectivity patterns underlying the modularity of individual semantic networks predicted individual differences in real-life creative behavior. In the second part, we examined the cognitive processes of semantic memory search that allow higher creative abilities and explored their brain correlates. We found two components that reflected 1) attentional focus allowing persistent search behavior and 2) the flexibility in memory. The first component related to divergent thinking while the second one related to convergent creative thinking. Finally, in the last part, by means of the semantic priming approach, we explored the different types of relationships that participate in the organization of semantic memory. We specifically investigated whether the implicit retrieval of a thematic and taxonomic category is facilitated by two words belonging to this category more than by a single exemplar. We found larger and additive priming in the double priming compared to the single priming condition at the behavioral and electrophysiological level. Our findings improved the actual knowledge on the organization of semantic memory into categories and how related concepts implicitly associate to each other in memory.

Altogether, these findings shed light on several neurocognitive mechanisms related to semantic memory structure and processes involved in creativity. This work may also provide new tools that could be useful in future research on creativity.

RÉSUMÉ

La créativité fait partie des fonctions cognitives de haut niveau à la base de nombreux domaines de l'activité humaine. Malgré son importance au sein de notre société et de notre vie quotidienne, la capacité créative n'est encore pas bien comprise. Des études dans le domaine indiquent que l'organisation de la mémoire sémantique et ses processus sous-tendrait la créativité. L'objectif de cette thèse est de mieux comprendre les relations entre mémoire sémantique et créativité, et identifier les corrélats cérébraux sous-jacents.

La première partie de cette thèse inclut deux études qui ont pour but explorer la créativité dépend des propriétés de la mémoire sémantique et des réseaux cérébraux qui les soutiennent.

Dans la première étude cognitive, nous avons exploré les relations entre l'organisation de la mémoire sémantique et la créativité. Á l'aide de méthodes de graphes, nous avons construit des réseaux sémantiques individuels pour explorer la structure de la mémoire sémantique et leurs propriétés, et les avons mis en relation avec les capacités créatives. Nos résultats démontrent que les différences individuelles dans les propriétés des réseaux sémantiques sont corrélées à la pensée divergente et au comportement créatif dans la vie réelle. Dans la deuxième étude, nous avons reproduit ces résultats et nous avons en outre exploré la connectivité fonctionnelle du cerveau supportant les propriétés des réseaux sémantiques qui prédisent la créativité dans la vie réelle. Nous avons découvert que des profils individuels de connectivité fonctionnelle cérébrale sous-jacents à la modularité des réseaux sémantiques individuels prédisent les différences individuelles dans le comportement créatif dans la vie réelle. Dans la deuxième partie de cette thèse, nous avons examiné les processus cognitifs de recherche en mémoire sémantique qui sont associés à la capacité créative et en avons exploré les corrélats cérébraux. Nous avons mis en évidence deux composantes qui dépendent de patterns de connectivité cérébrale distincts et qui reflètent 1) un comportement de recherche persistant requérant des processus de contrôle attentionnel et 2) une exploration flexible qui repose sur l'interaction entre processus contrôlés et associatifs. La première composante corrèle à la pensée divergente et alors que la seconde est liée à la pensée créative convergente. Finalement, dans la dernière partie de la thèse, nous avons utilisé l'approche d'amorçage sémantique pour explorer les différents types de relations qui participent à l'organisation de la mémoire sémantique. Nous avons spécifiquement examiné si l'activation implicite d'une catégorie thématique et taxonomique est facilitée par la présentation de deux exemplaires de cette catégorie plus que par un seul exemplaire. Nous avons trouvé un amorçage plus important et additif dans la condition de double amorçage par rapport à la condition d'amorçage simple au niveau comportemental et électrophysiologique.

Nos résultats soutiennent l'hypothèse d'une organisation de la mémoire sémantique en catégories à la fois thématiques et taxonomiques.

Pour conclure, ces résultats mettent en évidence des mécanismes neurocognitifs liés à la structure de la mémoire sémantique et aux processus impliqués dans la créativité. Ce travail propose de nouveaux outils qui pourraient être utiles dans de futures recherches sur la créativité ou la mémoire sémantique.

Chapter 1: Theoretical background

Introduction

We are all creative people but to a different extent. Creative thinking allows people to solve problems, innovate, and adapt to new situations. During the past year, all we had to face a novel situation related to the COVID-19 pandemic that led to many countries to impose a lockdown as a measure to control the spreading of the coronavirus SARS-CoV-2. This event impacted our everyday lives, forcing us to adapt our way of life and to find new solutions to the problems linked to either the disease or the lockdown's consequences. Coping with such a new situation stimulated creativity as people reported being more creative during the lockdown than before and engaging in more creative activities. But what makes us more or less creative?

Neurocognitive research is progressing in better understand the various cognitive capacities that support creative thinking. Individual differences in semantic memory organization and the processes over it may contribute to the individual's creative abilities. However, other cognitive processes and factors may also be involved. Understanding the relationship between semantic memory organization, its underlying processes, and its neural correlates in relation to creativity has become one of the greatest challenges in the neuroscience of creativity. In this manuscript, I will attempt to contribute to the knowledge on this topic.

Creativity

The development of high-level cognitive functions supporting goal-directed behaviors makes humans unique. Among these functions, creative thinking is essential to face the challenges of our society and our daily lives. The human capacity to solve new or unusual problems and find creative solutions is critical to cope with changes, innovate, and adapt to new situations. Consequently, creativity is at the basis of various domains of human activity, including arts, sciences, education, or economy. Creative behavior involves a wide range of aspects including everyday problem solving and the most significant discoveries of humanity in response to unsolved questions.

Creative thinking is of undeniable societal and individual interest since it is essential for the blossoming and progress of individuals and societies. It is also associated with individual wellbeing and the ability to cope with negative emotions (Karwowski et al., 2021;[START_REF] Lopez-Persem | Through thick and thin: changes in creativity during the first lockdown of the Covid-19 pandemic[END_REF]Mastria et al., 2018;Orkibi, 2021;Wu et al., 2019). The study of creative thinking is also important in neurodegenerative and neuropsychiatric pathologies. Creativity or reasoning tasks seem to be more sensitive to brain diseases than conventional neuropsychological tests [START_REF] Bieth | Contribution des études lésionnelles aux neurosciences de la créativité[END_REF][START_REF] Hart | Divergent Thinking in Alzheimer's and Frontotemporal Dementia[END_REF][START_REF] Ovando-Tellez | The contribution of the lesion approach to the neuroscience of creative cognition[END_REF][START_REF] Urbanski | Reasoning by analogy requires the left frontal pole: lesiondeficit mapping and clinical implications[END_REF]. Thus, the assessment of these higher functions could be useful in clinical practice. Overall, the critical role of creativity for societal, cultural, and economic progress, but also individual wellbeing urges us to better understand creative abilities, its complex components, and its neural substrates.

Definition

Creativity is a high-level cognitive function. In neuroscience, creativity is defined as the ability to generate something that is both new and appropriate to a given circumstance (Acar and Runco, 2019). In this context, something new refers to something (idea, object, artwork, service etc.) characterized by being original and uncommon. Creative thinking is involved in a wide range of aspects in human lives, allowing people to find original solutions to problems, to achieve something new, to adapt to new situations. Creativity is also critical for wellbeing, since more creative people seem to be happier and more satisfied with their lives [START_REF] Conner | Everyday creative activity as a path to flourishing[END_REF][START_REF] Shen | The mental welfare effect of creativity: how does creativity make people happy?[END_REF]. Thus, creativity includes coping with everyday situations when we encounter a new problem and enormous achievements in life.

To distinguish between the different levels of individual creativity, Kaufman andBeghetto (2009, 2007) proposed the Four C theory. Depending on the level of creative achievement, the authors described mini-C, Little-C, Pro-C, and Big-C creativity. In this theory, creativity is considered as a progressive trajectory that begins from mini-C creativity [START_REF] Beghetto | Toward a broader conception of creativity: A case for "mini-c" creativity[END_REF], which refers to creative ideas that are new and meaningful at the personal level. Then, Little-C creativity relates to everyday creative behaviors. It refers to the novel ideas or objects that other people recognize as creative. Next, Pro-C creativity is reached when people have a professional level after making progress and developing the creative abilities in certain activities. This level of creativity leads to the public acknowledgment of the products or ideas in the field. Finally, the Big-C creativity is related to eminent thinkers. It relates to the greatest inventions of humanity receiving global acknowledgment and whose legacy remains for decades. Thus, creativity can progress from the early to the later stages; however, creative thinking depends not only on the persistence and development of our abilities in a particular domain but also on multiple factors.

Factors influencing individual differences in creativity

Creativity is a multidimensional construct (Lubart et al., 2015;[START_REF] Lubart | An investment approach to creativity: Theory and data[END_REF] that depends on cognitive determinants, and various internal and external factors influence it. According to the multivariate approach to creativity, different types of resources are required for creativity, in relation to cognition, conation, emotion, and the environmental context. Thus, among others, personality, motivation, affective states, and environment have been shown to play an important role in creativity (Lubart et al., 2013).

Previous research has explored the different personality traits in relation to creative abilities. Among the five personality traits described in the Big Five Model personality theory [START_REF] Mccrae | The five-factor theory of personality[END_REF], openness to experience has been the most consistently related to creativity [START_REF] Feist | A Meta-Analysis of Personality in Scientific and Artistic Creativity[END_REF]Jauk et al., 2014;Vartanian et al., 2018). This personality trait refers to people with a higher disposition to explore, being curious, imaginative, and enjoying novel experiences. Recent research has also associated the other four personality traits. -neuroticism, extraversion, agreeableness, and conscientiousness -with creativity [START_REF] Clark | Creativity and the aspects of neuroticism[END_REF][START_REF] Feist | The Function of Personality in Creativity: Updates on the Creative Personality[END_REF]Furnham et al., 2008;[START_REF] Furnham | Personality and intelligence as predictors of creativity[END_REF][START_REF] Karwowski | Big Five Personality Traits as the Predictors of Creative Self-Efficacy and Creative Personal Identity: Does Gender Matter?[END_REF], but less consistently. Moreover, the openness trait has been consistently shown as the best predictor of creativity compared to the other personality traits (Karwowski and Lebuda, 2016;[START_REF] Silvia | Openness to experience, plasticity, and creativity: Exploring lower-order, high-order, and interactive effects[END_REF].

Intrinsic motivation is another factor contributing to individual differences in creativity.

The intrinsic motives for everyday creative activities have been related to high openness to experience (Benedek et al., 2019). Moreover, intrinsic motivation has been shown to mediate the relationship between the openness to experience trait and creativity [START_REF] Prabhu | Creativity and certain personality traits: Understanding the mediating effect of intrinsic motivation[END_REF].

Internal motivation plays a critical role in the involvement and persistence of people in different activities (Amabile, 2018(Amabile, , 1983;;Amabile and Pillemer, 2012;Benedek et al., 2019;Fischer et al., 2019;Mastria et al., 2018). Additionally, highest cognitive flexibility has been shown in conditions of strong internal motivation [START_REF] Amabile | The Work Preference Inventory: Assessing intrinsic and extrinsic motivational orientations[END_REF]. Both persistence and cognitive flexibility, are important component in creative thinking (Nijstad et al., 2010;[START_REF] Taylor | Dual pathways in creative writing processes[END_REF].

Contrary to extrinsic motivations, intrinsically motivated behaviors are rewarding in themselves [START_REF] Amabile | Motivating Creativity in Organizations: On Doing What You Love and Loving What You Do[END_REF][START_REF] Rhoades | Perceived organizational support: A review of the literature[END_REF]. Creative activities are predominantly motivated by intrinsic aims, such as enjoyment and developing potential in some fields (Benedek et al., 2019). Internal motivation is also important for solving problems. When people are intrinsically motivated, the attention and cognitive functioning increase, leading to better problem solving on goal-directed tasks [START_REF] Mcgraw | Evidence of a detrimental effect of extrinsic incentives on breaking a mental set[END_REF].

Other studies exploring the emotional and motivational influences on creativity has focused on the effect of affective states, including mood and emotions in different aspects of creative performance (Baas et al., 2008;Davis, 2009;De Dreu et al., 2008). Commonly, a positive mood refers to happiness, relaxedness, and relieved mood, while a negative mood is related to sadness, anger, and anxiety and stress. Most of studies exploring the mood-creativity relationship report that positive moods facilitate creativity (Baas et al., 2008). Moreover, people feel that creative ideas are generated easier when they are happy [START_REF] Baas | Conceiving creativity: The nature and consequences of laypeople's beliefs about the realization of creativity[END_REF]. However, incongruent results have been shown in relation to negative moods. Either a negative effect, no effect, or a positive effect on creative abilities have been reported [START_REF] Ashby | A neuropsychological theory of positive affect and its influence on cognition[END_REF][START_REF] Kaufmann | Expanding the Mood-Creativity Equation[END_REF]. Overall, it is concluded that the positive and negative moods influence creativity; however, whether the mood states facilitate or inhibit creativity has no consensus and may depend on contextual factors.

Besides internal factors, creative behavior is also influenced by external factors, such as the environment in which an individual lives or works. Our everyday environment considers physical contexts [START_REF] Dul | The Physical Environment and Creativity: A Theoretical Framework[END_REF] related to living conditions, professional and cultural environments, social contexts (Amabile and Pillemer, 2012;Benedek et al., 2019), and the needs of the society (Lubart et al., 2015). Social environments can also influence intrinsic motivations and thus, potentiate, or hamper creativity [START_REF] Amabile | Motivating Creativity in Organizations: On Doing What You Love and Loving What You Do[END_REF]Amabile et al., 1996). Thus, internal and external factors contribute to the complex behavior that is creativity. Several of these factors rely on intrinsic characteristics of the individual. However, there are also contextual factors, that can potentiate or hamper creative abilities or expressions.

The current and unprecedent situation related to the COVID-19 pandemic was a unique opportunity to explore how these factors influence creative abilities. The pandemic and the lockdown consequences placed us in a new context, changing our way of life, work time and conditions, and habits. This situation led the possibility to explore creativity in a more ecological framework allowing a better understanding of the conditions that encourage or hinder creativity (Karwowski et al., 2021;[START_REF] Lopez-Persem | Through thick and thin: changes in creativity during the first lockdown of the Covid-19 pandemic[END_REF]Mercier et al., 2020;[START_REF] Michinov | Stay at home! When personality profiles influence mental health and creativity during the COVID-19 lockdown[END_REF]Orkibi, 2021;Tang et al., 2020). For instance, our team developed a survey [START_REF] Lopez-Persem | Through thick and thin: changes in creativity during the first lockdown of the Covid-19 pandemic[END_REF]; ANNEX-1) to explore how creativity was impacted by various factors, such as affective states and free time during the COVID-19 pandemic and lockdown context. Specifically, we explored whether wellbeing factors related to affective experience, living conditions, social interactions, as well as workload or available free time impacted creativity during the lockdown. The results emerged from this study show that despite the negative subjective experience of the COVID-19 and lockdown situation, creativity was enhanced on average in the participants of the survey, and they engaged in more creative activities during the lockdown period. This enhancement was correlated with an improvement in emotional state related to wellbeing and increase in free time. Conversely, when negative changes in self-creativity or creative activities were reported, they were mainly related to affective factors. Other studies performed during the COVID-19 pandemic also reported an increase in everyday creativity during the lockdown (Karwowski et al., 2021;Mercier et al., 2020), and support the importance of multiple factors influencing creativity (Lubart et al., 2015).

Overall, the complex behavior of creativity depends on internal and external factors.

Emotional, contextual, and environmental factors have a critical impact on creativity, which can favor or constrain creative abilities and expressions. One critical question in creativity research is understanding how creative thinking can be achieved; what are the cognitive mechanisms behind the scene? Which are the mechanisms on which these factors act? And importantly, how these cognitive determinants are rooted in the brain?

Creativity process

Creativity, while valuable for our wellbeing and to face the challenges of our society, education, as well as our daily lives, is still poorly understood. Research on creativity is at the early stages. Although creativity is influenced by multiple internal and external factors, the cognitive determinants of creativity and its related processes play a critical role. Yet, various models of creativity have emerged [START_REF] Beaty | The roles of associative and executive processes in creative cognition[END_REF]Silvia, 2013, 2012;Guilford, 1967;[START_REF] Jung-Beeman | Neural Activity When People Solve Verbal Problems with Insight[END_REF]Mednick, 1962;Volle, 2018), aiming to explain the processes that allows people to come up with creative ideas. One of the first models of the creative process was proposed by [START_REF] Wallas | The art of thought, The art of thought[END_REF]. The four-step model of creativity identifies and describes the different processes involved in creativity. The first step is preparation, in which people begin gathering knowledge about a problem. This process involves internal and external processes, where people search in their knowledge and collect the necessary data and material. Next, the incubation step, which can vary from short to long periods. During this step, people are involved in other tasks, but the mind continues thinking about the problem, allowing associations of ideas without a conscious effort or focus on the problem. Incubation processes can vary on time and this step has been shown to be critical to producing creative ideas [START_REF] Sio | Does Incubation Enhance Problem Solving? A Meta-Analytic Review[END_REF]. Then, in the illumination step, people have an 'aha' or Eureka moment in which one idea suddenly comes to mind. And finally, the verification process is the moment of evaluation of the selected idea through its implementation. Besides this proposal of phase model of creativity, the interest in the cognitive processes underlying creative ability led to the development of different frameworks exploring the cognitive mechanisms of creativity.

Approaches of creativity in neuroscience

In cognitive neuroscience, three main theory-based approaches have been developed to explore creative abilities and the underlying cognitive and brain mechanisms: divergent thinking (DT), associative combination, and insight problem-solving. Building on these approaches, several tasks have been developed to measure creative abilities by testing the different cognitive models. In addition to these framework-based approaches, other selfreported measures have been validated in order to assess creative behavior, without exploring the underlying mechanisms of creativity, but quantifying creative activities and achievements in life.

Theory-based approaches of creativity 1.4.1.1 Divergent thinking approach

The DT approach is the most widely used in neuroscience to measure creativity [START_REF] Runco | Divergent Thinking as an Indicator of Creative Potential[END_REF]. The notion of DT was stablished by (Guilford, 1967[START_REF] Guilford | Creativity[END_REF] who proposed a theory-based approach of creativity to describe the process by which ideas are produced. In his structure of intellect model, two types of creative thinking processes were differentiated: DT and convergent thinking. DT is the ability to generate several ideas or solutions to an open question or problem; that is the starting point of these ideas' generation. Conversely, convergent thinking involves exploring different ideas to select the pertinent one, or to find the correct solution to a given problem. Since DT ability has been related to real-life creativity (Jauk et al., 2014[START_REF] Jauk | The relationship between intelligence and creativity: New support for the threshold hypothesis by means of empirical breakpoint detection[END_REF][START_REF] Kim | Meta-Analyses of the Relationship of Creative Achievement to Both IQ and Divergent Thinking Test Scores[END_REF][START_REF] Plucker | Is the Proof in the Pudding? Reanalyses of Torrance's (1958 to Present) Longitudinal Data[END_REF], it represents a potential measure of creative abilities.

Moreover, higher DT ability has been associated with higher creative performance in specific domains such as music (Beaty et al., 2013;Benedek et al., 2014a) and dance [START_REF] Fink | Creativity and personality in professional dancers[END_REF].

Based on the DT approach, several tasks have been developed to measure creative abilities. These tasks commonly require generating several ideas, either in verbal or figural domains, in response to a problem or a specific requirement. The Torrance test of creative thinking (TTCT; [START_REF] Torrance | Creativity testing in education[END_REF][START_REF] Torrance | Predictive Validity of the Torrance Tests of Creative Thinking*[END_REF]) is a well-validated [START_REF] Kim | Can We Trust Creativity Tests? A Review of the Torrance Tests of Creative Thinking (TTCT)[END_REF] battery composed of seven verbal and three figural tests of DT assessment. Among the tasks included in the TTCT, the alternative uses task (AUT) is the most used in the neuroscience of creativity (Figure 1A).

The AUT is a verbal test in which participants are asked to generate as many alternatives uses as possible for typical objects (e.g., a tire, a knife, or a bottle).

The four most common indices to score the DT tasks are fluency, originality, flexibility, and elaboration. The fluency refers to the number of responses generated in response to a given stimulus. Originality relates to the uniqueness, uncommonness or unusualness of the responses.

Flexibility considers the number of different categories, or unique categories in response to the stimulus. Elaboration refers to the expansion of the ideas and level of details provided. Among these scores, the flexibility may be less consistently explored in the usual scoring due its high correlation with the fluency score [START_REF] Hebert | Torrance: His Life, Accomplishments, and Legacy[END_REF].

properties of these ideas are not typically considered (i.e., if they are alternative or common uses, or if there are inappropriate responses).

Besides fluency score, the quantification of the originality has been critically discussed.

Different subjective scores have been proposed in which external raters or the same participants evaluate the level of creativity of their ideas generated. The consensual assessment technique [START_REF] Amabile | Social psychology of creativity: A consensual assessment technique[END_REF][START_REF] Kaufman | Beyond New and Appropriate: Who Decides What Is Creative?[END_REF] considers independent judges, experts or not, who evaluate the level of creativity of the ideas. However, a big sample size may require the subjective rating of a high number of ideas implying increase time cost for the raters. Silvia et al. (2008) validated a new method using only a subset of responses to be analyzed. The authors proposed a top scoring method, in which participants select their two most creative ideas (or three; Benedek et al., 2013) which are evaluated for creativity by external raters. Critically, this method allows to dissociate the fluency from the originality scores that have been shown to be highly correlated (Benedek et al., 2013;Silvia et al., 2008). Alternatively, [START_REF] Silvia | Old or New? Evaluating the Old/New Scoring Method for Divergent Thinking Tasks[END_REF] proposed a new selection of the subset of ideas to be evaluated through the differentiation of new and old (or already seen) ideas by the participants among their ideas generated. The new ideas are judged as more creative by both external raters and participants themselves (Silvia et al., 2017). Overall, the main and common advantage in these subjective methods is that the quantification of originality by external raters allow accounting for the uncommonness and appropriateness of the ideas. However, these measures are rater dependent and time demanding, and importantly, ratings given by external judges to creative ideas might be influenced by their creative abilities [START_REF] Ceh | Assessing Raters: What Factors Predict Discernment in Novice Creativity Raters?[END_REF].

In addition to subjective measures, other objective measures of originality have been proposed. Originality has been quantified based on the frequency or uniqueness of the responses, the more infrequent or uncommon responses being considered as the more original ones. The uncommon or infrequent responses are selected based on the frequency of the given response in a normative sample [START_REF] Kim | Can We Trust Creativity Tests? A Review of the Torrance Tests of Creative Thinking (TTCT)[END_REF], or among the participants of a given sample (see [START_REF] Runco | Divergent Thinking as an Indicator of Creative Potential[END_REF]. Although this method allows a more objective quantification of originality, several limitations have been described. First, originality can be correlated to the fluency parameter, since the production of a higher number of ideas increase the probability of generating a more original idea (Silvia et al., 2008). This effect has been also described in the manual of the scores in the TTCT tasks [START_REF] Ball | Torrance tests of creative thinking streamlined scoring workbook, figural A[END_REF]. Second, these measures do not consider the appropriateness of the ideas (i.e., a common idea can be unique since participants are asked to generate alternative ideas rather than common). Third, the probability of uniqueness of responses decreases with increasing sample size.

Another objective measure of originality that have been proposed is based on the semantic associative distance between the stimulus and the ideas generated [START_REF] Acar | Assessing associative distance among ideas elicited by tests of divergent thinking[END_REF][START_REF] Beketayev | Scoring Divergent Thinking Tests by Computer With a Semantics-Based Algorithm[END_REF][START_REF] Hass | Conceptual Expansion During Divergent Thinking[END_REF]. The semantic distance is the probability with which a concept is associated to another concept in memory, reflecting the pre -establish association between these concepts in a population. In neuroscience of creativity, semantic distance measure is often evaluated using the Latent Semantic Analysis (Beaty et al., 2014b;Beaty and Johnson, 2021;[START_REF] Dumas | Understanding Fluency and Originality: A latent variable perspective[END_REF]Green et al., 2012;Jones and Estes, 2015;[START_REF] Landauer | A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge[END_REF][START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF]. This analysis is based on a large dataset of text corpus and estimate the frequency of co-occurrence between words in these datasets.

Based on this analysis, the semantic distance measured as the frequency of co-ocurrence has been used to quantify originality of individual's responses, evidencing its reliability and validity (Beaty et al., 2014a;Gray et al., 2019;[START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF]. For instance, Prabhakaran et al. (2014) investigated how semantic distance relates to creativity using a verb generation task.

The authors presented nouns to the participants and instructed them to think creatively in order to generate verbs associated to the given nouns. Participants that generated more semantically distant verbs in response to the nouns had a better performance in the TTCT test and creative achievements. Recently, Beaty et al. (2021a) developed an automated scoring of verbal creativity via semantic distance. The authors used a multi-model approach to semantic distance estimation based on word similarity (e.g., frequency of co-occurrence) and text corpus from more naturalistic language (e.g., subtitles from movies), evidencing that semantic distance measured from these different models reliable correlates with different creativity measurements. Moreover, Beaty et al. (2021a) examined different items of the AUT task (i.e., the common objects given as a cue) for which this multi-model approach yielded acceptable reliability and whose semantic distance correlated positively with various creativity measurements. Thus, using this novel approach is a promising method for scoring AUT tasks with evidence of its reliability and validity (Beaty et al., 2021a.;Beaty and Johnson, 2021;[START_REF] Dumas | Measuring divergent thinking originality with human raters and text-mining models: A psychometric comparison of methods[END_REF][START_REF] Maio | Is the Reliability of Objective Originality Scores Confounded by Elaboration[END_REF].

Other semantic distance measurements have been proposed based on several word networks [START_REF] Acar | Assessing associative distance among ideas elicited by tests of divergent thinking[END_REF] or associative norms quantifying semantic associations in a given language (Ferrand and Alario, 1998;[START_REF] Nelson | What is preexisting strength? Predicting free association probabilities, similarity ratings, and cued recall probabilities[END_REF]. Overall, semantic distance between the cue word and the ideas generated by the participants during the AUT task is a useful tool to explore originality more objectively. Moreover, semantic distance measures show a positive correlation with other measurements of creativity (Beaty and Johnson, 2021;[START_REF] Dumas | Measuring divergent thinking originality with human raters and text-mining models: A psychometric comparison of methods[END_REF][START_REF] Orwig | Creative Connections: Computational Semantic Distance Captures Individual Creativity and Resting-State Functional Connectivity[END_REF].

Finally, the creativity assessment via DT tasks is a challenge in neuroscience.

Particularly, different objective and subjective measures of originality have been proposed which have their advantages and limitations. Hence, the more accurate method for the quantification of originality in DT tasks is still debated [START_REF] Barbot | Creativity assessment in psychological research: (Re)setting the standards[END_REF]Forthmann et al., 2019a[START_REF] Forthmann | The Be-Creative Effect in Divergent Thinking: The Interplay of Instruction and Object Frequency[END_REF][START_REF] Reiter-Palmon | Scoring divergent thinking tests: A review and systematic framework[END_REF].

Associative combination

Another approach that has been influential in the neuroscience of creativity field is the associative combination. In the 60's Mednick (1962) developed the concept of associative thinking as key to creativity, and proposed a theoretical model based on the relationship between associative ability and creativity. This model hypothesizes that creative abilities are related to semantic associations in memory, and thus, creative abilities depend partly in the organization of the concepts in memory. Based on Mednick's associative theory, the creative thinking process is defined as the ability to associate remote elements into new combinations that are in some way useful or to meet specific requirements. Hence, the more remote the elements are, the more creative the process of combining them is. This theoretical model suggests that differences in creative abilities are related to a different associative hierarchy of the concepts in memory characterizing high and low creative people. Mednick proposed that more creative people have flatter associative hierarchies than less creative people, characterized by steeper associative hierarchies. The steeper associative hierarchy in less creative individuals implies that for a given concept, the stronger associated elements to it will be reached easily, so that individuals are unlikely to elicit weak associates or remote concepts. Therefore, a steeper hierarchy results in more stereotypical responses. For example, the presentation of the word 'dog' is likely to elicit the word 'cat' as a quick response in people with a steeper associative hierarchy.

Contrary to the steep hierarchy, a flatter hierarchy in high creative individuals allows reaching several close and remote associations to the concept easily since there are no stereotypical or strong associations between concepts (Kenett et al., 2014;[START_REF] Mednick | Continual association as a function of level of creativity and type of verbal stimulus[END_REF]Mednick, 1962). Thus, a creative subject can present a slow initial generation of ideas, but prolonged in time, allowing the emergence of uncommon words (Benedek and Neubauer, 2013;[START_REF] Mednick | Continual association as a function of level of creativity and type of verbal stimulus[END_REF]Mednick, 1962) and the generation of a higher number of ideas in terms of fluency and originality. For example, the presentation of the word 'dog' can elicit more uncommon associated words such as 'tree' or 'loyal' rather than the common association 'cat'. This flatter organization of the associations in memory may allow to make links between more distant concepts.

Several studies have supported this theory showing that more creative individuals link distant concepts more easily than less creative individuals (Beaty et al., 2014b;Benedek and Neubauer, 2013;Rossmann and Fink, 2010). In addition, more creative people have a more flexible organization of semantic memory or less constrained word associations, allowing to make more uncommon associations than less creative people (Beaty et al., 2014b;Bendetowicz et al., 2017;Benedek et al., 2012b;Benedek and Jauk, 2018;Kenett et al., 2014;Volle, 2018).

However, besides this evidence, empirical research has not fully supported Mednick's associative hierarchies' assumptions (see Benedek and Neubauer, 2013).

Based on the associative theory of creativity (Mednick, 1962). Mednick developed the Remote Associates Test (RAT; [START_REF] Mednick | The Remote Associates Test[END_REF], aiming to capture the ability to form associative elements into new combinations. In this task, three cue words are presented. At first glance, the three words seem unrelated (e.g., rat, blue, and cottage), and people have to find a fourth word related to each of the three cue words in different ways, serving as a link for the words (e.g., cheese).

An adaptation of the RAT task is the Combination of Associates Task (CAT; Bendetowicz et al., 2018Bendetowicz et al., , 2017)). Based on the premise of Mednick (1962) that combining more remote elements implies a more creative process (Mednick ,1962), the CAT task was developed to capture the difference in performance when varying the remoteness of semantic associations or semantic distance between the cue words and the response word (Figure 1B). This task is composed of 100 trials in which three cue words are presented and participants must come up with a fourth word acting as an associative link of the three cue words. Among the total trials, half of them (50 trials) have a close semantic distance between each cue word and the word response (e.g., to marry, together, family), being the trials the less difficult to solve (e.g., find the response union). While the other half of the trials are more difficult (e.g., defend, to prove, office), having a more remote semantic distance between each cue word and the word response (e.g., find the response lawyer). The semantic distance (association strength) between the cue words and the response in the two types of trials is quantified using free association norms (Debrenne, 2010). The creative performance is measured by calculating an index between the easier trials (lower semantic distance) and the more difficult trials (higher semantic distance), the latter requiring the combination of more remote elements to find the solution. This index reflects the ability to solve the distant trials, that is to combine more remote semantic associations. This task has been used to explore creativity in relation to brain structure in healthy subjects and in patients with frontal lesions (Bendetowicz et al., 2017). The findings from these studies show a correlation between CAT scores and the measure of originality in the TTCT task and creative achievement measurements.

Other versions of RAT have also been created based on semantic associations (Olteţeanu et al., 2019), lexical associations by forming compound words between the cue words and the word response (Bowden and Jung-Beeman, 2003a;[START_REF] Jung-Beeman | Neural Activity When People Solve Verbal Problems with Insight[END_REF][START_REF] Shen | The Development and Validity of a Chinese Version of the Compound Remote Associates Test[END_REF][START_REF] Wu | A Systematic Review of Creativity-Related Studies Applying the Remote Associates Test From 2000 to 2019[END_REF], and also using pictures instead of words [START_REF] Becker | Assessing creativity independently of language: A normed Language Independent Remote Associate Task (LI-RAT)[END_REF][START_REF] Olteţeanu | A Visual Remote Associates Test and Its Validation[END_REF].

Insight problem solving

The insight problem-solving approach has also been widely used to study creative thinking. Similar to the associative combination approach, insight problem solving typically requires finding one expected solution. Although there is still no consensus about the different features characterizing insight problem solving, in neuroscience this approach typically relates to problems whose statement can evoke implicit constraints that restricts the responses space. This restriction in the response space may triggers inappropriate heuristics that lead to a mental impasse, that can be interpreted as the blockage of mental representations of the problem's elements based on people's experience or understanding. It likely occurs due to a misdirected search in memory triggered by the implicit constraints of the problem or existing knowledge (Cassotti et al., 2016b), leading to a mental fixation that makes retrieval of the relevant information difficult, and people cannot to progress further to solve the problem [START_REF] Duncker | On problem-solving[END_REF].

In creative problem-solving, people need to overcome these heuristics and change their mental representations of the problem's elements or finding new connections between remote or unrelated elements. Thus, problem-solving may require the restructuration of the problem [START_REF] Weisberg | Prolegomena to theories of insight in problem solving: A taxonomy of problems[END_REF], and breaking pre-established ideas or constraints to explore the problem differently. In some cases, the solution comes to mind suddenly and effortlessly, with the subjective experience of Aha! or Eureka phenomenon [START_REF] Aziz-Zadeh | Aha!": The neural correlates of verbal insight solutions[END_REF][START_REF] Gilhooly | Differentiating insight from non-insight problems[END_REF]Kounios and Beeman, 2014;[START_REF] Sandkühler | Deconstructing Insight: EEG Correlates of Insightful Problem Solving[END_REF][START_REF] Topolinski | Gaining Insight Into the "Aha" Experience[END_REF]. This type of problem-solving is usually considered as an insight solving (Kounios and Beeman, 2014;[START_REF] Weisberg | On the "Demystification" of Insight: A Critique of Neuroimaging Studies of Insight[END_REF].

Certain problems, such as the RAT trials and the riddles, have been identified as strongly elicitors of the Eureka, hence, reflecting resolution by insight [START_REF] Bowden | New approaches to demystifying insight[END_REF]. Insightbased problem solving is usually assessed by the individuals' subjective report of a Eureka phenomenon after finding the solution to a given problem [START_REF] Aziz-Zadeh | Aha!": The neural correlates of verbal insight solutions[END_REF][START_REF] Jung-Beeman | Neural Activity When People Solve Verbal Problems with Insight[END_REF] which is commonly accompanied by the feeling of confidence and pleasure regarding the response coming to mind [START_REF] Laukkonen | Getting a grip on insight: real-time and embodied Aha experiences predict correct solutions[END_REF].

The insight problem solving has been described as the sudden and conscious awareness of the solution that is preceded by its unconscious processing [START_REF] Aziz-Zadeh | Aha!": The neural correlates of verbal insight solutions[END_REF][START_REF] Bowden | New approaches to demystifying insight[END_REF][START_REF] Jung-Beeman | Neural Activity When People Solve Verbal Problems with Insight[END_REF]Kounios and Beeman, 2014;[START_REF] Rothmaler | New insights into insight: Neurophysiological correlates of the difference between the intrinsic "aha" and the extrinsic "oh yes" moment[END_REF][START_REF] Topolinski | Gaining Insight Into the "Aha" Experience[END_REF][START_REF] Weisberg | On the "Demystification" of Insight: A Critique of Neuroimaging Studies of Insight[END_REF]. Previous research has shown the role of unconscious processes in idea generation and selection during the creative process, which has been associated with the incubation step [START_REF] Dijksterhuis | Where Creativity Resides: The Generative Power of Unconscious Thought[END_REF][START_REF] Dijksterhuis | A Theory of Unconscious Thought[END_REF][START_REF] Ritter | Creativity: The role of unconscious processes in idea generation and idea selection[END_REF]. Moreover, during the problem solving of anagrams, participants are more likely to show insight-like solutions when words that are related to the solution are presented subliminally prior to it [START_REF] Bowden | The effect of reportable and unreportable hints on anagram solution and the aha! experience[END_REF]. Similarly, during a semantic priming paradigm, participants have greater priming for the solution word when its recognition evoke an insight (Bowden and Jung-Beeman, 2003b), suggesting that the subjective experience of insight partly occurs due to the semantic activation or unconscious processing of the solution that led to its faster recognition [START_REF] Bowers | Increasing breadth of semantic associations with left frontopolar direct current brain stimulation: A role for individual differences[END_REF][START_REF] Langley | A computational model of scientific insight[END_REF][START_REF] Ohlsson | Information-processing explanations of insight and related phenomena[END_REF][START_REF] Yaniv | Activation and metacognition of inaccessible stored information: Potential bases for incubation effects in problem solving[END_REF].

Insight problem solving is commonly opposed to the analytical manner of problemsolving. Analytical solving problem is described as following logical steps and generating rules and hypotheses to the problem, and once they are generated, people are progressively evolving towards the solution. Contrary to the insight problem solving, analytical problem solving allows people to report the different steps and the reasoning used to find the solution (Aziz-Zadeh et al., 2009;[START_REF] Kounios | The origins of insight in resting-state brain activity[END_REF][START_REF] Lavric | Differences in working memory involvement in analytical and creative tasks: an ERP study[END_REF].

In neuroscience, the self-report of the Eureka subjective phenomenon continues to be the key methods to explore insight-based problem-solving. One of the most used tasks to explore insight problem solving is the RAT task and its different adaptations [START_REF] Dietrich | A review of EEG, ERP, and neuroimaging studies of creativity and insight[END_REF]. RAT trials can be solved with insight which can be captured by the self-reported feeling of Eureka by the individuals after solving the problems [START_REF] Bowden | New approaches to demystifying insight[END_REF]. This subjective report of Eureka is used to assess insight problem solving (Figure 1B). During the RAT problem solving, people should access to more remote associations in order to find the appropriate solution. However, the fixation on related, but inappropriate associates of the cue words may impair the performance in this task [START_REF] Smith | Incubation and the persistence of fixation in problem solving[END_REF][START_REF] Storm | Mental fixation and metacognitive predictions of insight in creative problem solving[END_REF]. Thus, finding the solution on the RAT trials may require breaking with preestablished and strong relationships to the three cue words and searching in memory for concepts that can be used to reinterpret the problem [START_REF] Ohlsson | Restructuring revisited: II. An information processing theory of restructuring and insight[END_REF] in order to find the solution that satisfies the constraints of association.

Other approaches to explore creativity

Besides the framework-based tasks, other self-report measurements have been developed for assessing creativity [START_REF] Batey | A psychometric investigation of everyday creativity[END_REF][START_REF] Carson | Reliability, Validity, and Factor Structure of the Creative Achievement Questionnaire[END_REF]Diedrich et al., 2018;[START_REF] Dollinger | Need for uniqueness, need for cognition and creativity[END_REF]Hocevar, 1979). They are questionnaires attempting to capture a vast range of aspects of creativity in several creative domains which are difficult to quantify objectively.

However, an important challenge when exploring these aspects of creativity is to cover a broad range of achievements and domains to provide a more complete measure of real-life creativity. Thus, different questionnaires have been developed in order to capture several aspects of creative activities and achievements in real life across different domains.

Creative activities are usually measured as the frequency in which participants engage in activities in different creative domains (e.g., "how often you make a craft"). The Creative Behavior Inventory (CBI; Hocevar, 1979); revised version by [START_REF] Dollinger | Need for uniqueness, need for cognition and creativity[END_REF] was one of the first scales to measure creative behavior in research. This is a four-point ordinal response scale with 28 items assessing people's engagement in creative activities during adolescence and adult life. This scale has been shown to be correlated to DT tasks [START_REF] Silvia | A dimensional analysis of creativity and mental illness: Do anxiety and depression symptoms predict creative cognition, creative accomplishments, and creative self-concepts?[END_REF], creative drawing [START_REF] Dollinger | Identity and Creativity[END_REF] and openness to experience trait [START_REF] Dollinger | Creativity and conservatism[END_REF]. A similar scale is the Biographical Inventory of Creative Behavior (BICB; [START_REF] Batey | A psychometric investigation of everyday creativity[END_REF] that considers the involvement in creative activities in a shorter time, limited to the past year. The score of this scale correlates to DT tasks and openness to experience trait (Furnham et al., 2008).

Although both scales correlate to other creativity measures, these measurements have some limitations. For instance, the overall score in both scales have a highly skewed distribution and the BICB questionnaire is a categorical scale that does not consider subscales.

Other scales explore creative achievements, that are usually evaluated as the level of accomplishment in a creative domain during life. The Creative Achievement Questionnaire (CAQ; [START_REF] Carson | Reliability, Validity, and Factor Structure of the Creative Achievement Questionnaire[END_REF] is one of the most used questionnaires comprising 96 achievements among ten creative domains. The self-report checklist of achievement in the different creative activities ranges from no engagement in the creative domain to reaching the sale of a creative product. Importantly, the overall CAQ score has been shown to correlate to the ratings given by experts to artistic creations and the personality trait of openness [START_REF] Carson | Reliability, Validity, and Factor Structure of the Creative Achievement Questionnaire[END_REF].

However, this self-report scale attempts to capture Pro-C and Big-C creativity [START_REF] Kaufman | Beyond Big and Little: The Four C Model of Creativity[END_REF], therefore it focuses on significant accomplishments. Since Big-C creativity is uncommon, the distribution of the CAQ scores is typically skewed. Moreover, differences in creative achievement in mini-C or little-C creativity may be not captured using this scale.

During the last years, the Inventory of Creative Activities and Achievements questionnaire (ICAA) was developed in order to measure creative activities and achievements in a more balanced way across several and relevant creative domains. This scale may better capture everyday creativity, considering both the engagement in creative activities and the quality of the creative achievements within the past 10 years. The ICAA comprises six different questions evaluating creative activities and 11 different questions evaluating creative achievements among eight different domains (Diedrich et al., 2018), including literature, music, arts and crafts, creative cooking, sports, visual arts, performing arts, and science and engineering. The main advantage of this questionnaire is that within the different items, it assesses the different levels of creative achievements in life, especially everyday creativity that is scarcely captured by the other tests. Therefore, the ICAA considers a wide range of creative achievements, covering from personal achievements to publicly acknowledged ones. A total score across domains is commonly used as a measure of creative behavior (Jauk et al., 2014) for both creative activities and creative achievements. The total score in the creative activities measure was shown to be correlated with fluency and originality in divergent thinking tasks, and both creative activities and achievements correlated with the personality trait of openness (Diedrich et al., 2018). However, as the other self-reported measures, the ICAA scores, and especially the creative achievement score, were shown to be highly skewed. One of the advantages of the ICAA is it explores both creative activities and achievements across several and relevant domains. In addition, this scale considers that some creative productions or ideas can vary from mini-C to Big-C creativity, and therefore can be acknowledged as creative at the personal level or by others. This scale considers different aspects of creativity ranging from something (ideas, products, etc.) that can be considered creative by the relatives (e.g., decoration for the cover of a notebook) to creations that are acknowledged by the more general public (e.g., the creation of a new type of spacecraft), that is a critical point to capture from little-C to Pro-C creativity.

Overall, the self-report measures attempt to capture creative behavior in real life through the exploration of creative activities and achievements across different creative domains. On the other hand, the framework-based tasks attempt to cover the underlying processes and mechanisms described in the different approaches exploring creativity. Several versions of creativity tasks have been developed and adapted to explore creative abilities in different cultures and languages. All these approaches contribute to better understand creativity and their underlying mechanisms.

Cognitive processes involved in creative thinking

Evidence from several studies in creativity has identified two main categories of cognitive processes that are involved in creative thinking, which are associative processing and cognitive control processes. These processes allow searching, reorganizing, and combining ideas or concepts from our stored knowledge, in particular from semantic memory, to make it possible for creative ideas to emerge. The strength of semantic associations stored in memory, that determines their spontaneous activation by a given context, has been put forward as a creativity component as early as in the 60s (Mednick, 1962). However, memory search is not only determined by the structure of memory, but it also relies on controlled processes that allow efficient retrieval and navigation within it. Since both associative and controlled processes rely on semantic memory, it is a key component in creative thinking (Abraham and Bubic, 2015;Benedek and Jauk, 2018). Thus, although describing the extensive research on semantic memory is out of the scope of this thesis, it is important to first describe some general and important aspects of semantic memory for a better understanding of the different processes involved in creativity.

Semantic Memory

Semantic memory is one of the memory systems that stores the set of conceptual representations we have about the world and ourselves [START_REF] Tulving | Episodic and semantic memory, in: Organization of Memory[END_REF]. This memory system stores knowledge independent of time and context [START_REF] Binder | Where Is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies[END_REF]Binder and Desai, 2011).

Thus, semantic memory relates to semantic knowledge, that is critical for everyday activities in all human domains [START_REF] Yee | Semantic memory[END_REF]. Based on the controlled semantic cognition framework, the conceptual knowledge is one of the systems of semantic cognition, that in interaction with control processes guide the efficient semantic retrieval (Lambon Ralph et al., 2017;Rogers et al., 2015). This distinction attempts studying the representation of the semantic memory organization and processes separately. However, how semantic memory is organized and how processes allow navigating on it is remains an open question.

Previous studies have contributed to better understand how concepts are organized in semantic memory by means of the semantic priming paradigms. Unlike other semantic-related tests, the priming effect does not rely on explicit or effortful access to semantic information since it attempts to measure the implicit or automatic access to semantic information. However, previous research has reported that even using this approach, more controlled and strategic processes can also occur (Colombo and Williams, 1990;de Groot, 1984;den Heyer et al., 1983;Neely, 1991Neely, , 1977;;Neely et al., 1989). Semantic priming typically measures how response speed and response accuracy in processing a target word is facilitated when it is preceded by a related word (cat -dog), as compared to an unrelated word (lemon -dog) [START_REF] Mcnamara | Semantic priming: Perspectives from memory and word recognition, Semantic priming: Perspectives from memory and word recognition[END_REF].

The most typical task used to measure the priming effect is the lexical decision task. Other paradigms have been developed using for instance naming tasks or relatedness judgment tasks.

Depending on the aim of the research paradigms also vary regarding the type of stimuli used, including verbal or non-verbal, auditory or visual stimuli, or the number of primes used (single or double primes; Balota and Paul, 1996).

Using semantic priming measures, previous studies have shown that different types of semantic relationships can yield priming effects. In particular, a series of studies show that both thematic (items that share a common context without necessarily sharing similar features, e.g., 'rabbit' -'carrot' (Lin and Murphy, 2001) and taxonomic (items sharing specific features such as attributes and functional properties, e.g., 'dog' -'cat'; Gelman and Meyer, 2011;Mirman et al., 2017;Murphy, 2002) relationships between prime and target yield a significant priming effect (Chen et al., 2014b;Hagoort et al., 1996;Khateb et al., 2003;Maguire et al., 2010;Mirman et al., 2017). These findings suggest that semantically related concepts (taxonomic and thematic) are connected to each other in memory and that concepts may be partly organized into semantic categories in memory (Chen et al., 2014b;Jones and Golonka, 2012;Maguire et al., 2010) (for reviews, Hutchison, 2003;Jones and Estes, 2012;Lucas, 2000).

The findings from previous studies using semantic priming approach converge with the idea that the categorization process is at the basis of the construction of our knowledge of the world (Cohen, 2005). The categorization process allows grouping different elements based on their common characteristics. Based on our knowledge, we categorize everything we perceive, and this allows us to make sense of our environment and experiences. Thus, the categorization of concepts is a critical and an adaptive mental process that enables the organized storage of information in memory and its efficient retrieval. Moreover, categorization can occur automatically relying on semantic associations (Praß et al., 2013), suggesting that it depends on the organization of semantic memory.

Based on different approaches of creativity, categorization process was shown to contribute to creative abilities in different ways. For example, reaching more creative ideas during the DT tasks may depends on the ability to explore various conceptual categories, moving from one semantic conceptual field to another during the generation of ideas [START_REF] Acar | Latency predicts category switch in divergent thinking[END_REF][START_REF] Beaty | Why do ideas get more creative across time? An executive interpretation of the serial order effect in divergent thinking tasks[END_REF]Guilford, 1967;Mastria et al., 2021). Indeed, the flexibility score of the AUT can include both the number of categories generated by the individuals during the task and the number of switching between conceptual categories (Acar et al., 2019;Acar andRunco, 2019, 2017). Thus, flexibility during DT may reflect the ability of thinking in multiple directions, exploring different semantic categories and remote association that may depends on the associative nature of semantic memory.

Categorization is also important for abstract reasoning and problem solving. Reasoning by analogy is essential for our reasoning, problem-solving and abstract thinking skills [START_REF] French | The computational modeling of analogy-making[END_REF]Kotovsky and Gentner, 1996). It is defined as the ability to find similarities between different mental representations thus, it depends on the ability to detect a similarity between dissimilar elements. [START_REF] Green | Automatic activation of categorical and abstract analogical relations in analogical reasoning[END_REF] used a semantic priming paradigm to explore the influence of category activation on analogy reasoning. The authors showed that both analogical and categorical judgments of four-word sets primed words that referred to category relations in a subsequent task, while only analogical judgments primed words that referred to analogical relations. This finding suggests that activation of category concepts plays a fundamental role in analogical thinking [START_REF] Green | The Micro-Category account of analogy[END_REF][START_REF] Green | Automatic activation of categorical and abstract analogical relations in analogical reasoning[END_REF]. Similarly, [START_REF] Chrysikou | When shoes become hammers: Goal-derived categorization training enhances problem-solving performance[END_REF] explored the implication of goal-derived categorization in insight problem-solving. The author showed that the training in goal-derived category construction improved insight problem solving performance. Moreover, this result was observed with and without informing the participants about the relevance of the training for the problem-solving tasks. Thus, these studies suggest that categorization process play an important role in abstract reasoning and problem solving, two approaches related to creative thinking.

Besides the categorization process, the role of semantic memory in creativity has been well established across several lines of research (Beaty et al., 2014b;Benedek et al., 2012b;Benedek and Jauk, 2018;Benedek and Neubauer, 2013;Volle, 2018). In addition, semantic memory has been considered the scaffolding of creativity and imagination (Abraham and Bubic, 2015). Paradoxically, some studies have also described constraints of semantic knowledge on creativity, such as functional fixedness (Camarda et al., 2018b;Cassotti et al., 2016b;[START_REF] Chrysikou | Functional Fixedness in Creative Thinking Tasks Depends on Stimulus Modality[END_REF][START_REF] Chrysikou | Following the Wrong Footsteps: Fixation Effects of Pictorial Examples in a Design Problem-Solving Task[END_REF], where common ideas or previous knowledge constrains the exploration of novel and more creative ideas in memory [START_REF] Adamson | Functional fixedness as related to problem solving; a repetition of three experiments[END_REF][START_REF] Agogué | The Impact of Type of Examples on Originality: Explaining Fixation and Stimulation Effects[END_REF]Cassotti et al., 2016b;[START_REF] Duncker | On problem-solving[END_REF][START_REF] Purcell | Design and other types of fixation[END_REF][START_REF] Smith | Constraining effects of examples in a creative generation task[END_REF]. For instance, previous experiences with an object impair generating novel uses for the object during creative problem solving [START_REF] Chrysikou | Functional Fixedness in Creative Thinking Tasks Depends on Stimulus Modality[END_REF][START_REF] Chrysikou | Following the Wrong Footsteps: Fixation Effects of Pictorial Examples in a Design Problem-Solving Task[END_REF][START_REF] Duncker | On problem-solving[END_REF]. In addition, the mental fixation also occurs due to the examples given during task instructions (e.g., showing a picture of the common use of an object) which impairs the generation of more original ideas and problem solving (Camarda et al., 2018b;[START_REF] Chrysikou | Functional Fixedness in Creative Thinking Tasks Depends on Stimulus Modality[END_REF][START_REF] Chrysikou | Following the Wrong Footsteps: Fixation Effects of Pictorial Examples in a Design Problem-Solving Task[END_REF].

Overall, different creativity theories emphasize the importance of semantic memory in creativity, indicating that more creative ideas can emerge from connecting weakly related concepts (and avoiding closely related concepts) in semantic memory during the creative process (Beaty et al., 2014b;Benedek et al., 2012b;Mednick, 1962;Volle, 2018). Some of the most studies semantic memory related processes in creative cognition are associative thinking processes or abilities.

Associative processes in creativity

The role of associative processes in creativity have been proposed early in the 1960s by Mednick (1962). Previous research in neuroscience of creativity supports aspects of this theory and indicates that creative cognition relies on associative processes allowing the spontaneous activation of semantic memory knowledge (Beaty et al., 2014b(Beaty et al., , 2021;;Benedek et al., 2012b;He et al., 2021;Marron et al., 2018Marron et al., , 2020)). Spontaneous associative thinking refers to the process by which the activation of a given concept leads to the spontaneous activation of their associated concepts in memory [START_REF] Collins | A spreading-activation theory of semantic processing[END_REF]. Hence, associative thinking relies on the organization and strength of associations between concepts in semantic memory (Benedek et al., 2017;Kenett et al., 2014;Kenett and Faust, 2019).

The role of associative processes in creative thinking has been theoretically proposed in the associative theory of creativity (Mednick, 1962) and has been recently gaining ample empirical support (Beaty et al., 2014b(Beaty et al., , 2021;;Benedek et al., 2012bBenedek et al., , 2020;;He et al., 2021). The associative theory suggests that creative ideas depend on associative thinking abilities as they emerge spontaneously through associative processes that unfold in semantic memory for finding new meaningful connections between remote concepts (Mednick,1962). Moreover, new connections between more remote concepts are thought to yield more original ideas. Therefore, the role of associative thinking in creativity relies on the flexible organization of associations between the concepts in the individual's semantic memory (Kenett and Austerweil, 2016;Kenett et al., 2014;Mednick, 1962).

Several studies have attempted to isolate the associative processes underlying creative abilities for their exploration (Benedek et al., 2012b;He et al., 2021;Marron et al., 2018Marron et al., , 2020)).

Different tasks have been proposed to examine more spontaneous thinking related to associative processing with minimal control demands in relation to creative behavior. For example, the free association task is commonly used to explore associative fluency (Benedek et al., 2012b). In this task, participants are asked to generate as many words as possible in relation to a cue word during a limited period of time. Another task is the chain-free association task (Benedek et al., 2012b;He et al., 2021;Marron et al., 2018Marron et al., , 2020) ) that consists of generating a chain of associates in which the first word relates to the cue word, and then following associates are in relation to the previously generated word (e.g., 'cat,' 'dog,' 'bone,' 'meat,' 'food'). Different scores can be measured based on this task, such as the fluency, flexibility, and semantic remoteness between the words. Associative fluency and flexibility explored with these tasks have been related to fluency and originality in DT tasks (Benedek et al., 2012b;He et al., 2021;Marron et al., 2018). In addition, the semantic remoteness between the generated words was correlated with divergent thinking ability (He et al., 2021). A more deliberate variant of associative flexibility has been explored using the associative combination task (Benedek et al., 2012b;He et al., 2021). This task asks to the participants to generate a concept that is associated to a pair of unrelated words. A higher ability in associative combination (Benedek et al., 2012b) was shown to correlate with the performance in divergent thinking task.

Another task that was developed to explore associative thinking is the free generation of associates task (Bendetowicz et al., 2018). This task allows exploring jointly spontaneous associations and intentional remote thinking. In a first condition, participants are asked to generate the first word coming to mind in response to several cue words successively. In a second condition, they are asked to generate an unusual word association. The generated word responses to each cue word allow exploring the commonness and uniqueness of the generated words given spontaneously by the participants. The authors of this task found that intentional remote thinking scores correlated with the originality and fluency scores in divergent thinking tasks (Bendetowicz et al., 2018). Similar word-to-word tasks were developed by Benedek et al. (2020) to explore spontaneous associations, intentional remote thinking and associative combination. The authors found that intentional remote thinking and associative combination were correlated to fluency and originality in DT task, and to creative personality, while associative combination correlated to creative activities in real life measured with the ICAA questionnaire. Overall, although isolating associative processes from control processes in challenging, the different tasks attempting to assess associative processes in relation to creativity have supported its important role in creative behavior.

Previous priming studies have explored a purer measure of the spontaneous associative processes in relation to creativity (Atchley et al., 1999;[START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF][START_REF] Radel | The role of (dis)inhibition in creativity: Decreased inhibition improves idea generation[END_REF]. As explained in previous sections, associative priming paradigm typically measures how people are faster and more accurate in processing a target word when it is preceded by a semantically related prime word, as compared to an unrelated prime word (priming effect).

High creative individuals have shown higher susceptibility to priming and more readiness to accept word associations during priming paradigm [START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF]. In addition, [START_REF] Radel | The role of (dis)inhibition in creativity: Decreased inhibition improves idea generation[END_REF] showed that decreased cognitive resources for inhibition led to a higher semantic priming for weakly associated concepts (remotely associated) and to the facilitation of frequency and originality of ideas during DT tasks. Therefore, more automatic associative processing unfolds in semantic memory contributes to higher creative abilities. These findings suggest that high creative people have higher strength of association between more distant concepts, and thus, better associative abilities [START_REF] Aberg | The "Creative Right Brain" Revisited: Individual Creativity and Associative Priming in the Right Hemisphere Relate to Hemispheric Asymmetries in Reward Brain Function[END_REF]Atchley et al., 1999;[START_REF] Faust | Semantically convergent and semantically divergent priming in the cerebral hemispheres: lexical decision and semantic judgment[END_REF].

Overall, associative thinking has been related to creative abilities as measured within several approaches, such as divergent thinking (Beaty et al., 2014b(Beaty et al., , 2021;;Benedek et al., 2012b;Lee and Therriault, 2013;Marron et al., 2020;Rossmann and Fink, 2010), insight problem solving [START_REF] Mednick | Continual association as a function of level of creativity and type of verbal stimulus[END_REF]Mednick, 1962), analogical reasoning [START_REF] Green | Thinking Cap Plus Thinking Zap: tDCS of Frontopolar Cortex Improves Creative Analogical Reasoning and Facilitates Conscious Augmentation of State Creativity in Verb Generation[END_REF], as well as to creative achievements in real life (Benedek et al., 2020;[START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF][START_REF] Merten | Creativity, personality and word association responses: associative behaviour in forty supposedly creative persons[END_REF][START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF]. Previous research supports the role of associative thinking in creativity showing that more creative individuals have more flexible semantic associations and connect more distant concepts or words (Benedek et al., 2020;[START_REF] Olson | Naming unrelated words predicts creativity[END_REF]. Moreover, previous studies have shown that more creative individuals rate unrelated words as less semantically distant (Rossmann and Fink, 2010) and are faster in judging the relatedness of concepts compared to less creative individuals [START_REF] Vartanian | Divergent thinking ability is related to faster relatedness judgments[END_REF]. These findings suggest that remote elements in memory may be more strongly interconnected in creative individuals.

In sum, although Mednick's theory has been the most influential in promoting associative processes in creative thinking, recent research further supports the role of these associative abilities in creativity (Beaty et al., 2014b(Beaty et al., , 2021;;Benedek et al., 2012bBenedek et al., , 2020;;He et al., 2021). Previous research suggests that a better associative ability, typically represented by faster or easier associations of more semantically distant concepts, may benefit from an efficient organization of the concepts in semantic memory (Volle, 2018). Thus, semantic memory structure is considered relevant to creative ability as associative processes depend on the strength of semantic associations between concepts and how they are stored in memory (Beaty et al., 2014b(Beaty et al., , 2021;;Benedek et al., 2012b;He et al., 2021;Marron et al., 2018Marron et al., , 2020)).

Control processes in creativity

Besides associative processes, the role of several executive functions underlying control processes has been consistently reported in relation to creative abilities. Among these functions, inhibition, working memory, attentional control and controlled retrieval play an important role in creative thinking, allowing the control of thoughts and actions necessary to accomplish goaldirected tasks (Beaty et al., 2017;Benedek and Fink, 2019;Benedek and Jauk, 2018;Cassotti et al., 2016a;Edl et al., 2014;Lee and Therriault, 2013).

The generation of creative ideas requires cognitive inhibition, which commonly means inhibiting prepotent ideas that come first to mind [START_REF] Friedman | The relations among inhibition and interference control functions: a latent-variable analysis[END_REF]. Thus, inhibition is critical to generate and select more appropriate and creative responses (Cassotti et al., 2016a). Inhibition has been shown to be important in creative abilities as measured with the different approaches. It has been correlated to DT tasks and self-reports of creative achievements (Benedek et al., 2012a(Benedek et al., , 2014c;;Edl et al., 2014;[START_REF] Zabelina | Creativity as flexible cognitive control[END_REF], and it also may be important to inhibit high-frequency responses for solving the RAT (Gupta et al., 2012).

Based on the dual process model of cognition (see [START_REF] De Neys | Conflict detection, dual processes, and logical intuitions: Some clarifications[END_REF][START_REF] De Neys | Bats, balls, and substitution sensitivity: cognitive misers are no happy fools[END_REF][START_REF] Houdé | Interference and Inhibition, Psychology of[END_REF][START_REF] Krämer | Thinking, Fast and Slow[END_REF], Cassotti et al. (2016a) propose a cognitive model of the generation of creative ideas. This model considers three different systems: an intuitive-heuristic system (System 1), an oppose deliberate-analytic system (System 2) and inhibitory control (System 3). This triadic model of creativity (Cassotti et al., 2016a) proposes that generating a creative solution relies on the inhibition (System 3) of the common ideas in fixation (system 1) in order to activate the conceptual expansion reasoning (System 2). The failure in inhibiting the intuitive responses (System 1) can lead to a mental fixation that difficult creative ideas emerge (Camarda et al., 2018a;Cassotti et al., 2016a). Thus, cognitive inhibition is important in overcoming fixation in problem-solving situations [START_REF] Agogué | The Impact of Type of Examples on Originality: Explaining Fixation and Stimulation Effects[END_REF]Cassotti et al., 2016b;[START_REF] Dietrich | A review of EEG, ERP, and neuroimaging studies of creativity and insight[END_REF][START_REF] Storm | Overcoming fixation: Creative problem solving and retrievalinduced forgetting[END_REF][START_REF] Storm | Forgetting as a consequence and enabler of creative thinking[END_REF].

In addition to inhibition, working memory allowing people to form and manipulate mental representations is also important for creativity. This capacity enables the maintenance of the relevant information for task requirements and goals, and is required for the controlled search of the information within semantic memory [START_REF] Unsworth | The nature of individual differences in working memory capacity: Active maintenance in primary memory and controlled search from secondary memory[END_REF]. Working memory capacity and the ability to update the incoming information during a task have been associated with higher DT ability and the prediction of creativity (Benedek et al., 2014c;[START_REF] De Dreu | Working memory benefits creative insight, musical improvisation, and original ideation through maintained task-focused attention[END_REF][START_REF] Oberauer | Which working memory functions predict intelligence?[END_REF]. Moreover, working memory has been shown to serve as a resource for attention control [START_REF] Engle | Working memory capacity as executive attention[END_REF][START_REF] Kane | A controlled-attention view of working-memory capacity[END_REF] which enable people to maintain the attentional focus during a task which is critical for creativity (Benedek, 2018;Frith et al., 2021b). Attentional flexibility is also required for creativity as it allows people to combine the information from internal and external inputs (Zabelina et al., 2016). Overall, attention control supported by working memory ability is relevant to keep focused on the task-goals and to persist in one idea over time while attentional flexibility allows combining information from different inputs and switching between ideas (Zabelina et al., 2016).

One particular type of cognitive control relevant to creativity is semantic control (Beaty and Silvia, 2013;Benedek et al., 2012a;Forthmann et al., 2019b;Gilhooly et al., 2007;Lee and Therriault, 2013;Nusbaum and Silvia, 2011;Silvia et al., 2013). Research on semantic memory characterizes the neurocognitive semantic control system as the component that allows to selectively retrieve and manipulate meaningful information depending on context demands (Gao et al., 2021;Jackson, 2021;Jefferies and Wang, 2021;Lambon Ralph et al., 2017;Patterson and Lambon Ralph, 2016;Rogers et al., 2015;Vatansever et al., 2021). The ability to retrieve semantic information from memory may depend on the executive demands of the tasks [START_REF] Corbett | Exploring multimodal semantic control impairments in semantic aphasia: Evidence from naturalistic object use[END_REF][START_REF] Jefferies | Semantic impairment in stroke aphasia versus semantic dementia: a case-series comparison[END_REF]Noonan et al., 2013). For instance, controlled processes are relevant for the selection of goal-relevant knowledge over irrelevant competitors [START_REF] Fletcher | Sculpting the Response Space"-An Account of Left Prefrontal Activation at Encoding[END_REF]Hirshorn and Thompson-Schill, 2006;[START_REF] Moss | Selecting Among Competing Alternatives: Selection and Retrieval in the Left Inferior Frontal Gyrus[END_REF][START_REF] Thompson-Schill | Effects of Repetition and Competition on Activity in Left Prefrontal Cortex during Word Generation[END_REF], 1997) and for the semantic retrieval and selection depending on the goal-relevant information (Badre et al., 2005;Badre and Wagner, 2002;[START_REF] Wagner | Recovering meaning: left prefrontal cortex guides controlled semantic retrieval[END_REF] which may be critical in creativity tasks. The controlled access to knowledge process is also required in the case of words with multiple meanings, where one of them is usually dominant, and the subordinates need to be activated (Lambon Ralph et al., 2017;Noonan et al., 2013;Whitney et al., 2011a), and more creative people have been shown to be better able to activate the representation of multiple aspects of potentially incongruous information, including competitive lexico-semantic meanings (Atchley et al., 1999).

Besides the critical role of cognitive control in creativity, previous studies have shown that low control processes in certain contexts may favor creativity and that the optimal level of cognitive control is task-dependent [START_REF] Amer | Cognitive Control As a Double-Edged Sword[END_REF][START_REF] Chrysikou | Creativity in and out of (cognitive) control[END_REF][START_REF] Chrysikou | The costs and benefits of cognitive control for creativity[END_REF]Chrysikou et al., 2014;[START_REF] Wiley | Working Memory Capacity, Attentional Focus, and Problem Solving[END_REF]. To further support this idea, Chrysikou et al. (2014) proposed the Matched Filter Hypothesis for cognitive control. Based on this framework, creativity may benefit of cognitive control when using rule-based tasks that rely on manipulating information that do not exceed the capacity of representations in working memory, such as cue semantic retrieval tasks. In this type of close-ended tasks, the responses or ideas generated are guided by explicit rules, thus cognitive control mechanisms are required for an optimal performance. Contrary, when using more spontaneous and automatic open-ended tasks, cognitive control may not benefit creativity as they involve the processing of information that exceeds working memory limitations.

Overall, the role of executive functions underlying control processes is critical for creativity. It allows finding and combining more remote ideas or concepts to generate creative ideas. In addition, they allow a more flexible searching over memory and an efficient retrieval of information in given contexts. However, depending on the goals of the tasks, cognitive control can have a different role, favoring or constraining creative abilities.

Interaction between controlled and associative processes in creativity

During the last years, research in the field of neuroscience of creativity has shed light on the importance of the interaction between associative and controlled processes in creativity (Beaty et al., 2014b;Benedek and Jauk, 2018;[START_REF] Dietrich | The cognitive neuroscience of creativity[END_REF][START_REF] Mok | The interplay between spontaneous and controlled processing in creative cognition[END_REF]Volle, 2018). For instance, Beaty et al (2014b) using multivariate structural equation modeling showed that both associative and control processes underlie the production of novel ideas, thus, supporting the involvement of both processes in creativity. Moreover, the role of both associative and control processes has been also supported by studies exploring brain correlates of creativity (Beaty et al., 2016;Bendetowicz et al., 2018).

One theoretical approach suggests that the interaction between associative and controlled processes underlie different roles in generating and evaluating creative ideas, respectively (Ellamil et al., 2012;[START_REF] Finke | Creative cognition: Theory, research, and applications, Creative cognition: Theory, research, and applications[END_REF][START_REF] Sowden | The shifting sands of creative thinking: Connections to dual-process theory[END_REF]. The generation of several solution ideas is underlain by associative processes. This generation process is constantly shifting with the evaluation process underlying control processes to verify the appropriateness of the ideas. This interplay between processes continues until one solution is selected.

In addition, it has been proposed that associative and controlled processes are related to the semantic memory structure and processes of search over it allowing the generation of creative ideas. Based on this proposal, associative processes allow the spontaneous associations that partly rely on the individual differences in semantic memory organization (Mednick, 1962).

In the other hand, controlled processes are required for the guidance of searching in memory, controlling the generation of ideas depending on the task goals, and for selecting the appropriate and more creative ideas (Volle, 2018). However, how spontaneous and controlled processes interact dynamically in creative thinking is still unclear. Particularly, how associative and controlled processes interplay to enable the search for ideas in memory? What makes this memory search creative?

How do we search in memory and how it relates to creativity?

Several studies have attempted to elucidate how people search concepts in memory (Abbott et al., 2012(Abbott et al., , 2015;;Hills et al., 2012Hills et al., , 2015a;;Troyer et al., 1997Troyer et al., , 1998;;Troyer, 2000). Semantic memory search and retrieval is commonly measured with semantic or phonemic fluency tasks (Abbott et al., 2015(Abbott et al., , 2012;;Benton, 1968). In these tasks participants are asked to retrieve concepts from memory with specific instructions (e.g., exemplars of a semantic category or words starting with a specific letter). The cognitive processes involved in fluency tasks are not fully understood, and there is an open debate on how people search in their semantic memory during these tasks (Abwender et al., 2001;Ross, 2003;Ross et al., 2007;Shao et al., 2014;Unsworth, 2017;Unsworth et al., 2011). Fluency tasks are thought to capture cognitive processes such as switching between categories, inhibition, updating, and initiation [START_REF] Perret | The left frontal lobe of man and the suppression of habitual responses in verbal categorical behaviour[END_REF]Troyer et al., 1997;Unsworth et al., 2011).

Previous studies explain semantic fluency data as the result of a two-processes retrieval (Hills et al., 2012;Troyer, 2000;Troyer et al., 1997). The first process is clustering, which refers to the production of semantically related words, during which the searching of concepts is carried out in the same subcategory or field within the semantic memory (e.g., for the category of animals, a subcategory refers to mammals). The second process is switching, which refers to jumping into another subcategory or field during the generation of the words (i.e., jump from mammals to birds). Both processes interplay during the fluency tasks (Troyer, 2000;Troyer et al., 1997), and it has been described that clustering processes rely on automatic semantic processing while switching processes rely on controlled processes, such as flexibility and shifting (Troyer et al., 1998).

To account for the search mechanisms of clustering and switching, Hill et al. (2012) developed the memory foraging theory using mathematical models. This theory proposes that searching in memory is similar to spatial optimal foraging, with a trade-off between local exploitation (i.e., semantic memory retrieval process) and global exploration (i.e., executive control processes) [START_REF] Hills | Animal foraging and the evolution of goal-directed cognition[END_REF]Hills et al., 2012Hills et al., , 2015a)). The basis of this theory relies on the analogy between searching in memory and the foraging for food of animals in the environments for patches (cluster) before switch to searching in new patches. Thus, this dynamic process involves switching between global search in memory patches and local search of associates within patches. Hill et al. (2015a) further explored whether these patches in memory were defined in terms of taxonomic or associative relationships. They concluded that semantic memory may involve different types of local dependencies, including category representations and associative relationships between related concepts.

During the last years, it has been proposed that semantic fluency data can result from a single-process (rather than double-process) retrieval (Abbott et al., 2015(Abbott et al., , 2012)). The authors describe a different semantic representation that is a random walk. They show that a random walk equally models performance in a fluency task, similar to the two-process foraging search model proposed by Hills et al. (2012). Moreover, random walk represents an uncontrolled search process in memory and can be considered as a computational implementation of the spreading activations in semantic networks [START_REF] Collins | A spreading-activation theory of semantic processing[END_REF]. However, whether searching over memory is a semantic foraging process or a random walk process is an open debate (Abbott et al., 2015;Avery et al., 2021;Jones et al., 2015).

The framework of semantic foraging offers a useful quantitative method to characterize the responses in diverse generation tasks relevant to creativity. For instance, a few studies have separated exploitation/exploration processes operationalized in a visuospatial creativity task (Hart et al., 2017). Based on search processes over memory and particularly, exploring the cluster-switch strategy, other studies have examined clustering and switching behaviour in a DT task (Hass, 2017;Mastria et al., 2021;Nusbaum and Silvia, 2011). For instance, Hass (2017) proposed that DT tasks require similar cognitive search processes than semantic fluency tasks, since both tasks require generating a maximum of concepts in response to a cue word or idea. Based on this idea, the authors explored the responses in the DT tasks via the formation of temporal and semantic clusters. The results showed that unlike retrieval process during semantic fluency task, the generation of alternative uses during the AUT task does not elicit the same degree of temporal clustering or semantic clustering, but less clustering. This difference may be related to the fact that DT task are more constrained by the instructions, and less structured, than responses in the fluency tasks. The authors concluded that the generation of ideas during the AUT task converge with the predictions of random-sampling theories of search and retrieval from semantic memory.

Conversely, a recent study identified different patterns of brain dynamics underlying the two distinct cognitive mechanisms of switching and clustering processes supporting flexibility in a DT task (Mastria, et al., 2021). In addition to this finding, the switching ability during the fluency tasks has been related to a higher performance on DT tasks (Nusbaum and Silvia, 2011).

Switching is a form of cognitive flexibility [START_REF] Monsell | Task switching[END_REF] and has been associated with higher DT performance [START_REF] De Dreu | Working memory benefits creative insight, musical improvisation, and original ideation through maintained task-focused attention[END_REF]Nusbaum and Silvia, 2011;[START_REF] Pan | Different effects of cognitive shifting and intelligence on creativity[END_REF]. This ability enables people to generate higher creative ideas through new ways of exploration and the alternation between different contents. Nijstad et al. (2010) proposed the dual pathway to creativity model that highlights the critical role of cognitive flexibility and cognitive persistence in generating creative ideas. In this model, working memory influences creativity by improving cognitive persistence while cognitive flexibility allows switching between different categories or ideas. Hence, creative persistence, supported by working memory and control attention, allows better attention control to keep and extend the search deep within the same idea or category in memory. Overall, these studies suggest that clustering and switching reflect separable processes supporting creative ideation.

Brain correlates of creativity

Brain correlates of creativity in healthy subjects

Based on the different frameworks to explore creative thinking, several studies have examined the brain correlates of creativity in healthy subjects. Although neurocognitive research on creativity is still at a very early stage, brain correlates have been explored at the structural and functional level, identifying brain regions for creativity. Most of the studies using neuroimaging techniques consistently report the involvement of the prefrontal cortex (PFC) in creativity tasks. Yet, the functional specialization of the PFC's different sub regions is still debated. Additionally, parietal and temporal brain regions have been also associated to creativity tasks (Gonen-Yaacovi et al., 2013; Figure 2).

Previous meta-analyses (Boccia et al., 2015;Gonen-Yaacovi et al., 2013) of neuroimaging studies on creativity have clarified the different brain regions involved in creativity. The lateral and medial PFC, and posterior parietal cortex, and more specifically the left inferior frontal gyrus, inferior frontal junction, and left angular gyrus have been associated to different creativity tasks and domains across studies (Gonen-Yaacovi et al., 2013). In addition, the left dorsolateral PFC (Boccia et al., 2015), the pre-supplementary motor area, and the right inferior frontal gyrus [START_REF] Chen | Mapping the artistic brain: Common and distinct neural activations associated with musical, drawing, and literary creativity[END_REF] has been shown to be involved in verbal, musical and visuospatial creativity, suggesting the role of these region in general domains of creativity.

Figure 2. Global activation likelihood estimation (ALE) meta-analysis of creativity.

Clusters of activations associated with various creativity tasks are rendered in the brain structure. From Gonen-Yaacovi et al. (2013) Other studies have examined whether distinct domains of creativity relate to distinct brain regions. At the domain specific-level, previous studies show that verbal creativity relies on the left inferior and middle frontal gyri, posterior part of the left temporal cortex (Gonen-Yaacovi et al., 2013), the left middle and superior temporal gyri, inferior parietal lobule, postcentral and supramarginal gyri, right lingual gyrus, anterior insula, and cerebellum (Boccia et al., 2015; Figure 3). Other overlapping and different brain regions have been indicated as brain correlates of visuospatial and artistic domains, including musical, drawing, and literary creativity (Boccia et al., 2015;[START_REF] Chen | Mapping the artistic brain: Common and distinct neural activations associated with musical, drawing, and literary creativity[END_REF]. Visuospatial creativity has been related to the right inferior and middle frontal gyri, bilateral thalamus, and left precentral gyrus (Boccia et al., 2015; Figure 3) while musical creativity is related to the bilateral medial PFC and cingulate gyrus, the left middle frontal gyrus, left inferior parietal lobule, right fusiform gyrus, and cerebellum (Boccia et al., 2015; Figure 3). In addition, pre-supplementary motor areas, bilateral inferior frontal gyrus and left precentral gyrus have been correlated to musical creativity when comparing to the two other artistic creativity domains. Overall, this evidence suggests that different creativity domains depend on different brain regions, including different sub regions within the PFC and that regions that are general vs specific to particular domains or tasks coexist in the creativity networks.

In addition to the general-specific domain duality of creativity, the brain lateralization in relation to creativity is a widely debated topic and have been explored in different studies.

Although most of the studies describe a bilateral distribution of the brain regions underlying creativity, a predominant activation has been reported in regions of the left hemisphere [START_REF] Cogdell-Brooke | A meta-analysis of functional magnetic resonance imaging studies of divergent thinking using activation likelihood estimation[END_REF]Gonen-Yaacovi et al., 2013) during diverse creativity tasks, and especially (but not exclusively) in verbal tasks (Boccia et al., 2015).

Figure 3. Single ALE meta-analysis on specific domains of creativity. Clusters of activation

from the ALE meta-analysis on studies assessing musical (green), verbal (blue), and visuospatial (red) creativity. From Boccia et al., (2015) Current evidence from neuroimaging studies suggests that two distinct forms of functional lateralization support differences in distinct creativity modalities [START_REF] Chen | Brain hemispheric involvement in visuospatial and verbal divergent thinking[END_REF]. [START_REF] Chen | Brain hemispheric involvement in visuospatial and verbal divergent thinking[END_REF] suggest that verbal creativity relies on a balanced inter-hemispheric functional connectivity involving brain regions related to complex semantic processing and cognitive control processes. In contrast, visuospatial creativity relies predominantly on brain regions of the right hemisphere. However, previous studies have shown that higher activation of the right hemisphere is also associated with coarser semantic coding in verbal tasks (Abraham et al., 2012b;[START_REF] Chiarello | Exploring cerebral asymmetries for the verb generation task[END_REF]Jung-Beeman, 2005). In addition, visuospatial creativity, such as non-verbal tasks in artistic creativity, has been associated to the activation of brain regions in both hemispheres [START_REF] Chen | Mapping the artistic brain: Common and distinct neural activations associated with musical, drawing, and literary creativity[END_REF]. Thus, evidence from different studies suggests that creativity in any modality requires a bilateral engagement and exchange of information between brain regions of both hemispheres.

Overall, creativity tasks involve a combination of domain-general, domain-specific, and task-relevant abilities that are likely supported by distinct brain systems. Previous studies indicate that the processing of general and specific domains, modalities, or processes of creativity are distributed bilaterally in the whole brain However, the cognitive role of these regions and how they are organized and connected for creativity is only beginning to be understood.

Brain networks related to creativity in functional connectivity studies

A cognitive function as complex as creativity likely required many regions working together, rather than local processing. During the last years, the development of novel methods for exploring functional connectivity have allowed the examination of large-scale functional networks underlying creativity. Functional connectivity describes the temporal correlation in the high amplitude, low-frequency spontaneously generated BOLD signal between brain regions (Fox and Raichle, 2007;[START_REF] Raichle | A default mode of brain function[END_REF]Raichle, 2015a). Based on functional connectivity, previous research has described the architecture of human brain's large-scale intrinsic functional networks [START_REF] Fox | The human brain is intrinsically organized into dynamic, anticorrelated functional networks[END_REF]Fox and Raichle, 2007;[START_REF] Fransson | Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis[END_REF][START_REF] Greicius | Functional connectivity in the resting brain: a network analysis of the default mode hypothesis[END_REF][START_REF] Seeley | Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control[END_REF][START_REF] Yeo | The organization of the human cerebral cortex estimated by intrinsic functional connectivity[END_REF]. Particularly, these studies have shown that spontaneous correlations occurring in the absence of tasks or stimuli (resting state) are not random. Conversely, during free-task conditions different brain regions belonging to known anatomical systems are functionally related [START_REF] Beckmann | Investigations into resting-state connectivity using independent component analysis[END_REF][START_REF] Yeo | The organization of the human cerebral cortex estimated by intrinsic functional connectivity[END_REF].

Using resting state data, several studies have estimated the large-scale brain networks by parcellating the brain into functional systems [START_REF] Glasser | A multi-modal parcellation of human cerebral cortex[END_REF][START_REF] Power | Functional network organization of the human brain[END_REF][START_REF] Schaefer | Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI[END_REF]. These studies have identified various brain regions that belong to the different intrinsic functional networks, such as default mode network (DMN), frontoparietal control network (FPCN), Salience network (SalN), ventral attention, dorsal attention, somatomotor, temporoparietal, limbic, and visual networks. Importantly, the variability in the intrinsic brain activity has been related to variability in human behavior (Fox et al., 2007).

Studies using this technique in relation to creativity have shown the involvement of large-scale brain systems -intrinsic functional networks -in creative thinking (Beaty et al., 2016). Particularly, a growing number of studies have reported the importance of the functional interactions within and between DMN, FPCN and SalN during creative cognition and artistic performance (Beaty et al., 2018(Beaty et al., , 2017[START_REF] Beaty | Default and Executive Network Coupling Supports Creative Idea Production[END_REF]Ellamil et al., 2012).

The DMN is composed of three different subsystems. First, A midline core involving the anterior medial PFC and the posterior cingulate cortex. Both regions are identified as the hubs regions within the DMN, interacting with the other two subsystems. Second, the dorsal medial PFC subsystem, including temporoparietal junction, lateral temporal cortex, temporal pole, and the dorsomedial PFC. Third, the medial temporal lobe subsystem that includes the ventral medial PFC, posterior inferior parietal lobe, retrosplenial cortex, parahippocampal cortex, and hippocampus (Andrews-Hanna et al., 2010; Figure 4).

The DMN was initially described as the network of the passive state, in the absence of goal-directed tasks [START_REF] Raichle | A default mode of brain function[END_REF]. However, this network has also been shown to be involved in specific tasks (Lanzoni et al., 2020;Murphy et al., 2018;[START_REF] Price | Converging Evidence for the Neuroanatomic Basis of Combinatorial Semantics in the Angular Gyrus[END_REF]Wang et al., 2021; see also [START_REF] Dohmatob | Dark control: The default mode network as a reinforcement learning agent[END_REF]. The DMN support, among others, spontaneous cognition, internally generated thoughts, mind wandering and daydreaming, contextual associations, semantic and episodic memory, control-demanding of working memory tasks, and semantic goal maintenance (Andrews-Hanna et al., 2010, 2014;[START_REF] Binder | Where Is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies[END_REF][START_REF] Buckner | The brain's default network: Anatomy, function, and relevance to disease[END_REF][START_REF] Christoff | Experience sampling during fMRI reveals default network and executive system contributions to mind wandering[END_REF]Christoff et al., , p. 200, 2016;;Fox et al., 2015;Humphreys et al., 2015;Murphy et al., 2018;Raichle, 2015a;Wang et al., 2021;Wirth et al., 2011;[START_REF] Xu | Activation and Connectivity within the Default Mode Network Contribute Independently to Future-Oriented Thought[END_REF]. Additionally, this network may get involved whenever cognition involves heteromodal memory representations (Murphy et al., 2018;[START_REF] Pylkkänen | The neural basis of combinatory syntax and semantics[END_REF]Wang et al., 2021).

In the neuroscience of creativity, several studies have shown that higher functional connectivity of DMN regions during resting state correlate with creative performance (Beaty et al., 2014a;[START_REF] Fink | Modulation of resting-state network connectivity by verbal divergent thinking training[END_REF]Takeuchi et al., 2012;[START_REF] Wei | Increased resting functional connectivity of the medial prefrontal cortex in creativity by means of cognitive stimulation[END_REF]. During creativity tasks, the role of the DMN has been related to the unconstrained associative thinking that plays an important role in the spontaneous processes of creative idea generation [START_REF] Beaty | Default and Executive Network Coupling Supports Creative Idea Production[END_REF]Marron et al., 2018). A few recent studies support the role of the DMN in associative processing related to creativity. For instance, Marron et al. (2020) explored the spontaneous generation of association in relation to creative abilities using a chain-free association task. Using a seedbased functional connectivity, the authors showed that higher functional connectivity within DMN during resting state correlated positively to associative fluency. Moreover, the spontaneous associative fluency mediated the relationship between functional connectivity in DMN during resting state and creativity (Marron et al., 2020).

In relation to creativity, the DMN have been shown to interact with the FPCN during creative performance. The FPCN network is composed of the dorsolateral PFC, the dorsal anterior cingulate cortex, the superior parietal cortex, and the dorsal part of the inferior parietal lobule [START_REF] Power | Control-related systems in the human brain[END_REF][START_REF] Yeo | The organization of the human cerebral cortex estimated by intrinsic functional connectivity[END_REF]; Figure 4). The FPCN is usually related to controlled processes in tasks requiring externally oriented attention, including working memory, relational integration, inhibition, memory retrieval, attentional control, planning and flexibility, and task-set switching (Dixon et al., 2018;Foulon et al., 2018;[START_REF] Friedman | The role of prefrontal cortex in cognitive control and executive function[END_REF]Le Bouc et al., 2020;[START_REF] Menon | The role of PFC networks in cognitive control and executive function[END_REF][START_REF] Nee | Integrative frontal-parietal dynamics supporting cognitive control[END_REF][START_REF] Niendam | Metaanalytic evidence for a superordinate cognitive control network subserving diverse executive functions[END_REF][START_REF] Seeley | Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control[END_REF].

Although evidence from resting-state and on task functional connectivity research suggests that the DMN and FPCN tend to act in opposition [START_REF] Fox | The human brain is intrinsically organized into dynamic, anticorrelated functional networks[END_REF], the functional coupling between these functional networks has been described in relation to creative cognition in several studies. Most of these studies have explored the brain network configurations during task performance, which allows examining the dynamic organization and interaction between brain regions underlying specific tasks. Moreover, the DMN-FPCN coupling have been shown using different approaches such as verbal divergent thinking tasks (Beaty et al., 2016[START_REF] Beaty | Default and Executive Network Coupling Supports Creative Idea Production[END_REF], music improvisation [START_REF] Bashwiner | Resting state functional connectivity underlying musical creativity[END_REF][START_REF] Pinho | Addressing a Paradox: Dual Strategies for Creative Performance in Introspective and Extrospective Networks[END_REF][START_REF] Pinho | Connecting to Create: Expertise in Musical Improvisation Is Associated with Increased Functional Connectivity between Premotor and Prefrontal Areas[END_REF], poetry composition [START_REF] Liu | Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process[END_REF] and visual arts (Ellamil et al., 2012), suggesting this is domain-general. This dynamic interaction supports the view that creative abilities result from the interaction between associative and controlled processes of creativity, relying on the DMN and the FPCN, respectively. Beaty et al. (2016) proposed that the DMN and the FPCN may have different roles in creative processes, where DMN supports creative idea generation and FPCN, the idea evaluation processes. of the DMN, FPCN and SalN networks that dynamically interact to support creative task performance.

Using a recent approach in neuroimaging applied to functional brain connectivity, Beaty et al. (2018) demonstrated that the individual differences in engaging DMN, FPCN and SalN during creative performance is predictive of individuals' creative ability in DT tasks. The authors used the connectome-based predictive modeling method (CPM; Shen et al., 2017). The CPM approach allows identifying patterns of functional connectivity that predict complex cognitive functions via a machine learning method. The advantage of using this method rather than exploring uniquely functional connectivity is that CPM allows reliable predictions of behavior in novel individuals based on their patterns of functional connectivity during a specific task or during resting state (Beaty et al., 2018;[START_REF] Finn | Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity[END_REF]Frith et al., 2021a;Rosenberg et al., 2016;Shen et al., 2017). Hence, exploring functional connectivity patterns during creative performance is relevant to determine the neural bases of creative behavior.

Role of specific brain regions correlates for distinct creativity processes?

Several studies using the functional connectivity approach evidence that creative behavior relies on the interaction of large-scale functional networks (Beaty et al., 2018(Beaty et al., , 2016)). However, creativity is a complex function during which several cognitive processes are at play. Thus, the cognitive mechanisms associated with these interactions remain poorly understood.

In an attempt to clarify the role of the multiple brain systems in the cognitive processes that participate in creative behavior, we can interpret the findings from previous research in the light of functional connectivity networks. Particularly, from studies using other neuroimaging approaches such as fMRI, voxel-based morphometry, and non-invasive stimulation techniques.

Using fMRI data Ellamil et al. (2012) explored the neural correlates of generation and evaluation processes during creative drawing. The authors showed that medial temporal lobe regions, DMN related, were associated with creative generation. In addition, creative evaluation was associated with FPCN and DMN related brain regions, and with the activation of other brain regions such as, the rostrolateral prefrontal cortex, insula, and temporopolar cortex. Thus, these findings support the role of DMN and FPCN in idea generation and idea evaluation during the creative process, respectively.

Other studies have established the critical role of brain regions that could correspond to FPCN for creative abilities (Figure 4). For instance, higher activation and brain volume of the caudal and rostral lateral PFC, posterior parietal and cingulate cortex have been the most reported FCPN-related brain regions correlating to DT tasks [START_REF] Bechtereva | PET study of brain maintenance of verbal creative activity[END_REF][START_REF] Chávez-Eakle | Cerebral blood flow associated with creative performance: A comparative study[END_REF]Gonen-Yaacovi et al., 2013;Jung, 2013;[START_REF] Jung | Neuroanatomy of creativity[END_REF][START_REF] Takeuchi | White matter structures associated with creativity: evidence from diffusion tensor imaging[END_REF]. During creativity tasks, posterior areas of the PFC have been related to the free generation of novel ideas, including the left dorsolateral PFC, left supramarginal gyrus, and anteromedial PFC. In addition, the dorsolateral PFC has been shown to play a role in control processes related to creative abilities, such as focused attention, working memory, and selection processes. The activation of the rostral lateral areas of the PFC have also been related to the combination of ideas or remote information, specifically, the left anterior inferior frontal gyrus, the left posterior middle temporal gyrus, and the right inferior frontal gyrus (Boccia et al., 2015;Gonen-Yaacovi et al., 2013;Wu et al., 2015). Moreover, the brain activation of the inferior frontal gyrus was shown to increase when more original and appropriate ideas are generated, supporting the role of executive processes for overcoming dominant and common responses (Benedek et al., 2014b). These findings support the critical role of PFC, brain region related to FPCN, in creativity using different approaches, and highlight the distinct role of the PFC in different processes underlying creativity.

More causal studies using non-invasive brain stimulation also support the diverse functions of the PFC by showing divergent results regarding the role of the left lateral PFC in creativity. Targeting this region with anodal (to activate) stimulation have shown improved performance in RAT tasks (Cerruti and Schlaug, 2009;[START_REF] Metuki | Enhancing cognitive control components of insight problems solving by anodal tDCS of the left dorsolateral prefrontal cortex[END_REF]Zmigrod et al., 2015). In addition, cathodal (to inhibit) stimulation has shown an improvement in DT task performance [START_REF] Chrysikou | Noninvasive transcranial direct current stimulation over the left prefrontal cortex facilitates cognitive flexibility in tool use[END_REF][START_REF] Ivancovsky | Transcranial direct current stimulation (tDCS) targeting the left inferior frontal gyrus: Effects on creativity across cultures[END_REF][START_REF] Kleinmintz | Participation of the left inferior frontal gyrus in human originality[END_REF]Mayseless and Shamay-Tsoory, 2015). Thus, the PCF may have a task-dependent role in creativity, supporting the idea that distinct creativity tasks require a different balance between associative and controlled processes (Chrysikou et al., 2014;[START_REF] Weinberger | Using Transcranial Direct Current Stimulation to Enhance Creative Cognition: Interactions between Task, Polarity, and Stimulation Site[END_REF]. However, by using this technique the functional network related to the brain region stimulated cannot be specified and thus, it is unclear whether PFC regions belong to FPCN or to other functional networks.

Other studies have supported the role of FPCN in executive processes related to creativity. For instance, Benedek et al. (2014b) examined brain correlates of the generation of new ideas, as opposed to the retrieval of old ideas. The authors found stronger activation in the left inferior parietal cortex which is a parietal core region of the FPCN. The authors suggested that idea generation requires a state of focused internally-directed attention involving controlled semantic retrieval. This finding is consistent with the role of FPCN in moderating attention during goal-directed though [START_REF] Benedek | Brain mechanisms associated with internally directed attention and self-generated thought[END_REF]. In another study, Beaty et al. (2017) found that various brain regions from different brain networks participate in more control processes of creativity. The authors reported the interactions between brain regions of DMN, FPCN and SalN during response inhibition in constrained idea production. Particularly, they observed a higher activation in posterior cingulate cortex and bilateral angular gyri (DMN), right anterior insula (SalN), and left dorsolateral prefrontal cortex (FPCN) during high compared to low constrained idea production. The authors interpreted this pattern as a greater coordination among brain systems supporting memory retrieval and cognitive control.

Besides FPCN related brain regions, other studies have shown that creative performance also correlated to the activation and structural differences of DMN related regions (Fink et al., 2014;Jauk et al., 2015;Jung, 2013;[START_REF] Jung | Neuroanatomy of creativity[END_REF][START_REF] Takeuchi | White matter structures associated with creativity: evidence from diffusion tensor imaging[END_REF][START_REF] Zhu | Relating Inter-Individual Differences in Verbal Creative Thinking to Cerebral Structures: An Optimal Voxel-Based Morphometry Study[END_REF]Figure 4). A recent study explored associative processes in relation to creativity and identified their brain correlates (Marron et al., 2018). The authors explored the spontaneous generation of association using a chain-free association task while participants underwent a fMRI session.

This task has been used to explore associative spontaneous thinking related to creative abilities minimizing controlled processes. The authors found that better associative abilities correlated with the activation of DMN brain regions. Particularly, these brain correlates were found within and between the medial prefrontal cortex, posterior cingulate cortex, left temporal, parietal junction, and middle temporal gyrus. Additionally, a negative correlation between associative abilities and the activation of the left IFG -brain region related to FPCN -was also reported. This finding supports the role of the DMN in associative spontaneous thinking related to creativity processes and is in line with the consistently reported role of DMN in spontaneous cognition, mind wandering, and self-generated thoughts (Andrews-Hanna et al., 2014, 2010;[START_REF] Axelrod | The default network and the combination of cognitive processes that mediate self-generated thought[END_REF][START_REF] Mason | Wandering Minds: The Default Network and Stimulus-Independent Thought[END_REF][START_REF] Yan | Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load[END_REF].

Using non-invasive brain stimulation other studies support the role of brain regions that could correspond to DMN. Targeting the medial PFC was shown to improve the remoteness of word associations during a word-generation task in certain conditions (Brunyé et al., 2015;[START_REF] Green | Thinking Cap Plus Thinking Zap: tDCS of Frontopolar Cortex Improves Creative Analogical Reasoning and Facilitates Conscious Augmentation of State Creativity in Verb Generation[END_REF]. In addition, the inhibition of this region decreased mind wandering (Bertossi et al., 2017), a mental activity shown to be related to creativity abilities.

Other brain regions related to creativity overlap with brain regions of semantic cognition, which has been described as neural substrates of semantic representation and semantic control in the framework of controlled semantic cognition [START_REF] Binder | Where Is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies[END_REF]Lambon Ralph et al., 2017;Noonan et al., 2013[START_REF] Noonan | Elucidating the Nature of Deregulated Semantic Cognition in Semantic Aphasia: Evidence for the Roles of Prefrontal and Temporo-parietal Cortices[END_REF]Whitney et al., 2011b; Figure 5). The left anterior inferior temporal gyrus has been identified as the brain correlates of the meaning representations, while the posterior middle temporal gyrus and inferior frontal cortex are related Overall, robust results based on healthy individuals' studies using structural and functional analyses indicate the essential role of the DMN, the FPCN, and the SalN regions in creativity. The brain regions belonging to these functional networks underlie distinct creative processes. Importantly, there is an overlap between the brain regions related to creativity and semantic cognition.

Critical brain regions for creativity processes in patient studies

Several studies have been focused on a better understanding of cognitive processes and brain regions underlying creative abilities. The brain correlates of creative abilities have been elucidated through several studies using neuroimaging data to explore correlations between the performance of the individuals in creativity tasks and brain activity, brain anatomy, or the functional connectivity between brain regions. However, since the data from neuroimaging studies in healthy subjects are mainly correlational, few brain-lesion studies have been performed to elucidate how creative abilities are affected in patients and identify the regions or networks whose integrity is critical for creativity.

The role of DMN and FPCN in creativity processes have been shown in patient studies, indicating their importance in associative and controlled processes of creativity, respectively. Bendetowicz et al. (2018) showed the critical role of both DMN and FPCN in the CAT task.

The performance of such tasks depends on the organization of spontaneous associations between words or concepts, that is, associative thinking, and on the generation and combination of remote associates. Patients with damage to the rostromedial PFC belonging to the DMN showed low performance in CAT due to the rigidity in semantic associations impairing the generation of unusual words. Patients with damage to the left rostrolateral PFC belonging to the FPCN had difficulties in controlled combination processes that are critical to integrating the generated associates to produce the solution and succeed in the CAT task. Thus, this finding supports the critical interplay of DMN and FPCN functional networks for creativity.

Other studies in patients support the critical role of the supposedly FPCN-related regions in creativity. In patient studies, the broad damage in FPCN related brain regions, such as the frontal and temporal regions due to neurodegenerative diseases, has been related to a decreased performance in divergent thinking tasks (Rankin et al., 2007). Damage to frontolateral or temporoparietal regions impaired the performance in several creativity tasks (Abraham et al., 2012a). The atrophy of these regions and parietal areas correlated with divergent thinking scores (de Souza et al., 2014). In patients with frontotemporal and semantic dementia, the grey matter atrophy in the left middle frontal gyrus was correlated to the DT performance [START_REF] Paulin | The effect of semantic memory degeneration on creative thinking: A voxel-based morphometry analysis[END_REF]. Studies in patients with focal lesions also support the critical role of the FPCN regions in creativity. However, few patient studies challenge the benefit of control processes in creativity indicating that they are detrimental to creativity.

Studies in patients also support the critical role of DMN related brain regions in creativity (Duff et al., 2013;Shamay-Tsoory et al., 2011;[START_REF] Warren | What relates newspaper, definite, and clothing? An article describing deficits in convergent problem solving and creativity following hippocampal damage: Hippocampal Damage Impairs Convergent Problem Solving[END_REF]. In addition, the damage to the rostral part of the medial PFC has been associated with decreased production of original ideas in divergent thinking tasks (Shamay-Tsoory et al., 2011), especially in the right medial PFC. Other studies have demonstrated the role of the hippocampus in relation to creativity. This DMN related brain has been related to reduced performance in both associative combination tasks and divergent thinking (Duff et al., 2013;[START_REF] Warren | What relates newspaper, definite, and clothing? An article describing deficits in convergent problem solving and creativity following hippocampal damage: Hippocampal Damage Impairs Convergent Problem Solving[END_REF]. In addition, patients with semantic dementia with predominant bilateral atrophy in the anterior temporal lobe [START_REF] Paulin | The effect of semantic memory degeneration on creative thinking: A voxel-based morphometry analysis[END_REF] had a decreased performance on the DT task that correlated to the grey matter volume in the left medial temporal lobe, centered on the left anterior hippocampus.

Overall, these findings suggest that damage to the DMN-related regions, such as the medial PFC and the medial and anterior temporal regions impair distinct aspects of creative thinking, possibly altering associative thinking. In addition, they highlight the link between the DMN and the memory systems.

Finally, despite their importance, only a few studies report the consequences of brain lesions on creativity. The findings from lesion studies allowed a more causal relationship between cognitive processes involved in creative abilities and brain regions or networks. To highlight strong support of the lesion studies to the neuroscience of creativity, we reviewed the scientific literature in the neuroscience of creativity in patients [START_REF] Ovando-Tellez | The contribution of the lesion approach to the neuroscience of creative cognition[END_REF]ANNEX-2). This review showed how lesion studies have been critical to determining the importance of diverse brain regions in the different cognitive processes underlying creativity, as well as the characterization of the functional networks to which these regions belong. In addition, we included a summary of these patient studies in a recently published chapter of creativity [START_REF] Bieth | Creativity[END_REF]. Overall, although most of the lesion studies have shown how the damage to specific brain regions relies on a decreased performance in creativity tasks, few studies have shown an increased creative ability in patients. Despite these opposed results, lesion studies strongly support the critical role of brain regions belonging to the DMN and the FPCN for creativity, as well as memory systems, and the critical interplay of these functional networks for creativity.

Overview

Research in the neuroscience of creativity is progressing in mapping the cognitive processes that support creative thinking, such as associative processing and various executive functions such as switching (Nusbaum and Silvia, 2011) and broad retrieval ability (Silvia et al., 2013) supporting control processes. Findings from neuroimaging studies in healthy subjects and patients further support the role of these cognitive mechanisms in creativity. These studies converge in the importance of three main large-scale brain networks for creativity: DMN, FPCN and SalN networks. Importantly, distinct brain regions belonging to these functional networks may be relevant for distinct creative processes.

Creativity theories emphasize the importance of connecting weakly related concepts rather than closely related ones in semantic memory during the creative process (Bendetowicz et al., 2018;Benedek et al., 2012b;Mednick, 1962). Despite the empirical evidence supporting the role of semantic memory for creative thinking, the relationship between semantic associations and creativity has not been thoroughly investigated. One of the main difficulties for exploring this relationship relies on the challenge of representing semantic memory.

However, recently developed computational approaches allow us to represent semantic memory as a complex network and to explore their properties in relation to creative thinking.

Network science: A tool to study semantic memory organization in cognitive neuroscience

Generalities of network science

Network science has been used across various scientific fields to study complex systems, including human cognition (Siew et al., 2019;Sporns, 2011). These methodologies are based on mathematical graph theory providing quantitative methods to explore complex systems as a network, such as social networks, brain networks (connectomes), and semantic networks (SemNets). The networks can be conceptualized as graphs composed of nodes and links, or edges, connecting the nodes to each other. Depending on the local and global efficiency, three different types of networks are characterized: regular, random, and complex networks. Regular networks are characterized by high local connectivity and low global connectivity. In contrast, the random networks have low local connectivity and high global connectivity. Complex networks have an optimized balance between local and global efficiency [START_REF] Watts | Collective dynamics of 'small-world' networks[END_REF], and they are commonly used to study complex systems in the The relationship between the nodes and the links within the graphs or networks are typically represented in adjacency matrices. The adjacency matrix is another representation of a graph allowing to explore the organization of the graph data in a different way. It is formed by columns and rows that represent the nodes, whose matching cells represent the links connecting those nodes. Depending on the directionality of the graphs, the undirected graphs have a symmetric upper and lower diagonal of the matrix; therefore, the information on both sides is equal. Conversely, in the directed graphs, the upper and the lower parts are asymmetric, and the information varies depending on the direction of the relationships. Depending on the values of the links, in the WUN graphs, the cells of the matrix are represented by the weight of the links, while in the UUN graphs, the links are represented by ones or zeros.

Characterization of the network properties

Networks can be characterized by different measures allowing to quantify their local and global connectivity, as well as the properties of their components. At the node level, it is possible to measure how connectivity varies across the nodes. The node degree is usually computed to characterize the number of connections that each node has to the rest of the network [START_REF] Fornito | Fundamentals of Brain Network Analysis[END_REF][START_REF] Fornito | Graph analysis of the human connectome: promise, progress, and pitfalls[END_REF]. Usually, the hub nodes have higher number of connections than average, thus, are facilitators of the information integration within the network.

At the network level, several local and global topological properties can be examined to characterize the organization of the nodes, such as the level of segregation, functional integration and small-world connectivity (Rubinov and Sporns, 2010; Figure 7). The measures of segregation describe the extent to which the network has specialized functions or shared properties between nodes that are densely interconnected within the network. Mainly, the measures of segregation quantify the number of modules or communities that are present in the network. The main measure of segregation is the modularity (Q) that describes how much a network is partitioned into sub-communities (Newman, 2006; Figure 7A). Thus, the larger Q is, the more the network comprises sub-networks. Q is calculated as the degree to which the networks can be subdivided into non-overlapping groups of nodes with a maximally number of links within groups and a minimal number of links between groups [START_REF] Girvan | Community structure in social and biological networks[END_REF]. The number of delineated communities can also be quantified as a network property. In addition to Q, the clustering coefficient (CC) is often interpreted as a measure of segregation as this metric is related to functional specialization of the networks, particularly in connectomes.

The small-worldness (S) property represents an optimal balance of functional integration and segregation within the network [START_REF] Sporns | Small worlds inside big brains[END_REF]. The S property describes complex networks characterized by functionally specialized or segregated modules with a robust number of links connecting the modules, and thus, facilitating the integration of the information [START_REF] Watts | Collective dynamics of 'small-world' networks[END_REF]. A network with an S property usually characterizes complex system networks such as brain and semantic networks, presenting a large clustering (higher CC) and small path length (shorter ASPL) [START_REF] Watts | Collective dynamics of 'small-world' networks[END_REF].

Overall, the different graph metrics or measures serve as a useful tool to quantify the properties of the network. These measures reflect the level of connectivity within the network (CC), the distances connecting different concepts within the network (ASPL and D) that relate to the efficiency or integration in the network, the different communities and level of segregation of the network (Q). These network measures can be then applied to different types of networks (e. g., brain networks or semantic networks) to have a more comprehensible description of their properties and representations. However, note that this list is not exhaustive since other measures have been also described. Here we focused on the main properties that have been described in the literature in relation to creativity.

Different types of networks in neuroscience

Brain networks

One of the most typical uses of network science applied to neuroscience is the exploration of brain networks. The brain networks or connectomes are studied using different imaging methods such as electroencephalogram (EEG) and fMRI [START_REF] Fornito | Fundamentals of Brain Network Analysis[END_REF]Rubinov and Sporns, 2010). Based on these approaches, and depending on the nature of the links between nodes, brain networks can represent either anatomical relationships (based on diffusion-based tractography MRI methods) or functional connectivity (for both EEG and fMRI). In brain networks, regions of interest are defined and constitute the network nodes. Depending on the type of the network, the links connecting the nodes represent either the anatomical tracts or the functional associations between the nodes [START_REF] Friston | Functional and effective connectivity in neuroimaging: A synthesis[END_REF]. In anatomical connections, links are typically white matter, while in functional connections, links represent temporal correlations in connectivity between brain regions (Figure 8).

Other important characteristics should be also considered to build brain networks. For instance, the directionality of the links within the network is commonly undirected since as it is not clear what they represent., They are commonly removed, or analyzed in a separate network, to allow the brain network metric computations more interpretable [START_REF] Fornito | Fundamentals of Brain Network Analysis[END_REF].

Semantic Networks

In addition to brain networks, recent research has shown how network science (Baronchelli et al., 2013;Borge-Holthoefer andArenas, 2011, 2010;Siew et al., 2019) provides a powerful tool to represent and quantify the properties of cognitive systems, including semantic memory. The use of the computational network science methodologies based on mathematical graph theory has allowed the exploration of the organization of the concepts in semantic memory as well as the quantification of their properties via SemNets.

In SemNets, nodes can represent different words or concepts, and the links connecting them represent the strength of association between the concepts (Figure 9). Thus, these elements serve as a proxy of the organization of these words in their individual SemNets. The directionality of the links within the network can vary if considering the causal relationships between words (e.g., in cases where specific words are frequently cued by a word, but not the inverse). For example, the word yellow is commonly evoked by the word banana; however, the word banana triggers other associated words more frequently than the word yellow, such us, sun, green and red. Most of the SemNets built to exploring the semantic associations between words are undirected networks, characterizing whether there is a relationship between the concepts without evaluating the direction of the relationship. SemNets can be also represented by WUN and UUN graphs with the same characteristics described previously.

nodes, where more similar concepts have a higher association strength within the network.

Besides creativity, this method has been used to explore network property differences between the first and second language in bilinguals [START_REF] Borodkin | When pumpkin is closer to onion than to squash: The structure of the second language lexicon[END_REF], children with cochlear implants and normal hearing [START_REF] Kenett | Semantic organization in children with cochlear implants: computational analysis of verbal fluency[END_REF], and compare young and older adults [START_REF] Cosgrove | Quantifying flexibility in thought: The resiliency of semantic networks differs across the lifespan[END_REF].

Free association task can also be used to build SemNets (Kenett et al., 2014). In this task, participants are asked to generate the first word coming to their mind in response to a given cue, i.e., for a given cue word table, response words are generated such as chair, meal, or plate. The associative strength between two words is the proportion of participants who gave this word association. Hence, the associative strength is commonly determined by the frequency to which a concept is given in response to a cue word among the group of participants and the extent of similar associative responses given to a pair of target words within the network. This associative strength can be used to form the links between the nodes (words) in a SemNet. Thus, the more similar associations generated and the greater number of participants generating these association responses to a pair of target words, a higher association strength between this pair of words in the network. However, this method can only examine group networks and is not sensitive to individual differences of participants.

The recent approach developed by Benedek et al. (2017) offers a novel method to build SemNets at the individual level based on a relatedness judgment task. In this task, participants are presented with different pairs of words and are asked to judge the relationships between the words using a visual scale ranging from 0 to 1. To operationalize the building of an individual's SemNet, the pairs of words of the task are combinations of an initial set of words that are used as the nodes in the individual SemNets. Since participants judge all possible pair word combinations, the links connecting the words can be formed using the semantic relatedness judgments given by the participants to the different pairs of words (Benedek et al., 2017;[START_REF] Yan | Left temporal pole contributes to creative thinking via an individual semantic network[END_REF]. Building individual representations of SemNets is a critical factor when studying SemNet metrics in relation to creativity.

Semantic network properties and creativity

During the last years, by means of the network science methods, SemNet structure have been used a proxy of the organization of the concepts in semantic memory. Using SemNets, we can represent how the concepts are associated in memory and thus, we can obtain more differentiated measures of the individual's semantic memory structure. Using a SemNet approach, few studies have explored the properties of the semantic memory organization in relation to creativity (Benedek et al., 2017;He et al., 2021;Kenett, 2018;Kenett et al., 2014;Kenett and Faust, 2019). One of the pioneering studies using this method was Kenett et al. (2014). The authors explored the properties of the SemNets in groups of high and low creative individuals as measured with DT and convergent thinking tasks. The SemNets were built based on free associations generated in response to 96 cue words. The authors identified several SemNet properties differentiating the groups of high and low creative individuals. The SemNet of high creative individuals was characterized by being more connected, more compact, with a shorter distance between concepts, and less modular than the SemNet of less creative individuals (Figure 10A). A few years later, Benedek et al. (2017) developed the relatedness judgment task to build individual SemNets and, thus, to capture the individual differences in network properties related to creative abilities. The authors used the associative relatedness ratings given by the participants to all possible word pair combinations of 28 cue words to estimate the properties of their individual SemNets that served as a proxy of the organization of these words in the semantic memory (Figure 10B). The results of this study replicated the findings of Kenett et al. (2014), revealing that the individual properties of SemNet correlate to creative abilities measured with the AUT task at the individual level. Thus, these findings support the relationship between SemNet properties and creative thinking (Kenett and Faust, 2019). Moreover, these results suggest that exploring the properties of SemNets is essential to better understand the cognitive mechanisms allowing original ideas to emerge. models of spreading activation, semantic priming occurs, at least in part, because a prime word leads to the pre-activation of the target [START_REF] Anderson | A spreading activation theory of memory[END_REF][START_REF] Collins | A spreading-activation theory of semantic processing[END_REF][START_REF] Mcnamara | Semantic priming: Perspectives from memory and word recognition, Semantic priming: Perspectives from memory and word recognition[END_REF]Neely, 1991). Hence, the semantic relatedness between concepts is usually considered a key predictor of associative processing since the activation of a specific concept leads to a higher probability of activation of the strongest associated concepts [START_REF] Anderson | A spreading activation theory of memory[END_REF][START_REF] Collins | A spreading-activation theory of semantic processing[END_REF][START_REF] Mcnamara | Semantic priming: Perspectives from memory and word recognition, Semantic priming: Perspectives from memory and word recognition[END_REF]Neely, 1991).

Associative priming is thought to depend on automatic spreading activation processes (spontaneous associations). However, in certain conditions, it could also involve additional controlled or strategic processes. For instance, a long stimulus onset asynchrony (SOA) duration could engage cognitive control processes such as inhibition, attention-based expectancy, and post lexical-semantic matching (Neely et al., 1989). In a SemNet, the preactivation of a target concept (a node) could occur due to the automatic spreading activation triggered by the activation of a previous related concept within the network. That is, when a concept is activated (e.g., by presenting a word), its activation spreads to its associated concepts through the links connecting them, from the closest associates to the more distant ones (Collin and Loftus, 1975). This activation spreads to all the concepts connected to it, but quickly dissipating as the distance (i.e., the number of links) increases [START_REF] Balota | Depth of automatic spreading activation: Mediated priming effects in pronunciation but not in lexical decision[END_REF][START_REF] Heyer | On the nature of the proportion effect in semantic priming[END_REF][START_REF] Mcnamara | Semantic priming: Perspectives from memory and word recognition, Semantic priming: Perspectives from memory and word recognition[END_REF].

Then, how the structure of semantic memory affects semantic priming effect? Moreover, since the spreading activation within the network quickly dissipates, how many steps (links) between the prime and a target word can the spreading activation traverse? These were some of the questions that attempted to answer Kenett et al. (2017). The authors used a semantic priming paradigm to examine whether the path length (i.e., number of links separating any two concepts) within the network can be used as a measure of semantic distance and how it relates to associative priming. The authors used a SemNet representing the organization of words in the Hebrew mental lexicon [START_REF] Kenett | Global and local features of semantic networks: evidence from the Hebrew mental lexicon[END_REF] to estimate the semantic distance between words.

Then, participants were presented with different pairs of words varying in path length and were instructed to judge them as related or unrelated. The authors found that shorter path length (up to 4 steps separating word-pairs) within the Hebrew network were judged as related along with an increase in reaction time with increasing the path length. Conversely, pairs of words separated by 4 or more steps (longer path length) were mostly judged as unrelated with shorter reaction times. Thus, during the semantic judgments, reaction time increased at shorter path lengths, but after 3 steps, reaction times decreased, and word pairs were judged as unrelated easily. The authors concluded that priming effect (spreading activation within the network) can potentially reach remote associates, that is, concepts separated by up to 4 steps within the network. Moreover, they validated the use of path length as a measure of semantic distance within a network. [START_REF] Kumar | Distant connectivity and multiple-step priming in large-scale semantic networks[END_REF] replicated these findings indicating that the priming effect within a network extends to distant concepts, but it also dissipates with increasing the distance between the concepts. Overall, these results support the relationship between SemNet structure and semantic associations, since semantic distance, as measured with path length correlated with the subjective judgements of associative strength and semantic priming effect.

Search process in semantic memory networks and creativity

As mentioned in the previous section, how people search for concepts or ideas in memory is still debated. One of the theoretical approaches attempting to explain this process is based on a random walk (Abbott et al., 2015(Abbott et al., , 2012)). Recent research explored how random walk models can capture the performance in creativity tasks which require cognitive search [START_REF] Bourgin | Empirical Evidence for Markov Chain Monte Carlo in Memory Search[END_REF][START_REF] Smith | The Role of Sequential Dependence in Creative Semantic Search[END_REF]. Using this approach, Kenett and Austerweil (2016) simulated random walks over the SemNets of low and high creative individuals. The authors found that a random walk over the SemNet of high creative individuals visits more unique words (nodes) within the network and moves further through the network. Their results indicate that differences in SemNet structure are sufficient to produce differences in creative performance between the participants. Thus, it is likely that differences in creative thinking can be captured by random walk search process relying in the memory representation (i.e., differences in SemNet structure), rather than the process over memory.

This finding shed light on how semantic memory organization as measured with

SemNet properties capture the more spontaneous searching in memory. However, how people intentionally search over memory, how SemNets allow to explore these processes, and what influence this search towards generating creative solutions remains to be explored. Moreover, whether creative people have different search processes over memory is still unclear.

Summary and unresolved questions

Creativity is a multifaceted human capacity that depends on individual differences in cognitive functions interacting with other internal and external factors [START_REF] Lubart | Psychologie de la creativite[END_REF]Lubart et al., 2015). The cognitive mechanisms underlying creative abilities are not yet fully understood, but previous studies have supported the significant role of associative abilities in creativity (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2012b). Yet, how creativity depends on the organization of semantic memory, i.e., on the organization of semantic associations in creativity, and what brain systems realize it remains poorly understood.

Recent research has demonstrated how network science methodologies allow exploring the structure of semantic memory via SemNets. Using this method, a pioneering study shown how individual differences in creative abilities as measured by the DT approach relate to SemNet structure (Benedek et al., 2017). This finding raised the question whether this link is task-dependent (specific to the AUT that was used, or to verbal material) or if it can be generalized to creative behavior in real life. Do individual differences in creative behavior in various domains also rely on the SemNet structure? If this relationship exists, can we identify the underlying neural mechanisms? Overall, the link between SemNet properties, neural mechanisms and creativity remains to be characterized.

Another fundamental unresolved question is how we search in memory for creative ideas. Previous research has shown that creative idea generation relies on associative processes, that can be facilitated by particular semantic memory structure reflected in the SemNet properties (Kenett et al., 2016) but also on controlled processes to guide the search (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2012a;Benedek and Jauk, 2018). However, the memory search processes that find creative ideas are not understood. How memory search processes relate to SemNet structure and executive control functions? Do they differ in more creative individuals? Besides differences in SemNet structure, whether more creative people also navigate differently over semantic memory compared to less creative people remains to be clarified. Previous studies have attempted to characterize, isolate, and capture the processing components of semantic search using fluency tasks (Troyer et al., 1998), with inconsistent results. Importantly, these studies have not explored them in relation to SemNet properties and creative abilities. Additionally, the underlying neural mechanisms of search processing components remains unclear.

Finally, to better understand how semantic associations and search processes relying on a more (or less) flexible SemNet structure contribute to creativity, it is important to improve our knowledge of how concepts are organized in memory. How semantic memory is organized and whether the organization of the concepts in memory allows us to implicitly relate concepts remains poorly explored empirically.

To fill these gaps of knowledge, we performed several studies combining diverse behavioral and neuroimaging methodologies. In the first part of my thesis, we combined recent developed methods in network science applied to neuroscience at the behavioral and neural level in order to explore the semantic and brain networks underlying creative abilities. This part contains two different studies: The first study is a behavioral research in which we developed a relatedness judgment task with a controlled material in French that enabled us to explore individuals' differences in SemNet organization in relation to various creativity measures, including creative behavior in real life. In the second study, we combined behavioral and neuroimaging data to explore the functional connectivity patterns underlying semantic memory structure related to creative behavior in real life, and thus, linked neural and cognitive levels to measure real-life creativity. In the second part of my thesis, we combined behavioral and neuroimaging data to explore the brain functional connectivity patterns underlying flexible search in memory by means of clustering and switching processes. In addition, we explored its relationship with the individual organization of semantic memory, executive functions, and creative abilities. Finally, in the third part of my thesis, we used the semantic priming approach to explore which are the different types of relationships that participate in the organization of semantic memory and allow them to be implicitly activated. We developed a double priming paradigm to explore whether taxonomic and thematic categorization can occur implicitly.

Objectives and general plan of the thesis

The general objective of my thesis aims to better understand the role of semantic memory structure and the processes acting on it in creativity, and explore the underlying brain patterns of functional connectivity. The studies addressing this main objective will be organized in three parts. o To explore whether the implicit retrieval of a thematic and taxonomic category is facilitated by two semantically related words belonging to this category more than by a single exemplar of this category measured by the mean amplitude of the N400 component.

PART 1: Semantic and brain networks underlying creative abilities

In collaboration with Dr. Mathias Benedek and Dr. Yoed Kenett

Building individual semantic networks and exploring their relationships with creativity

Introduction

Creative processes can be studied using different approaches. The associative theory defines creativity as forming new combinations by associating remote elements (Mednick, 1962). Based on this approach, it is hypothesized that creative abilities depend partly on the organization of semantic associations in memory. Several studies have shown that more creative people have better associative abilities (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2012b;Lee and Therriault, 2013;Marron et al., 2020;Rossmann and Fink, 2010), that may be supported by semantic memory structure (Benedek et al., 2017).

The network science methodologies' contribution to neuroscience makes it possible to explore complex systems such as semantic memory via SemNets [START_REF] Kenett | Global and local features of semantic networks: evidence from the Hebrew mental lexicon[END_REF]. Using this approach, we can represent the organization of the concepts in memory, allowing us to explore the properties of the networks by computing different graph metrics characterizing the networks. By means of this approach, a few previous studies have shed light on how the organization of the concepts in semantic memory can help understand creative cognition (Benedek et al., 2017;Kenett et al., 2014;Kenett and Faust, 2019).

These studies have shown that highly creative individuals are characterized by more compact, more connected, less modular, and shorter path lengths within the SemNet than low creative individuals (Benedek et al., 2017;Kenett et al., 2014). Therefore, the SemNet of high creative individuals is characterized by a more efficient structure and, thus, a better flow of information within semantic memory with a higher flexibility of associations. These results were consistent at the group (Kenett et al., 2014) and the individual level (Benedek et al., 2017).

In the study of Benedek et al. (2017), the authors developed a relatedness judgment task to capture the differences in SemNet organization at the individual level and explore the relationships between creative abilities and individual SemNet properties. However, in this pioneering study, creative ability was measured using the DT approach by means of the AUT task, without considering other aspects of creativity.

In this first study of my thesis, we used the approach of Benedek et al. (2017) to investigate SemNet properties and correlate them to creative abilities at the individual level.

The main objective of this work was to replicate previous results from Benedek et al. (2017) with a different material and extend them to diverse creativity measures. Thus, we first developed and validated the verbal material to be used in the study, and then explored the relationships between SemNets and creativity tasks, in order to replicate and extend previous findings.

The first step of this study was to develop the French version of the RJT task to be able to explore SemNet properties at the individual level. We developed a new computational method in order to control for the linguistic properties and optimize the semantic distance between all pairs of words used in the RJT task. This method consisted of building a French

SemNet based on French semantic association norms. Then, we used a novel computational method in order to select a set of words to be used in the task. These words were controlled by linguistic properties and the semantic distance between each other measured by the path length within the French network. We used all the possible pair word combinations of the selected words as trials for the task. Then, the relatedness ratings given by the participants in the task were used to estimate their individual SemNets. We validated this task by correlating the relatedness judgments of the participants to the different word pairs and the theoretical steps (semantic distance) within the French SemNet (as in Kenett et al., 2017).

The second step of this work was to characterize and correlate the SemNet properties to creative abilities. We included the AUT task (as in Benedek et al., 2017) and completed the set of tasks with other measures of creativity from distinct frameworks; the associative combination using the CAT task, and real-life like estimation with the ICAA questionnaire to relate these scores to the graph-based metrics of the individual SemNets. The use of different approaches of creativity allowed us to replicate but also extend previous results.

Article

Building Individual Semantic Networks and Exploring their

Relationships with Creativity Introduction

The associative theory of creativity hypothesizes that creative abilities are related to individual differences in the organization of semantic associations in memory (Mednick, 1962).

In support of this theory, several findings showed that more creative individuals had less common word associations or a less constrained organization of semantic associations (Beaty et al., 2014;Bendetowicz et al., 2017;Benedek et al., 2012;2017;Kenett et al., 2014;Rossmann & Fink, 2010;Volle, 2018) and that in brain-damaged patients, rigid semantic associations were associated with poor creative abilities (Bendetowicz et al., 2018;[START_REF] Ovando-Tellez | The contribution of the lesion approach to the neuroscience of creative cognition[END_REF].

Thus, the properties of semantic associations play a critical role in the cognitive processes that bring forth original ideas. Recently, computational methods exploring semantic memory structure in creativity are paving the way to uniquely study its role in creativity. One such computational approach is based on network science methodologies (Kenett, 2018;Kenett & Faust, 2019).

Network science is based on mathematical graph theory, providing quantitative methods to investigate complex systems, such as semantic memory, as networks (Siew et al., in press).

In semantic networks, concepts or words are represented as nodes that are connected to each other by edges (denoting semantic similarity between concepts). The few studies that have applied semantic networks in the field of creative thinking indicate that studying the properties of semantic networks is a promising approach to explore creativity (Kenett, 2018;Kenett & Faust, 2019). For example, Kenett et al. (2014) investigated the semantic networks of low and high creative participants, based on free associations generated by both groups to a list of cue words. The authors showed that the semantic networks of low creative participants were less connected and more spread out compared to high creative participants.

However, aggregating over participants into groups may obscure individual differences related to creativity. To address this issue, Benedek et al. (2017) developed a new method to estimate individual semantic networks, based on semantic judgment ratings. Participants rated the relations between all possible pairs of 28 cue words, chosen to represent seven different categories. These relatedness ratings served as a proxy to the organization of these cue words in an individual's semantic memory. The authors showed how individual-based semantic network metrics correlated with individual-based creativity scores (Benedek et al., 2017) for specific types of filtered networks. However, in their study, the authors subjectively selected such cue words, and also applied a specific task, the Alternative Uses Task (AUT), to measure creative ability.

The general aim of the current study was to replicate and extend the relationships between the properties of individual semantic networks and creative abilities found by Benedek et al. (2017). Individual semantic networks were estimated using a modified version of Benedek et al. (2017) in which we controlled for the selection of the cue words based on a computational method. Participant's creativity was more extensively assessed via a creativity battery, including the AUT used in the original study, a problem-solving task, and a creative achievement questionnaire. Specific network metrics of the individual semantic networks were computed and were correlated with the obtained creative scores.

Materials and Methods

Participants

Twenty-three healthy individuals aged between 22 and 37 years (26.96 ± 4.25) were included in the study. Participants were French-native speakers, right-handed with no neuropsychiatric disease. Two participants were excluded from the graph analysis because they rated >70% of word pairs as unrelated. This study was approved by the French ethical committee Sud Mediterrannée IV. Participants gave written consent and were paid for their participation.

General Overview

The study was composed of two parts. In the first part, the associative judgment task (AJT) was devised to estimate individual's semantic networks. The AJT was adapted from Benedek et al. (2017) by constructing new verbal material controlled for linguistic and semantic properties. In the AJT task, participants are asked to rate the semantic relatedness of pairs of words. In the second part, participants performed the AJT and a set of creativity tasks. AJT ratings were used to estimate the individual AJT-based semantic networks and network metrics were correlated with creative scores.

Part 1: The Associative Judgment Task (AJT)

We first used computational methods in order to develop and select a new set of cue words to be used in the AJT, accounting and controlling for semantic and linguistic properties.

This was achieved by 1) estimating a large French semantic network, based on a large database of semantic association norms in French, and 2) by selecting a set of cue words, based on the properties of this network.

Creation of a French Semantic Network.

To construct the French version of the AJT, we estimated a French semantic network of 1,081 words, based on French verbal association norms [START_REF] Debrenne | Le dictionnaire des associations verbales du français et ses applications. Variétés, variations et forme[END_REF]; http://dictaverf.nsu.ru/dictlist). This dataset was collected by asking French native speakers to provide the first word that came to mind after receiving a cue word. We selected words for which at least 400 participants provided a response. The final data contains 1,081 cue words and 26,268 responses from the participants.

The French semantic network was estimated using a network approach developed to analyze free association data (Kenett et al., 2014). According to this approach, each node represents a cue word and edges between nodes represent the association between these nodes.

These associations represent the similarity profiles across any pair of cue words, i.e., the overlap of associative responses generated by the sample to each of the cue words.

The network was estimated in the following way: First the associative responses were preprocessed to standardize responses (correction of typos, elimination of non-words and articles, and spelling homogenization). Second, a data matrix was constructed such that each column is a cue word, and each row is a unique associative response. Thus, each cell denotes how many participants generated response i to cue word j. Third, the correlation between any pair of cue words was calculated using Pearson's correlation. This resulted in a 1,081 by 1,081 matrix where each cell denotes the semantic correlation between node i and node j. To minimize noise and possible spurious associations, we finally applied the planar maximally filtered graph filter (Kenett et al., 2014). To examine the structure of the networks, the edges were binarized so that all edges were converted to a uniform weight (i.e., 1). This allowed us to compute the shortest path between nodes in the network, serving as the theoretical semantic distance between them (Kenett et al., 2017).

Selection of AJT Stimuli.

To select the verbal material to be used in the AJT, we developed a new computational method that allowed us to objectively select words with specific associative and linguistic properties from the French semantic network.

From the French semantic network, hierarchical tree structures were created recursively, using each node as a seed and searching for its neighbors. For each iteration, the neighbors of the neighbor's nodes were searched. In total we performed 4 iterations, considering that Kenett et al. (2017) demonstrated that most participants judged as unrelated the words separated by more than 4 steps in a force choice task. However, this tree procedure generated nodes that were separated by more than 4 steps when they belong to distinct branches, which allowed us to also

In total, participants performed 6 different runs of 100 trials each (except the last run with 95 trials). Each run was composed of 4 blocks of 25 trials and separated by 20 seconds rest periods with a fixation cross. The trials were pseudo-randomly ordered within blocks with the constraint that each block contained a similar proportion of word pairs of each theoretical step. This order was fixed across participants. Before starting the task, participants performed a short practice. In addition, we checked that all participants were familiar with the 35 AJT words.

Relatedness ratings were coded for each participant and values were averaged separately for each theoretical distance and overall (see Fig. 2).

Estimating AJT-based individual semantic networks. Participant ratings of the word pairs during the AJT task served as a proxy of the organization of these words in their individual semantic network. We created a n by n matrix in which n represented the words used in the AJT task and each matching cell represented the semantic relatedness judgement given by the participant for these two words.

We employed two network filtering methods, one that had revealed significant relationships to creative abilities in previous work (Benedek et al., 2017) and a more conservative method that keeps more information in the network. In the first filtering method, we applied a fixed minimum relatedness threshold to the data and only edges with a weight of at least 50 were maintained. Since the value of 50 is the middle of the AJT scale, only edges corresponding to moderate to high semantic relatedness were kept and set to 1 whereas all the others were removed, resulting in an unweighted undirected network (UUN). In the second filtering method, all the edges were kept with their weight, so it preserved the variability in semantic judgments and resulted in a weighted undirected network (WUN). In this graph, each edge was weighted by the relatedness judgement given by the participant. For both networks, when the participant judged two words as unrelated (rating = 0), the two corresponding nodes had no edges linking them.

Based on the metrics previously related to creative abilities (Benedek et al., 2017;Kenett et al., 2014), we computed the following network metrics to characterize the structure of an individuals' semantic networks: the clustering coefficient (CC), the average shortest path length (ASPL), the diameter of the graph (D), smallworldness (S), betweenness centrality (BC), and modularity (Q). CC measures the degree to which nodes in a graph tend to cluster together.

ASPL measures the average shortest number of steps that separate any pair of nodes, and D represents the longest path in the network. S is computed as a ratio between CC and ASPL. BC corresponds to the fraction of all shortest paths in the network that contain a given node. Q refers to the percentage of the network that is integrated into small-community structures.

Analyses were performed with the Brain Connectivity Toolbox in Matlab (Rubinov & Sporns, 2010).

Creative Assessment. Creativity was assessed using the Combined Associates Task (CAT), the Alternative Uses Task (AUT) and the Inventory of Creative Activities and Achievements (ICAA).

The CAT is an adaptation of the Remote Associates Task (Mednick, 1962) developed by Bendetowicz et al. (2017;2018) and assesses the ability to form new combinations between remotely associated words. In this task, participants are asked to find a word linked to three cue words with no apparent associations in a maximum of 30 seconds. CAT defines close and distant trials depending on the semantic distance between the cue words and the solution. 40

trials with an equal number of close and distant trials were administered. To quantify the data, four scores were analyzed. CAT_Solving is the sum of correct responses. CAT_Close and CAT_Distant correspond to the sum of correct responses in close and distant trials respectively, and CAT_Index corresponds to the difference in performance between distant and close trials, corrected by the averaged performance and was shown to reflect creative processes (Bendetowicz et al., 2017).

During the AUT, participants were asked to generate original uses for a common object in three minutes. At the end of the three minutes, the participants selected their two most creative responses, as top-two scoring has been observed to be an effective approach to assess creativity (Benedek et al., 2013;Silvia et al., 2008). This procedure was repeated for three objects: tire, bottle and knife. The corresponding nouns naming the objects were presented on the screen during the 3 minutes. Scores for fluency and originality were assessed for each object.

AUT_Fluency refers to the total number of ideas generated by the participant and AUT_Originality counts the number of infrequent ideas (given by less than 5% of the participants) among the top-two ideas of the participant.

The ICAA questionnaire was used to quantify everyday creative activities and achievements (Diedrich et al., 2018). This questionnaire contains two parts. In the first part, participants answered questions focused on 8 different specific domains. For each domain, the

AJT behavior and Creativity Scores

To test whether creativity also relates more directly to AJT behavioral measures (Rossman & Fink, 2010), Pearson correlations were computed between the creativity scores and AJT relatedness ratings, overall and separately for each theoretical step from 1 to 6. The mean AJT relatedness ratings correlated positively with AUT_Originality, ρ(19) = .54, p < .05: Participants judging word pairs as more related overall produced more original ideas at the AUT task.

Correlations with the other creativity scores were not significant.

When

Discussion

This study aimed to investigate the link between individual differences in the organization of semantic associations and creativity using computational methods based on graph theory.

Individual semantic networks were estimated using an adapted version of the method from Benedek et al. (2017) by controlling the selection of the words based on computational methods.

To this purpose, we first estimated a unique and large-scale semantic network in French. Next, we developed a method allowing to select a set of words in French while controlling for their semantic distance. Then, the selected words were used in a semantic relatedness judgement task and these relatedness ratings were used to estimate individual semantic networks. Several metrics characterizing the structure of these networks were computed and related to creative assessment scores.

Our results showed that the theoretical semantic distance correlated with the relatedness judgments of the participants, thus converging with the results of Kenett et al. (2017).

Theoretical distance relies on the properties of a semantic network estimated from a free verbal association task submitted to a large number of independent volunteers and from the similarity between the generated associates of all cue words. This semantic network allows to measure a theoretical distance as the number of steps separating two nodes in the network. That this measure was strongly related to the subjective similarity judgement of our participants between these cue words validate the use of path length computed on such semantic network as a measure of semantic distance (also converging with results from Kenett et al., 2017). However, it is important to note that while the correlations were highly significant, the Kendall τ coefficients were of moderate size (mean of ± .22). One possibility would be that the relationship between the theoretical distance and rated distance between selected stimuli may not be linear across the full range of steps. In addition, other factors could impact these subjective relatedness ratings. For instance, subjective ratings showed a high inter-individual variability that could in part be explained by creative abilities, as indicated in the second part of our study.

The next step of the current study consisted of a behavioral experiment aiming to examine the relationships between the organization of semantic memory and creative abilities. The findings showed that some network metrics for both WUN and UUN networks were related to creativity measures including the originality of ideas generated during the AUT (AUT_Originality) and the creative activities in life assessed with ICAA (ICAA_1). However, those network metrics were not significantly correlated to the number of ideas generated in the AUT (AUT_Fluency)

and creative achievements (ICAA_2) measured with the same tasks, nor to CAT scores.

Indeed, the results showed that participants with more original ideas in the AUT and/or more creative activities in their real life (ICAA_1), exhibit WUN networks that are less spread out (shorter D and ASPL), were more clustered (higher CC), showed greater small-world connective properties (higher S) and the nodes tended to have a more homogeneous connective role in the network (lower BC). Similarly, AJT-based UUN networks were also less spread out with shorter path length, less modular (lower Q) and with uniform nodes (lower BC) but with reduced small-world properties (S) in more creative participants.

Importantly, these findings replicate and expand the results from Benedek et al. (2017) who used UUN networks and showed similar correlations between CC and ASPL metrics and AUT;

we additionally observed correlations between the AUT and other metrics (BC, Q, D). In WUN networks additional correlations were shown between network metrics (D and ASPL) and ICAA_1. These correlations indicate that the organization of semantic memory measured by network metrics is also a relevant factor in real life creativity.

Overall, the current findings suggest that more creative participants exhibit a more clustered and densely connected semantic network whereas less creative participants have a more spread out and fragmented network. These results are in line with the few previous studies that examined semantic memory and creativity (Benedek et al., 2017;Kenett et al., 2014;2018;Kenett & Faust, 2019). Together these studies strengthen the view that exploring the organization of semantic associations using individual networks is both relevant and valuable for the neuroscience of creativity and support the associative theory of creativity.

Additionally, our method allows us to explore the relationships between AJT ratings and creativity measures. The AJT ratings averaged across all theoretical distances and separately for each theoretical distance greater than 1 was positively correlated with originality in AUT.

This finding indicates that participants who produced more original ideas also identified word pairs as more related, especially for pairs of words being theoretically more distant. This result is consistent with Rossmann and Fink ( 2010) that showed a positive relationship between originality and the evaluated associative distance between unrelated word pairs. These findings suggest that creative people are able to perceive connections between concepts that others may not see. Conversely, the mean AJT rating for theoretically close word pairs (1 step apart)

positively correlated with the total number of correct responses in CAT when considering all trials (CAT_Solving) or close trials only (CAT_Close). Participants who found close links in the CAT also judged theoretically close pairs as highly related. However, contrary to what was expected, the correlations with AJT ratings failed to reach significance when considering distant trials only (CAT_Distant) or the difference in performance between distant and close trials (CAT_Index). It is possible that distant trials involve additional processes that are less dependent on semantic associations (Bendetowicz et al., 2018). We cannot rule out that the small number of CAT trials used in this experiment may have influenced this result.

Finally, our results indicate that network metrics provide insight why people rate concepts as more or less related and that they are relevant quantitative measures to study creativity.

However, statistical analyses revealed that no correlation with creativity scores survived the corrections for multiple comparisons. The small sample size may explain the lack of power, and more participants will be included in this study to address this issue. Nevertheless, the trends in the results and their consistency with previous studies are encouraging. Overall, the findings suggest that exploring individual semantic networks based on a controlled verbal material is a promising approach to study creativity.

Conclusions and Perspectives

To conclude, our data indicate a close relationship between the organization of semantic associations represented by semantic networks and creative abilities. Although the results will need to be confirmed in a larger sample, which is an ongoing project, the current study is consistent with previous studies performed in this field and offers improvements in semantic network methods. Our results are notably in agreement with previous studies that showed a link between creative abilities and the ability to make semantic connections between unrelated concepts. Developing new methods to measure the ability to make new semantic connections is an important challenge to better understand the mechanisms of creative cognition (Benedek & Fink, 2019). The analysis of individual semantic networks is one of the most promising approaches to achieve this goal.

The results of this study also offer interesting hypotheses to test regarding the brain substrates that underlie creative abilities. For instance, the same paradigm can be combined with functional MRI to explore how brain network activity and connectivity covary with the ability to connect distant concepts as measured by semantic network metrics. Moreover, graph theory can be used to study how brain network connectivity relates to the organization of semantic networks in the context of creativity.

Finally, the current study provides valuable insight regarding the fruitfulness of the newly created French semantic network. This network could be especially useful for measuring the semantic distance of words produced by participants in cognitive tasks or building new French task material in which semantic distance needs to be controlled.

Discussion

The result of this study strongly supports previous findings associating creative abilities and the properties of the semantic memory structure measured via SemNet. In this study, we developed an RJT task to explore individual SemNet properties in relation to creative abilities in the French population. We replicated and extended the results in Benedek et al. (2017) by adding two amendments. First, we developed a computational method to control the selection of the cue words to be used in the RJT. This method allowed us to optimize the semantic distance between all possible pairs of words. Second, we expanded previous results to other creativity tasks.

The RJT task developed by Benedek et al. (2017) to explore individual SemNet properties in relation to creative abilities has been shown to be suitable for building individual SemNets and exploring their graph-based metrics. However, linguistic properties of words vary in relation to the specificities of each language, and linguistic aspects can highly influence verbal tasks. Thus, one of the critical steps of this study was the development of a novel method that enabled us to control linguistic properties and the theoretical semantic distance between all the word pairs used as stimuli in the RJT task.

The results in this study allowed us to validate our method of selecting the task's cue words and the RJT task. The participants' relatedness judgments to the different pairs of words were highly correlated to the theoretical distance between the pairs of words, indicating that more semantically distant words in the French SemNet were judged as less related (or more unrelated) by the participants. Critically, this correlation was shown at the individual and the group level. Additionally, we show a decrease in the relatedness judgments of the participants to the word-pairs up to 4 steps of semantic distance, and word-pairs with 4 or more steps were mostly judged as unrelated (as in Kenett et al., 2017). In this task, the theoretical semantic distance between the word-pairs was measured by the path length (i.e., number of steps)

separating the words (i.e., nodes) in the French SemNet. Since this measure was correlated with the subjective ratings given by the participants in the RJT task, these findings suggest that the novel French SemNet serves as a useful tool to measure semantic distance between words (Kenett et al., 2017).

To the best of our knowledge, no French SemNet based on associative norms has been built before. We built a network that represents a relatively high number of word associations.

Although, this number is not significant with respect to all the words that exist, it allows exploring the semantic distance between a range of concepts in French that is not limited to the words we used in the RJT task. Thus, the path length between the words within the network can be used as a semantic distance measure to create novel verbal material for cognitive tasks.

Moreover, the French SemNet is currently being used in the laboratory to explore the semantic distance of words generated by individuals in diverse cognitive tasks in ongoing projects.

Another critical step of this study was using a complete set of creativity tasks to assess creative abilities. It allowed us to replicate the results of previous studies by showing that AUT scores correlated with several SemNet metrics, particularly CC and ASPL (as is Benedek et al., 2017). Moreover, we extended these results by showing that AUT was also correlated to modularity and diameter of the SemNets. Furthermore, we showed that the mean RJT ratings to words-pairs with theoretical distances higher than 1 (i.e., > 2 steps separating them within the French SemNet) was correlated to the originality score in the AUT task. Thus, more creative individuals rated theoretically remote pairs of words as more related. This result converges with (Rossmann and Fink, 2010) findings showing that highly creative individuals provide lower estimates of the remoteness of unrelated words than less creative individuals. Contrary, we did not observe correlations between the CAT scores and SemNet properties. However, this study used a short version of this task, and the sample size was small. Thus, using a bigger sample size is required to further explore these relationships.

Critically, we also extended previous findings by showing the relationship between the individual properties of SemNet and real-life creativity measured with the ICAA questionnaire.

More creative individuals in real life had WUN SemNets that were less spread out (shorter D and ASPL), more connected (higher CC) and with greater small-world connective properties (higher S). These correlations shed light on the importance of the organization of semantic memory measured by network metrics for real life creativity. However, these results remain to be replicated in a bigger sample size.

In summary, the individual SemNet properties in high and low creative individuals in this study converged with the findings in previous studies (Benedek et al., 2017;Kenett et al., 2014). These results support the close relationship between the organization of an individual's semantic memory and creative abilities, and importantly they extend previous results, shedding light on the role of semantic memory structure in creative behavior. Importantly, we validate the RJT task based on French associative norms which is now suitable to be used in further studies in French. These findings open the question of the brain mechanisms underlying the part of creativity rooted in the SemNet structure. Do the SemNet properties that characterize more creative people rely on particular brain organization? We address this question in the next study, exploring whether individual differences in brain network connectivity support the SemNet properties that relate to creativity.

Brain functional connectivity patterns underlying semantic network properties in relation to creative behavior

Introduction

In the previous study, we developed and validated the RJT task as a tool that allows us to build individual SemNets. In addition, we demonstrated the relationship between individual SemNet properties and creative abilities and creative behavior, including measures of real-life creative activities and achievements. Here we aim to explore which are the brain correlates of the SemNet metrics related to real-life creative behavior. Particularly, we examined whether individual differences at the neural level in relation to SemNet properties also explain creativity in real life.

Creative behavior is critical in different domains of human life, allowing us reaching a wide range of achievements going from solving the problems we may encounter every day to more outstanding achievements in different creative activities. Yet, creative behavior in real life as measured by validated self-report questionnaires and the underlying brain correlates have been scarcely explored in the neuroscience of creativity. How brain connectivity supports particular properties of the semantic memory structure is also largely unknown. And finally, how creative behavior depends on semantic memory structure is poorly understood. Previous studies have shown that creative activities and achievements in real life are related to associative processes (Benedek et al., 2020;[START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF]. Benedek et al. (2020) show that creative activities relate to the ability of generating bi-associations, that is, to find associative links between two unrelated concepts. [START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF] further showed that the ability to generate more creative associations to a cue word was related to higher creative achievements in real life. However, no study has explored SemNet properties in relation to creative behavior in real-life. Based on the findings of the previous study, we expected creative activities and achievements of individuals to be related to individual differences in their semantic memory structure in this bigger group of participants.

At the neural level, few studies have explored creative behavior in real life using different approaches including evaluation of big-C samples (Chen et al., 2014a;Chrysikou et al., 2020bChrysikou et al., , 2020a;;[START_REF] Japardi | Functional magnetic resonance imaging of divergent and convergent thinking in Big-C creativity[END_REF][START_REF] Jung | Neuroanatomy of creativity[END_REF][START_REF] Shi | Different brain structures associated with artistic and scientific creativity: a voxel-based morphometry study[END_REF][START_REF] Wertz | Neuroanatomy of creative achievement[END_REF].

Structural brain differences between the groups of high-creative and average-creative individuals in relation to CAQ scores have been reported (Chrysikou et al., 2020b). Highcreative achievers have greater cortical surface area in the bilateral superior parietal gyrus and in the right postcentral gyrus. However, the same neural mechanisms between eminent and noneminent thinkers have been shown but to a different degree, involving the activation of brain regions related to DMN and FPCN networks during the AUT task (Chrysikou et al., 2020b).

Very few studies have investigated brain correlates of everyday creative achievement using the CAQ questionnaire. [START_REF] Jung | Neuroanatomy of creativity[END_REF] reported that higher creative achievement scores were related to lower cortical thickness in the left lateral orbitofrontal cortex and higher volume in the right angular gyrus. Other studies have reported that individuals with higher CAQ scores have a decreased volume of the rostral anterior cingulate cortex (Chen et al., 2014a), and with differences in the volume measure in brain regions belonging to FPCN and SalN network depending on the engagement in scientific or artistic creativity [START_REF] Shi | Different brain structures associated with artistic and scientific creativity: a voxel-based morphometry study[END_REF]. A recent study failed to replicate these findings [START_REF] Wertz | Neuroanatomy of creative achievement[END_REF]. However, the authors indicated that brain volume measures related to the total CAQ score, scientific, and artistic creativity scores included many regions from the FPCN and DMN networks.

Evidence from functional connectivity analysis emphasize the interaction of FPCN and DMN in relation to creative achievements as measured with the CAQ questionnaire (Sunavsky and Poppenk, 2020). The functional coupling between DMN and FPCN have also been shown in different domain-specific of creativity (see Beaty et al., 2016), including verbal divergent thinking tasks [START_REF] Beaty | Default and Executive Network Coupling Supports Creative Idea Production[END_REF], musical improvisation [START_REF] Pinho | Addressing a Paradox: Dual Strategies for Creative Performance in Introspective and Extrospective Networks[END_REF][START_REF] Pinho | Connecting to Create: Expertise in Musical Improvisation Is Associated with Increased Functional Connectivity between Premotor and Prefrontal Areas[END_REF], poetry composition [START_REF] Liu | Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process[END_REF], and visual arts [START_REF] De Pisapia | Brain networks for visual creativity: a functional connectivity study of planning a visual artwork[END_REF]Ellamil et al., 2012).

However, the neurocognitive determinants of individual differences in creativity related to the flexibility of semantic associations are still unclear and unexplored Among the different neuroimaging techniques, the whole-brain functional connectivity approach allows exploring in a more holistic and functional view brain connectivity to draw a clearer picture of how creativity is supported by brain organization (Beaty et al., 2016). Intrinsic functional networks identified during resting state are also present during task performance [START_REF] Cole | Intrinsic and taskevoked network architectures of the human brain[END_REF] and shape task activations [START_REF] Cole | Functional connectivity change as shared signal dynamics[END_REF]. These task-specific functional connectivity changes occurring during task performance are largely subject specific [START_REF] Cole | The Functional Relevance of Task-State Functional Connectivity[END_REF][START_REF] Gratton | Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation[END_REF]. Critically, the patterns of brain configurations during tasks can enhance individual signals allowing a more accurate prediction of cognitive behavior [START_REF] Finn | Movie-watching outperforms rest for functional connectivity-based prediction of behavior[END_REF]. In addition, exploring functional connectivity during a task allows a better control on what participants do during the fMRI acquisition and thus likely allows a more homogeneous exploration of brain configuration related to processes of interest, improving sensitivity to behavior differences.

A recent approach in neuroimaging research is the CPM method (Shen et al., 2017), which uses machine learning methods to identify patterns of functional connectivity that predict complex cognitive functions. Beaty et al. (2018) used this method to explore how unique brain functional connectivity patterns during the AUT task can predict differences in divergent thinking ability. However, whether creative achievements and semantic memory structure can be predicted by brain connectivity patterns have not been explored, or not in combination. Do individual differences in brain network connectivity support the ability to connect distant concepts as measured by SemNet metrics, and its relationship to creativity?

The present study explored in combination the relationships between real-life creativity, network properties of individual SemNets, and brain functional connectivity patterns. As in the previous study, we first examined the organization of the individual SemNets using network metrics. To this end, the relatedness judgments of the participants during the RJT task were used to build their individual SemNets, and to quantify the properties of these networks. We assessed real-life creativity, using the ICAA questionnaire. Using a cross validation with a leave one out method, we identified the SemNet metrics that reliably predicted differences in reallife creativity, which were highlighted as cognitive markers of creativity. Next, we explored functional brain connectivity using the CPM method. We examined whether the SemNet metrics previously identified as cognitive markers of creativity can be predicted from the individual's functional connectivity patterns during the RJT task. Finally, we demonstrated the role of the SemNet in mediating the link between functional brain connectivity and creative achievements.

Article

Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure

Marcela Ovando-Tellez 1* , Yoed N. Kenett 2 , Mathias Benedek 3 , Matthieu Bernard 1 , Joan Belo 1 , Benoit Beranger 4 , Theophile Bieth 1,5 & Emmanuelle Volle 1*

Introduction

Creativity is key to our ability to cope with change, innovate, and find new solutions to address societal challenges. Understanding the complex and multidimensional construct of creativity is thus fundamental to support societal, cultural, and economic progress. Creative behavior in real life depends on individual differences in cognitive ability, in addition to personality and environmental factors (1). The cognitive mechanisms underlying creative abilities are not yet fully understood, but pertinent theories of creative cognition consistently highlight the role of specific associative processes and the need to uncover meaningful links between unrelated concepts (2)(3)(4)(5)(6)(7).

The associative theory of creative thinking hypothesizes that creative abilities are related to the organization of associations in semantic memory (6). In support of the associative theory, several studies found that more creative individuals are able to link distant concepts more easily:

they provide lower estimates of the remoteness of unrelated words than less creative individuals (8), are faster in judging the relatedness of concepts ( 9), have less common or constrained word associations in free generation tasks (3,10,11), and a more flexible organization of semantic memory (2,7,(12)(13)(14). Other studies have specifically demonstrated the significant role of dissociation ability and associative combination in creative thinking (2-4, 15, 16). In addition, in brain-damaged patients, rigid semantic associations were associated with poor creative abilities (17,18). Associative thinking has been related to creative abilities as measured within several existing frameworks, such as divergent thinking (2-4, 8, 19), insight problem solving (6), analogical reasoning (20), as well as to creative achievements in real life (11,15,21). For instance, creative behavior in real life have been related to associative processes including generating remote semantic associations (11) or bi-associations (15). Overall, there is ample evidence that the way in which concepts are associated and activated via associative processes relies on the structure of semantic memory, which influences the ability to connect remote concepts into novel ideas (5,7,(22)(23)(24), and thereby semantic memory has a critical role in creativity.

Recent research has demonstrated how computational network science methodologies (25) based on mathematical graph theory allow exploring the structure of semantic memory via semantic networks (SemNets). Applying these methods, several studies have shown that creative abilities can be related to the structure of semantic memory (5,13,14,23,24,26). 13) have since been replicated across different languages (5,26,28),

demonstrating the validity of this approach.

In a previous study (26), we replicated and extended the approach developed by Benedek et al. (13) with two improvements: We controlled the selection of the cue words using a computational method optimizing the distribution of theoretical distances between words, and we assessed creative abilities and behaviors using a more diverse set of tools. Importantly, our previous study showed that the network metrics of individual SemNets, built from the relatedness judgments of the participants given to several pairs of words, correlated with several measures of creativity, including a questionnaire of creative activities and achievements (29).

Hence, individual SemNets measures allow exploring underlying cognitive mechanisms of creativity by revealing how different aspects of the semantic memory structure relate to creativity and to creative behavior (5). However, the neurocognitive determinants of individual differences in creativity related to the structure of semantic memory are still unclear and unexplored.

Existing MRI-based neuroimaging studies have identified a large set of brain regions involved in creative cognition (7,(30)(31)(32). A growing body of creativity neuroscience research has highlighted the importance of functional interactions within and between several brain networks, including the executive control network, salience network and the default mode network (30). Additionally, semantic and episodic memory regions (32)(33)(34)(35) and the motor and premotor regions have been shown to play a role in creative cognition (32,36). Evidence from studies in semantic cognition has further linked creativity to neural interactions between semantic representations and control processes (37). These components of semantic cognition draw on different brain regions (38) -with anterior temporal lobes (partially overlapping with default mode network) critical for heteromodal conceptual information in long-term memory, and regions interspersed between default mode network and multiple-demand network supporting semantic control (39,40). The advantage of a whole-brain functional connectivity approach is to provide a holistic and functional view of how brain networks relate to creative thinking. For example, resting-state functional connectivity within and between these networks was shown to predict creative abilities (31) and task-based functional connectivity within and between these networks increased during a creativity task, compared to a control task (30,31).

A recent approach in neuroimaging research is connectome predictive modeling (CPM; 41, which uses machine learning methods to identify patterns of functional connectivity that predict complex cognitive functions, including divergent thinking ability (31,41,42). Unlike previous research that focused on the brain connectivity associated with specific creativity tasks (e.g., divergent thinking), the current study explores the neurocognitive determinants of real-life creativity by studying the neural basis of semantic memory organization related to creative behavior. We hypothesized that the associative mechanisms reflected by SemNet metrics are relevant to real-life creative activities and achievements and can be predicted by functional connectivity patterns, involving, in particular, the control, default, and salience networks (31).

To this end, we first examine the properties of individual SemNets via network metrics and identify SemNet metrics that reliably predict differences in creative achievement and activities and thus constitute cognitive markers of real-life creativity. We then explore the functional connectivity of brain networks predicting individual differences in these SemNet markers. We use the CPM method and analyze functional brain connectivity during the performance of the semantic relatedness task that is used to estimate individual SemNets. This allows us to identify the task-based functional connectivity patterns predicting individual differences in SemNet properties. As an internal validation, we additionally explore whether the predictive models built in the task-based functional connectivity are generalizable to participants resting-state functional connectivity. Finally, we examine whether SemNet properties mediate the link between these brain connectivity patterns and real-life creativity, thus linking functional connectivity to real-life creativity via individual differences in semantic memory organization.

Results

Individual Semantic Network metrics and creativity

First, we explored the properties of individuals' SemNets in relation to creativity.

Similar to previous studies (5,13,26), we estimated participants' individual semantic memory network as weighted (WUN) and unweighted (UUN) undirected networks based on performance in the semantic relatedness judgment task (RJT; Figure 1). During the RJT, participants judged the relatedness between all possible pairs of 35 words (595 ratings).

In undirected networks the relation between node a and node b is equal to the relation between node b and node a which conforms to the nature of the judgment task. WUN and UUN differ in either assuming weighted or unweighted network links. The WUN is a more informed type of SemNet as it retains the weights of all links between the words (nodes). The UUN is a binary network that only retains the links between words (nodes) that are moderately or highly associated (i.e., ratings greater than 50 on the visual scale; Figure 1A) and sets their weights to 1. We built these two types of networks-UUN and WUN-as previous studies had suggested that they are particularly sensitive to creative ability (5,13,26), with subtle differences across creativity measures (5,13,26). Therefore, we aimed to explore whether the properties of a more (WUN) or less (UUN) informed networks are better captured by the individual's brain functional connectivity patterns during the RJT task. We then computed established network measures in cognitive network research (25), for WUN and UUN networks that have been previously related to creative abilities (5,13,14,24,26) 1). Based on previous studies that related SemNets to creative abilities (5,13,14,24,26), we expected higher creative behavior (higher C-Act and/or C-Ach scores) to be related to more connected and flexible (higher CC and S), less spread out (lower ASPL), or less segregated (lower Q) networks. Given that most previous work had focused on creative potential, we had no strong expectation on differences between predictions regarding WUN and UUN networks in the context of creative behavior.

We then examined how SemNet metrics predict real-life creativity by applying linear regression models, regressing creativity on each SemNet metric with leave-one-out crossvalidations: We iteratively fitted predictive linear models in N-1 participants and tested the model in the left-out participant. The significance of the model prediction was assessed by the correlation between the predicted value of C-Act (or C-Ach) computed by the model and the observed value using permutation testing. These analyses revealed that both real-life creative activities and achievements are predicted from different individual SemNet metrics (Figure 2).

The Spearman correlations showing the direction and size of the relationships between SemNet metrics and creativity are reported in Table 2. C-Act was predicted from WUN ASPL and UUN Q. C-Ach was predicted from WUN Q and UUN Q. Overall, more creative individuals had less modular SemNets. To ensure that the results were not affected by age, sex, education, and IQ, we explored the correlations between these variables and SemNet metrics and creativity scores (SI Table S1). The results remained significant after controlling for the variables that correlated with either the SemNet metrics or the creativity scores (SI Figure S1).

Prediction of creativity-related SemNet properties from brain connectivity

We applied the connectome-based predictive modeling (CPM) approach (31,41) to explore whether task-based functional connectivity patterns predict semantic memory network metrics that related to creativity (i.e., Q in WUN and UUN, and ASPL in WUN; see Table 2;

The applied CPM approach is illustrated in Figure 3). We used a functional brain atlas to define 200 brain nodes belonging to 17 functional networks networks (43). For each participant, Pearson correlations of the BOLD signal between all unique pairs of brain regions (i.e., nodes; n = 19,900) were computed to estimate the task-related functional connectivity of the whole brain connectivity network (Figure 3A). We then identified relevant links of the brain connectivity network that positively (positive model network) or negatively (negative model network) correlated with the SemNet metric across participants (Figure 3B). Next, we adapted the classical CPM method (41) to better take into account the network properties of the brain model networks. Instead of using the sum of the connectivity in the model networks, we computed two key network metrics describing small-worldness properties of human brain networks networks (44,45): their CC (brain-CC) and efficiency (brain-Eff; Figure 3C). We selected these brain network metrics since the small-world organization of the brain networks has been widely supported in network neuroscience (44). Such research has characterized the brain by a high clustering (brain-CC) and long-distance connections allowing efficient local and global information processing and integration (brain-Eff; measured as the inverse of the ASPL). Long-distance connections (as measured by brain-Eff) were shown to support the functional diversity of the whole brain acting as an integrated brain network (46). In addition, brain-Eff has been previously correlated to personality traits related to creative behaviors (47).

We then ran six separate linear models regressing each SemNet metric (Q for WUN and UUN, and ASPL for WUN) on each model network metric (brain-CC and brain-Eff). We used leaveone-out cross-validations, iteratively fitting predictive linear models in N-1 participants and tested these models on the left-out participant (Figure 3D). Finally, the model prediction was assessed by the Spearman correlation between the predicted value from the model and the observed values.

We then tested the relation between predicted and observed CPM models on the various SemNet metrics, using 1,000 iteration permutation testing (41; Figure 4). The CPM-based prediction from brain-CC was significant for the WUN Q metric (rs = .386, p = .004). The CPM-based predictions from brain-Eff were significant for the WUN Q metric (rs = .476, p = .001) and the UUN Q metric (rs = .272, p = .036). The CPM-based predictions of WUN ASPL from both brain-CC and brain-Eff, and UUN Q from brain-CC were not significant, showing either a negative correlation between predicted and observed values (indicating the prediction model failure) or not significant p-value after permutation testing. These results remained significant after controlling for variables (SI Figure S2) that correlated with either the SemNet metrics or ICAA scores (SI Table S1), i.e., sex, education, and IQ, suggesting that our findings are robust to individual differences in these factors. In summary, CPM analyses on task-based functional connectivity showed that brain connectivity CC and efficiency allowed reliable predictions of SemNet Q.

Functional anatomy of the predictive brain connectivity patterns

To characterize the functional brain connectivity patterns predictive of SemNet metrics, we explored the links of the model networks that account for SemNet properties relevant to creativity. Unique positive and negative model networks were identified for each SemNet metric (41) (Figure 3B) and used to compute their network properties (brain-CC and brain-Eff; Figure 3C). Since SemNet modularity (Q) was negatively correlated with both creativity measures (C-Act and C-Ach; Table 2) as expected from previous studies (5,13,14,26), we focused on the description of the negative model network predicting UUN Q (Figure 5) or WUN Q (SI Figure S3). In this model network, we considered the links that were shared in all iterations of the leave-one-out analysis, as the links in the model network can slightly vary at each iteration.

For the standard CPM negative model network of UUN Q, we identified 452 links.

Connectivity of these links related to lower SemNet Q, which again predicted higher real-life creativity. These links represented connections mainly within and between temporal, parietal, limbic and prefrontal lobes (Figure 5A-B). When we explored the distribution of these links at the functional networks level, based on the functional networks included in the Schaefer atlas (43), most of the links were part of the somatomotor, salience and default mode networks (Figure 5C). The highest number of links were found between control and default mode networks (8.2%), followed by links within the salience network and between somatomotor and visual networks. 5D). In summary, the main patterns of functional connectivity that predicted lower SemNet Q (i.e., related to higher creativity) had a whole-brain distribution and involved the control, default mode, salience and somatomotor networks.

Internal validation: Prediction of creativity-related SemNet properties from restingstate functional connectivity

We then explored whether the predictive models built based on the task-based functional connectivity data can be generalized to the prediction of SemNet metrics from the resting-state functional connectivity. To this end, we used the positive and the negative network models from the task-based functional connectivity data from all participants to build the predictive model for UUN Q and WUN Q (using the same method as for the task-based functional connectivity prediction described above but with all participants included). We then applied these predictive models to the participants resting-state data, by computing brain-Eff and brain-CC of the model networks during resting state for each participant. The model prediction was assessed by the Spearman correlation between the predicted value from the model and the observed values. The CPM-based prediction from brain-CC of resting state connectivity was significant for the WUN Q metric (rs = .210, p = .043). The CPM-based predictions from brain-Eff of resting state connectivity was significant for the WUN Q metric (rs = .267, p = .010), but not for the UUN Q metric (rs = .103, p = .327) (Figure 7). Thus, the predictive model of creativity-related SemNet properties built on task-based functional connectivity data was also partly significant when applied to the brain intrinsic connectivity data of the individuals. This result suggests that properties of memory structure, and particularly the modularity metric, are also represented in intrinsic functional connectivity. Critically, this result support the CPM-based predictive model's robustness and generalization to the resting state fMRI data (31).

Mediation Analysis

In the previous analyses, we found a relationship between SemNets and real-life creativity, and between brain functional connectivity and SemNets. In a final step, we analyzed whether the relationship between functional brain connectivity and real-life creativity is mediated by the SemNet properties. Hence, we conducted mediation analyses that focused on the indirect effect of functional connectivity on creative activities and achievements, using either C-Act or C-Ach as the dependent variable for each significant CPM model. To simplify interpretations, since UUN Q had a negative correlation with creativity, its value was reversed (UUN QR) to be positively correlated with creativity. Hence, lower modularity of the SemNet can be described as a higher flexibility of the SemNet (23,24,48).

Since C-Act was significantly predicted by the SemNet metric UUN Q, we explored the mediating role of UUN Q on the relationship between the properties of the functional brain network predicting UUN Q (brain-Eff) and C-Act (Figure 6A). As shown in the previous analyses, the regression coefficient between brain-Eff and UUN QR was statistically significant (beta = .305, p < .001), as was the regression coefficient between UUN QR and C-Act (beta = .443, p = .002). The total effect and the direct effect were not statistically significant (beta = .116, p = .328; beta = -.019, p = .872). In all mediation analyses, we calculated the indirect effect as the product of path a (i.e., the regression coefficient between brain functional connectivity and SemNet metric) and path b (i.e., the regression coefficient between SemNet metric and creativity; Figure 6). We tested the significance of the indirect effect using a bootstrapping method. The bootstrapped indirect effect was (.305)*(.443) = .135, and the 95% confidence interval ranged from 0.024 to 0.320. Thus, the indirect effect was statistically significant (p = .002). Hence, SemNets UUN Q mediated the relationship between the efficiency of functional brain connectivity (brain-Eff) and creative activities (C-Act): The higher the efficiency of the negative model network that predicts UUN Q, the lower the SemNet Q, and the higher are real-life creative activities.

C-Ach score was predicted from SemNet WUN Q and UUN Q metrics. We explored the mediating role of UUN Q between the functional connectivity of the negative model network predicting it (brain-Eff) and C-Ach (Figure 6B). The mediation analysis showed that the regression coefficient between brain-Eff and UUN Q R was statistically significant (beta = .305, p < .001), as was the regression coefficient between the C-Ach and UUN QR (beta = .241, p = .005). The total effect and the direct effect were not statistically significant (beta = .142, p = .056; beta = .069, p = .353). The bootstrapped indirect effect was (.305)*(.241) = .073, and the 95% confidence interval ranged from 0.018 to 0.140. Thus, the indirect effect was statistically significant (p < .001).

Hence, SemNets UUN Q mediated the link between the efficiency of brain functional connectivity (brain-Eff) and real-life creative achievements (C-Ach): The higher the efficiency of the negative model network that predicts UUN Q, the lower the modularity of SemNet, and the higher the real-life creative achievements.

Similarly, we explored the mediating role of WUN Q on the relationship between the properties of the functional connectivity of the negative model network predicting it (brain-Eff and brain-CC) and C-Ach (Figure 6C). Using brain-Eff as an independent variable, the regression coefficient between brain-Eff and WUN QR was significant (beta = .286, p = .004), as was the regression coefficient between C-Ach and WUN QR (beta = .183, p = .015). The total effect and the direct effect were not statistically significant (beta = .094, p = .183; beta = .042, p = .560). The bootstrapped indirect effect was (.286)*(.183) = .052, and the 95% confidence interval ranged from 0.005 to 0.110. Thus, the indirect effect was statistically significant (p = .018).

Using brain-CC as independent variable, the regression coefficient between brain-CC and WUN QR was significant (beta = .280, p = .008), as was the regression coefficient between C-Ach and WUN QR (beta = 0.192, p = .01) (Figure 6D). The total effect and the direct effect were not statistically significant (beta = .068, p = .365; beta = .014, p = .850). The bootstrapped indirect effect was (.280)*(.192) = .054, and the 95% confidence interval ranged from 0.006 to 0.130. Thus, the indirect effect was statistically significant (p = .018).

Hence, SemNet WUN Q mediated the link between the efficiency (brain-Eff) and the clustering coefficient (brain-CC) of functional brain connectivity and real-life creative achievements (C-Ach): The higher the efficiency and clustering of the negative model network that predicted WUN Q, the lower SemNets Q, and the higher the real-life creative achievements.

In summary, individual SemNets Q measured in WUN and UUN networks mediated the relationship between brain functional connectivity and real-life creativity.

Discussion

Our results demonstrate that brain functional connectivity can be used to predict individual differences in real-life creativity via quantitative properties of semantic memory structure. Thus, we uniquely link neural and cognitive levels to measure real-life creativity. We conceptualize long-standing associative approaches of creativity by means of state-of-the-art methodologies and link various strands of research to enable a deeper understanding of how the organization of brain networks and semantic memory networks relate to creative behavior.

Recently developed computational approaches allowed us to predict complex cognitive functions from brain connectivity (41,42) and to explore the organization of semantic memory at the individual level using SemNets (5,13,26). Using these novel approaches, we revealed unique patterns of brain functional connectivity that reliably predict differences in real-life creativity via semantic network structure. Using the CPM approach, we show that brain connectivity during semantic relatedness judgments predicted individual differences in the modularity (Q) of SemNets that was identified as a behavioral marker of creativity. Specifically, the efficiency and clustering of whole-brain connectivity patterns predicted differences in reallife creativity mediated by SemNet modularity.

According to the associative theory of creativity ( 6), high creative individuals are characterized by a more flexible organization of concepts in their semantic memory, allowing them to retrieve remote associations more easily (4,17,49). A recent study revealed the mediating role of associative abilities between semantic memory structure and creativity as measured by verbal creativity (5). Here, we show that individual semantic memory network properties also relate to real-life creativity: individuals with a more compact and less modular organization of their semantic memory exhibit more creative accomplishments. Part of this effect was consistent across both indicators of creative behavior (C-Act and C-Ach were predicted from Q) while there were also subtle differences (C-Act was additionally predicted by ASPL), which indicates differences in reliance on semantic memory structure in different realization of real-life creative behavior. Importantly, this finding was also consistent using two different thresholding methods for the SemNet (WUN and UUN), and remained significant when controlling for sex, education, and IQ. Our finding is in line with the previously reported relationship between semantic associative ability and creative behavior in real-life (11,15), and additionally shows how this relationship could be explained by individual differences in semantic memory structure, in particular as reflected by SemNet modularity. Less modular networks may allow more flexible thinking, with a higher connectivity between weakly related elements facilitating their combination. Overall, our findings strengthen and extend previous findings on the relation between properties of semantic memory structure at the individual level and creativity.

Furthermore, we showed that SemNet modularity represents both a behavioral marker of real-life creativity and a mediating mechanism underlying the effect of brain functional connectivity on real-life creative activities and achievements. The higher the efficiency and overall connectivity of the brain predictive network, the more flexible the semantic network (characterized by being more compact and less modular), and the more creative the participant is. Previous studies exploring the cognitive processes involved in creativity have revealed brain regions and functional networks associated with different creativity tasks (30,32). The use of SemNets allowed us to explore mechanisms that appear more broadly relevant to associative basis of creative cognition, avoiding the specificities of existing tasks. Using a whole-brain functional connectivity approach, we identified the task-based functional connectivity patterns related to semantic network properties predicting real-life creativity (activities and achievements). An internal validation of the predictive models using resting-state fMRI data suggests that the whole-brain predictive models we built in the task-based functional connectivity are robust and generalizable to the individual's resting state functional connectivity. However, while the validation was significant for the CPM predictions of WUN metrics, this did not apply to UUN metrics, which remains to be explained.

The predictive patterns included functional connections distributed across the whole brain, the densest being observed between brain networks previously linked to creativity (30,31,36,50). The major contributions to the prediction of creativity resulted from functional links between control and default mode network, within salience network, and between somatomotor and visual networks. The default mode network has been associated with several aspects of cognition involving heteromodal memory representation including self-generated thoughts and spontaneous associations (17,19), as well as control-demanding activities such as working memory and semantic goal maintenance (40). The control network is associated with controlled processes such as attentional control, working memory, inhibition, memory retrieval, and flexibility, which are necessary to accomplish the objectives of a specific task (51). The functional coupling between control and default mode networks has been reported in relation to creative cognition in several studies using different approaches (30,31,52). Additionally, higher intrinsic connectivity of semantically-relevant control and default mode network regions was associated with better performance in a task largely similar to the remote associates task commonly used to study creativity (37). Hence, our results can be interpreted in the light of the controlled semantic cognition framework (38), where conceptual representations interact with control processes when semantic retrieval is tailored to suit the circumstances. In addition to control and default mode network, the salience network has also been reported to play a critical role in creativity. It has been associated with attentional switching and detection of salient external or internal stimuli and appears to play a role in triggering the engagement of control and default mode networks during creativity tasks (30). Overall, our finding converges with previous correlational studies in creativity (30) and semantic cognition (37), and predictive studies of creativity using CPM approach (31,42) indicating the essential role of the functional connectivity within and between control, default, and salience networks for creative thinking abilities.

A considerable number of functional connections between somatomotor and visual networks also contributed to the prediction of creativity via SemNet properties. Both networks have been associated with creativity in previous studies (36,42), but independently. The motor system has been related to creativity as measured by different approaches, including verbal creativity, music improvisation, and visuospatial creativity (36,50). The brain regions of visual networks also appear to play an important role mainly in artistic creativity (50) and their activation was previously correlated with higher creative achievements (53). A recent study using the CPM approach showed the contribution of visual networks in the overlapping brain patterns predicting creativity and intelligence (42). Our study adds to this previous work by

showing the involvement of the coupling of motor and visual networks in creativity. The role of motor and visual regions in creativity can be plural. In the context of our RJT task used to estimate SemNets, semantic relatedness judgments may evoke visual representations and motor experiences associated with the concepts (38). It is then possible that less modular SemNets reflect less segregated motor and visual memory contents in higher creative individuals than in less creative ones, and closer connections between remote concepts in memory. Overall, our finding further supports and expands existing knowledge on the functional interaction within and between control, default mode and salience networks for creativity (31) by showing their link with real-life creativity and characterizing their role in the associative mechanisms captured by SemNet metrics. The current findings also shed light on the contribution of the coupling between regions of the visual and motor networks for creativity.

To further characterize the predictive patterns of functional brain connectivity, we identified the nodes with the highest number of connections being localized in the medial prefrontal cortex, insula, the extra-striate inferior region, parietal medial and temporoparietal regions, and temporal pole in the right hemisphere. Most of these regions have been reported

to play a role in creative cognition. In a brain lesion study, the medial prefrontal cortex of the default mode network has been shown to be relevant in associative processes underlying creative cognition (17). Moreover, this brain region and the insula of the salience network have been highlighted as essential regions for verbal creativity (30,31). The right lingual gyrus, part of the extra-striate cortex, is also recruited in verbal creativity tasks (32) in relation to the originality of semantic associations (15), and to internally directed attention reflecting increased visual imagery (54). Other temporal areas, including the right temporoparietal regions and temporal pole have been associated with verbal and visual creativity (55), including insight problem solving (56), mental imagery (57), and creative achievements (58). The involvement of the anterior temporal pole is consistent with its role as a semantic hub (38) and in abstract thinking and categorization (59,60).

One surprising result is that the highest degree brain nodes related to real-life creativity were distributed within the right hemisphere. Previous analyses reported a left dominance for creativity regions in functional (32,34), connectivity (31), and structural ( 61) imaging studies.

Most verbal creativity tasks highlight the critical role of brain regions of the left hemisphere, particularly in the prefrontal and temporal cortex, possibly related to linguistic/semantic processing processing (32,[START_REF] Abraham | Creative conceptual expansion: A combined fMRI replication and extension study to examine individual differences in creativity[END_REF]. Here, we also identified left-lateralized highly connected nodes contributing to the prediction of differences in real-life creativity in the left ventral prefrontal cortex of the control network and in the insula of the salience network, regions that have been shown critical for verbal creativity (18,31), as well as in regions underlying semantic cognition (38)(39)(40).

Yet, the right dominance of the predictive patterns in our study was unexpected because our study focused on the semantic basis of creative cognition and used a verbal task. The strong engagement of the right hemisphere might be related to the process of judging remote concepts during the RJT. Previous studies have indeed associated the right hemisphere with a relatively coarser semantic coding [START_REF] Jung-Beeman | Bilateral brain processes for comprehending natural language[END_REF] and the activation of broader semantic fields by words or contexts [START_REF] Chiarello | Exploring cerebral asymmetries for the verb generation task[END_REF]. Moreover, the engagement of broad associative processes in the right hemisphere has been related to hemispheric brain asymmetries in dopamine function [START_REF] Aberg | The "Creative Right Brain" Revisited: Individual Creativity and Associative Priming in the Right Hemisphere Relate to Hemispheric Asymmetries in Reward Brain Function[END_REF]. More creative individuals may rate distant words as more related during the RJT than less creative ones, which might rely on a higher functional connectivity with or within the right hemisphere. Hence, these findings show that diverse regions previously reported as central to creative cognition participate together in the predictive connectivity patterns of real-life creativity through a less segregated organization of semantic memory (lower SemNet modularity). Whether and how SemNet modularity reflects remote thinking that would rely more specifically on the right functional connectivity remain to be addressed in future studies.

Finally, the current SemNets-related results converge with and expand the few recent neuroimaging studies exploring the associative processes of creativity. Higher associative abilities in a free chain association task have been related to higher resting-state functional connectivity within the default mode network (19) and to larger gray matter volume in the left posterior inferior temporal gyrus (33). In both studies, higher associative abilities mediated the relationship between a priori selected regions of the brain and creativity. One recent study showed that efficiency in SemNets mediated the link between gray matter volume in the left temporal pole and a divergent thinking task (28).Our findings advance this knowledge in several critical ways. First, by using SemNets, we were able to estimate the organization of semantic memory, which offers some mechanistic perspective on remote and associative thinking, and showed its role in real-life creativity. Second, we employed a whole brain approach without focusing on a priori regions or networks. Third, we explored functional connectivity not during rest, but during the RJT, while all participants performed the same trials. This approach minimized individual differences in mental activity during scanning. It importantly gave access to the functional connectivity configuration that occurs during semantic relatedness judgments that reflect semantic associations. Overall, the unprecedented combination of recently developed network approaches allowed us to demonstrate that individual differences in brain structure predict creative behavior mediated by semantic memory structure, thereby characterizing what aspects of brain and memory structure are relevant to support creative behavior.

Some limitations to this study need to be acknowledged. First, our sample is relatively small and the use of additional external validation would add strong support to our findings.

Additionally, we observed significant sex differences in some SemNet metrics in WUN and UUN networks. We performed analyses at the behavior and brain level to control for the impact of sex differences (as well as education and IQ) on our results, which supported the robustness of the findings. However, the sample size did not allow us to explore the CPM predictions separately for women and men. Thus, future studies should explore how SemNet metrics differences between women and men influence creative abilities. Second, we used the SemNet approach that is rooted in the associative theory of creativity (6) to estimate individual semantic memory networks based on relatedness judgments of word pairs. The RJT-based SemNet metrics may not capture all the complexity of associative thinking. Thus, more work is needed to replicate our findings, using alternative methods to estimate individual's SemNets. It further remains to be explored how the results generalize across different performances in distinct creative domains. Third, the stability of the semantic networks properties with different sets of words remains to be properly tested. However, comparing the current results to previous studies that used the RJT with different words in different languages to estimate individual SemNets provides indirect support for the reliability of network solutions (5,13). Moreover, a recent study explored semantic network metrics across two timepoints without any manipulation task in between for a baseline group of the study. The authors found no differences across the two semantic networks in this group, thus providing further support for the stability of these network estimation approaches [START_REF] Kenett | Novel conceptual combination can dynamically reconfigure semantic memory networks[END_REF]. Finally, real-life creativity is not exclusively predicted by semantic memory. Many other internal and external factors are important to creativity, such as personality, motivation, emotions and environment (1,[START_REF] Karwowski | How Is Intelligence Test Performance Associated with Creative Achievement? A Meta-Analysis[END_REF][START_REF] Lubart | Creative potential and its measurement[END_REF]. Despite these other potential dimensions and sources of variability, the brain connectivity patterns allowed us to predict reallife creativity through the individual differences in semantic memory structure, suggesting its strong influence on creative activities and achievements.

In conclusion, the current findings uniquely link brain functional connectivity, semantic memory structure, and real-life creativity by combining advanced network-based methods in novel ways. By exploring semantic memory organization using SemNet methods, we were able to predict creative abilities independently of narrow frameworks or tasks. Our connectomebased predictive modeling approach identified brain connectivity patterns that predicted creative behaviors rooted in semantic memory properties. By converging these two approaches together, our study illustrates how the network organization of the brain and of memory can be related to each other, leading to exciting new frontiers of scientific inquiry. Participants declared no history of neurological or psychiatric disease, no evolutive neuropsychiatric condition, no psychotropic medication, no drug abuse or cognitive difficulties.

Materials and Methods

Participants

In total, eight participants were excluded from the fMRI analysis: Six participants were excluded because of the discovery of MRI brain abnormalities, one participant fell asleep during the acquisition of the data (precluding the analysis of both MRI and RJT data), and another participant had a claustrophobia episode at the beginning of the MRI scanning. The latter participant performed the RJT task outside the scanner and since his data did not deviate with respect to the group (scores and metrics were within ±2 standard deviations of the group mean) it was kept in the behavioral analyses (also see SI Table S2 the results without this participant).

The final sample was hence composed of 94 participants aged between 22 and 37 years (mean 25.4 ± 4. the actual task but using different stimuli. In addition, all words used in the RJT were displayed to participants to check that they were familiar with all of them (Details of the task training are described in SI S1). After the fMRI session, participants completed a set of creativity tasks on a computer outside the scanner that lasted around three hours.

Relatedness judgement task (RJT)

Task and material description

The RJT has been used to estimate individual-based SemNets and to explore the structure of semantic memory (5,13,26). The task requires participants to judge the relatedness of all possible pairs of words from a list of cue words. These judgements are then used to estimate an individuals' semantic memory network of these words. and the links were weighted by the normative associative strength between words. Next, we computed the shortest path between words and the minimal number of links between each pair was considered as the theoretical semantic distance between the words. Finally, we applied a computational method to select the RJT words that optimized the repartition of the theoretical semantic distance between all possible pairs of these words. The optimal solution included 35

words, resulting in a total of 595 word-pairs that represented the 595 RJT trials.

Each trial began with the displaying word pair on the screen along with a visual scale below ranging from 0 (unrelated) to 100 (strongly related). The stimuli were displayed for 4 seconds in total, divided into a reflection period of 2 seconds to ensure a comparable minimum judgement time and a response period of 2 seconds. During the first two seconds, the participants studied the word pair but couldn't move the slider yet. Two seconds after stimuli onset, the response period began, the cursor appeared in the middle of the visual scale, and the participants were allowed to move the slider on the visual scale to indicate their rating using a trackball. Participants were instructed to validate their response by clicking the left button of the trackball. The position of the cursor on the scale at the moment of the validation was recorded as the relatedness judgment. When participants did not validate their response, we recorded the slider position at the end of the two-second response period. After the response period, a blank screen was shown during the inter-trial interval jittered from 0.3 to 0.7 seconds (steps = 0.05; Figure 1A).

Task trials were distributed into six runs composed of 100 trials each, except for the last run (95 trials). Each run consisted of four blocks of 25 trials each (except the last block of the sixth run with only 20 trials), separated by a 20 second rest period with a cross fixation on the screen. Trials were pseudo-randomly ordered within blocks, such that each block contained a similar proportion of word pairs of each theoretical semantic distance. At the beginning and end of each run, participants had a ten second rest period with a cross fixation on the screen.

During the last two seconds of fixation cross periods, the cross changed color, warning the participant that the task was about to start. Participants had a self-paced break inside the scanner between runs.

Assessment of individual semantic network structure.

Building individual semantic networks

The relatedness ratings given by the participant to each pair of words was used to weight the links of the individual SemNet where each word is a node. We represent each of these networks as a 35x35 matrix with one column and one row for each word and cell values correspond to the judgment given by the participant during the RJT (Figure 1B). Based on previous studies and on our pilot study (5,13,26), we estimated two types of networks, a weighted undirected network (WUN) and an unweighted undirected network (UUN; Figure 1C) which vary on the type of threshold applied to the network. The WUN is a more informed type of the SemNet since it keeps the weights of all links between the words. The UUN is a less informed approach, retaining links above a defined threshold, and the links with a weight below the threshold are removed. We defined the threshold as rating value of 50 (the middle of the visual scale) to keep the links between words that were considered moderately or highly associated by the participants. The weights of the remaining links are uniformly set to 1.

Calculation of the individual semantic network metrics

We estimated the properties of the individual SemNet independently for the UUN and the WUN graphs. Based on previous studies relating SemNet to creative abilities (5,13,14,24,26), we computed the SemNet metrics that have been correlated to creativity: ASPL, CC, Q and S metrics. The Average Shortest Path Length (ASPL) is the average shortest number of steps needed to be taken between any pair of nodes. In semantic networks, path length reflects how related two concepts are to each other [START_REF] Kenett | The semantic distance task: Quantifying semantic distance with semantic network path length[END_REF]. The Clustering Coefficient (CC) measures the network's connectivity. It refers to the probability that two neighbors of a node will themselves be neighbors. In semantic networks, higher CC relates to higher overall relatedness between concepts. Modularity (Q) measures how a network is divided (or partitions) into smaller subnetworks; a higher Q relates to more sub-communities in the network [START_REF] Newman | Modularity and community structure in networks[END_REF]. Such subcommunities can reflect semantic categories in a semantic network. In creativity research, for example, more creative individuals often exhibit a more connected (higher CC), less segregated (lower ASPL and Q) semantic network than less creative individuals and these differences were related to flexibility of thought (24). The small-worldness (S) property of the network is calculated as the ratio between ASPL and CC and describes how much the nodes that are not directly linked can be reached through connections between their neighbors. In semantic networks, higher S has been linked to higher flexibility of thought (14). The computations were performed in Matlab (https://www.mathworks.com), via the Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/visualization; 71).

Assessment of real-life creativity

Outside the scanner, we used the Inventory of Creative Activities and Achievements Overall, creative activities capture the differences in the frequency of everyday creative behaviors, whereas creative achievements capture differences in publicly acknowledged creative accomplishments (29). A summary of the questions is reported in the supplementary material (SI S3), and the descriptive statistic for each specific domain explored in the ICAA is presented in the supplementary material (SI Table S3).

Relationships between individual Semantic Network metrics and creativity

We explored whether individual SemNet properties were predictive of real-life creative activities (C-Act) and achievements (C-Ach; Figure 1E). In independent analyses, we performed linear regressions using leave-one-out cross-validations to predict C-Act and C-Ach scores for each of the SemNet metrics (ASPL, CC, Q, and S of WUN and UUN SemNets). The analyses consisted of building a predictive linear model iteratively in N-1 participants using their SemNet metrics (e.g., WUN Q SemNet metric) and testing it in the left-out participant.

The model was applied on the SemNet metric of the left-out participant to compute a predicted value of the ICAA scores. The significance of the prediction was evaluated via Spearman correlations between the predicted and the observed creativity scores. When the correlations between observed and predicted values were positive with p < .05, we assessed its statistical significance using 1,000 iteration permutation testing. We report the Rho coefficient and the pvalue of the permutation test. Note that Spearman correlations are used for behavioral analyses as creative activities and achievements are typically skewed (72). We also ran Spearman correlations between SemNet metrics and ICAA scores to better represent the statistical association between the different SemNet metrics and creativity (Table 1). Additionally, we explored the correlations between SemNet metrics and creativity, and the following factors:

age, education, sex, and IQ. As a proxy of the IQ, participants performed the matrix reasoning subtest of the Wechsler Adult Intelligence Scale test (WAIS). The results of these correlations are reported in the supplementary material (SI Table S1). The prediction models of the significant results were re-analyzed by controlling for the factors that significantly correlated with SemNet metrics or creativity scores (SI Figure S1).

MRI Data Acquisition and Preprocessing

Neuroimaging data were acquired on a 3T MRI scanner (Siemens Prisma, Germany) with a 64-channel head coil. Six functional runs were acquired during each six task runs using The preprocessing of the on-task fMRI data was performed for each run separately and for the resting-state data using the afni_proc.py pipeline from the Analysis of Functional Neuroimages software (AFNI; https://afni.nimh.nih.gov). The different preprocessing steps of the data included despiking, slice timing correction and realignment to the first volume (computed on the first echo). We then denoised the preprocessed data using the TE-dependent analysis of multi-echo fMRI data (TEDANA; https://tedana.readthedocs.io/en/stable/), version 0.0.9 (73). The advantage of using multi-echo EPI sequences is that it allows better cleaning of the data by assessing the BOLD and non-BOLD signal through the ICA-based denoising method, improving the reliability of the functional connectivity-based measurement (74). The TEDANA pipeline consisted first of an optimal combination of the different echo time series.

Then, the dimensionality of the optimally combined data is reduced through the decomposition of the multi-echo BOLD data using principal component analysis (PCA We used the Schaefer brain atlas to define our ROIs that consisted of 200 ROIs distributed into 17 functional subnetworks than can be summarized in eight main functional networks (43). For each ROI, we extracted the BOLD signal during the RJT (averaged across voxels) and computed Pearson correlation coefficients of all pairs of ROIs. As a result, we obtained for each participant a 200x200 matrix with the correlation coefficients between all ROIs. These matrices were Z-Fisher-transform and rescaled by the maximal weight to have values between -1 to 1

)
for the subsequent analyses. This matrix corresponds to the functional connectivity network of each participant in which ROIs are the nodes and correlation coefficients the links.

A connectome-based predictive modeling approach

We used a CPM approach approach (https://www.nitrc.org/projects/bioimagesuite/; 31,41) to explore how SemNet properties can be predicted from functional connectivity patterns during the RJT task. We focused the CPM analyses on the SemNet metrics that predicted creativity scores following the method described in Shen et al. ( 41) (Figure 3). We used a leaveone-out cross-validation that consisted in building the model iteratively on N-1 participants and test the prediction on the left-out participants.

Since head motions during the fMRI acquisition can affect the CPM results, we verified that there was no correlation between motion patterns during the fMRI acquisition and the SemNet metrics. We estimated the mean FD, that is the sum of the absolute values of the derivatives of the six realignment parameters (77), and computed Spearman correlations between the mean FD and all SemNet metrics. The correlations revealed no significant correlation between the motion patterns and WUN ASPL (r = -.052, p = .622), WUN Q (r = .133, p = .203) and UUN Q (r = .127, p = .225).

The first step of the CPM consists of selecting the significant features of brain connectivity to build the "model brain networks". In the training set (N-1), we selected the links of the functional connectivity matrix (correlation coefficients between the ROIs) that significantly correlated with the tested SemNet metric (threshold p < .05) either positively (the positive model network) or negatively (the negative model network) across participants (Figure 3A-B). Since SemNet metrics had non-Gaussian distributions, we used Spearman correlations.

In these model networks of brain connectivity, negative links were removed (78). We normalized the values of the links (i.e., the correlation coefficients between ROIs) to have the same range of values for the calculation of the brain networks in the following step.

The second step consists in estimating functional connectivity properties within each participant's positive and negative model networks. This is one amendment from the classical protocol (41) to better take into account the structural properties of functional brain connectivity patterns. Instead of summing the links in the model networks (as in the classical CPM method), we estimated the network properties of the positive and the negative model networks using network metrics (Figure 3C). We computed two different whole-brain model network metrics:

1) Network efficiency (brain-Eff), measuring rapid and efficient integration across the network (79) and 2) CC (brain-CC), key property describing a small-world properties network characterizing the human brain (44). The brain-Eff metric was calculated as the average of the inverse shortest path length. The computation of the brain-CC metrics was similar to the CC of the SemNet described above in the "Calculation of the individual semantic network metrics" section.

The third and fourth steps consist in building the predictive model using the computed network properties and then applying it to a novel participant (the left out one for each iteration; (41). Since we used within-data set cross-validation, for the significant predictions, it was necessary to evaluate the predictive power of the CPM using permutation testing to assess the statistical significance of the results. To this end, we randomly shuffled the values of the SemNet metric 1,000 times, and we ran the new random data through the pipeline of our predictive model in order to generate an empirical null distribution and estimate the distribution of the test statistic given by the correlation between predicted and observed values.

Additionally, the prediction models of the significant results were analyzed by controlling for the factors that significantly correlated with SemNet metrics or creativity scores (SI Table S1).

To perform this analysis, we varied the third step of our CPM analysis by including sex, education, and IQ measures to the predictive model. We evaluated the predictive model by performing a Spearman correlation between the predicted and the observed SemNet metric (SI

Figure S2

). The CPM analyses were performed using Matlab Statistical Toolbox (Matlab R2020a, The MathWorks, Inc., USA). The pipeline for the CPM is an adaptation from the protocol by Shen et al. (41).

Functional anatomy of the predicting brain model networks

To explore the patterns of connectivity predicting the SemNet metrics, we characterized the main nodes and links of the significant model networks. We examined the distribution of the connections at the lobar level (between and within brain lobes) and at the intrinsic network level (within and between the eight main functional networks defined by the Schaefer atlas).

Finally, we explored the brain distribution of the six highest degree nodes (i.e., ROIs), which are the nodes with the highest number of connections. Due to the nature of the cross-validation approach (running one model for each iteration on N-1 participants), each iteration likely resulted in slightly different links in the model networks. Therefore, we considered the links that were shared between all iterations. The data visualization and plots were performed using To generalize our predictive models, and as an internal validation, we performed the prediction of the SemNet metrics from the resting-state functional connectivity. To this end, we used the on-task functional connectivity properties (brain-Eff and brain-CC) within each participant's positive and negative model networks to build the predictive model. We applied the predictive model in the resting-state data of the participants using the resting-state functional connectivity properties within each participant's positive and negative model networks during the resting-state acquisition (31). As for task-based analyses, mean FD was included in the models. The Spearman correlations between the predicted and the observed SemNet metrics were estimated to evaluate the prediction.

Mediation Analysis

To test whether the patterns of functional connectivity that predict SemNet properties are also relevant for real-life creativity, we ran mediation analyses. For significant CPM predictions, we tested whether the SemNet metrics mediated the relationship between the patterns of brain functional connectivity and creativity. As for the CPM analyses, the mediation analyses focused on the SemNet metrics that correlated with creativity scores. Hence, they explored an indirect effect of the functional brain connectivity on creativity through the SemNet properties.

The mediation analysis (81) consisted in calculating the product of (a) the regression coefficient of the regression analysis on the independent variable (i.e., brain functional connectivity metric, brain-CC or brain-Eff of the positive or the negative model networks) to predict the mediator (i.e., SemNet metrics) and (b) the regression coefficient of the regression analysis on the mediator to predict the dependent variable (i.e., creativity score), when controlling for the independent variable. We also calculated the regression coefficient of the regression analysis on the independent variable to predict the dependent variable without controlling for the mediator (total effect) and when controlling for it (direct effect; Figure 6).

The indirect effect was calculated as the product of path a and path b. All the variables entered in the mediation analyses were normalized. To decrease the impact of the skewness, we logtransformed the variables with skewed distributions (C-Ach and C-Act, and SemNet metrics).

The variables that had a negative correlation with creativity were reversed (multiplied by -1).

The selection of the positive or the negative network to be used on the mediation analysis depended on which of them is expected to be positively correlated to the creativity score. We tested the significance of the indirect effect using bootstrapping method, computing unstandardized indirect effects for each 5,000 bootstrapped samples, and the 95% confidence interval was computed by determining the indirect effects at the 2.5 th and 97.5 th percentiles. The mediation analyses were performed using the PROCESS macro (81) in SPSS 22.0 (IBM Corp.

in Armonk, NY, USA).

from SemNet properties estimated in (b). The SemNet metrics were used to build predictive linear models in N-1 participants. The predictive model was tested on the left-out participant using its SemNet metric (m) to predict its creativity scores. RJT = relatedness judgment task; SemNet = semantic network; ASPL = average shortest path length; CC = clustering coefficient; Q = modularity; S = small-worldness.

Tables We developed a computational method to select the verbal material of the RJT task by controlling for linguistic properties and the semantic distance between all possible pairwise combinations between the selected words (26). To this aim, we first built a French semantic network of 1,081 words, based on French verbal association norms (http://dictaverf.nsu.ru/dictlist). Then, we computed the shortest path between all pair of nodes (i.e., 1,081 words) in the network, serving as the theoretical semantic distance between the words [START_REF] Kenett | The semantic distance task: Quantifying semantic distance with semantic network path length[END_REF].

Second, we developed a computational method to select words with specific associative and linguistic properties in this French semantic network. The method searched for solutions that included a total of 35 words (more words would increase the duration of the RJT task exponentially, 35 words led to 595 word pairs which took 1 hour to judge in the RJT), that were nouns, with a lexical frequency greater than 20 occurrences per million and a number of syllables lower or equal than 3. In addition, words used in the creativity tasks were excluded from the selection. The selection method searched for solutions that optimally balanced the theoretical semantic distance between word pairs. In other words, we selected the words that optimized the proportion of pairs composed of words separated by 1, 2, 3, 4, or 5 or more steps.

Discussion

In this study, we used the unprecedented combination of recently developed methods in network science to link brain functional connectivity, semantic memory structure, and real-life creativity. Individual SemNets, used as a proxy of the organization of the concepts in semantic memory, along with the exploration of the network properties of the brain configurations during the RJT task, allowed us to demonstrate how individual differences in brain structure that predict creative behavior are mediated by semantic memory structure.

As in the previous study, we built the semantic memory networks based on the relatedness judgments of the participants given to pairs of words during the RJT task. We replicated our findings, showing the relationship between individual SemNet metrics and the measurements of real-life creativity with the ICAA questionnaire. In addition, we used a predictive leave-one-out approach that shows how semantic memory structure predicts creative activities and achievements. In particular, less segregated (lower modularity) and more efficient (shortest path length) SemNets predicted creativity. SemNet modularity may be a potential index of flexibility and spreading of activation of the concepts in memory (Kenett et al., 2018) and higher modularity may constrain the spread of information leading to increased rigidity of thought (Kenett et al., 2016) related to lower creativity. Overall, semantic memory structure assessed by SemNets properties may be a valuable component of creativity.

We also characterized the brain correlates underlying the creativity-relevant SemNet metrics. The CPM revealed functional connectivity patterns predicting individual differences in the SemNet properties. These patterns corresponded to unique brain functional connectivity configurations over the whole brain during the relatedness judgment of semantic associations.

Since the modularity of the SemNet was the property that accounted for the differences in reallife creativity, we focused on this SemNet metric. We were able to predict the modularity of the individual SemNets from brain functional connectivity patterns during the RJT task. We additionally showed that the modularity of the individual SemNets mediated the relationship between brain connectivity patterns and real-life creativity. Hence, we identified aspects of brain and memory structure that are relevant to support creative behavior in real life.

The functional connectivity patterns we identified involve several of the main largescale brain networks that have been associated with creativity tasks and creative abilities, including the medial prefrontal cortex related to DMN, bilateral insula and right parietal medial region related to SalN network, the left ventral prefrontal cortex of the FPCN, the extra-striate inferior region of the visual network, and temporoparietal regions and temporal pole in the right hemisphere. We additionally describe the coupling of the visual and the motor networks. Thus, a whole-brain distribution involving FPCN, DMN, SalN, somatomotor and visual networks predicted individual differences in creative behavior in real life.

Some limitations of our study must be acknowledged. First, we only explored interindividual variability, but not directly the mechanisms underlying the different processes.

Second, we found significant differences between individual SemNets in relation to sex, and further studies should be performed to explore these differences and how they relate to creative abilities. Third, we found subtle differences between the SemNet properties in relation to tasks. The authors used four different association tasks (Benedek et al., 2012b) to capture the associative abilities of the participants. Individuals with higher SemNet efficiency had better associative abilities that facilitated verbal creativity. Critically, associative abilities had a mediating role in the relationship between SemNet efficiency and verbal creativity. Thus, it is likely that SemNet structure support associative processes that are part of creative thinking, whose individual differences can predict higher or lower creative behavior in real life.

In conclusion, despite creativity being a complex multidimensional construct, we found evidence that the structure of semantic memory contributes to creative behavior in real life. In addition, our results show how the organization of semantic associations in memory as well as the underlying organization of brain networks participates in creative behavior. Convergent results from recent studies also support the role of semantic memory structure in creativity and indicate that associative processes are at play. One emerging question then is whether creative people not only have distinct semantic memory structure, but also are able to better navigate in their memory. How processes operating over semantic memory also contribute to higher creative abilities? SemNet than low creative individuals (Benedek et al., 2017;Kenett et al., 2014). Such a SemNet structure has been related to more creative search behavior (Kenett and Austerweil, 2016).

Moreover, the link between SemNet structure and verbal creativity seems mediated by associative abilities (He et al., 2021). However, the generation of creative ideas also relies on controlled processes to guide the search (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2012a;Benedek and Jauk, 2018) allowing efficient retrieval and navigation within memory (Hills and Kenett, 2021;Jefferies et al., 2020;Lambon Ralph et al., 2017). Thus, semantic retrieval relies on both automatic and strategic components (Unsworth et al., 2011). Then, how do we search in memory for creative ideas? What are the processes of semantic memory search that support higher creative ability? And what are the brain correlates realizing these processes? How people search across semantic memory and which processes influence this search towards finding creative solutions is still poorly understood. Moreover, it is still unclear whether high and low creative people have different search processes. This study aims to explore the neurocognitive components of semantic search that relate to creative abilities.

The search processes over semantic memory is typically measured with fluency tasks.

During this task participants generate a list of words for a specific category (i.e., category fluency) or words starting with a specific phoneme (lexical fluency), allowing to assess the retrieval processes. Troyer et al. (1998) characterized two components that interact during fluency tasks, clustering and switching (Troyer, 2000;Troyer et al., 1998Troyer et al., , 1997)). The authors described clustering as a more automatic process of semantic retrieval subserved by the temporal lobe in which individuals generate concepts that belong to a same subcategory (e.g., in semantic fluency of animals, generate pets). Conversely, switching involves executive processes related to the frontal lobe and characterize the process in which participants jump from one subcategory to another (e.g., going from pets to wild animals). This description appears partly in line with the two neural subsystems of semantic cognition: conceptual knowledge, and control processes guiding retrieval (Lambon Ralph et al., 2017;Rogers et al., 2015). Conceptual knowledge has been associated to more temporal regions and particularly anterior temporal lobe, while control processes guiding retrieval relate to more lateral regions of prefrontal cortex and other brain regions including the posterior middle temporal gyrus, the intraparietal sulcus, the pre-supplementary motor area and the anterior cingulate-ventromedial PFC (Noonan et al., 2013). However, it is still unclear whether clustering reflects pure automatic processes relying on temporal areas and switching more controlled processes relying on frontal regions (Davey et al., 2015;Gao et al., 2021;Ross, 2003;Ross et al., 2007;Unsworth et al., 2011;Wang et al., 2018).

Different approaches attempt to better understand semantic search and what clustering and switching processes capture. Hills et al. (2012) reframed the clustering-switching processes into the exploitation-exploration model. The authors proposed that memory search may involve the same processes as spatial optimal foraging, where switching to a different cluster occurs when the retrieval rate within the current one falls below a threshold (Hills et al., 2012). Later, Abbott et al. (2015) proposed an alternative explanation, based on random walk modelled on a semantic network. However, whether search in memory reflects a foraging search or random walk is still debated. Furthermore, how search processes reflected during the fluency tasks relate to creative abilities remains unclear.

Since creative thinking has been shown to be related to diverse executive functions (Beaty and Silvia, 2013;Benedek et al., 2012a;Forthmann et al., 2019b;Gilhooly et al., 2007;Silvia et al., 2013), the switching process during fluency tasks may capture controlled processes that are particularly critical for the search of creative ideas (Hass, 2017;Nusbaum and Silvia, 2011). Moreover, cognitive flexibility during search in memory may be particularly critical for creative ideas, converging with the important role of flexibility in the generation of creative ideas in DT tasks (Hass, 2017). Overall, how flexibility in search processes relates to individual differences in SemNet organization, executive functions that underlie cognitive control, and creativity remains to be explored.

Finally, neuroimaging studies have shown the involvement of several brain areas during category fluency tasks, including the left inferior frontal gyrus, medial prefrontal areas and premotor regions, cingulate cortex, insula, middle frontal gyrus, and anterior and posterior temporal regions (Foulon et al., 2018;Le Bouc et al., 2020;Li et al., 2017;Price, 2010;Wagner et al., 2014). However, how these brain correlates relate to specific search processes during the fluency tasks remains unclear. Troyer et al. (1998) emphasized a clustering-temporal and switching-frontal dissociation. However, empirical studies have not fully supported this hypothesis (Reverberi et al., 2006;Ross et al., 2007;Whitney et al., 2011b). Furthermore, functional connectivity studies on creativity have shown that associative and controlled processes of idea generation are supported by the interaction of several large-scale brain networks (Beaty et al., 2018(Beaty et al., , 2016; R. E. Beaty et al., 2019;Bendetowicz et al., 2018) including the DMN, FPCN, and SalN, rather than individual regions. Thus, assessing functional brain connectivity associated with individual differences in clustering and switching processes may contribute to better understand the brain systems that underlie distinct memory search components.

To better understand the neurocognitive components of semantic search that relate to creativity, we developed a novel free associative fluency task and characterized different search processes related to clustering and switching processes. This task has two main characteristics that parallel and differ from the typical semantic fluency tasks, respectively: First, as in the associative fluency task used in Benedek (2012b) , this task has unconstrained instructions, asking participants to generate free associations of words in relation to a cue word. Second, our free associative fluency task uses ambiguous words as cues (AmbiFlu). The fact that ambiguous words have different meanings allow the easy identification of the clustering and switching processes during the task. This is an important point as in typical fluency tasks, the borders between different categories are not evident (e.g., 'cat' can be categorized as mammal, pet, feline).

We assessed clustering and switching performance based on the number of responses within and between the different meanings. From the sample of the previous studies, a subgroup composed of 93 participants performed the AmbiFlu task. As in the previous studies, participant completed the RJT task during a fMRI session then, they completed a set of creativity tasks and neuropsychological tests. We explored how search processes related to clustering and switching during the AmbiFlu task correlated to individual differences in creative abilities as measured by two main approaches: DT and associative combination. To better understand the processes underlying the clustering and switching components, we explored their relationships with SemNet properties and executive functions processes. Moreover, we explored the individual's unique patterns of brain functional connectivity that predict both clustering and switching related components.

Based on the literature, we expected both clustering and switching related components to be correlate with creativity measures. According to Troyer's hypothesis, switching-related measures were expected to be related to higher executive control and cognitive control brain networks, while the clustering-related component may reflect semantic memory structure and rely on semantic memory regions.

Abstract

How do creative ideas arise in our mind and in our brain? A common assumption is that ideas result from searching, reorganizing, and combining stored knowledge, particularly from semantic memory. However, the specific mechanisms acting on memory to yield creative ideas are not well understood. This study aims to explore the neurocognitive components of semantic search that relate to creative abilities.

To this end, we designed a new associative fluency task in which search is initiated from an ambiguous, polysemous word (AmbiFlu). The AmbiFlu task enabled us to distinguish two We explored how these two components related to creative abilities, assessed by a divergent thinking (generating many and unusual ideas) and a convergent thinking task (finding solutions to problems by combining information in novel ways). To better understand the processes underlying the components, we examined their relationships with semantic memory structure and executive abilities. Semantic memory structure was explored by building semantic memory networks via computational network science methods. Finally, we used a connectomebased modeling approach to explore the brain functional connectivity patterns that predicted the clustering and switching components.

We found that both clustering and switching components correlated with creativity performance, yet differently: the clustering component correlated with fluency in divergent thinking, while the switching component correlated with the ability to combine remote associates in convergent thinking. Furthermore, the switching component, but not clustering, correlated with both semantic memory structure and executive functions tests and was predicted by functional brain connectivity between and within the default mode, control and salience networks. These results suggest that flexibility in memory search relies on interactions between spontaneous and controlled processes guiding the search. In contrast, the predictive connectivity patterns of the clustering component relied on interactions between control, salience, and attentional regions. Thus, clustering appears to capture controlled processes, possibly related to attentional focus allowing persistent memory search, and likely different from processes assessed by our executive function measures. Overall, our clustering and

Introduction

How do creative ideas arise in our mind and in our brain is a key unresolved question.

Ideas don't come from nowhere: It is commonly assumed that they result from searching, reorganizing, and combining stored knowledge (Abraham and Bubic, 2015;Beaty et al., 2014b;Benedek et al., 2012b;Benedek and Fink, 2019;Kenett and Thompson-Schill, 2020;[START_REF] Kenett | Investigating the structure of semantic networks in low and high creative persons[END_REF]Kounios and Beeman, 2014;Mednick, 1962), in particular from semantic memory.

But the specific mechanisms acting on memory to yield creative ideas are not well understood.

Therefore, this study aims to identify processes of semantic memory search that support higher creative ability, and explore their brain correlates realizing these processes.

The role of semantic memory in creativity has been well established across several lines of research (Beaty et al., 2014b;Benedek et al., 2012b;Benedek and Jauk, 2018;Benedek and Neubauer, 2013;Kenett, 2018;Kenett and Faust, 2019;Volle, 2018). The strength of semantic associations stored in memory, that determines their spontaneous activation by a given context, has been put forward as a creativity component as early as in the 1960s (Mednick, 1962).

Although associative cognition has proved challenging to empirically measure, studies have linked it to creativity using divergent thinking (generating many and unusual ideas) but also convergent thinking (finding solutions to problems by combining information in novel ways)

tasks (Beaty et al., 2021b;Bendetowicz et al., 2018;Benedek et al., 2012b;Benedek and Neubauer, 2013;Gray et al., 2019;Kenett and Austerweil, 2016;Marko et al., 2019;Marron et al., 2020Marron et al., , 2018)).

The organization of semantic associations stored in semantic memory can be studied using semantic memory networks via computational network science methods (SemNets; (Baronchelli et al., 2013;Borge-Holthoefer et al., 2011;Siew et al., 2019). Network science is based on mathematical graph theory, and provides quantitative means to represent complex systems, such as semantic memory, as networks. In a semantic memory network, nodes represent concepts, and edges represent the relationship between these concepts based on some similarity measures (Hills and Kenett, 2021;Siew et al., 2019). SemNet studies have explored how creative abilities are related to the semantic memory structure (Kenett and Faust, 2019). In this relatively new stream of research, the SemNets of more creative individuals were found to be more connected, less spread out and less segregated than those of less creative individuals (Benedek et al., 2017;[START_REF] Bernard | Building Individual Semantic Networks and Exploring their Relationships with Creativity[END_REF]He et al., 2021;Kenett et al., 2014;Kenett and Faust, 2019;[START_REF] Ovando-Tellez | Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure[END_REF]. Such a SemNet structure has been linked to more creative search behavior (Kenett and Austerweil, 2016). Moreover, the link between SemNet structure and verbal creativity seems mediated by associative abilities (He et al., 2021).

Memory search is not only determined by the structure of memory, but it also relies on controlled processes that allow efficient retrieval and navigation within it (Hills and Kenett, 2021;Jefferies et al., 2020;Lambon Ralph et al., 2017). Research on semantic memory characterized a neurocognitive semantic control component that allows to selectively retrieve, manipulate, and select meaningful information depending on context demands (Gao et al., 2021;Jackson, 2021;Jefferies and Wang, 2021;[START_REF] Ralph | Neural basis of category-specific semantic deficits for living things: evidence from semantic dementia, HSVE and a neural network model[END_REF]Patterson and Lambon Ralph, 2016;Rogers et al., 2015;Vatansever et al., 2021). Controlled retrieval has been consistently related to creative abilities (Beaty and Silvia, 2013;Benedek et al., 2012b;Forthmann et al., 2019;Gilhooly et al., 2007;Silvia et al., 2013), as have been diverse executive abilities including working memory or inhibition of obvious and common responses (Beaty et al., 2017(Beaty et al., , 2014b;;Benedek et al., 2014;Edl et al., 2014).

Hence, both associative and controlled memory components are thought to be jointly involved in creativity (Beaty et al., 2014b;Beaty et al., 2021b;Benedek et al., 2012a;Benedek and Jauk, 2018). Then, what makes memory search creative? What memory search components are related to higher creativity and how? Can we identify these components? Semantic memory search and retrieval is typically assessed with fluency tasks (Abbott et al., 2015;Benton, 1968;Hills et al., 2015bHills et al., , 2012;;Troyer et al., 1997). In the category fluency task, participants are asked to retrieve as many members of a category as possible in a limited amount of time (typically one or two minutes). To date, the cognitive components of fluency tasks are not fully understood (Abwender et al., 2001;Ross, 2003;Ross et al., 2007;Shao et al., 2014;Unsworth, 2017;Unsworth et al., 2011) and depend on the instruction and cues that are given (e.g., searching for words belonging to a given category, beginning with a given letter (lexical), or associated with a given word in any ways (associative). However, to characterize, isolate and capture the processing components of a semantic search task is challenging. Troyer et al. (1998) characterized two components that interact during fluency tasks, clustering and switching (Troyer, 2000;Troyer et al., 1998Troyer et al., , 1997)). During clustering, individuals generate words that belong to a same subcategory (e.g., birds within the animal category). During switching, individuals jump into another subcategory (i.e., from birds to amphibians). Troyer and colleagues suggested that clustering reflects automatic semantic retrieval while switching involves executive processes. Although this hypothesis is coherent with semantic cognition that describes two interacting systems for representing information and semantic control (Badre et al., 2005;Jefferies, 2013;Jefferies and Wang, 2021;Lambon Ralph et al., 2017;Thompson-Schill et al., 1997), the alignment of clustering -switching with associative -executive components, respectively, is far from established (Davey et al., 2015;Gao et al., 2021;Ross, 2003;Ross et al., 2007;Wang et al., 2018). Hills et al. (2015bHills et al. ( , 2015aHills et al. ( , 2012) ) reframed clustering and switching in the context of an exploitation-exploration trade off and showed that memory search may involve the same processes than spatial optimal foraging: switching to a different cluster occurs when the retrieval rate within the current one falls below a threshold (Hills et al., 2012). By analogy, they propose a cross-domain alignment of clustering-switching with local-global perception or focal-diffuse attention (Hills et al., 2015b). An alternative view, raised by Abbott et al. (2015Abbott et al. ( , 2012)), argued for a simpler, one-process search model that is based on a random walk model and equally models performance in a fluency task, similar to the two-process foraging search model proposed by Hills et al. (2012). However, whether searching over memory is a semantic foraging process or a random walk process is an open debate (Abbott et al., 2015;Avery et al., 2021;Jones et al., 2015). Yet, the framework of semantic foraging offers a useful quantitative method to characterize the responses in diverse generation tasks relevant to creativity. For instance, a few studies have separated exploitation/exploration processes operationalized in a visuospatial creativity task (Hart et al., 2017) or clustering and switching behaviour in a divergent thinking task (Hass, 2017;Mastria et al., 2021;Nusbaum and Silvia, 2011), suggesting that they reflect separable processes supporting creative ideation.

Overall, more work is needed to understand the search processes underlying verbal fluency and in particular those related to creative ideation (Hills and Kenett, 2021;Kenett in press). Although broad retrieval ability has been reliably associated with creative abilities (Beaty et al., 2014b;Forthmann et al., 2019;Gilhooly et al., 2007;Silvia et al., 2013), the cognitive components of fluency tasks that have a role in creativity remains to be clarified.

Switching may capture controlled processes are particularly critical for the search of creative ideas (Hass, 2017;Nusbaum and Silvia, 2011), as suggested by correlations between creative thinking and diverse executive functions (Beaty and Silvia, 2013;Benedek et al., 2012a;Forthmann et al., 2019;Gilhooly et al., 2007;Silvia et al., 2013). In addition, creative people have been shown to be able to switch between alternative meanings and consider things from different perspectives, allowing to re-structure their mental representations (Atchley et al., 1999;[START_REF] Best | The Relationship Between Subthreshold Autistic Traits, Ambiguous Figure Perception and Divergent Thinking[END_REF][START_REF] Blake | Divergent Thinking Influences the Perception of Ambiguous Visual Illusions[END_REF]Olteţeanu et al., 2019;[START_REF] Schilling | A "Small-World" Network Model of Cognitive Insight[END_REF][START_REF] Wiseman | Creativity and ease of ambiguous figural reversal[END_REF]. However, it is unclear whether switching across semantic categories or domains during generation phases of creative ideas relies on the structure of semantic memory or on controlled processes supporting effective memory retrieval. Similarly, clustering has been hypothesized to rely on the semantic memory system (Troyer et al., 1997), but how it relates to controlled retrieval and to the structure of semantic memory, in particular as captured using

SemNets is an open question.

Finally, as fluency tasks involve the interaction between several cognitive components, assessing their neural correlates would contribute to a better understanding of memory search.

Functional neuroimaging studies (Li et al., 2017;Price, 2010;Wagner et al., 2014) and lesion studies (Foulon et al., 2018;Le Bouc et al., 2020) have shown that category fluency relies on the coordinated activity of several brain areas including the left inferior frontal gyrus, medial prefrontal areas and premotor regions, cingulate cortex, insula, middle frontal gyrus, and anterior and posterior temporal regions. The specific role of each region and how they interact during the associative and controlled aspects of memory search is not fully understood. Troyer and colleagues (Troyer et al., 1998) proposed that clustering depends on the temporal lobe memory regions, while switching reflects executive frontal lobe processes. However, an empirical demonstration of such dissociation is weak and existing studies show a more ambiguous picture (Reverberi et al., 2006;Ross et al., 2007;Troyer et al., 1998;Whitney et al., 2011b). Besides, functional connectivity studies on creativity have shown that associative and controlled processes of idea generation are supported by the interaction of several large-scale brain networks (Beaty et al., 2019(Beaty et al., , 2018(Beaty et al., , 2016;;Bendetowicz et al., 2018) including the default mode network, executive control network, and salience network, rather than individual regions.

The default mode network is thought to support associative thinking allowing to generate candidate ideas while the executive control network participates in constraining the search and monitoring the responses. How these networks relate to memory search processes is unclear [START_REF] Kim | Default network activation during episodic and semantic memory retrieval: A selective meta-analytic comparison[END_REF]Marron et al., 2020Marron et al., , 2018)). Assessing functional brain connectivity associated with individual differences in clustering and switching may provide a useful approach to capture the brain systems that underlie distinct memory search components.

The aim of this study is to explore the neurocognitive components of semantic search that relate to creative abilities. To this end, we designed a new associative fluency task in which search is initiated from an ambiguous word (AmbiFlu), which enables to assess clustering and switching based on the number of responses within and between the different meanings.

Ambiguous, polysemous words are characterized by having different meanings, thus more than one concept may be activated in memory for the same word (e.g., for the word bank, there are at least two meanings: river bank -bank account). By using a free-association task to assess typical unconstrained association behaviour in an ambiguous context (Benedek et al., 2012b), we expected to capture spontaneous, associative and controlled processes involved in memory search. Ambiguous words are relevant to the exploration of creativity for several reasons: First, creative people have been shown to be better able to activate the representation of multiple aspects of potentially incongruous information, including competitive lexico-semantic meanings (Atchley et al., 1999) or figures [START_REF] Best | The Relationship Between Subthreshold Autistic Traits, Ambiguous Figure Perception and Divergent Thinking[END_REF][START_REF] Wiseman | Creativity and ease of ambiguous figural reversal[END_REF]. Second, ambiguous words have proven useful in isolating control demands in previous studies on semantic memory (Noonan et al., 2013;Whitney et al., 2011bWhitney et al., , 2011a)). Third, ambiguous words are helpful in investigating the clustering and switching framework, because they allow a clear separation of the clusters based on the different meanings. Cluster definition is indeed a limitation in previous methods using category fluency tasks, as subcategories are difficult to define or have blurred borders (e.g., by context (zoo) or by taxonomy (birds); Ross et al., 2007).

Although a few computational approaches have defined clusters based on semantic similarity measures, they remain dependent on normative data or text corpuses [START_REF] Goñi | The semantic organization of the animal category: evidence from semantic verbal fluency and network theory[END_REF]Hass, 2017;[START_REF] Lerner | Network graph analysis of category fluency testing[END_REF].

The AmbiFlu task enabled us to distinguish clustering and switching performance, two central cognitive components of memory search (Hills et al., 2012). We then explored how these two components relate to creative abilities as well as to individual differences in semantic memory structure using SemNets (Benedek et al., 2017;[START_REF] Bernard | Building Individual Semantic Networks and Exploring their Relationships with Creativity[END_REF]He et al., 2021;[START_REF] Ovando-Tellez | Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure[END_REF], and executive abilities (Le Bouc et al., 2020). Finally, using a connectome-based modeling approach (Beaty et al., 2018), we explored the functional connectivity patterns that predicted the clustering-and switching-related components. While we expected that both clustering-and switching-related components would correlate with creativity measures, based on the seminal work from Troyer et al. (1998) we hypothesized that switching-related measures would relate to higher executive control and cognitive control brain networks. Because previous research has demonstrated that producing a chain of related words (associates) involves spontaneous and unconstrained mode of retrieval with little executive demands (Gray et al., 2019;Marron et al., 2018) we expected the clustering-related component to rather reflect semantic memory structure and rely on semantic memory regions.

Materials and methods

Participants

General procedure

After being informed on the experiment and overall visit, the participants first completed the relatedness judgment task (RJT) inside the MRI scanner. Then, outside the scanner, they completed two creativity tasks (AUT and CAT), neuropsychological tests assessing executive processes relevant to creativity, and the AmbiFlu task.

Ambiguous word fluency task

Task material, design and scoring

In the ambiguous word fluency (Ambiflu) task, participants are required to give all the single words that they could think of as associated with a given cue word for one minute. As cue words, we selected three ambiguous, polysemous French words: somme (sum), glace (ice), and rayon (ray; note that English translation does not convey all the meanings of these words in French), that have high lexical frequency (> 20 occurrences per million in several corpus and movies; according to the French lexicon project, http://www.lexique.org/) and at least five different meanings according to the French dictionary (Supplementary Table 1). For each cue word, participants generated words associated with the cue word, without being informed that the words had different meanings. The cue words were read out by the experimenter and also presented visually on a paper sheet. The participants gave their responses orally, which were written down by the experimenter. Participants' responses were cleaned (orthographic errors and typos) and coded by EV as referring to one of these meanings (Supplementary Table 1).

The list of the different meanings of each cue was determined using a French linguistic resource for research (Centre National de Ressources Textuelles et Lexicales; https://www.cnrtl.fr/).

We adapted methods by Troyer et al (1998) to quantify five different measures related to clustering and switching with respect to the different meanings of ambiguous cue words. For each participant, and for each cue word, several measures were computed: 1) Fluency (the total number of different words generated during the time allowed); 2) The number of different meanings the responses refer to; 3) the number of switches between meanings quantified as the number of time two successive words refer to two different meanings; 4) the biggest cluster size, that was the largest number of successive responses that refer to the same meaning during the task; and 5) the rank of the first switch, that was the rank of the first response when the participant referred to a new meaning. Because there is a debate on how to best measure switching (Abwender et al., 2001;Ross et al., 2007;Troyer et al., 1997) we measured both the total number of different meanings and every transition from one meaning to another and used a principal component analysis approach (see below). We also measured the rank of the first switch in an attempt to capture how much participants stayed in the same meaning before considering alternative ones. The measured variables were calculated on each cue word separately and averaged across the three cue words for each participant.

Principal component analysis of the ambiguous word fluency task measures

To reduce the set of dimensions of the AmbiFlu task and because several measured variables were strongly correlated (see the correlation matrix between the five measured variables in Supplementary Table 2), we conducted a principal component analysis (PCA).

We ran the PCA on the five AmbiFlu measures averaged with oblique rotation (direct oblimin)

to rotate loadings and identify the variables contributing the most to each component in the 86

participants. The PCA analysis was based on a correlation matrix. Components with eigenvalues over Kaiser's criterion of 1 were kept with confirmation of their relevance using the scree plot. Sampling adequacy for the analysis was assessed with the Kaiser-Meyer-Olkin measure (standard threshold=0.507).

The PCA identified two factors with eigenvalues higher than one that together explained 83% of the variance. Table 1 shows the components loadings after oblimin rotation. The first one captures fluency in responses, cluster size, and how much initial responses stayed in the same cluster. Hence, this first component likely reflects clustering of responses within semantic meanings. The second component captures the number of meanings explored and transitions between them, thus likely reflecting switching-related processes (Troyer et al., 1997). The correlation between the two components was not significant (rs=-.153, p= .160) supporting their discriminant validity.

Overall, these results indicate that although nuanced aspects of clustering and switching were separately assessed, they essentially reflect distinct factors that can be named clustering and switching. We proceeded using these component scores for further analyses.

Creativity assessment

To assess creative abilities, we used two validated creativity tasks: Divergent thinking was assessed with the alternative uses task (AUT; Guilford, 1967); convergent thinking was assessed with the combination of associates task (CAT; Bendetowicz et al., 2018Bendetowicz et al., , 2017;;Mednick, 1962).

Alternative Uses task

In the Alternative Uses Task (AUT), participants were given 3 minutes to continuously generate alternative uses for each of three common objects (i.e., a tire, a bottle, and a knife).

The name of the object was pronounced by the examiner and displayed on the screen during the 3 minutes. The participants wrote down their responses on the computer using the keyboard and could see all their responses during the task period. At the end of the 3 minutes, participants were asked to select their 2 most creative ideas for each object (top-2; Benedek et al., 2013;Silvia et al., 2008).

All the generated responses were coded in order to homogenise similar ideas that were differently formulated. Then, classical scores for divergent thinking task were computed.

Fluency quantified the total number of responses generated by the participants summed across the 3 objects (AUT-fluency). The uniqueness score (AUT-uniqueness; was the sum of number of ideas across the 3 objects that were generated by less than 5 % of the other participants. The overall frequency of responses quantified the frequency with which each idea was generated within the group (AUT-commonness) that was averaged across responses and objects. Finally, 5 independent judges who were lab members familiar to creativity ratings, were given written instructions to rate the creativity of each response following the top-scoring method (AUTratings; Benedek et al., 2013;Silvia et al., 2008). They used a Likert scale from 0 (not creative) to 4 (highly creative). The inter-rater reliability showed an intraclass correlation coefficient equal to .74. The database to compute the AUT-uniqueness and the AUT-commonness included 8 additional subjects who performed the set of creativity tasks but not the AmbiFlu.

Combination of Associates task

The Combination of Associates Task (CAT) is an adaptation of the Remote Associates Test (Mednick, 1962), a classical task assessing convergent thinking in creativity. In the CAT, participants completed 100 trials composed of triplets of cue words that at first glance seem to be unrelated. On each trial, they were asked to provide a solution word that relates to each of the cue words. In developing the CAT, we revisited the classical Remote Association Test (Mednick, 1962), by varying the semantic distance between the cue words and the solution, as described in previous articles (Bendetowicz et al., 2018(Bendetowicz et al., , 2017)). In close trials (n = 50), the solution had a semantically close relationship to the cue words, in distant trials, the solution is remotely related to the cue words (n = 50). On each trial, participants were given 30 seconds to find a solution. They were asked to press the space bar as soon as they thought of the solution, and to write it down on the computer within 5 seconds using the keyboard. Next, they were asked to report whether the solution came to their mind with a feeling of insight or Eureka, i.e., when the solution arose suddenly and effortless to mind (Kounios and Beeman, 2014). They were given 5 seconds to press the 'v' key if they found the solution via insight, or the 'n' key otherwise. We quantified the accuracy in CAT as the total percentage of correct responses (CAT-CR). To differentiate the performance of the participants in the close versus the distant trials, we calculated an index as the difference in accuracy in the close and distance conditions, divided by the overall accuracy (CAT-index). This index reflects the ability to solve the more distant trials. Finally, CAT-eureka score was the percentage of reported Eureka among correct trials.

Building individual-based semantic networks

Relatedness judgment task

The RJT task was used to estimate the individual semantic networks (Benedek et al., 2017;[START_REF] Bernard | Building Individual Semantic Networks and Exploring their Relationships with Creativity[END_REF]He et al., 2021). This task consists in judging the semantic relationship between all possible pairwise combinations of 35 words controlled for linguistic properties and semantic distance between all word pair combinations. The 35 words were selected based on their linguistic properties and the distribution of the theoretical distance (strength of association) between each pair of words (see [START_REF] Bernard | Building Individual Semantic Networks and Exploring their Relationships with Creativity[END_REF] for details). In total, the participants performed 595 RJT trials. For each trial, the word pair was presented on the screen along with a visual scale below going from 0 to 100. Participants were instructed to rate the relationship or semantic association between the two words using a slider. They were encouraged to use all the values of the scale for their judgments going from 0 (totally unrelated words), to 100 (totally related words). Each trial was composed of a 2-second thinking period and a 2-second response period. The thinking period started with the display of the words and visual scale. Participants were instructed to think about the relatedness between the words. After this 2-second thinking period, the slider appeared in the middle of the scale. Participants could then freely move the cursor on the scale using a mouse and validating their response by a left click. This response period ended with the validation or when the 2 seconds of the response period had elapsed. The final position of the slider in the scale after validation was considered as the semantic relatedness rating. An inter trial interval jittered from 0.3 to 0.7 seconds (mean 0.5; interval: 0.05) separated two successive trials. Trials were grouped in 24 blocks of 25 trials each (except the last block that lasted 20 trials) and organized into six runs of four blocks each.

Blocks were separated by 20 seconds rest periods, and runs by self-paced rest periods. Each run lasted around 9 minutes. Trials were pseudorandomly attributed to blocks with the constraint that the distribution of the theoretical distances between word pairs was equivalent between blocks (see [START_REF] Bernard | Building Individual Semantic Networks and Exploring their Relationships with Creativity[END_REF]Ovando-Tellez et al., 2021 for details).

correspond to the relatedness judgments given by the participant during the RJT. For the unweighted undirect network (UUN) cell values correspond to a binary value of 1 for judgments above or equal to 50, and 0 for judgments below 50. From these adjacency matrices we built the WUN and UUN graphs and estimated their SemNet metrics (Benedek et al., 2017;[START_REF] Ovando-Tellez | Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure[END_REF].

Computation of the individual semantic network metrics

We computed the SemNet metrics for the UUN and the WUN graphs separately and computed the network metrics [START_REF] Vico Fallani | Graph analysis of functional brain networks: practical issues in translational neuroscience[END_REF][START_REF] Humphries | Network 'Small-World-Ness': A Quantitative Method for Determining Canonical Network Equivalence[END_REF]Latora and Marchiori, 2001;[START_REF] Rubinov | Weight-conserving characterization of complex functional brain networks[END_REF]. The SemNet metrics were selected based on previous studies showing their relevance for creativity (Benedek et al., 2017;[START_REF] Bernard | Building Individual Semantic Networks and Exploring their Relationships with Creativity[END_REF]He et al., 2021;[START_REF] Ovando-Tellez | Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure[END_REF]. We estimated the global efficiency (Eff), clustering coefficient (CC) and modularity (Q). Eff is calculated as the inverse value of the average of shortest path lengths. It represents how efficient is the structure of the network to allow the transition of information. CC is computed for each node as the proportion of neighbours that are neighbours to each other. Within the SemNets, higher clustering represents more connected or related concepts. Q is the level of segregation of the network, and it is measured as the level of division of the network into smaller subnetworks or communities. In SemNets, these communities can represent different semantic categories. A more flexible SemNet has been related to higher Eff and CC, and lower Q (Benedek et al., 2017;Bernard et al., 2019;He et al., 2021;Kenett et al., 2014;Kenett and Faust, 2019;[START_REF] Ovando-Tellez | Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure[END_REF]. The computation of the SemNet metrics were performed in Matlab, via the Brain Connectivity Toolbox [START_REF] Rubinov | Weight-conserving characterization of complex functional brain networks[END_REF].

Executive process tests

Digit span test

The digit span test of the Wechsler Adult Intelligence Scale (WAIS; Wechsler, 2008) was used to assess working memory ability. We used the criteria of performance of this task based on the WAIS manual. This task consisted of two parts in which participants repeated string of numbers that increased in size. In the first part (forward-span), participants were instructed to repeat 16 different strings of numbers going from 2 to 9 digits in the same order.

In the second part (backward-span), they were instructed to repeat 16 different strings from 2 to 8 digits, but reversely. Participants were given with a pair of strings as examples before the actual tasks. To quantify the total score, we gave 1 point for each correct recall. Both parts were quantified separately.

Trail making test

The trail making test (TMT) assesses set shifting [START_REF] Reitan | Trail Making Test: Manual for Administration and Scoring[END_REF]. This task is composed of two parts. In the first part (A), participants were presented with numbers distributed randomly on a sheet of paper. They were instructed to link the numbers (from 1 to 25) in increasing order with a pen, as fast as possible. In the second part (B), participants were presented with numbers (from 1 to 13) and letters (from A to L) distributed randomly within the sheet. They were instructed to link the numbers and letters alternately in increasing order, as fast as possible. The time to complete each part was measured. We quantified the difference between the time to complete the second minus the first part (TMT-shifting).

Category and letter fluency tasks

Participants performed a category fluency task (with the category animals; [START_REF] Cardebat | Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level[END_REF], and a letter fluency task (with the letter 'F'). For each task, participants were given 2 minutes to continuously generate as many words as possible. We recorded and transcribed the responses of each participant. Based on [START_REF] Cardebat | Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level[END_REF], we quantified the total number of responses generated by the participants for each task separately.

Stroop test

The Stroop test [START_REF] Stroop | Studies of interference in serial verbal reactions[END_REF], measuring inhibition, was based on the French version by [START_REF] Chatelois | Batterie informatisée d'évaluation de la mémoire inspirée de l'approche cognitive. [A computerized memory test battery based on the cognitive approach[END_REF]. In the first part, participants were presented with 100 written names of different colors and were instructed to read these names aloud. In the second part, participants were presented with 100 coloured squares and were instructed to name the colour of the ink. In the third part, participants were presented with 100 written names of colors with a different color ink. Participants were instructed to say the colour of the ink aloud Each part was timed and the total time to complete each part was recorded. Based on [START_REF] Chatelois | Batterie informatisée d'évaluation de la mémoire inspirée de l'approche cognitive. [A computerized memory test battery based on the cognitive approach[END_REF], we quantified the interference effect (stroop-interference) as the difference in time to complete the third (colour naming) and second part (ink naming with interference).

MRI Data Acquisition and Pre-processing

Whole-brain imaging was acquired on a 3T MRI scanner (Siemens Prisma, Germany) with a 64-channel head coil. The fMRI data was acquired during the RJT runs using multi-echo echo-planar imaging (EPI) sequences. Each run consisted of 335 volumes acquired with repetition time (TR) = 1,600 ms, echo times (TE) for echo 1 = 15.2 ms, echo 2 = 37.17 Functional volumes of each run were first despiked, slice timing corrected and realigned to the first volume (computed on the first echo) using the afni_proc.py pipeline from the Analysis of Functional Neuroimages software (AFNI; https://afni.nimh.nih.gov; [START_REF] Cox | AFNI: software for analysis and visualization of functional magnetic resonance neuroimages[END_REF]. In a second step, the data were denoised using the TE-dependent analysis of multi-echo fMRI data (TEDANA; https://tedana.readthedocs.io/en/stable/), version 0.0.9 [START_REF] Kundu | Integrated strategy for improving functional connectivity mapping using multiecho fMRI[END_REF](Kundu et al., , p. 20, 2012;;The tedana Community et al., 2021). The TEDANA pipeline consisted of an optimal combination of the echo time series followed by the reduction of the data using PCA and independent component analysis (ICA) to decompose the multi-echo BOLD data, and classify the BOLD components as BOLD or non-BOLD. The removal of the later eliminates the thermal and physiological noise including the artefacts generated by the movements, respiration, and cardiac activity. The advantage of acquiring multi-echo EPI sequences is the possibility of assessing the BOLD and non-BOLD signal through the ICA-based denoising method, improving the reliability of the functional connectivity-based measurement (Lynch et al., 2020).

In the last step of the preprocessing, the data was co-registered on the T1-weighted structural image using the Statistical Parametric Mapping (SPM) 12 package running in Matlab (Matlab R2017b, The MathWorks, Inc.,USA) and normalized to the Montreal Neurological Institute (MNI) template. To spatially normalize the fMRI data, we used the transformation matrix computed from the normalization of the T1-weighted structural image, using with the default settings of the computational anatomy toolbox (CAT 12; http://dbm.neuro.uni-jena.de/cat/;

Gaser and Dahnke, 2016) implemented in SPM 12.

To covary out the task-related signal from each run, the denoised and normalized fMRI data were entered in a general linear model in SPM. We regressed out of the BOLD signal 24 motion parameters (standard motion parameters, first temporal derivatives, standard motion parameters squared, and first temporal derivatives squared) and the onsets and durations of each task related events (reflection period, response period, inter trial interval, cross fixation periods, and change of the cross-fixation colour). We then standardized and detrended the residuals of the GLM for each run, and concatenated the six runs, removing the between runs rest periods.

This preprocessed data was used in the subsequent analyses.

Building individual-based brain functional connectivity matrices

The functional connectivity matrix of each participant was computed using Nilearn v0.3 [START_REF] Abraham | Machine learning for neuroimaging with scikit-learn[END_REF] in Python 2.7 (van Rossum, 1995). We defined regions of interest (ROIs) based on the Schaefer brain atlas [START_REF] Schaefer | Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI[END_REF] rescaled by the maximal weight to be in the range of -1 to 1 for the subsequent analyses.

Connectome-based predictive modeling (CPM) of AmbiFlu components

We used the CPM approach to analyze how cognitive components of the AmbiFlu task rely on whole brain functional connectivity. We followed the method described in Shen et al.

(2017) regressing cognitive component on brain networks with a leave-one-out crossvalidation. The CPM analysis consisted of five steps, and a validation process. Since we used a leave-one-out validation, the first three steps were performed in N-1 participants, and the fourth step was performed in the left out individual.

In the first step, we selected the behaviourally relevant edges of the brain connectivity individual networks to build the "model brain networks". We selected the connections in the the observed values of the AmbiFlu component. Hence, we ran a separate analysis for the AmbiFlu components. Since we used within-data set cross-validation, when Spearman correlations were significant, it was necessary to evaluate the predictive power of the predictions using permutation testing. To this end, we randomly shuffled the observed values 1,000 times, and we ran the pipeline of our predictive model using the new random data. Thus, we generated an empirical null distribution and estimated the distribution of the test statistic

given by the correlation between predicted and observed values. The CPM analyses were performed using Matlab Statistical Toolbox (Matlab R2017b, The MathWorks, Inc., USA).

We explored the functional anatomy of the positive and negative model networks of each score by localizing and describing the main nodes and links of the significant model networks. Since we employed a leave-one-out method, for each iteration the number of links can be slightly different. For a better interpretation of the results, we considered the links that were significant in all iterations. We examined the distribution of the connections at the region level (between and within brain lobes) and at the intrinsic network level (within and between the eight main functional networks defined by the Schaefer atlas). Finally, we identified the highest degree nodes (i.e., ROIs), which are the nodes with the highest number of connections within the predictive networks. With the leave-one-out approach, the model networks were estimated N times on N-1 participants, and thus each iteration likely resulted in slightly different model networks. To obtain a reliable representation of the predictive model networks, we selected for this description the connections in the model networks that were shared between all iterations. The data visualisation and plots were performed using BioImage Suite Web

Results

The descriptive statistics of creativity, SemNet metrics and executive tests are reported in Supplementary Table 3. We explored the relationships between the identified principal components of the AmbiFlu task and the other behavioural measures, including AUT scores, CAT scores, SemNet metrics and executive function scores, using Spearman correlations. The correlations are reported uncorrected and corrected for multiple comparisons using the false discovery rate (FDR) in Figure 2 and Supplementary Table 4.

Relationship between AmbiFlu clustering and switching and creativity

We examined correlations between clustering and switching components of AmbiFlu and the two classic creativity tasks used. Spearman correlations analyses revealed significant correlations between the clustering component and AUT-fluency (p < .001), AUT-uniqueness (p < .001), and AUT-commonness (p = .013; the switching component was positively correlated with the accuracy in CAT task CAT-CR (p = .032). The other correlations between the AmbiFlu components and creativity scores were not significant (p > .05). The correlations between clustering and both AUT-fluency and AUT-uniqueness remained significant after FDR correction for multiple comparisons. These results indicate that individuals with higher clustering in the AmbiFlu task generate a higher number and more unique and infrequent ideas in the AUT, whereas individuals with higher switching in the AmbiFlu task may have higher ability to combine remote elements in the CAT task (see Figure 2 and Supplementary Table 4). Overall, the clustering component was more related to divergent thinking (AUT) while the switching component was more related to convergent thinking (CAT).

Relationship between AmbiFlu clustering and switching and individual semantic network metrics

For each individual we built individual SemNets based on the RJT ratings (Figure 1).

We tested whether clustering and switching in AmbiFlu related to individual differences in the SemNet structure, using Spearman correlations (Figure 2; Supplementary Table 4). The analysis showed a significant correlation between the switching component and Eff in both the WUN (p = .023) and UUN networks (p = .007). Additionally, the switching component correlated negatively with Q (p = .023), and positively with CC (p = .019) in the UUN network.

The correlation between efficiency in the UUN network and the switching component remained significant after FDR correction. These results suggest that individuals with higher switching during the AmbiFlu task had a more efficient SemNet, that was more connected and less modular.

Relationship between AmbiFlu clustering and switching and executive functions

We explored the Spearman correlations between the Ambiflu components and executive functions tests measuring cognitive inhibition (Stroop test, Stroop-interference;[START_REF] Chatelois | Batterie informatisée d'évaluation de la mémoire inspirée de l'approche cognitive. [A computerized memory test battery based on the cognitive approach[END_REF], set-shifting (TMT-shifting; [START_REF] Reitan | Trail Making Test: Manual for Administration and Scoring[END_REF], working memory (digit spans; Wechsler, 2008), and category and letter fluency tasks for broad retrieval abilities (category-fluency and letter-fluency; [START_REF] Cardebat | Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level[END_REF]) (see Supplementary Table 3 for descriptive statistics).

The clustering component correlated positively to category-fluency (p = .014). The switching component was positively correlated to backward span (p = .032), category-fluency (p = .001) and letter-fluency (p = .008), and negatively correlated to TMT-shifting (p = .019)

and stroop-interference (p = .017). After FDR correction, the correlation of the switching component with category-fluency and letter-fluency remained significant (see Figure 2 and Supplementary Table 4). While both components of the AmbiFlu related to retrieval abilities in the semantic fluency task, only switching seems to be related to executive abilities.

Prediction of AmbiFlu clustering and switching from brain connectivity

To identify the brain substrates of the clustering and switching components, we explored the functional connectivity patterns predicting these scores, using a CPM method (Beaty et al., 2018;[START_REF] Ovando-Tellez | Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure[END_REF]Rosenberg et al., 2016;Shen et al., 2017). The results (Figure 3) revealed significant correlations between the predicted and observed clustering component (r = .339, p = .002) and between the predicted and observed switching component (r = .427, p < .001). To test the robustness of the prediction, we analysed the relation between modelpredicted and observed components, using 1,000 iteration permutation testing. Both predictions remained significant after the permutation testing (p = .019 for the clustering component; p =

.004 for the switching component). These findings suggest that patterns of brain functional connectivity allow robust predictions of both clustering and switching components.

distribution of the links across intrinsic functional networks based on Schaefer's atlas [START_REF] Schaefer | Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI[END_REF]

Discussion

This study investigated neurocognitive correlates of memory search and related them to creative cognition. We developed a free association task, based on ambiguous words, a task that we show captures two main components: A clustering component related to fluency and cluster size of responses; and a switching component related to the diversity and flexibility in these responses and their meanings. We found that clustering and switching components were differently related to divergent and convergent thinking, and that the switching component, but not clustering, was associated with individual differences in semantic memory structure (measured by SemNet metrics) and executive control. The application of network science methods at the neural level further allowed us to identify differences in their respective predictive brain connectivity patterns.

Both clustering and switching components correlated with creativity performance.

However, they differed in their relationships with divergent and convergent creativity tasks.

The clustering component correlated with AUT measures of fluency and originality (number of total and unique ideas), suggesting that it captures processes involved in divergent thinking.

This result converges with previous studies that related performance in various kinds of verbal fluency tasks to fluency and originality in divergent thinking tasks (Beaty et al., 2014b;Beaty et al., 2021b;Benedek et al., 2020Benedek et al., , 2017Benedek et al., , 2012b;;Gilhooly et al., 2007;Hass, 2017;Marron et al., 2018;Silvia et al., 2013), showing that broad retrieval ability is associated with creative ability (Forthmann et al., 2019;Silvia et al., 2013). It is important to mention that clustering did not correlate with a measure of AUT originality that is not confounded by fluency in AUT (AUT ratings of top-creative responses), which may suggest that the observed correlations with AUT-uniqueness were driven by the fluency (Benedek et al., 2013;Forthmann et al., 2020;Silvia et al., 2008). Effective exploitation of semantic clusters may thus drive fluent idea generation.

In contrast, we did not observe correlations between the switching component and divergent thinking scores in the AUT task, but with the ability to combine remote associates in the CAT task, a measure of convergent thinking ability. The link between flexible retrieval abilities and CAT is in line with the associative theory of creativity, where higher flexibility of thought supports the generation and combination of remote associations (Kenett and Faust, 2019;Marko et al., 2019;Mednick, 1962;Volle, 2018). The clustering component may align with the persistence pathway of the dual model to creative outcomes (Nijstad et al., 2010) that relies on broad exploration, whereas the switching component may align with the flexibility pathway. However, the switching component also related to executive functions, confirming the contribution of control processes guiding the search in memory (Volle, 2018). Importantly, the link between CAT performance and the switching component is consistent with previous findings showing that optimal search in the Remote Associates tests involves continuous switching between clusters of associates (Davelaar, 2015, but see Smith et al., 2013) and involves controlled processes (Bendetowicz et al., 2018;Gupta et al., 2012;Smith et al., 2013).

The relationships of the AmbiFlu components with SemNets metrics and executive function tests may help to better understand the underlying processes. As expected, the et al., 2001;Reverberi et al., 2006;Ross et al., 2007;Troyer et al., 1998), and is thought to rely on cognitive control. Similarly, the processing of ambiguous words likely involves executive processes to retrieve multiple unrelated meanings of the words (Noonan et al., 2013;Whitney et al., 2011b), manipulate and update the content of working memory, inhibit previously visited meanings that would not be profitable to revisit, and switch between semantic fields.

Based on the associative theory of creativity (Mednick, 1962), we expected components of the AmbiFlu task to capture memory search processes related to individual differences in the semantic memory organization explored via the SemNet properties. The switching component indeed correlated with SemNets efficiency (significant correlations with modularity and CC did not survive the FDR corrections), in agreement with a previous result [START_REF] Marko | The structure of semantic representation shapes controlled semantic retrieval[END_REF], indicating that more efficient information spread in semantic memory network is associated with more effective switching. Hence, self-paced switching, allowing to alternate between diverse meanings during the AmbiFlu task, is related to individual differences in semantic memory structure. Overall, switching in AmbiFlu seems to rely on both semantic memory structure and executive control processes, which combined allow flexibility in memory search.

Contrary to our expectations, the clustering component did not correlate with SemNets metrics suggesting that it is not driven by individual differences in the organization of semantic memory. Because the clustering component relates to both cluster size and total fluency of participants performance, and because the participants were asked to give unconstrained AmbiFlu free associations during the AmbiFlu task, we expected it to reflect spontaneous associative fluency (He et al., 2021;Marron et al., 2018). However, the absence of significant correlations with semantic memory structure questions this interpretation and we cannot exclude that additional processes or strategies are captured by this component. For instance, people generating responses in larger clusters may tend to stay longer in the same cluster until it is exhausted. Staying within a cluster may require a focused, sustained, goal-directed attention to suppress distractors or competitors from outside this cluster. Thus, the clustering component might capture associative but also controlled processes to effectively exploit a semantic cluster.

This interpretation converges with the exploration-exploitation trade-off extended to semantic search (Hills et al., 2015b(Hills et al., , 2012)), where switching to a different cluster occurs when the retrieval rate within the current one falls below a threshold. Hence, our clustering component may reflect the ability to maximally exploit a given meaning or the tendency to persist longer in an exploitation mode. This would suggest that the AmbiFlu task is useful in identifying appropriate cluster boundaries (Hills et al., 2015a). Further studies modeling these processes rather than capturing individual behaviors are needed to clarify this point.

Overall, the switching component relates to individual differences in the organization of semantic memory networks, executive function abilities, and the ability to combine remote associates measured with the CAT task. These results suggest that switching captures a selfpaced flexible search behaviour during the AmbiFlu task that relies on both semantic memory structure and executive processes. This behaviour may be particularly relevant during convergent thinking tasks such as the CAT, where switching between different types of relationships between the cues words and the solution word facilitates performance (Davelaar, 2015;Smith et al., 2013). Conversely, the clustering component is related to semantic retrieval and divergent thinking, and may capture a persistent behavior involving controlled processes to maximally exploit a cluster. Hence, effective clustering involves more cognitive control than previously expected, yet probably not the type of control that was assessed by our set of executive function measures that did not correlate with clustering.

The cognitive mechanisms captured by our clustering and switching components can be further discussed in the light of the brain functional connectivity patterns predicting the components. The CPM approach allowed us to identify specific brain connectivity patterns robustly predicting individual differences in each component. The brain model network predicting clustering and switching components involved networks classically involved in creativity tasks, performance, and behavior, namely the ECN, DMN, and salience networks (Beaty et al., 2019(Beaty et al., , 2016;;[START_REF] Ovando-Tellez | Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure[END_REF]Volle, 2018), but predictive patterns also clearly differed. The clustering predictive model network predominantly relied on internetwork connectivity between the ECN, DAN, salience, somatomotor and visual networks. The involvement of the salience and ECN is consistent with patient studies showing that these networks are critical for category fluency tasks (Foulon et al., 2018;Le Bouc et al., 2020). Here, the ability of retrieve multiple semantically related concepts seems to be supported by interactions between control and attentional networks with visual and motor regions.

Importantly, the temporal pole, a region considered as a hub region of semantic knowledge (Binder and Desai, 2011;Ralph et al., 2017) participated in the model network though its connection with the salience network. The involvement of task-positive networks and their interaction with a semantic memory region may be consistent with the proposed role of goaldirected attention in the clustering component allowing a sustained processing and a focused search in semantic memory (Dixon et al., 2018).

Key nodes of the clustering model network were related to the ECN. Although consistent with our interpretation of the clustering component above, this result may seem unexpected and not in line with Troyer et al (1998Troyer et al ( , 1997) ) that related clustering to temporal regions of semantic memory while switching related to frontal control processes during a category fluency task. It is possible that our findings differ from Troyer's hypothesis because a fluency task starting with an ambiguous word, even with an unconstrainted instruction, requires control to stay efficient and focused [START_REF] Eddington | How meaning similarity influences ambiguous word processing: the current state of the literature[END_REF], and indeed, processes that are captured by clustering and switching measures vary depending on fluency task types (Ross et al., 2007;Troyer et al., 1998). However, the alignment of clustering and switching in fluency tasks on temporal and frontal functions has already been contested. For instance, in Troyer et al. (1998), patients with temporal lobe lesions were impaired on category fluency switching in comparison to controls and (Reverberi et al., 2006) showed that performance of lateral frontal patients had a disorganized search strategy rather than a switching deficit. In addition, a brain stimulation study targeting the DLPFC showed an impact on clustering (and not switching) during a category fluency task [START_REF] Vannorsdall | Altering Automatic Verbal Processes with Transcranial Direct Current Stimulation[END_REF]. These findings suggest that the processes reflected in the clustering measure are not purely associative. Overall, it is likely that our clustering component captures not only semantic memory associations but also top-down regulation of semantic search.

In contrast to the clustering model network, the predictive patterns of the switching mainly involve DMN-related brain regions, in addition to regions of the salience and visual networks. The most densely connected brain regions in the switching model network included several regions of the DMN, in particular in the inferior parietal lobule bilaterally, retrosplenial cortex, and medial prefrontal cortex. The involvement of the DMN in our task was expected since previous studies have shown that generation of spontaneous associations involves brain regions of the DMN (Beaty et al., 2014a;Bendetowicz et al., 2018;Marron et al., 2020Marron et al., , 2018)).

DMN-related brain regions are thought to be essential for the spontaneous processes (Andrews-Hanna et al., 2014) contributing to creative abilities, especially the medial prefrontal cortex (Beaty et al., 2016(Beaty et al., , 2014a)). Additionally, the DMN has been associated with several aspects of cognition involving heteromodal memory retrieval (Lanzoni et al., 2020;Murphy et al., 2018) and semantic goal maintenance (Wang et al., 2021); its interaction with the ECN correlates with the summation of multiple concepts during a semantic task close to the CAT (Evans et al., 2020). Importantly, the connectivity predicting switching involves connections of DMN regions with other networks, in particular with ECN regions and the temporal pole. The connectivity between DMN and ECN uniquely predicts switching, not clustering in AmbiFlu. This finding is consistent with the behavioral findings suggesting that interactions between associative and control processes support flexible search in memory. This interpretation is also in line with previous neuroimaging findings showing that DMN-ECN interactions reflect the top-down regulation of spontaneous cognition, with ECN orienting and constraining the DMN-related generative processes (Beaty et al., 2016;Beaty et al., 2021a;Bendetowicz et al., 2018;Spreng et al., 2010). Their link with the salience network may allow to trigger the switching between engagement of controlled and associative modes of thought, supported by ECN and DMN, respectively (Beaty et al., 2016;Spreng et al., 2010;Uddin, 2015;Volle, 2018).

Also aligned with this hypothesis, the left inferior frontal gyrus, a critical region of the semantic control system (Jefferies, 2013;Jefferies et al., 2020;Noonan et al., 2013;Ralph et al., 2017;Vatansever et al., 2021;Whitney et al., 2011b), plays a role in the switching predictive network, through its connectivity with the DMN, salience and visual networks, and with the temporal regions of the ECN. This result is further in line with a series of studies demonstrating the critical role of the left inferior frontal gyrus in fluency tasks (Hirshorn and Thompson-Schill, 2006;Le Bouc et al., 2020), in controlled semantic retrieval and selection (Badre et al., 2005;Badre and Wagner, 2007;Ihara et al., 2007;Thompson-Schill et al., 1997), and in resolving competition between incompatible representations (Hirshorn and Thompson-Schill, 2006).

How the ECN regions based on the Schaeffer parcellation [START_REF] Schaefer | Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI[END_REF] align with the semantic control system remains to be directly addressed (see for instance Gao et al., 2021;[START_REF] Wang | A gradient from long-term memory to novel cognition: Transitions through default mode and executive cortex[END_REF]. In addition, echoing the correlation between the switching component and CAT, the predictive network includes node regions of the ECN and DMN that have been shown critical for CAT performance in patients (Bendetowicz et al., 2018) in the rostral IFG and medial PFC.

The involvement of the ECN is also consistent with the correlation of the switching component with executive tests that typically require regions of the ECN [START_REF] Derrfuss | Involvement of the inferior frontal junction in cognitive control: Meta-analyses of switching and Stroop studies[END_REF]Le Bouc et al., 2020;[START_REF] Mandonnet | Network-level causal analysis of set-shifting during trail making test part B: A multimodal analysis of a glioma surgery case[END_REF][START_REF] Mandonnet | A network-level approach of cognitive flexibility impairment after surgery of a right temporo-parietal glioma[END_REF]. Overall, these results are in line with several studies showing that since ambiguous words activate multiple meanings, the processing of these words involves processing demands related to executive semantic system [START_REF] Hoenig | Neural correlates of semantic ambiguity processing during context verification[END_REF]Noonan et al., 2013;Rodd et al., 2005;Whitney et al., 2011a;[START_REF] Zempleni | Semantic ambiguity processing in sentence context: Evidence from event-related fMRI[END_REF].

In addition to regions of the DMN and ECN, the left orbital frontal cortex (limbic network) is a high degree node in the switching predictive model. This brain region has been associated to inhibitory control (Le Bouc et al., 2020;Volle et al., 2012), and damage to this region had been associated with difficulties in suppressing inappropriate behaviours and perseverative responses [START_REF] Horn | Response inhibition and impulsivity: an fMRI study[END_REF][START_REF] Viskontas | Symptoms of Frontotemporal Dementia Provide Insights into Orbitofrontal Cortex Function and Social Behavior[END_REF], functions that likely play a role in avoiding inappropriate or perseverative responses during AmbiFlu performance.

Overall, switching in AmbiFlu may be supported by interactions between DMN and ECN or semantic control system (as well as with their interaction with the salience, visual and somatomotor networks), highlighting the contribution of controlled processes to explore diverse semantic fields and the importance of the DMN for self-paced switching behaviour.

Interestingly, switching related to salience-DMN connectivity whereas clustering was related to salience-ECN connectivity, which may help to further tease apart the involvement of Salience-DMN-ECN connectivity patterns observed in more complex forms of creative cognition (see Beaty et al., 2019Beaty et al., , 2016)).

Some limitations of this study should be mentioned. First, the sample is relatively small, especially for conducting the CPM approach. However, the results are robust to statistical procedures based on permutation testing. Second, the brain network predicting the clustering component is composed by a relatively small number of links, questioning the importance in these links in the overall brain connectivity. Some correlations did not survive multiple comparisons likely due to a lack of power. Future studies should consider a higher number of participants and external validations of the results to fully account for these issues. Finally, replication of these results with more cue words for the AmbiFlu task would be useful to drawn more general conclusions on the underlying processes.

In conclusion, the current findings contribute to better understand the cognitive processes of memory search that relate to creativity and their neural correlates.

An investigation of the cognitive and neural correlates of clustering and switching components of memory search related to creative ability

Supplementary Table 1. Different meanings of the French ambiguous words. Each column has the different meanings of the three cue words used in the ambiguous word fluency task.

Each word has at least five different meanings. The total number of meanings for each cue word is shown in parenthesis. A few words could not be categorized and were labeled as undetermined (< 2 % overall responses across participants.

Somme ( 12) Glace ( 5) Rayon ( 11

Discussion

The results that emerged from this study contribute to better understand the neurocognitive components of search processes in memory and their relationships with creative abilities. Based on a free association task with ambiguous cue words, we identified two components of search processes related to clustering and switching that differently correlated with DT and associative combination tasks. Moreover, only the switching component was associated with individual differences in semantic memory structure, as measured by the SemNet metrics, and executive control. By the means of the network science methods at the neural level we characterized distinct predictive brain connectivity patterns of clustering and switching.

As expected from the literature, we found that both clustering and switching components correlated with creativity performance, yet differently. The clustering component correlated with fluency and originality in the AUT task, while the switching component correlated with the ability to combine remote associates in the CAT task.

The clustering component captured fluency and cluster size of responses during the AmbiFlu task. A higher semantic fluency was important for generating many and unusual ideas in DT tasks. The relationship between the clustering component and DT task converges with previous studies that related performance in distinct verbal fluency tasks to fluency and originality in DT tasks (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2020Benedek et al., , 2017Benedek et al., , 2012a;;Gilhooly et al., 2007;Hass, 2017;Marron et al., 2018;Silvia et al., 2013). However, the relationship we observed between clustering and originality scores were likely driven by the fluency during the task (Benedek et al., 2013;Forthmann et al., 2020;Silvia et al., 2008) as the unique score of originality that is not confounded by fluency in AUT (i.e., external ratings to the top-2 ideas)

was not correlated to the clustering component. This finding suggests that exploiting semantic clusters, rather than exploring different clusters yield to more fluent generation of ideas during DT tasks. Importantly, the flexibility score during DT tasks involves switching between different semantic fields or categories during the idea generation in the DT tasks. Thus, AUT flexibility could be likely captured by the switching component. Since we did not explore this measure, how higher flexibility during the AUT task relates to clustering and switching components remains to be explored.

Contrary to clustering, the switching component was correlated with the ability of combining remote associates as measured with the CAT task. The switching component captured the diversity of meaning and switches between them in the responses of the AmbiFlu task. Thus, a more flexible retrieval abilities during the AmbiFlu task captured by the switching component allowed a better performance in the CAT task. This finding is in line with Mednick's theory empathizing that higher flexibility of thought supports the generation and combination of remote associations (Kenett and Faust, 2019;Marko et al., 2019;Mednick, 1962;Volle, 2018).

When analyzing the clustering and switching components in relation to SemNet structure and executive functions it confirmed that these components capture distinct semantic and/or executive processes involved in memory search. Although both components were related to the category fluency task, uniquely switching component was correlated with executive abilities and SemNet structure. This result suggests that switching reflects a more flexible search during the AmbiFlu task that relies on both semantic memory structure and executive processes. A higher flexibility of thought is also critical for the CAT task, (Kenett and Faust, 2019;Marko et al., 2019;Mednick, 1962;Volle, 2018) as switching between different associations between the cues words and the solution word facilitate the performance in this task (Davelaar, 2015;Smith et al., 2013). Conversely, the clustering component was related to semantic retrieval and fluency in DT and may capture a persistent behavior to maximally exploit a cluster. This interpretation converges with the exploration-exploitation trade-off extended to semantic search (Hills et al., 2015b(Hills et al., , 2012)), where our clustering component may reflect the ability to maximally exploit a given meaning or the tendency to persist longer in an exploitation mode.

These different patterns between the search components identified in the AmbiFlu task and creativity tasks can be interpreted in the light of the dual model to creative outcomes (Nijstad et al., 2010). The clustering component relates to the persistence pathway allowing a more fluent generation of ideas in AUT task, while the switching component relates to the flexibility pathway, supporting the generation and combination of remote associations.

Furthermore, clustering and switching components were predicted from distinct functional connectivity patterns that further support this idea. The predictive connectivity patterns of the clustering component involved the interactions between brain regions of FPCN, SalN, attentional, somatomotor and visual networks. The involvement and interaction of these brain networks, and between SalN and semantic memory regions may indicate the role of goaldirected attention in the clustering component allowing a sustained processing and a focused search in semantic memory (Dixon et al., 2018). This pattern does not align with the clusteringtemporal and switching-frontal dissociation proposed by Troyer et al. (1998Troyer et al. ( , 1997)). It is likely that our clustering component captures not only semantic memory associations but also topdown regulation of semantic search.

The switching component was predicted by functional brain connectivity between and within the DMN, FPCN and SalN, and visual and somatomotor networks. The most densely connected brain regions in the switching model network included several regions of the DMN, in particular in the inferior parietal lobule bilaterally, retrosplenial cortex, and medial prefrontal cortex. These DMN-related brain regions are important for the spontaneous processes (Andrews-Hanna et al., 2014) contributing to creative abilities, especially the medial prefrontal cortex (Beaty et al., 2016(Beaty et al., , 2014a;;Bendetowicz et al., 2018;Marron et al., 2020Marron et al., , 2018)). The connectivity between DMN and FPCN predicting switching is consistent with its cognitive patterns suggesting that interactions between associative and control processes supporting flexible search in memory. Moreover, the involvement of the SalN suggest its role in mediating the switching between engagement of controlled and associative modes of thought, supported by ECN and DMN, respectively (Beaty et al., 2016;Spreng et al., 2010;Uddin, 2015;Volle, 2018).

In addition, the brain networks predicting both clustering and switching, also involved brain regions related to semantic representation and semantic control that may be required when processing ambiguous words. Moreover, the involvement of the inferior frontal gyrus in predicting switching is in line with previous studies demonstrating the critical role of this region in controlled semantic retrieval and selection (Badre et al., 2005;Badre and Wagner, 2007;Ihara et al., 2007;Thompson-Schill et al., 1997), and in resolving competition between incompatible representations (Hirshorn and Thompson-Schill, 2006). However, how these different regions contributing to the prediction of clustering and switching align with regions involved in semantic representation vs semantic control remains to be explored. Since we defined and characterized our brain regions based on the Schaeffer parcellation [START_REF] Schaefer | Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI[END_REF] further analyses should be performed to better understand how the different regions highlighted in our study and their functional networks overlap the differently the semantic control and representation systems (Lambon Ralph et al., 2017).

Finally, it is important to acknowledge that although the AmbiFlu is a free-association task based on ambiguous words. Ambiguous words can have a dominant meaning and the processing of other subordinate meanings may involve more control processes (Noonan et al., 2013;Whitney et al., 2011a). Therefore, processing ambiguous words, especially the subordinate meanings may imply the activation of multiple semantic representations associated with the different meanings of the words [START_REF] Onifer | Accessing lexical ambiguities during sentence comprehension: Effects of frequency of meaning and contextual bias[END_REF]Rodd et al., 2005). In our study, we observed the involvement of the inferior frontal gyrus in predicting the switching component. This brain region is critical for the semantic control system (Jefferies, 2013;Jefferies et al., 2020;Lambon Ralph et al., 2017;Noonan et al., 2013;Vatansever et al., 2021;Whitney et al., 2011b). It has been related to fluency tasks (Hirshorn and Thompson-Schill, 2006;Le Bouc et al., 2020), controlled semantic retrieval and selection (Badre et al., 2005;Badre and Wagner, 2007;Ihara et al., 2007;Thompson-Schill et al., 1997), and resolving competition between incompatible representations (Hirshorn and Thompson-Schill, 2006). The processing of ambiguous words, when they have a dominant meaning implies both the activation of the meaning representations and semantic control processes associated with inhibition or the broad retrieval of the meanings (Beaty et al., 2014b;Noonan et al., 2013;Silvia et al., 2013;Whitney et al., 2011a). People may require first inhibit more common associations associated to the dominant meaning to be able then, to explore other solutions related to the word associates to the subordinate meanings (Camarda et al., 2018a;Noonan et al., 2013;Whitney et al., 2011a). The cue words presented in this study had a relatively dominant meaning. How our results replicate with other cue words that better control dominance and vary in terms of the strength of the dominant meaning remain to be explored.

Overall, results from behavioral and neural level suggest that higher flexibility in

PART 3: Exploring different types of relationships in the organization of the semantic memory

1. Do we implicitly relate concepts?

Introduction

In the previous study we show how a flexible memory search partly relies on individual differences in the organization of the concepts in semantic memory networks. A more flexible search in memory allows exploring more diverse semantic fields and combining more remote associations to reach creative ideas. Semantic memory is the system that stores our general world knowledge (Binder and Desai, 2011;[START_REF] Tulving | Episodic and semantic memory, in: Organization of Memory[END_REF]. Based on this knowledge, we categorize everything we perceive which allows making sense of our environment and experiences in life. Categorization is the mental process at the basis of the construction of our knowledge of the world (Cohen, 2005) which allows grouping different elements based on their common characteristics. Hence, existing theories propose that concepts are organized into categories in memory as concepts sharing common characteristics may be more associated in memory [START_REF] Mcrae | Semantic memory, in: The Oxford Handbook of Cognitive Psychology[END_REF]Mirman et al., 2017). How different types of categories and different relationships between items are represented in semantic memory is debated [START_REF] Joelson | Properties of Categories in Semantic Memory[END_REF][START_REF] Yee | Semantic memory[END_REF]. In this study we aimed to explore different types of relationships that participate in the organization of semantic memory, and how semantically related concepts in memory can be implicitly retrieved.

Memory retrieval depends on the organization of the storage of information in memory.

Its organization into categories, grouping similar items together, may allow a more efficient retrieval. Based on the organization of the concepts in memory, people can retrieve concepts that have common characteristics. More strategic or controlled processes can also be involved.

Semantic memory retrieval is commonly assessed with semantic fluency tasks. In this task, participants are asked to retrieve different concepts of a given semantic category from memory (e.g., animals, vegetables, tools, and others). By analyzing the grouping of responses, previous authors have proposed that memory search in this task depends on the organization of memory storage into categories (Troyer et al., 1998). Our previous study shows how a semantic fluency task relate to semantic memory structure, but also involves executive processes (Troyer et al., 1998(Troyer et al., , 1997)). In the current study, we aimed at exploring semantic retrieval i) as reflected by the organization of semantic memory, i.e., occurring with minimal executive control demands, and ii) focusing on category retrieval, whether taxonomic or thematic (associative).

To explore category retrieval with minimal executive demands, we used a widely validated method in neuroscience to explore associations in memory, the semantic priming paradigm. Semantic priming shows how processing a target word is facilitated when it is preceded by a semantically related word (cat -dog), as compared to an unrelated word (lemon -dog) [START_REF] Mcnamara | Semantic priming: Perspectives from memory and word recognition, Semantic priming: Perspectives from memory and word recognition[END_REF]. This facilitation (priming effect) is typically measured by the response speed and accuracy at the behavioral level, and via evoked related potentials (ERP) at the neural level. Thus, the priming effect is supposed to reflect an implicit or indirect access to semantic information.

Associative priming is thought to depend on an automatic spreading activation by spontaneous associations within a semantic memory network. Hence, the semantic relatedness between concepts can be considered a key predictor of associative processing as the activation of a given concept leads to a higher probability of activation of the strongest and closest associated concepts [START_REF] Anderson | A spreading activation theory of memory[END_REF][START_REF] Collins | A spreading-activation theory of semantic processing[END_REF]Neely, 1977). However, it is difficult in semantic priming studies to isolate purely automatic processes, preventing controlled or strategic processes to occur. For instance, conscious presentation of the primes and long SOA durations may allow cognitive control processes to be involved in the semantic priming effect (Neely et al., 1989).

Using semantic priming measures, previous studies have shown that different types of semantic relationships can yield priming effects. Among others, previous studies show that thematic and taxonomic relationships between prime and target yield significant priming effects (Chen et al., 2014b;Hagoort et al., 1996;Khateb et al., 2003;Maguire et al., 2010;Mirman et al., 2017). These findings suggest that concepts with taxonomic and thematic relationships are connected to each other in memory and that concepts may be organized into semantic categories in memory (Chen et al., 2014b;Jones and Golonka, 2012;Maguire et al., 2010) (for reviews, Hutchison, 2003;Jones and Estes, 2012;Lucas, 2000).

Categorization process is typically evaluated with the Wechsler Adult Intelligence Scale (WAIS) similarity subtest. This task explores whether individuals can categorize two concrete or abstract items (e.g., "how are an orange and a banana alike?") by giving their taxonomic category ("fruits"). Thus, it requires people to perform an explicit categorization of the concepts. As measured by similarity tasks, previous studies show that categorization requires executive control functions (Garcin et al., 2018). Moreover, this process is typically deteriorated in patients with brain damage to regions related to the two neural systems of semantic cognition (Garcin et al., 2018[START_REF] Garcin | Similar or Different? The Role of the Ventrolateral Prefrontal Cortex in Similarity Detection[END_REF], the semantic representation and semantic control systems (Lambon Ralph et al., 2017). However, categorization can occurs automatically relying on semantic associations (Praß et al., 2013), suggesting that it depends on the organization of semantic memory. Hence, whether categorization, as it occurs in the similarity task, can also occur in a semantic priming paradigm without explicit instructions to categorize concepts is still unexplored. Particularly, whether taxonomic and thematic categorization can occur implicitly remains to be studied.

Besides its essential role in abstract thinking, exploring implicit categorization is relevant to the study of creativity because reaching more creative ideas during the DT tasks depends on the ability to explore various conceptual categories, more remote associations and moving from one semantic field to another [START_REF] Acar | Latency predicts category switch in divergent thinking[END_REF][START_REF] Beaty | Why do ideas get more creative across time? An executive interpretation of the serial order effect in divergent thinking tasks[END_REF][START_REF] Finke | Creative cognition: Theory, research, and applications, Creative cognition: Theory, research, and applications[END_REF]Guilford, 1967;Mastria et al., 2021). Moreover, concepts from different categories may have a higher semantic distance to each other than elements of a same category, which represent higher originality in DT tasks (Beaty and Johnson, 2021). The formation of categories also plays a fundamental role in analogical thinking [START_REF] Green | The Micro-Category account of analogy[END_REF][START_REF] Green | Automatic activation of categorical and abstract analogical relations in analogical reasoning[END_REF] and problem solving [START_REF] Chrysikou | When shoes become hammers: Goal-derived categorization training enhances problem-solving performance[END_REF]. [START_REF] Green | Automatic activation of categorical and abstract analogical relations in analogical reasoning[END_REF] showed that the activation of category concepts is required for reasoning by analogy tasks, thus, being essential for our reasoning, problem-solving, abstract thinking and creativity [START_REF] French | The computational modeling of analogy-making[END_REF]Green et al., 2012[START_REF] Green | Automatic activation of categorical and abstract analogical relations in analogical reasoning[END_REF]Kotovsky and Gentner, 1996). Additionally, [START_REF] Chrysikou | When shoes become hammers: Goal-derived categorization training enhances problem-solving performance[END_REF] The role of categorization in creativity is also supported by previous studies showing that different creativity tasks rely on the ability to find common characteristics or similarities between concepts. Benedek et al. (2012b) show that high DT performance is predicted by higher abilities in associative combination and dissociation tasks. The associative combination task measures the ability of fluently combine unrelated concepts, while the dissociation task measures the ability of finding semantically unrelated concepts. Hence, creativity tasks may be facilitated by the ability of finding similarities and dissimilarities between concepts, and thus, identifying semantically related or unrelated concepts which may rely on how concepts are organized in memory. Moreover, in the pioneering study exploring SemNet structure in relation to creative abilities (Benedek et al., 2017), the verbal material to build the networks was based on semantic categories. Critically, they found significant correlations between how these concepts are organized in memory as reflected by the SemNet structure.

To explore implicit categorization process and to better understand concepts' organization into taxonomic and thematic categories, we developed a double semantic priming paradigm. Based on the association norms, we selected pairs of semantically related words with a low strength of association to explore whether they can pre-activate the category to which they belong. Exploring the implicit categorization process serves as a tool to examine the retrieval of semantically related concepts in taxonomic and thematic categories and thus, can help to better understand whether semantic memory is organized into categories. Moreover, it can help to better understand how exemplars of different categories and its category can be implicitly retrieved from memory.

We explored whether the implicit retrieval of a thematic and taxonomic category is facilitated by two semantically related words belonging to this category more than by a single exemplar of this category. We measured the priming effect in behavior and electrophysiological recordings (evoked related potentials, ERP) of double prime compared to single prime in a lexical decision task (LDT).

Article

Does adding beer to coffee enhance the activation of drinks? An ERP study of semantic category priming

Marcela Ovando-Tellez 1 , Benjamin Rohaut 1,2,5 , Nathalie George

Introduction

Categorization is a complex cognitive process that allows brains to classify objects and events based on common characteristics (Cohen, 2005). Based on our knowledge, we categorize everything we perceive, and this allows us to make sense of our environment and experiences.

This process of categorization is essential for human thinking, learning or forming general concepts, and problem-solving (Gelman and Meyer, 2011;Kotovsky and Gentner, 1996).

However, the cognitive mechanisms by which we categorize or fail to categorize are poorly understood.

Categorization abilities are typically measured by the Wechsler Adult Intelligence Scale (WAIS) similarity subtest (Wechsler, 2008). In this test, subjects are asked to categorize two items (e.g., "How are an orange and a banana alike?") and name their taxonomic category ("fruits"). Previous studies report impairment in categorization tasks, such as the similarity task, in patients with frontal neurodegenerative diseases (Garcin et al., 2018;Lagarde et al., 2015), suggesting a critical role of executive control functions in this task. However, categorization has also been shown to occur automatically and to rely on semantic associations (Praß et al., 2013), suggesting that categorization depends on the organization of semantic memory. In particular, a given category seems to be implicitly pre-activated by an exemplar of this category (e.g., seeing "dog" pre-activates "animal") (Mirman et al., 2017). The critical role of executive control function in the similarity task may seem paradoxical with the notion that categorization occurs implicitly, based on semantic associations. Hence, how the category is activated during the similarity task remains an open question. More specifically, the question we raised is: do two exemplars of a given category pre-activate this category more than each exemplar would do alone, in a semantic priming paradigm? Addressing this question is essential to clarify the cognitive processes underlying categorization. It is also essential to better appreciate the cognitive difficulties of neurological patients.

The semantic priming paradigm is commonly used to explore implicit categorization and the organization of semantic memory, including the organization into categories (Chen et al., 2014;Jones and Golonka, 2012;Maguire et al., 2010) (for reviews (Hutchison, 2003;Jones and Estes, 2012;Lucas, 2000)). The principle of a semantic priming paradigm is to measure how much people are faster and/or more accurate in processing a target word when it is preceded by a semantically related prime word, as compared to an unrelated prime word. This measure reflects the semantic priming effect. The semantic priming effect is also explored with electrophysiological measures using event-related potentials (ERPs). ERPs provide temporal measures of the neural activity following stimulus presentation. Specifically, the N400 component, a negative deflection occurring approximately 400 ms after the stimulus onset and typically maximal at centro-parietal electrodes sites, is an electrophysiological landmark of semantic priming effects (for review see Kutas and Federmeier, 2011). The N400 appears very sensitive to semantic relatedness. Its amplitude is smaller when a word is preceded by a related rather than an unrelated word [START_REF] Kutas | Psycholinguistics electrified: Event-related brain potential investigations[END_REF][START_REF] Lau | A cortical network for semantics: (de)constructing the N400[END_REF]. The difference in the N400 amplitude in different semantic priming conditions is referred to as the N400 priming effect, and it can occur even in the absence of response time (RT) priming effect [START_REF] Chwilla | Mediated Priming in the Lexical Decision Task: Evidence from Event-Related Potentials and Reaction Time[END_REF][START_REF] Chwilla | Event-related potential and reaction time evidence for inhibition between alternative meanings of ambiguous words[END_REF], suggesting a higher sensitivity. Many studies using a semantic priming paradigm have shown a relationship between the N400 amplitude and the strength of prime-target associative relatedness [START_REF] Bentin | Event-related potentials, lexical decision and semantic priming[END_REF][START_REF] Holcomb | Automatic and attentional processing: An event-related brain potential analysis of semantic priming[END_REF][START_REF] Rugg | The Effects of Semantic Priming and Word Repetition on Event-Related Potentials[END_REF].

Using these behavioural and ERP measures, the semantic priming paradigm has allowed

showing that different types of semantic relationships can yield priming effects. In particular, a series of studies show that both thematic (items that share a common context without necessarily sharing similar features, e.g., "rabbit" -"carrot" (Lin and Murphy, 2001) and taxonomic (items sharing specific features such as attributes and functional properties, e.g., "dog" -"cat"; Gelman and Meyer, 2011;Mirman et al., 2017;Murphy, 2002) relationships between prime and target yield a significant priming effect (Chen et al., 2014;Hagoort et al., 1996;Khateb et al., 2003;Maguire et al., 2010;Mirman et al., 2017). However, whether thematic and taxonomic relationships have distinct semantic priming characteristics, is an open question.

In addition, the behavioural and electrophysiological semantic priming effects have been shown to occur for consciously perceived primes and target, but also unconsciously perceived primes in masked-priming paradigms [START_REF] Brown | The processing nature of the n400: evidence from masked priming[END_REF][START_REF] Naccache | Unconscious semantic priming extends to novel unseen stimuli[END_REF][START_REF] Rohaut | Unconscious semantic processing of polysemous words is not automatic[END_REF][START_REF] Van Gaal | Can the meaning of multiple words be integrated unconsciously?[END_REF]. This evidence suggests that a semantic priming effect can occur unconsciously. However, a conscious context and task setting effects related to control functions can influence even unconscious priming [START_REF] Naccache | Unconscious semantic priming extends to novel unseen stimuli[END_REF][START_REF] Rohaut | Unconscious semantic processing of polysemous words is not automatic[END_REF][START_REF] Van Gaal | Can the meaning of multiple words be integrated unconsciously?[END_REF], indicating that the interplay between unconscious, implicit and controlled processes during semantic priming is complex and not entirely elucidated.

Nevertheless, the priming effect on semantic categories, whether taxonomic or thematic, suggests that categorization could occur in the context of tasks, such as the similarity task, where two presented words converge to a given category.

A few studies on semantic memory have used a "double semantic priming" paradigm to explore whether two primes impacted the processing of the target more than each prime would do alone (Balota and Paul, 1996;Chwilla andKolk, 2005, 2003;Python et al., 2018a). In the double semantic priming paradigm, the influence of the convergence of two primes on the target processing is assessed by comparing the conditions in which the two primes are related to the target, to conditions in which only one of the two primes relates to the target. These studies demonstrated a larger double as compared to single priming effect with various primes-target relationships: exemplars-taxonomic category, e.g., copper + bronzemetal (Balota and Paul, 1996); associated contexts or characteristics-object, e.g., alley + window -house [START_REF] Lavigne | Time course of activatory and inhibitory semantic priming effects in visual word recognition[END_REF] or naked + shytowel [START_REF] Chwilla | Accessing world knowledge: Evidence from N400 and reaction time priming[END_REF]; mediated associations, e.g., lion

+ stripestiger [START_REF] Chwilla | Event-related potential and reaction time evidence for inhibition between alternative meanings of ambiguous words[END_REF]; exemplars-exemplar, e.g., helicopter + busairplane (Python et al., 2018a). Only one of these studies used exemplars of a category as primes and the category as a target (Balota and Paul, 1996) and showed a shorter RT in the double priming condition as compared to single priming, suggesting that categorization can occur implicitly.

The measurement of the N400 priming effect using multiple primes has been less explored [START_REF] Chwilla | Event-related potential and reaction time evidence for inhibition between alternative meanings of ambiguous words[END_REF]Python et al., 2018a). To our knowledge, only two studies have explored the double semantic priming effect by combining behavioural and electrophysiological approaches. Chwilla and Kolk [START_REF] Chwilla | Event-related potential and reaction time evidence for inhibition between alternative meanings of ambiguous words[END_REF] showed that the existence and the strength of the double priming effect depend on the behavioural task, are altered when primes are polysemous and are easier to observe in ERPs than in behaviour.

Although this study did not focus on category relationships, it demonstrates the importance of exploring the neural correlates of semantic priming as a complement to behavioural data.

Python and colleagues (Python et al., 2018a) examined category relationships, including thematic relationships and taxonomic relationships. In the taxonomic relationships, targets and primes were exemplars of a specific category (e.g., food or animals). The author described an increased semantic facilitation effect using multiple primes as compared to single primes.

Although single semantic priming studies did not show differences in priming effect between taxonomic and thematic relationships (Chen et al., 2014;Hagoort et al., 1996;Khateb et al., 2003;Maguire et al., 2010), in this double priming study, the behavioural priming effect was larger for thematic than taxonomic relationships. In contrast, the ERP priming effect was similar for both relationships. Hence, these findings suggest that categorization can be studied in semantic priming paradigms and may depend on the type of relationships between primes and target. However, this study does not allow to directly test whether exemplars of a category activate the name of that category because the targets used were exemplars of categories, not category names. Whether the double priming effect can be observed in the context of the similarity task, i.e., whether several exemplars prime the category name, is an unresolved question.

The current study aims to explore whether categorization -as it occurs in the similarity task -can also occur in a semantic priming paradigm, i.e., without explicit instructions. We explore more specifically whether two exemplars of a given category activate this category name more than each exemplar would do alone in a semantic priming paradigm. For this purpose, we designed a double semantic priming task. This task allowed us to test whether two words belonging to the same taxonomic category (e.g., "banana" -"orange") elicited a stronger priming effect on the target category word (e.g., "fruits") than each exemplar separately did.

Given the observed differences in priming paradigms between distinct types of relationships, we also examined this effect for thematic relationships where the target was contextually associated with the primes (e.g., primes "banana" and "cage" with the target "monkey"). We measured both the behavioural (RTs) and ERPs (N400) priming effects. We expected a larger priming effect in double-prime trials as compared to single-prime ones. "CPP Ile de France V", approval n o C14-17) approved the study. All sections of the experiment were performed in accordance with relevant guidelines and regulations. All participants provided written informed consent and received financial compensation for their participation.

Materials and Methods

Experimental paradigm

General principle

We developed a priming paradigm based on a Lexical Decision Task (LDT), in which participants decided whether a displayed chain of letters (the target) was a word or not. This target was preceded by two prime words, each of which could be either semantically related or unrelated to the target, so that there were three semantic relatedness to the target fell into three possibilities: 1) they were both semantically related to the target, 2) one was related and one unrelated, or 3) none was related to the target. Two types of relationships were explored:

taxonomic (e.g., "coffee" -"drink") and thematic (e.g., "banana" -"monkey") relationships, using a distinct verbal material. The participants performed two blocks of thematic trials and two blocks of taxonomic trials. We followed the general principles proposed by Balota and Paul (Balota and Paul, 1996) to design the double semantic priming paradigm: considering the relatedness between the prime and the target (related -unrelated) and the position of the prime (first -second), four different conditions were compared: Related-Related (RR), Related-Unrelated (RU), Unrelated-Related (UR), Unrelated-Unrelated (UU). The influence of the convergence of multiple primes on target processing was assessed by comparing the effect (either behavioural or electrophysiological) in the multiple prime condition (RR) with the effect of both single prime conditions (RU and UR). Three different effects can be observed: additive, over-additive, or under-additive effects. A simple additive effect occurs when the facilitation of the target processing in RR condition (double priming effect) corresponds to the sum of the facilitation in RU and UR conditions (sum of single priming effects). Over-additivity indicates that the double priming effect is larger than the sum of the single priming effects. Conversely, under-additivity means that the double priming effect is smaller than the sum of the single priming effects.

Experimental conditions:

We created a set of words for taxonomic relationships and another one for thematic relationships (see word lists in Supplementary Table S1). Each set consisted of 26 triplets of one category target word and two prime words. The prime words had a taxonomic relationship with the target (taxonomic set) or a thematic relationship with the target (thematic set). Four different conditions were obtained for both sets by recombining all the elements in each set and considering the relation between the primes and the target (related or unrelated). First, a double priming condition or RR condition consisted of each of the original lists of 26 triplets of words in which the two primes were related to the target. Second, RU and UR conditions were single priming conditions, in which only the first or the second prime, respectively, was related to the target word (the other prime being unrelated to this target). Finally, we created a fourth condition, UU, in which neither the first nor the second prime word were related to the target.

This last condition was the baseline condition.

Overall, each condition (RR, RU, UR, and UU for taxonomic and thematic relationships) included 26 trials. To operationalize the LDT, we also created trials with pseudowords as targets. These pseudoword targets were pseudo-randomly combined with the same prime pairs as in the RR (13 trials), RU (13 trials), UR (13 trials), and UU (13 trials). When the target was not a word, the relatedness to the target did not make sense anymore (UU, UR, and RU are equivalent), and only the relatedness between primes made sense (RR as opposed to UU, RU, UR). Hence, the combination of primes with pseudowords defined two conditions for pseudowords targets: RR (13 trials) and UU (39 trials) conditions (see Table 1 and Supplementary Table S2 andS3). Therefore, the probability of a pseudoword target occurring after two related (RR) or after two unrelated primes (UU) was identical (1/3). In total, this first set of trials included 156 taxonomic and 156 thematic trials (including word and pseudoword trials).

Another set of trials was obtained by reversing the order of the first and second prime words (which were displayed sequentially) for all the trials described above, while keeping the other parameters identical. This second set was built to counterbalance the order of the primes over conditions and allowed us to double the total number of trials. Overall, the participants performed four blocks of trials corresponding to the four sets of 156 trials that were formed: two taxonomic blocks (that differed in the order of the primes in each trial) and two thematic blocks (that also differed in prime order). Each prime was repeated six times: three times in the first position and three times in the second position (see Supplementary Table S3), and each target was repeated four times (Table 1). The order of the trials was pseudo-randomized in each session of 156 trials, with the constraint so that at least nine trials separated two repetitions of the same target. We used the software Mix (van Casteren and [START_REF] Van Casteren | Mix, a program for pseudorandomization[END_REF] to pseudo-randomize the trials. Each session lasted about 15 minutes. The order of the sessions was counterbalanced between subjects using the following latin square pattern:

1-2-3-4 / 2-3-4-1 / 3-4-1-2 / 4-1-2-3.
Before the task, each participant completed a training session of 6 trials with different triplets of words than those used in the main task. The task was coded and administered with MATLAB 2016b (The MathWorks, Inc., Natick, MA) using the Psychophysics Toolbox extensions [START_REF] Brainard | The Psychophysics Toolbox[END_REF][START_REF] Kleiner | What's new in psychtoolbox-3[END_REF]. this table provides the number of trials presented in one session. In RR-pseudoword trials, the two primes belong to the same category while in the UU-pseudoword trials the two primes are not related. The proportion of primes that belong to the same category is thus similar for words and pseudo-words trials (0.25). Trials were created from the original list of 26 triplets of words and a total 156 trials were used for each session (Four sessions in total: taxonomic and thematic;

Condition

prime order 1-2 or 2-1). French words are presented in brackets.

Selection of the verbal material:

We used nouns or adjectives that were concrete, composed of 3 syllables or less, and with a reasonably high lexical frequency (lemma frequency > 1 per million occurrences in the Lexicon book database; www.lexique.org; [START_REF] New | Lexique 2: A new French lexical database[END_REF].

Three databases of free association norms in French were used to select the words presented as primes and as targets: the norms of verbal associations for concrete words and abstract words [START_REF] Ferrand | Normes d'associations verbales pour 260 mots « abstraits[END_REF]Ferrand and Alario, 1998) and a dictionary of French verbal associations accessible online (Debrenne, 2010). These tools allowed us to assess the association strength between different pairs of words for the creation of word triplets (two primes and one target).

The association strength is the probability that a cue word elicits a specific target word in a verbal free association task. It is measured as the percentage of participants who produced the target in response to the cue word. We ensured that each prime word alone was not strongly associated with the target word (less than 15% of association strength), to avoid a ceiling effect and prevent the priming effect from being significantly boosted by direct verbal associations beyond the semantic relationship itself [START_REF] Tyler | Towards a distributed account of conceptual knowledge[END_REF].

Pseudowords were created by modifying target words using the Wuggy® software [START_REF] Keuleers | Wuggy: a multilingual pseudoword generator[END_REF]. This software allowed us to create pseudowords that matched the target words in terms of the number of syllables and letters, and the frequency of letters.

Pseudowords were words that do not belong to the French language, but pronounceable under French phonological rules. Pseudowords were then pseudo-randomly combined with pairs of primes to form trials so that a given target and the pseudoword generated from this target did not occur in the same trial.

Experimental task (Figure 1):

Participants were seated in front of the screen and asked to perform the LDT using two mouse buttons. Subjects who participated in the electrophysiological recording were seated inside an electrically shielded room (Faraday cage). Stimuli were presented in white letters on a black background. Each trial started with the presentation of a fixation cross displayed at the centre of the monitor. After 500 ms, the first prime was presented during 300 ms and was followed by a fixation cross for 100 ms. Then, the second prime was displayed during 300 ms and was followed by a new fixation cross during 800 ms, and then by the target that was displayed in bold font for 2 seconds. Hence, the total Stimulus-Onset Asynchrony (SOA) in all the trials was 1500 ms. During this period, participants were instructed to decide whether the target was a word or a pseudoword, by doing a left-or right-click on the mouse to select their response with the right hand. An inter-trial-interval whose duration was jittered from 1.8 to 2.2 seconds (with steps of 2.6 ms) followed. Subjects were instructed to focus only on bold words and to answer, "as accurately and as fast as possible". The mouse buttons corresponding to the answer "word" and "pseudoword" were counterbalanced between participants.

target onset and the signal was re-referenced to the average of all electrodes (retrieving the FCz electrode signal that was initially used as the reference). EEG signals were averaged for each experimental condition (RR, UR, RU, UU) separately. Only correct response trials with a word as a target were considered for the analysis. Note that our experimental design was optimized to analyze the target-evoked responses, minimizing contamination by the primes-evoked responses. Thus, the ERPs analysis did not consider the exploration of the priming effects triggered by the primes, which would be difficult to disambiguate from one another. We provide a figure of the ERP time course across the whole trial period in Supplementary Figure S1. All EEG preprocessing and analyses were performed using the FieldTrip toolbox running under MATLAB 2016b [START_REF] Oostenveld | FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data[END_REF].

Behavioural and electrophysiological analyses

Behavioural measures

Reaction time was measured as the duration from the target onset to the participants' button press. Median RTs on correct trials with a word as a target were computed for each condition and each participant. We chose median RTs for the analyses to limit the influence of extreme values in the results. The priming effect was measured by subtracting RT in the baseline condition (UU) to RT in the related conditions (UR, RU, or RR). The difference in RTs between RR and UU measured the double priming effect. We also calculated the overall single priming effect by averaging RTs from both single priming conditions (RU and UR) -labelled as RUR

RT -and subtracting it to UU.

ERP measures

Our a priori hypothesis focuses on the N400 component that typically occurs between 300-500 ms after the target onset [START_REF] Kutas | Brain potentials during reading reflect word expectancy and semantic association[END_REF]. A predetermined time-window of interest between 300-500 ms after the target onset was thus selected and analyzed. However, to provide a more comprehensive analysis of the evoked response, we analyzed other time windows of 200 ms duration each, going from 100 ms to 900 ms after the target onset.

Therefore, in total we analyzed the following time windows: 100-300 ms, 300-500 ms, 500-700 ms and 700-900 ms.

For each condition, we measured the mean amplitude of ERPs in the different time windows, averaging ERP data across nine electrodes around the central Cz position (FC1, FCz, FC2, C1, Cz, C2, CP1, CPz, CP2). We selected a priori the frontocentral and centroparietal sites because the N400 component is maximal at this location [START_REF] Martin | Brain potentials reveal semantic priming in both the 'active' and the 'non-attended' language of early bilinguals[END_REF]. To assess the N400 priming effect, we subtracted the N400 component of each of the conditions containing a related prime (RR, RU and UR) to the baseline condition (UU). We also computed the overall N400 single priming effect by averaging priming effects across both single priming conditions (UR and RU).

Statistical analyses

The following procedure was used for the behavioural data (RTs) and ERPs (N400s). We first tested for the semantic priming effect and for the differences according to the semantic category type (taxonomic or thematic). We performed a two-way repeated-measures ANOVA with semantic category type (2 levels, taxonomic and thematic) and condition (4 levels, RR, UR, RU, UU) as within-subject factors. For the ERPs analyses, independent ANOVAs were performed for each time window, and a Bonferroni correction was used for multiple comparisons. Then, as our interest was the existence of a double priming effect and the additivity properties of double as compared to single priming, we performed two additional two-way repeated-measures ANOVAs, in order to compare single and double priming effect (priming factor) across semantic category types (thematic vs taxonomic). In the first set of ANOVAs, we tested for a double priming effect in each time window (with a Bonferroni correction for this multiple comparison). For this, the two levels of the priming factor corresponded to the priming effects computed separately for the single (RUR -UU) and the double (RR -UU) priming conditions. When a double priming effect was significant, we tested for additivity or over-additivity of the priming effect. For this, the two levels of the priming factor corresponded to i) the sum of the priming effect for the two single priming conditions (that is, (RU -UU) + (UR -UU)) and ii) the double (UU -RR) priming conditions. For comparisons with more than one degree of freedom, we used the Mauchly's test to verify the assumption of sphericity and the Greenhouse-Geisser coefficient e to correct for deviations to this assumption. We report the Greenhouse-Geisser corrected p-values but the original, uncorrected degrees of freedom. The Greenhouse-Geisser epsilon (e) value is reported in cases where the assumption of sphericity was violated. When the main effect of priming condition was significant, we performed post-hoc pairwise comparisons between conditions with Bonferroni correction for multiple comparisons.

Finally, to explore whether the ERPs priming effects were significant in electrodes that we did not consider a priori in our analysis, we employed a cluster-based permutation approach in the Fieldtrip toolbox [START_REF] Oostenveld | FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data[END_REF]. We examined whether the priming effect in the double priming condition RR was significantly different from the baseline condition UU. This statistical procedure can optimally correct for the problem of multiple comparisons in EEG data.

All statistical analyses were performed using SPSS software (v22.0; LEAD Technologies, Inc.).

Results

Behavioural analysis:

Mean accuracy reached 98.4% (± S.D. = 1.2; Range = 95-100) of all trials. Only the correct trials were kept and analyzed. The mean and standard deviations of the median RTs are provided for all conditions in Supplementary Table S4. We first performed a two-way repeatedmeasures ANOVA with semantic category type (2 levels, taxonomic and thematic) and condition (4 levels, RR, UR, RU, UU) to explore priming effects across conditions. There was no significant main effect of semantic category type (taxonomic vs thematic) on median RTs (F < 1). However, there was a main effect of the priming condition (F(3,114) = 15.53; e = 0.75; p < 0.001). Post-hoc pairwise comparisons with Bonferroni correction for multiple comparisons revealed that RT was longer in the baseline condition UU when compared to double priming condition RR (p < 0.001) and to each single priming condition UR (p = 0.007) and RU (p < 0.001). RT was shorter in the double priming condition RR compared to the single priming condition UR (p = 0.005) but not compared to the single priming condition RU (p = 0.068) (Figure 2a). The interaction between semantic category type and conditions was not significant (F(3,114) = 1.61; p = 0.2).

To examine whether there was a double priming effect and whether it was over-additive, we ran two additional ANOVAs on the single and double priming effects. In the first ANOVA, the double priming effect (RR -UU) was compared to the average of the single priming effects (RUR -UU) to explore the existence of a double priming effect. There was no main effect of Figure 5. Cluster-based permutation analysis. Testing for the N400 effect and the ERPs effect in the whole epoch of analysis (0.1 to 0.9 seconds after the target onset), the cluster-based permutation tests revealed a significant difference between the double priming condition RR and the baseline condition UU (p < 0.01). The forty topographic plots equally spaced between 0.1 to 0.9 seconds are displayed, the black dots represent the 65 electrodes, and the significant clusters are indicated with stars. A single cluster was observed, indicating a significant difference between 0.34 and 0.48 seconds over the frontocentral and centroparietal electrodes.

Then, to explore over-additivity, we examined if the N400 double priming effect (RR-UU)

was larger than the sum of the N400 single priming effects ((RU -UU) + (UR -UU)). This analysis was performed in the 300-500 ms time window in which the double priming effect was significantly larger that the single priming effect. This final ANOVA did not reveal any significant effect or interaction (all F < 1). Thus, the N400 priming effect for the double priming condition was not significantly different from the sum of the priming effect for the single priming conditions, indicating a mere additivity of the priming effects.

Discussion

In the present study, we used a double semantic priming task to explore categorization of taxonomically and thematically related multiple primes in humans. We explored the priming effects through behaviour (as a time decrease to process the primed compared to the unprimed targets) and electrophysiology (as a decreased amplitude of the N400 in the primed conditions).

Our study yielded three essential results. First, both behavioural and ERP measures showed robust single (RU and UR) and double (RR) priming effects. Second, both behavioural and ERP measures demonstrated a larger priming effect for double priming than for single priming conditions. However, there was no evidence for an over-additivity of double priming compared to single priming. Finally, the more substantial priming effect of double compared to single primes was observed for both taxonomic and thematic relationships in the N400 analysis, whereas it was observed only for the taxonomic relationships in the behavioural analysis.

Categorization in a double vs single priming condition

Our results showed a greater priming effect with two primes than with single primes on both behavioural and ERP measures. We observed additivity but no over-additivity of multiple primes: the facilitation induced by multiple primes was not significantly larger than the sum of the facilitation yielded by each prime separately, for both behavioural and electrophysiological analyses.

Underlying processes of multiple priming effect

Our study confirms the additivity of two primes at the behavioural level in an LDT task.

This finding is consistent with previous studies showing an additive effect in multiple priming conditions at the behavioural level, in both semantic category types (Balota and Paul, 1996;[START_REF] Chwilla | Event-related potential and reaction time evidence for inhibition between alternative meanings of ambiguous words[END_REF][START_REF] Lavigne | Time course of activatory and inhibitory semantic priming effects in visual word recognition[END_REF].

Consistent with behavioural results, the ERP findings also revealed an additive pattern of double priming. We are aware of only one study that explored ERPs in a multiple priming task using a LDT paradigm. Chwilla and Kolk [START_REF] Chwilla | Event-related potential and reaction time evidence for inhibition between alternative meanings of ambiguous words[END_REF] showed different N400 additivity patterns varying according to the task (LDT or relatedness judgment task) and the type of words used as primes (ambiguous or unambiguous). In the LDT, the results showed an additive effect using both ambiguous and unambiguous words, which is consistent with our findings. In the relatedness judgment task, there was an over-additive effect with unambiguous words but an under-additive effect with ambiguous words. Another study that explored ERPs in a multiple priming task used a picture naming task (Python et al., 2018a). Although the authors did not explore additivity, they described an increased semantic facilitation effect with multiple primes compared to a single prime in both taxonomic and thematic categories. Overall, these findings suggest that a double priming effect can occur in priming paradigms that use different tasks, i.e., naming, LDT, relatedness judgement tasks, suggesting that distinct mechanisms can be involved and combined both at the semantic, lexical, and strategic levels.

The task and material used to explore a double priming effect are both critical and an overadditivity effect might be difficult to observe using an LDT, possibly due to its reliance on processes not optimally captured by this task.

The putative mechanisms of additivity remain debated. First, the multiple priming effect might depend on an enhanced influence of one of the primes. It refers to an associative "boost" [START_REF] Moss | Accessing different types of lexical semantic information: Evidence from priming[END_REF] in which the presence of a semantic association between each prime and the target generates a larger priming effect by "accumulation". We controlled the association strength between primes and target, to ensure that verbal associations beyond the semantic relationship itself did not boost the priming effect [START_REF] Tyler | Towards a distributed account of conceptual knowledge[END_REF]. For this reason, we consider that it is unlikely that the double priming effect was due to two separate priming effects or to an enhanced influence of the second prime on the target. We suggest that the multiple priming effect instead reflects the pre-activation of the target induced by primes that have convergent semantic relationships with this target. Previous studies using masked semantic primes and attentional blink paradigms have shown the sensitivity of the N400 priming effect to automatic semantic processes [START_REF] Deacon | Event-related potential indices of semantic priming using masked and unmasked words: evidence that the N400 does not reflect a post-lexical process[END_REF][START_REF] Kiefer | The N400 is modulated by unconsciously perceived masked words: further evidence for an automatic spreading activation account of N400 priming effects[END_REF][START_REF] Kiefer | Time course of conscious and unconscious semantic brain activation[END_REF][START_REF] Rolke | Missed prime words within the attentional blink evoke an N400 semantic priming effect[END_REF]. Hence, the higher facilitation in multiple primes conditions may reflect the organization of concepts into categories.

Second, top-down processes such as controlled strategies might be involved in the multiple priming effect. Several authors have argued against the involvement of purely automatic processes in priming experiments using long SOA (> 300 ms), because such duration allows controlled processing and strategies during task performance (Lucas, 2000;Neely, 1977). In the present study, we used a long SOA (1500 ms) to avoid the ERPs in response to target words to be affected by the ERPs in response to the second prime. Therefore, it is possible that both automatic and controlled processes contributed to the semantic priming effect.

More specifically, two controlled processes may be involved, the expectancy generation and semantic matching. Expectancy generation (Becker, 1980(Becker, , 1979;;Neely, 1991;Neely et al., 1989) is defined as the use of the semantic information of the prime to activate a set of potential words that could correspond or strongly relate to the following target. To limit the impact of the expectation component, we instructed participants not to pay attention to primes. However, as the target was a category, participants may make correct expectations about the following target. This expectation is more likely to be correct in the RR condition were the two primes belong to the same category, and especially in the taxonomic condition where there are fewer options than in the thematic condition. The expectation is less likely to be correct in the RU or UR conditions as the R and U primes do not belong to the same category. However, in our experiment, the relatedness proportion of RR trials (i.e., the proportion of related trials among the total of trials (including related and unrelated conditions) was low (25%), which does not typically favour the occurrence of strategic expectation processes during priming paradigms (de Groot, 1984;Neely et al., 1989). Another controlled process that can be involved in semantic priming is semantic matching (Colombo and Williams, 1990;den Heyer et al., 1983;Neely, 1991Neely, , 1977;;Neely et al., 1989), in which the participants verify the relation between the prime and the target. It is induced by the type of target-prime relatedness, and primarily occurs when most unrelated prime-target trials use pseudoword targets. With such proportion, unrelated pairs could bias the lexical decision to a "pseudoword" response, and related words, to a "word" response (Neely et al., 1989). In our paradigm, there were as many related as unrelated primes in both words and pseudowords trials, and it is thus less likely that semantic matching processes explain our results. It is noteworthy that the facilitation effect of the first word prime appeared greater than the effect of the second word prime, with a larger difference between RR and UR than between RR and RU conditions. Although this result is difficult to interpret due to a lack of statistical difference between RU and UR, it may suggest that the first prime played a larger role in the semantic facilitation effect than the second one. This tendency might however be better explained by expectancy generation triggered by the first prime than by semantic matching.

Furthermore, some masked priming paradigms showed that unconscious semantic processes are affected by the conscious context and engagement of executive attention [START_REF] Greenwald | Long-term semantic memory versus contextual memory in unconscious number processing[END_REF][START_REF] Naccache | Unconscious Masked Priming Depends on Temporal Attention[END_REF][START_REF] Rohaut | Unconscious semantic processing of polysemous words is not automatic[END_REF], highlighting the complexity of the role of controlled processes in implicit priming. Additionally, the increased activation of semantic associations in schizophrenic patients ("hyperpriming") with impaired frontal functions also suggests the role of control functions on semantic priming [START_REF] Dehaene | Conscious and subliminal conflicts in normal subjects and patients with schizophrenia: The role of the anterior cingulate[END_REF][START_REF] Kreher | Neural Evidence for Faster and Further Automatic Spreading Activation in Schizophrenic Thought Disorder[END_REF][START_REF] Spitzer | Indirect semantic priming in schizophrenic patients[END_REF]. "Hyperpriming" has also been described in neurological patients, including semantic dementia [START_REF] Laisney | When the zebra loses its stripes: Semantic priming in early Alzheimer's disease and semantic dementia[END_REF] and Alzheimer disease (Borge-Holthoefer et al., 2011;[START_REF] Giffard | The dynamic time course of semantic memory impairment in Alzheimer's disease: clues from hyperpriming and hypopriming effects[END_REF][START_REF] Giffard | The nature of semantic memory deficits in Alzheimer's disease: New insights from hyperpriming effects[END_REF][START_REF] Ober | Semantic priming in Alzheimer's disease: Meta-analysis and theoretical evaluation[END_REF], but also patients with post-stroke aphasia and left frontal lesions [START_REF] Dyson | Semantic processing in aphasia: evidence from semantic priming and semantic interference[END_REF]; see also Python et al., 2018b). Among other interpretations, this effect had been explained by a decreased competition or interference among fewer pre-activated or available knowledge. It may also be related to attentional or controlled deficits. The left inferior frontal region may be critical to shaping semantic facilitation by thresholding lexical selection. These studies highlight the complex intricacy of controlled and automatic processes during implicit priming. Hence, we cannot exclude that the categorization we observe also involves controlled processes exerted on implicit priming, and thus may engage at least in part the frontal lobe functions.

Overall, we show a double priming effect on both taxonomic (both behaviourally and on ERPs) and thematic (on ERPs only) relationships in a similarity priming task. These results indicate that some of the processes involved in the similarity task -allowing to activate the shared category between two items -can occur during a semantic priming task. Such processes may include automatic semantic processes and (mostly pre-lexical) controlled processes. The results also suggest that the N400 can be considered as an electrophysiological marker of such primed categorization.

Additive but not over-additive effect

The double priming effect was additive but not over-additive. One putative explanation relates to the design of our paradigm. According to the spreading activation theory, the activation of concepts decays with time. The long SOA used in our task may have allowed a decay in the pre-activation of the target over time, thus decreasing the double priming effect.

Another potential factor is the repetition of the primes (six times) and targets (four times)

within the same session that can impact the semantic priming effect. The repetition of each word can yield to a higher baseline level of activation of the concepts, generating a lower semantic priming effect (see Kutas and Federmeier, 2011). However, to limit this effect, we ensured that each word was presented only once for each condition, and we ensured that at least nine trials separated any target repetition. We also created two lists for each type of semantic association to counterbalance the order of the primes, and the order of the sessions alternated between the participants, which allowed us to balance the number of times each of the primes was presented in the first and in the second position.

Hypothetically, an additive rather than the over-additive effect in our double priming task might have a behavioural significance, because it could reflect an adaptive balance between over-constraining the activation of one given concept in response to given stimuli (overadditivity) and failing to activate the appropriate converging concept between them (underadditivity).

4.2

The amount of behavioural double priming effect varies with the type of semantic association.

In the behavioural analysis, the double priming effect was larger when primes were taxonomically related to the target, than when they were thematically related. Based on the results of Python and colleagues (Python et al., 2018a), we were instead expecting a larger behavioural priming effect for thematic than for taxonomic relationships. The reason for a lower double priming effect of thematic relationships remains unclear. Both automatic and controlled mechanisms can explain this result.

In the framework of the spreading activation model, when the first prime is presented, its activation propagates to neighbouring nodes. We propose that as taxonomically related concepts share various features, they are highly interlinked and close to each other in semantic memory. Therefore, a given target category could be primed by the cumulative effect of preactivation provoked by many neighbouring nodes, resulting in an increased target facilitation.

In contrast, thematically related words do not necessarily share similar features. Therefore, the presentation of thematically related primes may activate a much broader set of concepts that are not necessarily interlinked to each other. Thus, there may be no (or less) cumulative effect yielded by multiple primes.

In addition, and as mentioned above, although most factors were controlled in the paradigm to counteract the effect of controlled processes, some factors such as the long SOA and the conscious perception of the stimuli may have allowed controlled processes to occur. We believe that the expectancy generation process could have contributed to the difference in double priming RT between taxonomic and thematic categories. It is possible that due to the presentation of the same type of trials in the taxonomic sessions, namely, exemplars (primes)category (target) relation, the set of words activated according to the expectancy generation hypothesis was more limited than in thematic trials. In addition, thematic items included different types of functional relations and participants could have generated a broader set of expected targets. Then, the probability that the real target of the task corresponds to the expected one may have been lower for the thematic condition, which may have decreased the influence of expectancy generation in the semantic priming effect.

Regarding more typical effects such as the single priming effect, our findings are in agreement with several studies that have shown no difference in the RT priming effect between taxonomic and thematic (Chen et al., 2014;Hagoort et al., 1996;Khateb et al., 2003;Maguire et al., 2010). This result suggests that the semantic facilitation yielded by a single prime involves similar processes in both types of semantic categories. However, multiple primes may favour the access to taxonomic relationships behaviourally, which should be interpreted with caution since we did not observe differences between semantic category types in N400 amplitudes.

Conclusion

The present study provides evidence for an additive effect of double priming of taxonomic and thematic categories and suggests that categorization can occur without explicit instructions in a semantic priming task. At the behavioural level, the effect of double priming suggested that taxonomic relations may be stronger or more easily accessed than thematic relations. In contrast, the N400 double priming effect was equivalent for both types of semantic relations, highlighting the importance of the N400 as an electrophysiological marker of categorization.

Our findings have implications in understanding the cognitive processes at play during the similarity task in particular, and in categorization in general. The results also place our "similarity priming task" in a promising position as a tool to better characterize the patients' difficulties in abstract thinking, especially in the context of a frontal or temporal degenerative disease or in patients with schizophrenia. Finally, our research has broad significance in understanding how semantic memory is organized and accessed to, and how it shapes the way humans think and generate abstract concepts.

Acknowledgement:

The research leading to these results has received infrastructure funding from the French

Corresponding author

Correspondence to emmavolle@gmail.com

Additional Information:

Declarations of interest: None

Discussion

In this study, we used a double semantic priming paradigm to explore the implicit retrieval of semantically related words belonging to a same taxonomic or thematic category.

This paradigm allowed us to show that categorization process can occur implicitly, that is without explicit instructions to categorize. The results contribute to a better understanding of the organization of the semantic memory structure by accessing its information implicitly.

As expected, taxonomically and thematically related prime words elicited a priming effect on the target word (Chen et al., 2014b;Jones and Golonka, 2012;Maguire et al., 2010).

Moreover, our double semantic priming paradigm led to significant priming effects in the single priming conditions (RU and UR) and the double priming condition (RR). Thus, the related primes in both single and double priming condition facilitated the processing of the categorytarget word that was reflected in the priming effect. Importantly, this result further support that semantically related concepts without a strong association also elicit a priming effect, suggesting that they are associated in memory.

When comparing single and double priming, the priming effect resulting from the double priming condition was larger than in single priming conditions at the behavioral and electrophysiological levels. This result provides evidence of the additive effect of double priming of taxonomic and thematic categories., that is, the double priming effect in RR corresponds to the sum of the single priming effect in RU and UR conditions. Thus, the activation of an exemplar of a given taxonomic or thematic category pre-activates this category, and the activation of two semantically related exemplars of a given category increases the preactivation of this category [START_REF] Collins | A spreading-activation theory of semantic processing[END_REF]. Hence, the higher facilitation in multiple primes conditions may reflect the organization of concepts into categories.

This additive effect was more substantial at the electrophysiological level than in behavior. We observed a greater behavioral priming effect for double priming than single priming of a taxonomic but not of a thematic category. However, ERP showed a greater priming effect (a slighter N400) for double than single prime for both categories. This result can be explained since we controlled for the strength of association between the primes. Therefore, neither thematically nor taxonomically related concepts have a strong association, and the preactivation of these words may be supported by the shared features between the concepts (Mirman et al., 2017), but contrary to taxonomically related concepts, thematically related concepts share a common context without sharing similar features. Importantly, this finding suggests that electrophysiological markers may be more sensitive to priming effect of semantically related concepts, and especially to thematic relationships.

Critically, our findings demonstrate that taxonomic and thematic categories can be implicitly retrieved from semantic memory, without explicit instructions to categorize.

However, one important limitation in this study is that we used a long SOA to avoid overlapping the electrophysiological response of the primes and the target word. Therefore, it is likely that the long SOAs in our paradigm allowed controlled processing and strategies during the task performance (Lucas, 2000;Neely, 1977). However, during the development and the performance of the task, we implemented several strategies to avoid these control processes influencing our task.

This result may have implications and trigger future studies on creativity processes. For instance, higher flexibility in DT performance typically implies exploring and switching between different categories, and a higher number of categories identified in the ideas generated is related with more original ideas. Here, we show that concepts in memory may be organized in categories and thus, generating ideas or concepts that belong to different categories rely on exploring more remote elements in memory. In addition, previous studies show the role of categorization process in insight problem solving and analogy reasoning [START_REF] Chrysikou | When shoes become hammers: Goal-derived categorization training enhances problem-solving performance[END_REF][START_REF] Green | The Micro-Category account of analogy[END_REF][START_REF] Green | Automatic activation of categorical and abstract analogical relations in analogical reasoning[END_REF]. We show that categorization could occur without explicit instructions in a semantic priming task.

The double priming effect we describe here may be particularly relevant to creativity tasks requiring associative combination of concepts or ideas. The double priming effect represent the spontaneous convergence of two concepts into the facilitation of a target concept.

This ability may be critical in convergent thinking tasks, such as the CAT, in which the activation of three semantically distant concepts must converge in one target response. Previous studies have shown that creativity tasks rely on the ability to find common characteristics or similarities between concepts (Benedek et al., 2012b). This ability may facilitate the concurrent access to mutually remote associative elements and combine them properly. Thus, exploring differences in double priming facilitation in relation to creative abilities can contribute to better understand how the concurrent access to remote associative element in memory influences CAT performance. Overall, how implicit categorization of ideas or concepts contribute to a better performance in creativity remains to be directly explored.

In sum, our results indicate that we can implicitly access to the semantic information in memory using a double priming paradigm. In addition, the categorization process can occur implicitly (without explicit instructions) in a similarity priming task. This result suggests that the activation of the shared category between two semantically related items in the explicit similarity task occurs during a similarity priming task. Finally, these results suggest that the N400 component can represent an electrophysiological marker of categorization since the ERP priming effect was more sensitive than behavioral responses to detect the categorization process. Finally, this paradigm may be useful to explore individual differences in the double priming effect in relation to creative abilities, and how implicit categorization contribute to a better performance in different creativity tasks.

Chapter 3: General discussion

The associative theory of creativity proposes that creative ideas arise from finding new meaningful connections between remote concepts (Mednick, 1962). Moreover, associative combination abilities and the ability to generate remote and even unrelated concepts was shown to predict creative behavior (Benedek et al., 2020;[START_REF] Olson | Naming unrelated words predicts creativity[END_REF]. Besides Mednick's theory, associative thinking has been related to creative abilities as measured within several existing frameworks (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2020Benedek et al., , 2012b;;[START_REF] Green | Thinking Cap Plus Thinking Zap: tDCS of Frontopolar Cortex Improves Creative Analogical Reasoning and Facilitates Conscious Augmentation of State Creativity in Verb Generation[END_REF][START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF]Marron et al., 2020;[START_REF] Merten | Creativity, personality and word association responses: associative behaviour in forty supposedly creative persons[END_REF][START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF]Rossmann and Fink, 2010). Therefore, semantic memory structure is considered relevant to creative ability as associative processes depend on the strength of semantic associations between concepts and how they are stored in memory (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2012b;He et al., 2021;Marron et al., 2020Marron et al., , 2018)).

Evidence from the literature indicate that the organization of the concepts stored in semantic memory, and the processes allowing to navigate in memory, contribute to individual differences in creative abilities (Benedek et al., 2017;Kenett et al., 2014). Therefore, exploring the organization of semantic associations in memory can help to better understand the cognitive mechanisms underlying creative thinking. Through the several studies reported in this thesis, I explored the relationships between creative abilities, semantic memory structure, and memory search. The findings help to understand the role of semantic memory in creativity, the related cognitive processes, and the underlying brain correlates.

The development of the relatedness judgment task: A novel tools in cognitive neuroscience

During the last years, the contribution of computational network science methodologies in the neuroscience of creativity have allowed exploring the organization of semantic memory in relation to creative abilities and how concepts are organized in individuals' memory via SemNets (Benedek et al., 2017;Kenett et al., 2014;[START_REF] Yan | Left temporal pole contributes to creative thinking via an individual semantic network[END_REF]. The pioneering studies (Benedek et al., 2017;Kenett et al., 2014) developed different methods to build and explore SemNets in relation to creative abilities. These methods allowed them to build SemNets at the group (Kenett et al., 2014) and individual level (Benedek et al., 2017). To the best of our knowledge, the unique method that has been used to explore individual differences in the SemNets in relation to creative thinking is based on the relatedness judgment task [START_REF] Benedek | How semantic memory structure and intelligence contribute to creative thought: a network science approach[END_REF]. In support to this method, previous studies have shown how the relatedness judgments given by individuals relate to creativity (Rossmann and Fink, 2010;[START_REF] Vartanian | Divergent thinking ability is related to faster relatedness judgments[END_REF]. These studies show that more creative individuals rate unrelated words as less semantically distant (Rossmann and Fink, 2010) and are faster in judging the relatedness of concepts compared to less creative individuals [START_REF] Vartanian | Divergent thinking ability is related to faster relatedness judgments[END_REF].

Since semantic associations may vary between the different languages, in the first study of this thesis, we developed and validated a French version of the RJT task based (as in Benedek et al., 2017). To this aim, we built a French SemNet (Figure 11) based on the French semantic association norms (Dictaverf; Debrenne, 2010; http://dictaverf.nsu.ru/dictlist). The building of this semantic network, in combination with a novel mathematical method, allowed us to select the words of the RJT task by controlling for their linguistic properties, their lexical frequency, and the semantic distance between all possible combination of pairs of 35 words. This step of selecting the proper verbal material was an important step to validate the RJT task.

The main advantage of using the RJT task is that it allowed the building of individual SemNets, whose metrics were explored and compared between individuals with graph theory methodologies. Importantly, in our study, we replicated the findings in Benedek et al. (2017), demonstrating the validity of this method independently of the verbal material. In addition, recent studies using the task in Benedek et al. (2017) have also found correlations between individuals' SemNet organization and creative abilities (He et al., 2021;[START_REF] Yan | Left temporal pole contributes to creative thinking via an individual semantic network[END_REF], providing external validity. In Benedek et al. (2017), the selection of the material was based on semantic categories. Therefore, it is likely that more common semantic categories are shared between different languages. However, our study validated the RJT task based on associative norms that may be different between languages as they reflect language use and the way we perceive the world [START_REF] Anisfeld | Review of The Structure of Associations in Language and Thought[END_REF].

In addition to the French SemNet validation, the RJT task, is currently being used in ongoing projects in our laboratory to build individual SemNets. For instance, this method serves as a tool to examine local or global changes in individuals' SemNets due to different semantic contexts, such as problem-solving. Performance in problem-solving may partly depend on the associative strength of the concepts in memory. Moreover, previous studies have shown how semantic knowledge constraints creativity, for example, generating functional fixedness [START_REF] Chrysikou | Functional Fixedness in Creative Thinking Tasks Depends on Stimulus Modality[END_REF][START_REF] Chrysikou | Following the Wrong Footsteps: Fixation Effects of Pictorial Examples in a Design Problem-Solving Task[END_REF]. Thus, individuals' SemNets serve to explore how concepts relate to each other and how it can favor or constrain problem-solving.

Besides exploring SemNets properties in relation to healthy subjects, this approach is also promising in exploring semantic memory in patients. Currently, I am collaborating on an ongoing project aiming to explore semantic memory in people who have undergone awake brain surgery relative to healthy individuals. Since people with brain tumors in semantic-related brain regions may have mild semantic impairments after awake surgery [START_REF] Campanella | Semantic access dysphasia resulting from left temporal lobe tumours[END_REF][START_REF] Duffau | Usefulness of intraoperative electrical subcortical mapping during surgery for low-grade gliomas located within eloquent brain regions: functional results in a consecutive series of 103 patients[END_REF][START_REF] Santini | Cognitive outcome after awake surgery for tumors in language areas[END_REF][START_REF] Satoer | Cognitive functioning early after surgery of gliomas in eloquent areas: Clinical article[END_REF], exploring SemNet properties can serve as a tool to examine the damage to semantic memory when comparing with matched healthy controls or previous and posterior to the surgery. In addition, the SemNet properties of patients can be compared to the performance in classical neuropsychological tests to explore their specificity and sensibility to capture semantic impairments in this population. Thus, the use of this method may contribute to better understand the semantic impairments in this population. In future research, these impairments can be explored in relation to creative abilities [START_REF] Bieth | Creativity[END_REF].

In addition, the RJT task can also be used in future studies to explore how SemNet properties change in longitudinal studies -for instance, exploring how the organization of the concepts in memory changes across the life span and how it relates to creative abilities. Previous studies have shown that structural properties of the lexicon vary across different age cohorts [START_REF] Dubossarsky | Quantifying the structure of free association networks across the life span[END_REF][START_REF] Zortea | Graph analysis of semantic word association among children, adults, and the elderly[END_REF]. However, it remains unexplored how semantic memory shapes creative abilities across the life span via the changes in SemNet properties. In the same line, changes in SemNet properties can also be explored before and after specific semantic-related training, such as goal-derived categorization construction, as this type of training improves insight problem-solving performance [START_REF] Chrysikou | When shoes become hammers: Goal-derived categorization training enhances problem-solving performance[END_REF]. Indeed, whether the improved performance in insight problem solving after these training relies on changes in the individual's SemNet properties remains to be explored.

Overall, the development and validation of the RJT task allowed us to use this task as the main tool to build individual SemNets and identify the properties associated with individual differences in creativity. Moreover, in the field of neuroscience of creativity, the use of SemNets has been shown to be one of the most promising approaches to explore creative abilities. Importantly, future validation studies for test-retest are ongoing in the laboratory to further support the validity of the SemNet properties measures.

Individual semantic networks and creative abilities

RJT based semantic networks

The method we used to represent the organization of the concepts in semantic memory was based on the relatedness judgments given by the participants during the RJT task. This method to build individual SemNets was validated in our first study and was used to explore semantic memory organization in the two following studies. Hence, the exploration of individual SemNets served as a proxy of the semantic memory structure (Kenett et al., 2014;Kenett and Faust, 2019).

In semantic memory networks, the concepts are represented by nodes that are closely or remotely connected to each other via links. The links represent the strength of association between the concepts in memory. Based on the Collins and Loftus proposal (Collin and Loftus, 1975), when a given concept is activated in memory, its activation spreads to the connected concepts through the links, from the more closely associated to the more remotely associated concepts. Semantic distance between the concepts within a semantic network can be determined by the number of steps separating any two given concepts. Previous studies using the semantic priming approach examined how semantic distance as measured by the path length within the network (number of steps between any two concepts) is related to the subjective judgments of associative strength (Kenett et al., 2017;[START_REF] Kumar | Distant connectivity and multiple-step priming in large-scale semantic networks[END_REF]. Moreover, reaction times increase with more distant relationships (until 4 steps) but decrease for longer semantic distances (up to 4 steps of path length), while word pairs are judged as unrelated. These results suggest a priming effect in prime-target words that are closely related and that this effect spreads within the network but dissipates over time. Thus, remote concepts in memory are easily and quickly judged as unrelated, with no priming effect.

One of the critical steps in validating our RJT task was the confirmation that theoretical steps within the French semantic network, as a measure of semantic distance, correlated to the relatedness judgments given by the participants. We explored the correlation between the semantic distance (i.e., number of steps separating the words within the French SemNet)

between each pair of words of the RJT task and the relatedness judgment given by the participants. We observed that pairs of words with a higher semantic distance (i.e., a higher number of steps separating them within the French network) were judged as less related by the participants. Conversely, pairs of words with a shorter semantic distance (i.e., few numbers of steps separating them within the French network) were judged as more related by the participants. This pattern was observed at the individual and group levels. Moreover, word pairs with more than four steps separating them) were judged as less related during the RTJ task.

These results confirmed previous findings (Kenett et al., 2017), showing that pairs of words separated by more than four steps within a semantic network are usually rated as unrelated by participants. Moreover, this result suggests that RJT based SemNets reflect the organization of semantic memory in our participants. Critically, these results allowed us to validate the use of the path lengths (theoretical steps) within the French semantic network as a measure of semantic distance. Moreover, the correlation between the theoretical semantic distance and the relatedness judgments of the participants was later replicated by the group of participants of our neuroimaging studies with a bigger sample size (Figure 12).

Overall, the SemNet properties reflects semantic memory structure but what the relatedness judgments to the different pairs of words in the RJT capture remains unclear.

However, whether they capture purely associative processes or not is still unclear. Individual's SemNets are constructed from intentional relatedness judgments between words that could rely on cognitive control in addition to spontaneous semantic associations. Thus, further exploration is required to examine this question. Future studies using the associative priming approach could help to clarify the exact contribution of spontaneous associative processes in relatedness judgments and in SemNets.

Individual semantic network metrics and RJT ratings related to creative abilities

One of the main contributions of our behavioral study was to demonstrate the relationship between the individual SemNet metrics and creative abilities. Including a set of multiple creativity tasks in our study allowed us to extend previous findings (Benedek et al., 2017;Kenett et al., 2014) by relating semantic network metrics to unexplored creativity tasks and behaviors. As expected, subjects with greater creative abilities had more densely connected and less modular semantic networks. These results confirmed the role of semantic memory organization in creativity, not only based on DT measurements but also in and real-life creativity as assessed with the ICAA questionnaire. Furthermore, although the sample size of our first study was small (23 participants), the results were consistent and replicated in our second group of participants (in the neuroimaging studies), with a bigger sample size (94 participants), further supporting and validating the close relationship between the organization of semantic associations reflected by semantic network metrics and creative abilities.

The findings in our studies are in line with the general proposal of the associative theory (Mednick,1962) of creativity, by suggesting that higher creative individuals had a more flexible organization of their semantic memory. Thus, our findings extend previous studies reporting a strong relationship between associative abilities and creativity using different approaches, such as divergent thinking (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2012b;Lee and Therriault, 2013;Marron et al., 2020;Rossmann and Fink, 2010), insight problem solving (Mednick, 1962), analogical reasoning [START_REF] Green | Thinking Cap Plus Thinking Zap: tDCS of Frontopolar Cortex Improves Creative Analogical Reasoning and Facilitates Conscious Augmentation of State Creativity in Verb Generation[END_REF](Green et al., , 2012)), and creative achievements in real life (Benedek et al., 2020;[START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF][START_REF] Merten | Creativity, personality and word association responses: associative behaviour in forty supposedly creative persons[END_REF][START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF], suggesting that individual differences in semantic memory structure partly explain this relationship.

In addition to the individual SemNet properties, the selection of the verbal material by controlling semantic distance allowed the exploration of the semantic relatedness judgments of the participants in relation to the theoretical steps (strength of association) between the pairs of words. Based on this data, we could identify the creativity mechanisms that correlated to the higher (or lower) relatedness judgments given by the participants to semantically close (high strength of association) and to semantically distant (low strength of association) pairs of words.

In previous studies using relatedness judgment tasks, the theoretical distance between the pairs of words in the RJT task was not controlled, and the extent to which individuals judged semantically close or semantically remote related words could influence the results. In our behavioral study, we observed that the relatedness judgments given by the participants across the RJT task to a more remotely associated pair of words (steps > 2) correlated to originality scores in the AUT. This result converged with previous studies showing a positive relationship between originality and the judgments of the associative distance between unrelated pairs or words (Rossmann and Fink, 2010) and a higher semantic distance between freely generated words (Beaty et al., 2021b).

Previous research in the framework of semantic cognition suggests that the activation of semantic knowledge to suit the demands of a given task or context requires semantic control.

For example, retrieving semantic links between more weakly-related words according to associative norms involve a semantic control process (Badre et al., 2005;Hirshorn and Thompson-Schill, 2006;Jackson, 2021;Jefferies, 2013;Noonan et al., 2013;Whitney et al., 2011b), eliciting a strong engagement of the left-lateralized semantic control network (Noonan et al., 2013). Semantic control is likely involved in creativity tasks, especially when generating more creative and appropriate ideas, or in the CAT task, especially in the distant trials which require retrieving remote associations and combine them. Moreover, neuroimaging studies have shown the involvement of brain regions of the semantic control network during RAT-like tasks (Evans et al., 2020;Gonen-Yaacovi et al., 2013) and DT tasks (Beaty et al., 2017). Importantly, the different brain regions related to both semantic representation and semantic control were found in the brain predictive model networks of creative behavior, and more flexible memory search related to creativity in our studies.

Overall, in two groups of participants, we replicated and extended previous results in the literature. The RJT task allowed us to explore how the relatedness ratings and the individual differences in the SemNet metrics correlate to creative abilities. Thus, the SemNet metrics seems to be a helpful tool to measure the ability to make new semantic connections which is an important challenge to better understand the mechanisms of the creative cognition. Importantly, results from the studies reported in this thesis further support that a more flexible organization of semantic associations of creative people can be also explained by individual differences in brain connectivity.

Individual semantic network metrics as a cognitive marker of real-life creativity

In the second study of PART 1 of this thesis, we focused on real-life creativity for a neurocognitive exploration of its relationship to SemNet metrics. As in the behavioral study, the RJT task allowed us to build and explore the individual's SemNets in relation to creativity.

Specifically, we showed that real-life creative activities and achievements measured with the ICAA questionnaire could be predicted by individual SemNet properties, replicating and reinforcing our previous results. In addition, we showed the brain connectivity patterns predicting the creativity -relevant SemNet metrics. Overall, we identified and characterized the SemNet metrics as cognitive markers of creative behavior in real life by 1) relating them to creative activities and achievements and 2) predicting them from the brain functional connectivity patterns during the RJT task.

To operationalize our study, participants performed the RJT task while undergoing an fMRI session; thus, we could explore how intrinsic brain functional networks configurate differently in individuals during the RJT task when individuals judging semantic relatedness and how these patterns vary in relation to individual differences in SemNet. To explore the brain functional connectivity patterns during the RJT task, we used the CPM method which allowed us to predict differences in the properties of the individual SemNets based on the unique patterns of functional brain connectivity during relatedness judgments.

We propose the modularity metric of the SemNet as a cognitive marker of creative abilities in real life since individual differences in this metric reliably predicted individual differences in both creative activities and achievements in real life. SemNet modularity may be a potential index of flexibility and spreading of activation of the concepts in memory (Kenett et al., 2018), and higher modularity may constrain the spread of information leading to increased rigidity of thought (Kenett et al., 2016) and inefficient processing of concepts [START_REF] Siew | Community structure in the phonological network[END_REF]. Thus, we suggest that a less segregated SemNet allows people to have more flexible thinking, leading to higher creative activities and achievements in real life.

One interesting point in our results was that different metrics predicted creative activities and achievements at the behavioral level. While UUN Q predicted both creative activities and achievements, WUN ASPL predicted differences in creative activities, and WUN Q predicted creative achievements. Hence, modularity of SemNets appears as a critical property for both activities and achievements, while creative activities may also relate to efficiency (ASPL). Since previous work relating SemNet to creativity has mostly focused on creative potential, we did not have specific literature-based predictions of quantitative or qualitative differences between creative activities and creative achievements relationships with associative processes or brain connectivity. Although both creative activity and creative achievement are the main indicators of creative behavior (Diedrich et al., 2018), they distinctly reflect less and more complex forms of creative behavior, respectively. Previous studies have shown that both creative activities and achievements in real life are related to associative processes (Benedek et al., 2020;[START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF]. Thus, these subtle differences may refer to the relevance of certain aspects of semantic network organization for less versus more complex forms of creative behavior.

However, future studies should be performed to explore how distinct SemNet metrics relate to less or more complex creative behavior differently.

Although creativity in real life has been previously explored in relation to associative processes (Benedek, 2020;[START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF]Liu, 2021;[START_REF] Merten | Creativity, personality and word association responses: associative behaviour in forty supposedly creative persons[END_REF][START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF], it was unexplored in relation to semantic memory organization. Finally, it is important to acknowledge that creativity is a multifactorial construct. For instance, in the study we performed during the COVID-19 pandemic and related lockdown situation [START_REF] Lopez-Persem | Through thick and thin: changes in creativity during the first lockdown of the Covid-19 pandemic[END_REF], we show with how distinct factors influence everyday creative activities differently. We explored how everyday creative activities changed during the period of lockdown in France and the factors that could explain these changes. Individuals reported both a positive and negative direction of the changes in creativity compared to the time before the pandemic, specifically the past five years. These changes varied across activities and individuals. Positive changes were mostly related to more free time, while negative changes in creative activities were mainly related to affective factors (such as negative mood or stress).

Hence, despite creativity being a complex multidimensional construct, we found evidence that the structure of semantic memory contributes to creative behavior in real life in our studies.

Overall, the exploration of the SemNet metrics in combination with the functional brain connectivity served to identify a cognitive marker that predicts higher creative behavior. Thus, the results from this study further support the exploration of individual SemNets as a promising tool to better understand mechanisms underlying creative behavior.

Brain patterns predicting creativity via semantic network metrics

Behavioral results strongly supported the relationship between individual differences in SemNet metrics and creative abilities. We then explored the brain connectivity patterns related to these individual differences. Via mediation analyses, we showed how brain functional connectivity patterns during the RJT reliably predicted individual differences in the SemNet properties predicting creative behavior. This prediction was mediated by the modularity in the SemNet. This analysis allowed us to uniquely link cognitive and neural components by showing the relationships between SemNet metrics, creative abilities, and brain functional connectivity patterns.

To the best of our knowledge, only one previous study explored the relationships between individual SemNet properties, creativity, and brain correlates [START_REF] Yan | Left temporal pole contributes to creative thinking via an individual semantic network[END_REF],

showing that the efficiency of the SemNet mediated the relationship between gray matter volume of the left temporal pole and creativity. However, this work explored the relationship between individual SemNet and creativity, uniquely measuring creative thinking with the AUT task. Moreover, in contrast to our study, they explored structural brain differences rather than functional connectivity patterns. Exploring brain functional connectivity rather than using whole brain using voxel brain morphometry approach offer a better understanding of how brain region and functional networks interact during specific tasks or conditions. In our study, brain functional connectivity patterns allowed us to reveal the brain configuration patterns related to differences in creative mechanisms supported by semantic memory structure.

Other two studies examined the relationship between semantic memory, brain correlates, and creativity [START_REF] Liu | Semantic association ability mediates the relationship between brain structure and human creativity[END_REF]Marron et al., 2020). However, they explored associative processes relying on semantic memory rather than properties of the individual SemNets. These studies showed a mediating role of the semantic associations' ability in the relationship of creative abilities and brain correlates of the associative ability. However, they were restricted to pre-defined brain regions to explore differences in anatomy [START_REF] Liu | Semantic association ability mediates the relationship between brain structure and human creativity[END_REF] and functional connectivity during the resting state (Marron et al., 2020), rather than a wholebrain exploration which can obscure the involvement of other brain regions or pattern of functional connectivity related to creative abilities. Thus, from these studies remained unclear whether the differences in associative abilities they measured depend on the organization of the concepts in memory or whether the a priori definition of brain regions could obscure the involvement of other brain regions associated with individual differences in creativity rooted in semantic memory.

Although He et al. (2021) or require some control processes remains to be tested directly. The exploration of the brain correlates can provide some information about these questions.

Brain network prediction of creative abilities rooted in semantic memory

Combining several validated methods in neuroimaging, we explored the brain networks underlying the SemNet properties allowing people to be more creative. We identified the predictive brain model networks whose CC and efficiency predicted our SemNet markers of real-life creativity. Based on the neuroimaging studies in creativity, we expected these predictive model network to include the FPCN, DMN, and SalN networks.

Consistent with this hypothesis, our findings converge previous research supporting the role of several brain regions belonging to the DMN, FPCN, and SalN for predicting real-life creativity mediated by SemNet properties (Beaty et al., 2018(Beaty et al., , 2016[START_REF] Beaty | Default and Executive Network Coupling Supports Creative Idea Production[END_REF]Bendetowicz et al., 2018). The involvement of the DMN and FPCN related regions has been widely reported in relation to creativity in different domains (Beaty et al., 2018(Beaty et al., , 2016;;Bendetowicz et al., 2018).

Among others, it has been reported in verbal divergent thinking tasks (Beaty et al. 2016[START_REF] Beaty | Default and Executive Network Coupling Supports Creative Idea Production[END_REF], music improvisation [START_REF] Pinho | Addressing a Paradox: Dual Strategies for Creative Performance in Introspective and Extrospective Networks[END_REF][START_REF] Pinho | Connecting to Create: Expertise in Musical Improvisation Is Associated with Increased Functional Connectivity between Premotor and Prefrontal Areas[END_REF], poetry composition [START_REF] Liu | Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process[END_REF] and visual arts [START_REF] De Pisapia | Brain networks for visual creativity: a functional connectivity study of planning a visual artwork[END_REF]Ellamil et al., 2012). Importantly, these different creative domains are included in the ICAA questionnaire providing a further support of these brain networks in real life creativity. Moreover, neuroimaging studies indicate that brain regions of DMN-FPCN networks are activated differently during AUT tasks in people with low and high creative achievements (Chrysikou et al., 2020a). Here, we add to previous studies that individual differences in functional connectivity patterns within and between brain regions of these three main functional networks contribute to individual differences in real-life creativity via SemNet modularity.

The DMN-PFCN interaction could be also interpreted in the context of semantic cognition (Lambon Ralph et al., 2017), that describes two systems acting in interaction to support conceptual representations and control processes. These components of semantic cognition rely on different brain regions (Davey et al., 2015;[START_REF] Jefferies | Semantic impairment in stroke aphasia versus semantic dementia: a case-series comparison[END_REF][START_REF] Teige | Dynamic semantic cognition: Characterising coherent and controlled conceptual retrieval through time using magnetoencephalography and chronometric transcranial magnetic stimulation[END_REF]) that partly align with both DMN and FPCN (e.g., [START_REF] Davey | Exploring the role of the posterior middle temporal gyrus in semantic cognition: Integration of anterior temporal lobe with executive processes[END_REF]Gao et al., 2021;[START_REF] Montefinese | The interplay between control processes and feature relevance: Evidence from dual-task methodology[END_REF]Wang et al., 2021). Within this framework, creativity has been linked to interactions between semantic representations and control processes (Evans et al., 2020). How the interaction between these semantic cognition systems converges with the role of the coupling of DMN and FPCN networks in supporting creativity remains to be directly addressed.

Additionally, we identified the coupling of brain regions belonging to the visual and the somatomotor networks as a novel brain functional pattern supporting creativity. Recent research has highlighted the role of somatomotor networks [START_REF] Matheson | The role of the motor system in generating creative thoughts[END_REF] and brain regions belonging to the visual network in creativity [START_REF] Chen | Brain hemispheric involvement in visuospatial and verbal divergent thinking[END_REF][START_REF] Japardi | Functional magnetic resonance imaging of divergent and convergent thinking in Big-C creativity[END_REF]. In this work, we shed light on the critical role of the coupling of the brain regions of these DMN-FPCN and somatomotor-visual networks in real-life creativity rooted in the semantic memory structure. Thus, they could constitute neural markers of these creative mechanisms.

Additionally, in the neuroscience of creativity field, studies usually explore neural correlates related to specificities of creativity tasks, mainly the AUT task. One of the most relevant contributions of our studies is that we explored brain mechanisms of creative abilities rooted in semantic memory. Since participants performed the RJT task while they underwent an fMRI acquisition, we explored the functional connectivity patterns during this task at the whole-brain level and examined differences in the individual's brain configurations during semantic judgments that predict creativity. Thus, brain patterns were explored while participants performed similar processing of relatedness judgments. Overall, the mechanisms of creativity explored here were rooted in the semantic memory structure, and specifically, in the modularity of the individual's SemNets. Thus, the brain patterns we characterized are independent of the specificities of any creativity tasks; rather, they are involved in creativity processes underlying semantic memory, whose properties have been related to several creativity approaches.

The predictive brain network we report is relevant for a better understanding of the brain correlates underlying creative abilities. In this study, whole-brain regions belonging to the DMN, FPCN, salience, somatomotor and visual networks were involved in predicting differences in real-life creativity via SemNet Q. Thus, although several studies further support the role of DMN and FPCN in creativity, that was also supported by our findings, creative behavior relies in whole brain distributed functional networks. Critically, the brain correlates we described in our studies underlie the mechanisms of creative behavior that are rooted in semantic memory, that are captured by the SemNet metrics. Then, what are the specific mechanisms captured by the SemNet metrics remains to be fully understood.

Finally, since we explored the CPM method using cross-validation, within the analysis, the number of links of the brain networks related to SemNet differences was slightly different across the iterations. To explore the predictive brain networks, we were very strict in only considering the links that were correlated to the SemNet metrics across all the iterations. The fact that these links connecting whole-brain regions in the predictive network were consistent across all iterations allows more robust interpretations of our results. However, future studies must apply this data to novel functional connectivity data for an external validation which may be important to increase the robustness of our results.

In summary, across the two studies reported in the first part of my thesis, we show the role of the organization of the concepts in semantic memory in creative abilities, and importantly in real-life creativity. Using an unprecedented combination of methodologies, we show how differences in brain network properties, particularly efficiency and CC predict differences in real-life creativity. Few studies have explored brain correlates of real-life creativity as measured by self-reported questionnaires. These few studies have focused on assessing brain correlates in people with high creative achievements level that may not consider differences in everyday creative activities and achievements. Our findings converge with these results, showing that brain regions contributing to the prediction of real-life creative behavior belong to the three main functional networks underlying creative abilities, DMN, FPCN, and SalN. Moreover, we propose that the coupling of visual and somatomotor networks is also critical for real-life creativity. In the literature, only a few studies have explored unique brain patterns underlying cognitive behavior (Beaty et al., 2018;Frith et al., 2021a;Rosenberg et al., 2016). To the best of my knowledge, only few studies have explored CPM-based predictions in relation to creativity (Beaty et al., 2018;Frith et al., 2021a;[START_REF] Ren | Connectome-Based Predictive Modeling of Creativity Anxiety[END_REF]. Two of these studies (Beaty et al., 2018;Frith et al., 2021a). explored how brain patterns of functional connectivity while individuals perform the AUT task predict differences in the performance of this task. Here, we used the CPM method to explore a common aspect for different creativity tasks that rely on SemNet properties. Importantly, we add to previous studies by showing how brain network properties predict differences in real-life creativity. Thus, we uniquely linked neural and cognitive levels to measure creative behavior in real life.

Associative search processes over semantic memory and creative abilities

In the second part of my thesis, we contributed to better understand the neurocognitive mechanisms of search processes in semantic memory related to clustering and switching and how they vary in relation to creative abilities, the individual's organization of semantic memory, executive functions. Moreover, we characterized the brain correlates of these processes.

We adapted methods by Troyer et al. (1998) to quantify different AmbiFlu measures related to clustering and switching with respect to the different meanings of ambiguous cue words. Via principal component analysis, we identified two main components of memory search related to clustering and switching processes during the task. Troyer et al. (1998Troyer et al. ( ,1997) ) characterized the clustering and switching processes as two distinct processes during semantic retrieval, where clustering reflects more spontaneous processes related to temporal brain regions of semantic representations, while switching reflects more controlled processes related to frontal brain regions. However, the cognitive and neural patterns of our components did not fully align with this dissociation.

First, we identified the clustering component that related to DT tasks and semantic retrieval. We expected this component to capture more spontaneous processes based on previous research, partly relying on the SemNet structure. Contrary to our expectations and Troyer's proposal, the clustering component captured by our task did not reflect purely semantic associations. Moreover, it was not correlated to the SemNet structure. This component correlated to the fluency in the AUT, thus likely capturing search processes that are important during the generation of many creative ideas during DT tasks. Thus, generating a higher number of associations during the AmbiFlu task contributes to developing a higher number of original ideas during the AUT task.

Since the clustering component was not related to executive function assessments, but to category fluency task, we suggest that the clustering component captured by the AmbiFlu task may reflect the persistent behavior to maximally exploit a cluster during semantic retrieval.

Hence, effective clustering may involve cognitive control, yet probably not the type of control assessed by our set of executive function measures but related to focused attention.

Second, we identified the switching component that was correlated with the ability of combine remote associations as measured with the CAT task, SemNet structure and executive function measures. Based on the associative theory of creativity (Mednick, 1962) and previous findings indicating that associative abilities mediate the relationship between SemNet efficiency and verbal creativity (He et al., 2021), we expected that more spontaneous processes during the AmbiFlu task to be supported by the organization of the SemNets.

We observed a correlation between SemNets efficiency (significant correlations with modularity and CC did not survive the FDR corrections) and switching, indicating that more flexible semantic retrieval partly relies on the SemNet structure. In our previous study, we found that more creative individuals, as measured with the ICAA and the AUT task had a more flexible organization of concepts in their semantic memory, with higher CC and lower ASPL and modularity. Since in this study, the SemNet metrics were not correlated with the clustering process supporting DT ability, it suggests that the SemNet metrics may capture other processes that contribute to better performance in DT tasks, but they do not reflect the cognitive search process related to the persistence in exploiting a specific cluster captured by the clustering component, that was relevant for DT ability.

In this study, the SemNet structure favored a more flexible retrieval from semantic memory contributing to the ability to combine remote associates. Particularly, SemNet efficiency captured the self-paced switching during semantic retrieval that in combination to the executive control processes allowed higher flexibility in memory search. This efficient and more flexible memory search was related to the ability to retrieve remote associations more easily during the CAT task (Bendetowicz et al., 2018;Benedek et al., 2012b). Thus, the SemNet efficiency in our study may capture associative processes occurring in a more efficient SemNet structure, which are relevant for verbal creativity (He et al., 2021). Overall, SemNet may contribute to more flexible search processes that are important for several aspects of creativity.

The relationship between switching component and executive functions and CAT scores is line with high previous findings showing that higher flexibility of thought supports the generation and combination of remote associations (Kenett and Faust, 2019;Marko et al., 2019;Mednick, 1962;Volle, 2018). The interaction of these processes may support the guiding of the search to direct the retrieval of information in memory (Volle, 2018). Our results indicate that a more flexible search in memory relies on both spontaneous associations and controlled processes. Overall, the clustering and switching component in this study converge with the dual model to creative outcomes (Nijstad et al., 2010). The clustering component is aligned with the persistence pathway while the switching component aligned with the flexible pathway. Both components contributing to DT and convergent thinking tasks differently. The convergent thinking task was favored by more controlled processes captured by the executive function tasks. This result converges with the Matched Filter hypothesis (Chrysikou et al., 2014), indicating that RAT-type tasks require more cognitive control mechanisms to guide searching in memory depending on the explicit rules. Moreover, the neural results also showed distinct patterns that in a way align with these cognitive patterns.

The neural patters of the clustering and switching component further supported their cognitive patterns. Although we expected the clustering component to be related to more associative processes underlying by DMN (Marron et al., 2020;2018) we did not observe this pattern. Instead, brain patterns of clustering are in line with the idea that this component captures the persistence in exploiting clusters maximally which requires high focused attention to keep the search within the cluster. This persistence in search contributed to a more fluent generation of creative ideas. Conversely, the brain regions contributing to the prediction of the switching component were mostly related to the DMN. This result converges with previous results showing the role of the DMN related regions in associative processes in creative abilities (Marron et al., 2018). The role of the DMN related regions has been associated with selfgenerated thought and spontaneous associations (Bendetowicz et al., 2018;Marron et al., 2020Marron et al., , 2018;;Raichle, 2015b), but also supporting control-demanding in working memory tasks (Murphy et al., 2018), and semantic goal maintenance (Wang et al., 2021).

Importantly, brain regions of the DMN interacted with FPCN related regions showing an aligned pattern between cognitive and neural results. Thus, interaction between associative and controlled processes during semantic retrieval supported by DMN and FPCN interaction favor a more flexible search in memory. Furthermore, a flexible search in memory contribute to better performance in the combination of remote associates. The interaction between DMN and FPCN supporting CAT task was shown previously in patients (Bendetowicz et al., 2018) and it also correlates with the summation of multiple concepts during a semantic task close to the CAT task (Evans et al., 2020). Importantly, the involvement of the SalN network may allow to trigger the switching between engagement of controlled and associative modes of thought, supported by FPCN and DMN, respectively (Beaty et al., 2016;Spreng et al., 2010;Uddin, underlying categorization, the results yielded interesting results relevant to the neuroscience of creativity.

Using the semantic priming paradigm, we show that semantically related elements in memory facilitate the retrieval of the semantic category to which they belong supporting the idea that concepts are organized into categories in memory. The double priming effect we observe can be due to the spontaneous spreading activation of the semantically related elements in memory that converge in the pre-activation of its category [START_REF] Collins | A spreading-activation theory of semantic processing[END_REF].

Combining elements from the same category strengthen the associative activation of this category. If semantic memory is organized into categories, moving from one category to another may require a more flexible SemNet or search processes, which is what the findings of the third study of my thesis are suggesting. In the individual SemNets, how different categories form distinct communities or modules should be directly tested.

Previous research has shown how creativity can be predicted from associative abilities (Benedek et al., 2012b) that may rely on the SemNet structure (He et al., 2021). Performance in associative combinations and dissociation tasks predict the performance in DT tasks (Benedek et al., 2012b). These tasks depend on the ability to find similarities and dissimilarities between concepts. Here, we show that underlying processes of categorization as measured with the similarity task can be accessed implicitly. Thus, it is likely that relating concepts based on their similarities, as in the associative combination task, rely on associative processes in semantic memory, which can be explored in future studies using our double priming paradigm in relation to creativity. However, it is important to mention that exploring spontaneous associative processes via semantic priming paradigms can also involve more controlled and strategic processes (Becker, 1980(Becker, , 1979;;Colombo and Williams, 1990;den Heyer et al., 1983;Neely, 1991;Neely et al., 1989). Thus, even though the semantic priming approach is the most widely used method in neuroscience to explore automatic associative processing, to isolate purely associative processes is still a challenge.

Using the semantic priming approach, just a few previous works has attempted to explore spontaneous associative processes in relation to creativity. These studies show that more creative people have more readiness to accept different types of associations and are more susceptible to priming than less creative individuals [START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF]. In addition, when decreasing inhibitory control, the priming effect relying on more associative processes increased, and more original ideas were generated in DT tasks but not in RAT-type tasks. This result converges with the Matched Filter hypothesis (Chrysikou et al., 2014), indicating that RAT-type tasks require more cognitive control mechanisms to guide searching in memory depending on the explicit rules. However, the performance in this task has been also shown to rely on more spontaneous associative processes (Bendetowicz et al., 2018;[START_REF] Zhong | The merits of unconscious thought in creativity[END_REF].

Using the semantic priming paradigm, [START_REF] Zhong | The merits of unconscious thought in creativity[END_REF] showed increased accessibility to RAT responses following unconscious thought. The unconscious thought was related to an incubation period that facilitated the activation of more remote associations. This result suggests that semantic associations to the target word in the RAT are likely activated in memory by means of spreading activation. Thus, the double priming effect we observed in our semantic priming paradigm may reflect how the concurrent activation of semantically related concepts converges in the activation of a target word. Using this paradigm in relation to the CAT task, future search could contribute to better understand how the combination of associates relies on automatic associative processes in memory, in more or less difficult trials (higher or lower semantic distance of cues -target).

Besides creative abilities, based on the control semantic cognition framework, the findings of this study support the idea that semantic knowledge and semantic control can be studied separately. Due to the poor spatial resolution of EEG, we could not identify the brain regions related to the implicit categorization process, and we cannot elucidate whether the processes observed here rely on the brain regions of the semantic knowledge described in the literature (Lambon Ralph et al., 2017). However, we show the N400 component as an EEG marker of double priming effect which is sensitive to semantic relatedness (Kutas and Federmeier, 2011), that reflect the implicit categorization process in our study. Future studies could explore this implicit task using other neuroimaging techniques, such as fMRI, to better identify the brain correlates underlying the implicit categorization process. Furthermore, in combination with other cognitive tasks, the results of the double priming effect can contribute to better understand how spontaneous or associative is the process of relating or combining elements in memory which is relevant to find more creative ideas.

In addition, this paradigm may be useful in patients to better understand and assess their deficit in abstract thinking, categorization, and possibly creative thinking. Several lesion studies in patients with brain damage to frontal and temporal regions have shown that these patients have a decreased performance in creative abilities (Abraham et al., 2012a;Bendetowicz et al., 2018;de Souza et al., 2010;Rankin et al., 2007;Shamay-Tsoory et al., 2011) and categorization (Garcin et al., 2018[START_REF] Garcin | Similar or Different? The Role of the Ventrolateral Prefrontal Cortex in Similarity Detection[END_REF][START_REF] Giovannetti | Different underlying mechanisms for deficits in concept formation in dementia[END_REF]Lagarde et al., 2015). Therefore, this task can be applied in combination with creativity tasks to explore how decreased creativity is related to implicit categorization. Adaptations of our double priming task may also help to clarify whether difficulties in these patients relate to deficits in the semantic representation of the concepts relying on associative processes or semantic control during the retrieval. Based on the control semantic cognition framework, we would expect patients with damage to brain regions related to semantic control processes, such as patients with frontal lesions, would have a prevised double priming effect. In contrast, patients with damage to regions related to semantic representations, such as patients with a lesion in anterior temporal regions, may have a decreased priming effect [START_REF] Zhang | Knowing what you need to know in advance: The neural processes underpinning flexible semantic retrieval of thematic and taxonomic relations[END_REF].

Overall, the paradigm we developed allowed us to show that the categorization process can occur implicitly during the similarity priming task. The facilitation of multiple primes (two semantically related primes) was higher than with one prime to evoke the related category.

Critically, this result supports the idea that concepts are organized in taxonomic and thematic associations in memory (Mirman et al., 2017) and that they can be accessed during semantic priming paradigms. These results offer a promising tool to explore the organization of the concepts in memory and how the concurrent activation of concepts in memory converges in the activation of a related concept via associative processes in memory. This process may be related to CAT tasks, which require the combination of remote associations in memory. Thus, future studies could explore the double priming effect or implicit categorization we developed here in relation to creativity tasks.

Novel methodological approaches

Besides the theoretical implications of the findings of the reported studies, in this thesis, we also provide new methodological resources for future research in cognitive neuroscience.

One of the strengths of our studies, particularly in the first and second part of my thesis, was the unprecedented combination of whole-brain functional connectivity exploration, semantic network approach, and behavior. To operationalize our studies, participants performed the RJT task while undergoing an fMRI session; thus, we could explore the brain functional connectivity patterns during the RJT task. This unprecedented combination allowed us to uniquely link neural and cognitive levels to measure creativity in real life and explore the search processes in memory.

To explore the brain functional connectivity patterns during the RJT task in relation to real-life creativity, we combined two recently developed methods in neuroimaging. First, the CPM method allowed us to predict differences in the properties of the individual SemNets based on the unique patterns of functional brain connectivity during relatedness judgments. In addition, since network-based methods have been a valuable tool to characterize the functioning of the human brain, we took advantage of this method by applying it to the classical CPM.

Typically, the CPM approach predicts cognitive abilities from the strength of connectivity within the brain network. We amended the classical CPM method (Shen et al., 2017) to explore the brain network properties of the brain regions predicting the differences in SemNet properties rather than the strength of association between these brain regions. Thus, we applied recently developed network science methods to better examine the brain network properties within a validated CPM approach. This amendment to the existing classical method allowed us to identify the brain networks whose efficiency and the clustering coefficient were critical to predicting individual differences in the SemNet of the participants.

This new amendment to the CPM analysis allowed us to better consider the brain networks properties characterizing the network models that predict the SemNet properties. The functional connections of the brain networks are typically characterized by an efficient smallworld organization (Sporns, 2011) combining a high local neighborhood clustering (high clustering coefficient) and several long-distance connections (low average shortest path lengths), allowing a better efficiency. Moreover, previous studies show the relationship between more efficient brain networks and the personality trait of openness that has been highly related to creativity [START_REF] Beaty | Default and Executive Network Coupling Supports Creative Idea Production[END_REF]. Therefore, exploring these network properties in the predictive networks allowed a better interpretation of how these networks influence creative behavior by means of SemNet properties.

The characterization of the brain network properties combined with the CPM method can be a valuable approach to exploring the relationships between brain functional connectivity and cognitive behaviors. Previous studies have used the classical CPM method to predict cognitive measures from brain functional connectivity patterns, such as creative abilities, intelligence, and attention. Other studies have related the properties of the whole-brain networks to cognitive measures, including creativity. The combination of both methods could favor the better characterization of the brain network properties during different states (i.e., resting-state or during specific tasks) and its critical regions contributing to predict individual differences in specific cognitive functions, including creativity. However, one important limitation of the CPM method is that it requires an important number of participants to have robust results, especially when using the leave-one-out cross-validation method.

Overall, the network science applied to the cognitive neuroscience field is a valuable approach to explore creativity. In our study, individual differences in brain and semantic networks predicted individual differences in creative behavior in real life. Therefore, by means of the network science tools, we provide important findings on how brain functional connectivity and semantic memory structure influence creative behavior.

Besides the network science approach, in the studies using the RJT tasks, we used different recently developed methods for the acquisition and the analysis of our neuroimaging data that enhanced the quality of analyses and results. To acquire the fMRI data, we used a recently developed method in neuroimaging that is the multi-echo. Collecting data at multiple echo times allowed better denoising of the data via principal component analysis and independent component analysis. Therefore, this method improved the reliability of our functional connectivity-based measurement (Lynch et al., 2020) and thus, may have helped strengthening our results.

Finally, in the different studies reported in this thesis, we developed new tasks that could be useful in the field of neuroscience. First, we developed the AmbiFlu tasks. The usage of this task had several advantages when exploring semantic retrieval in comparison with typical fluency tasks. The AmbiFlu task improves and facilitates the quantification of the scores. Since ambiguous words have several meanings, this task allows a simple way to quantify the different processes of clustering and switching processes during the generation of the associated words.

These scorings are difficult to quantify since concepts share similar subordinate categories, and the borders between different subcategories are unclear. For instance, a cat can be associated with several subcategories for the category animals, such as a mammal, domestic, feline, and others. We consider that the method we used in this study allows a more precise quantification to identify the different clusters and switching during the generation of associates. In the AmbiFlu task, the word associates generated are distinguished between the different meanings of the ambiguous words.

Second, we developed the similarity priming task reported in the third study of this thesis. This task allowed us to explore the implicit categorization process of taxonomically and thematically related concepts in semantic memory. This is a promising tool to explore how the concurrent activation of concepts in memory yields to the activation of their shared category reflected by the double priming effect. This task can be used in future research to better understand the associative processes in memory in relation to creative abilities. By adapting different parameters of this priming task (short vs long SOAs, masked stimuli, relatedness proportion), future studies may better disentangle the involvement of purely associative vs. controlled processes in this task.

Limitations

In addition to the specific limitations mentioned in each of the articles reported in this thesis, we can discuss here two main limitations. First, one of the main limitations in our studies is the lack of direct support for the stability of the SemNet properties. To the best of our knowledge, no study has reported the stability of the properties of the SemNets with different sets of words, or explored its longitudinal stability. However, comparing the current results to previous studies that used the RJT with different words in different languages to estimate individual SemNets provides indirect support for the reliability of network solutions. In particular, the relationships between SemNet properties and creativity were similar as in previous studies (Benedek et al., 2017;He et al., 2021). In addition, indirect support of the stability of the semantic network approach comes from a recent preprint examining group-based semantic networks estimated from free association data (Kenett and Thompson-Schill, 2020).

In this preprint, the authors examine the effect of conceptual combination processing strategies on pre-and post-manipulation estimated semantic networks. A baseline group had their semantic network estimated across two timepoints without any manipulation task in between.

The authors found no differences across the two semantic networks estimated for the baseline group, thus providing further support for the stability of these network estimation approaches.

However, much more methodological work is needed in this developing field of cognitive network science (Siew et al., 2019). Importantly, future validation studies for test-retest are ongoing in our laboratory.

In addition, the meaning of the SemNet metrics is not fully understood. The recent study of He et al. (2021) showed the mediating role of the SemNet metrics in the relationship between associative abilities and creativity. Moreover, the results from our studies further support previous research exploring associative abilities in relation to creative thinking (Beaty et al., 2021b(Beaty et al., , 2014b;;Benedek et al., 2020Benedek et al., , 2012b;;[START_REF] Green | Thinking Cap Plus Thinking Zap: tDCS of Frontopolar Cortex Improves Creative Analogical Reasoning and Facilitates Conscious Augmentation of State Creativity in Verb Generation[END_REF][START_REF] Gruszuka | Priming and acceptance of close and remote associations by creative and less creative people[END_REF]Marron et al., 2020Marron et al., , 2018;;[START_REF] Merten | Creativity, personality and word association responses: associative behaviour in forty supposedly creative persons[END_REF][START_REF] Prabhakaran | Thin slices of creativity: Using single-word utterances to assess creative cognition[END_REF]Rossmann and Fink, 2010). However, future studies using the semantic priming approach can help elucidate whether these SemNet metrics capture purely associative processes or whether more controlled processes are also involved.

Finally, it is important to acknowledge that most of our studies are based on interindividual differences, and thus, they are mostly correlational. Although the findings in our studies allow drawing the hypothesis about the underlying mechanisms, future research should be performed to explore the mechanisms supporting the results reported in our studies.

Projections

From the results of the four studies presented in this thesis, we can envision several perspectives of future studies. First, the different tasks we developed throughout the different projects could be powerful tools to explore different cognitive processes in healthy subjects and patients. As mentioned before, the development of the French SemNet we used to select the RJT material can be valuable to create verbal material in other cognitive tasks whose semantic distance and linguistic properties need to be controlled. In addition, the French SemNet can serve as a measurement of semantic distance in a free generation of associated tasks since semantic distance has been shown to be a promising measure to explore creativity [START_REF] Acar | Assessing associative distance among ideas elicited by tests of divergent thinking[END_REF]Beaty and Johnson, 2021;[START_REF] Beketayev | Scoring Divergent Thinking Tests by Computer With a Semantics-Based Algorithm[END_REF].

As mentioned above, the AmbiFlu task we developed in the second part of this thesis can be a helpful tool to explore the clustering and switching processes involved in semantic retrieval. In our study, we contributed to better understand the cognitive processes of memory search that relate to creativity and their neural correlates. This characterization can contribute to drawing hypotheses for future studies.

Second, the exploration of the individual SemNets can be applied to the study of patients. Since the RJT is a valuable tool to build individual SemNet as a representation of semantic memory organization, this task can be used in patients with semantic-related brain lesions. This tool has been valuable to explore SemNet metrics, in patients with brain tumors in an ongoing project, particularly, to explore these properties before and after undergone brain surgery. Moreover, from this study, we can also explore how different SemNet properties relate to more typical neuropsychological tests used in the clinic to explore semantic memory. As mentioned previously, this is an ongoing project on which I am currently collaborating, and to date, we have included 17 participants with promising preliminary results. In the same line, the AmbiFlu task can be used in patients to explore search processing that relies on SemNet organization and examine whether brain damage to specific regions generates a dissociate effect in associative processes related to clustering and switching. This approach could help to better understand the brain correlates underlying memory search processes, in particular in patients.

Finally, the semantic priming paradigm can also be a useful tool in patients with difficulties in categorization processes and creative abilities. The population of patients with neurodegenerative diseases that affect frontal and temporal brain regions shows difficulties in the categorization process and creativity tasks (Abraham et al., 2012a;Bendetowicz et al., 2018;[START_REF] Borghesani | Neural dynamics of semantic categorization in semantic variant of primary progressive aphasia[END_REF]de Souza et al., 2010;Garcin et al., 2018[START_REF] Garcin | Similar or Different? The Role of the Ventrolateral Prefrontal Cortex in Similarity Detection[END_REF][START_REF] Giovannetti | Different underlying mechanisms for deficits in concept formation in dementia[END_REF]Lagarde et al., 2015;Rankin et al., 2007;Shamay-Tsoory et al., 2011). This double priming paradigm can be used to explore whether the implicit process of categorization underlying by more automatic retrieval of the concepts is preserved in patients with brain damage to frontal and temporal regions and how it relates to their difficulties in creativity tasks.

Thus, using more causal relationships, this approach can help elucidate the relationship between the implicit categorization process that relies semantic memory structure and creative abilities.

Third, based on the results of the studies in the first part of my thesis, the brain regions that were found to be essential to creative mechanisms rooted in semantic memory could be explored in more mechanistic studies. These brain regions could be examined in patients with cerebral lesions or under direct non-invasive brain stimulation to explore how their disruption or inactivation affects creative abilities. For instance, the use of brain stimulation applied to the brain regions with the highest node degree during the RJT task could also be used to explore the effect of the activation or inhibition of the activity of these nodes on the semantic judgments, memory search, and creativity. This approach may help regions distinguishing associative/executive processing involved in the RJT task.

Fourth, in addition to functional connectivity configurations during the RJT task, a follow-up project of this study considers exploring the brain activations during the task. Since the trials of the RJT vary in the theoretical semantic distance, ranging from 1 to 6 steps separating them into the French semantic network, we aim to explore the brain correlates variations regarding the theoretical steps or their relatedness judgments. Thus, this approach can help elucidate the different brain regions that contribute to judging the semantic relatedness of more closely and remotely associated concepts in semantic memory. In addition, other approaches such as representational similarity analysis can be performed to better understand the brain correlates of the different theoretical distances used in the trials of the RJT task. This approach could allow exploring how information is represented in the brain and varies in relation to the semantic distance when they are retrieved.

Finally, other techniques of neuroimaging could also be applied to the RJT task. During EEG recordings, it could be possible to explore the temporal dynamics underlying the relatedness judgments of the participants. It could allow exploring differences in the dynamic patterns depending on the semantic distance between words and the relatedness judgment given by the participants. Moreover, a semantic priming paradigm could also be important to explore the associative processes of creativity explored by the RJT task. It could contribute to determining how much of the results from the RJT task represent the associative mechanisms of creativity rather than adding control processes.

Overall, future research can apply the different protocols we used during the different studies of my thesis as tools of measurements in healthy and patient groups. Additionally, studies using different methodologies can explore the on-task fMRI data to better understand the relationships between the brain, semantic memory, and creative abilities.

Conclusions

This thesis contributed to better understanding the role of associative processing relying on semantic memory and search processes in creativity. Through the four studies and the findings reported here, we add to previous research in several ways. In the first part of this thesis, we evidence the strong relationship between semantic memory structure and creative

abilities measured with different approaches. Moreover, we uniquely linked brain functional connectivity, semantic network structure, and creative behavior in real life. The unprecedented exploration of neural and cognitive levels in relation to real-life creative behavior demonstrates that although creativity is a multifactorial process, individual differences in brain networks and semantic memory organization participates on creative activities and achievements in real life.

In the second part of this thesis, we contributed to better understand the neurocognitive mechanisms of memory search that relate differently to divergent and convergent thinking.

Based on behavioral and neural correlate explorations, we characterized the switching and clustering components of semantic search and their underlying brain functional connectivity predictors. Finally, by means of a double semantic priming paradigm, we identified an electrophysiological marker of implicit categorization. Using this paradigm shed light on how the concurrent activation of taxonomically or thematically related concepts in memory converges in the activation of their shared category, suggesting that concepts in memory are organized in categories. Alizée Lopez-Persem 1 *, Théophile Bieth

List of figures

Introduction

The context of the COVID-19 pandemic has led to the implementation of unprecedented restrictive measures in society. During spring 2020, 67 million French people entered the first strict lockdown, which dramatically changed our daily lives and wellbeing. These constraints imply a restriction of movement, the need to adapt working methods, a modification of daily life within households, and a decreased wellbeing (linked to health issues, restrictions of activities and social interactions, or economic consequences). Paradoxically, during this period, general and social media have reported that individuals and businesses seemed to show great adaptability to face the situation by finding innovative solutions and creative behaviors.

Creativity is defined in science as the ability to produce something that is both original and appropriate (Lubart, Zenasni, & Barbot, 2013). Creative abilities are involved in many human activities, from solving everyday problems to the great discoveries of our civilization. Creativity is a key mental capacity to cope with change, invent and innovate, and face the challenges of our society. The situation related to the COVID-19 and its consequences on our daily life are at the forefront of the current challenges we had and still have to cope with. Understanding the conditions that foster (or hinder) our creativity is essential, and the COVID-19 situation provides a unique opportunity to study some of these factors.

According to the multidimensional approach to creativity (Lubart, Mouchiroud, Tordjman, & Zenasni, 2015), creativity and creative behaviors rely on cognitive factors (our mental capacities) and individual personality, and is influenced by internal and external factors (Lubart et al., 2013). Intrinsic motivation is an essential component of creative behavior (Amabile, 1983(Amabile, , 2018;;Amabile & Pillemer, 2012;Benedek, Bruckdorfer, & Jauk, 2019;Fischer, Malycha, & Schafmann, 2019;Mastria, Agnoli, Zanon, Lubart, & Corazza, 2018) and drives involvement, intensity, and perseverance in activities. Affective states influence creativity, several studies showing that creative thinking tasks are mood-sensitive. Positive mood (such as joy) is often reported as facilitating creativity, and negative mood (such as stress, anxiety) as hindering creativity. However, whether positive or negative moods facilitate or inhibit creativity is still debated and may depend on contextual factors (Baas, De Dreu, & Nijstad, 2008;Davis, 2009;De Dreu, Baas, & Nijstad, 2008). Finally, creative activities and achievements are influenced by our environment, at home or in the workspace (living conditions, cultural, and professional contexts), our social context (Amabile & Pillemer, 2012;Benedek et al., 2019), and by the standards, needs, and values of the society (Lubart et al., 2015).

The situation related to COVID-19 likely had a major impact on many if not all the creativity dimensions or factors described above, by affecting our emotions, motivations, and wellbeing [START_REF] Bu | Loneliness during a strict lockdown : Trajectories and predictors during the COVID-19 pandemic in 38,217 United Kingdom adults[END_REF][START_REF] Li | Novelty Seeking and Mental Health in Chinese University Students Before, During, and After the COVID-19 Pandemic Lockdown : A Longitudinal Study[END_REF][START_REF] Michinov | Stay at Home ! When Personality Profiles Influence Psychological Adjustment and Creativity during the COVID-19 Outbreak[END_REF][START_REF] Roma | A 2-Month Follow-Up Study of Psychological Distress among Italian People during the COVID-19 Lockdown[END_REF][START_REF] Zabelina | Imagination, anxiety, and loneliness during the COVID-19 pandemic[END_REF], modifying our environment, our lifestyle, and availability, but also the standards and incentives of our society during this period, opening a new framework of thought [START_REF] Kapoor | Meaning-Making Through Creativity During COVID-19[END_REF][START_REF] Beghetto | How Times of Crisis Serve as a Catalyst for Creative Action : An Agentic Perspective[END_REF]. The few existing scientific studies that explored creativity during the COVID-19 pandemic have indeed suggested that the lockdown period facilitated everyday creativity (Karwowski et al., 2021;Mercier et al., 2020) or that creative abilities could help people dealing with the situation and improve their wellbeing [START_REF] Michinov | Stay at Home ! When Personality Profiles Influence Psychological Adjustment and Creativity during the COVID-19 Outbreak[END_REF]Orkibi, 2021;Tang, Ma, Naumann, & Xing, 2020). However, those studies did not assess how creativity changes related to environmental, contextual, or emotional changes.

The current study aimed at exploring how the lockdown and context related to COVID-19 impacted creativity and identifying the potential factors linked to these changes. We assumed that the lockdown could have stimulated creativity for at least two reasons. First, the confinement increased the availability of time and mind by reducing our usual daily outdoor activities and, in some cases, workload. Second, the new situation created a need or drive, pushing the individuals to adapt and invent new solutions to pursue their usual activities.

However, the situation may have caused negative subjective experience and distress, with stress and anxiety, a feeling of pressure, a lower mood, which can alter creativity. Last, because the restrictive measures targeted social interactions, we hypothesized that social distancing could also have harmed creativity. We carried out an online survey to estimate the impact of these potential factors on self-perceived creativity changes and changes in creative activities and achievements.

Methods

Ethics and participants

The study was approved by an ethical committee. All the data were collected anonymously online and in compliance with the General Data Protection Regulation (GDPR).

The participants were recruited online through social networks. Respondents were invited to complete the entire survey if they were French speakers over 18 years old, agreed to participate, and did not already participate.

In total, we collected data from 551 participants (365 females; mean age 42.31 years, age ranging from 18 to 84 years). Participants were French speakers and completed the survey between May 27 th and August 1 st of the year 2020. All questions were referred to the lockdown period, that was, in mainland France, from March 16 th to May 11 th of the year 2020. Data from 171 participants were removed because they did not complete more than 50% of the survey (which corresponds to the end of part VI, see below). The final sample of the survey considered the data collected from 380 participants (281 females; mean age 43.16 years, age ranging from 18 to 84 years). All demographic details about included participants are provided in Table 1 and Supplementary Table S1.

Survey and scores

The survey was programmed in Qualtrics as a self-administered questionnaire. We collected several types of information using independent questions distributed into nine distinct parts in the following order: (I) demographic information, (II) conditions of lockdown, (III) affective experience, (IV) subjective creativity change (SCC), (V) BFI-O, (VI) the participant's behavior regarding a list of 28 activities, (VII) their ideational behavior regarding a list of three topics, (VIII) complementary information about demographics and lockdown situation, and (IX) the top-creative productions they carried out during the lockdown. We grouped those nine parts into five main types of data: demographic data, creativity-relevant measures, and potential factors linked to a change in creativity. The creative ideation part (VII) was not included in the current study, which focuses on activities instead of ideation. The types of data we collected are detailed below, indicating for each one to which part of the questionnaire the item belongs.

Demographic data

We collected individual information about participants. It includes basic demographic information (age (I), gender (I), education level (I), socio-professional activity (VIII), and main field of activity (VIII)) but also more specific information relative to the lockdown period (residential area type (II), own residence (II), country of residence (VIII), access to private or large public outdoor spaces (II), whether professional activity was COVID-19 related (II), professional situation (II), the occurrence of a serious problem that could have limited the initiation of activities (and in this case, how long it limited one's activities) (II). responses as the survey progressed (See Supplementary Figure S3 for a summary). The sequence was repeated for each of the 28 activities as follows.

For each activity, participants were first asked whether they had performed the activity during the last five years (lockdown period included). If not, the sequence stopped, and they were asked about the next activity. If so, they were asked whether they did it during the lockdown period and at which frequency ('much less,' 'less,' 'as much', 'more', or 'much more' than before the lockdown period). They could also indicate whether they did it only during the lockdown period, i.e., if the activity was new for them. The frequency change was converted into a ranking value from -2 ('much less') to 2 ('much more'). In case the activity was not performed during the lockdown period, we assigned the value of -3. The frequency change score was computed for each participant as the mean of these ranking values across activities. We also computed the proportion of activities for each participant for which there was a negative, a positive, or no change.

Then, for each activity reported either less or more performed during the lockdown period than before, multiple-choice questions were displayed to explore the reasons for the change in frequency (different options were proposed for negative and positive changes, see Supplementary Table S4) with the possibility to select several responses. Participants also had the option to write down their answers if none of the options were satisfying. We manually checked these additional reasons and created two new a posteriori categories of motives for positive changes ('Wellbeing' and 'Favorable conditions'). For each participant, and separately for positive and negative changes, we computed the number of times each reason was selected divided by the total number of activities for which the question was asked.

To check for consistency between reasons explaining changes in engaging in specific activities and the general subjective experience of the lockdown, we ran bivariate correlations between the proportions of the main activity-based selected reasons (that were related to free time and affective states) and the components of our previous PCA that related to changes in SCC (See 'Statistical analyses'). For activity-related reasons, we computed the difference in the percentage of positive and negative reasons as follows: Free time-related reasons were the difference between the proportion of 'More Time' and of 'No time.' Affective change-related reasons were the difference in proportion between 'Inspired' and both 'Concerned and worries' / 'Did not feel like it.' The difference was computed (instead of only positive or negative) as positive and negative reasons might capture the same effect. If an individual selects 'More time' as often as 'No time' across activities (scores around zero), it might reflect a small change in experienced free time.

Finally, in case they did the activity as much or more often during the lockdown period than before, we asked:

• How they valorized it. Participants were asked to make a multiple choice among 11 options to assess the level of achievement of the performed activity. Each option represented an amount of points that reflected the level of achievement (Supplementary Table S4). Based on this question, a Creative Achievement Score was computed using a procedure similar to the ICAA scoring. The values corresponding to all the selected options for a given activity were summed. If participants shared their creative activities at least beyond the immediate surroundings (friends, family, cohabitant, or colleague), they automatically reached 8 points, and the three first proposals were not considered in the sum. Then, participant's scores were averaged across activities for each participant to compute an individual creative achievement score.

• To report their feeling of having encountered obstacles during its realization using a visual scale from 0 (no obstacles) to 100 (many obstacles). The obstacle score was computed as the mean of obstacles rating across activities for each participant.

When the series of questions related to one activity (and dependent on participant's responses) was complete, the same sequence was proposed for the next activity until the end of the list of 28 activities.

Finally, at the end of the survey (IX), we asked participants to freely report the five most creative realizations (or top-productions) they carried out during the lockdown. They entered their responses directly in a text box without any time or word limit and with the possibility to leave empty boxes. An external panel of five experts in creativity research rated each of the top-productions for their creativity using a 5-point Likert scale from 0 ('not creative') to 5 ('highly creative'). Good reliability was observed between judges (ICC=0.95). Then, the ratings were averaged across judges for each top-production, and we computed for each participant a Rating Score (RS) as the sum of averaged ratings of his/her productions. The RS aims to reflect the creativity of the activities achieved during the lockdown. Hence, it may both depend on the pre-existing creativity of the participants and on the effect of the lockdown situation on their creative thinking and behavior.

Psychological and physical conditions of confinement:

In the survey, we asked specific questions about how participants experienced the lockdown. The aim was to identify subjective and objective factors that can explain creativity changes based on our hypotheses. We collected four categories of information:

(a) Environmental conditions: we asked the participants to report the number of cohabitant and the number of available rooms in the residence place (survey part II), allowing us to compute the objective available space per cohabitant (i.e., the number of rooms per inhabitant). We also collected the subjective feeling of change in physical constraints (III).

(b) Social relationships: participants were asked whether they exchanged with as many people during the lockdown period as before (II) and the feeling of a change in loneliness (III). These variables aim to reflect changes in social relationships during the lockdown period. All the questions related to a change refer to a difference between the period before and during the lockdown. All subjective information in part III was collected using multiple-choice questions with eight available choices: seven ranging from 'much less' to 'much more' and one allowing a neutral response ('I do not know'). Change in free time and in social interactions had only five available choices ranging from 'much less' to 'much more.' Those variables were considered as continuous or ordinal, and the sign was reversed for the ones with a negative meaning (indicated with 'R' in figures and tables, such as loneliness) in order to obtain homogeneous valence across them (the higher, the better).

Statistical analyses

All analyses were performed using Matlab Statistical Toolbox (Matlab R2020a, The MathWorks, Inc., USA), SPSS (v22.0; IBM Corp.), and R (v3.6.2). Independent variables were defined as the reported responses by the participants (categorical variables) or as a score based on participant responses (quantitative variables; see above). Variables of interest could be in France (77%), at home (85%), in urban areas (50%). 63% had access to private or large public outdoor spaces, 60% were confined with one or more individuals, and 87% declared not having encountered a severe problem during the lockdown. They were working remotely for 54% of them. 83% of the respondents had a professional activity not related to the fight against the pandemic. All demographic details are provided in Table 1 and Supplementary Table S1.

Living conditions and subjective experience of the lockdown situation

In the survey, participants were asked subjective and objective questions to gather information about their lockdown experience. These items aimed at estimating their environmental conditions (available space per cohabitant, i.e., number of rooms per inhabitant; feeling of a change in physical constraints), social relationships (change in the number of social interactions; feeling of a change in loneliness), available time (number of working hours per week; feeling of a change in time availability), and subjective affective changes (feeling of a change in mood, motivation, anxiety or stress, and pressure) (see methods for more details).

Those ten measures were used to assess the living conditions and subjective experience of the lockdown situation.

As expected, on average and in terms of subjective experience, our sample felt more physically constrained (mean=0.33+/-0.08, t(379)=4. 16, p=4.10 -5 ), more lonely (mean=0.32+/-0.07, t(379)=4.69, p=4.10 -6 ), and with more free time (mean=0.6+/-0.07, t(379)=8. 82, p=4.10 - 17 ). In terms of affective changes, they felt more anxious or stressed (mean=0.32+/-0.08, t(379)=3.96, p=9.10 -5 ), and had a poorer mood (mean=-0.18+/-0.07, t(379)=-2.39, p=0.02). On average, they did not feel significantly more or less motivated (mean=0.12+/-0.09, t(379)=1.39, p=0.17), nor experienced more (or less) psychological pressure (mean=0.09+/-0.09, t(379)=1, p=0.32).

Within-factor measures were consistent. Indeed, the subjective report of change in physical constraints was correlated with the number of rooms per inhabitant (mean=1.19+/-0.03, r=0.10, p=0.049), the subjective report of change in loneliness was correlated with the quantitative change in social interactions (mean=-0.22+/-0.07, t(379)=-3.31, p=1.10 -3 r=0.18, p=5.10 -3 ). The subjective report of change in free time was correlated with the number of working hour per week (20% worked more than 42 hours/week, 19% between 36 and 42 hours/week, 15% between 29 and 35 hours/week, 13% between 22 and 28 hours/week, 14% between 15 and 21 hours/week, 9% between 8 and 14 hours/week, 10% less than 7 hours/week, r=0. 53, p=6.10 -24 ).

Additionally, affective changes in mood, motivation, anxiety or stress, and pressure were all correlated to each other (all p<8.10 -11 ).

Subjective creativity changes during the lockdown

Measures of creativity

When prompted to subjectively report how much they think their creativity changed during the lockdown (SCC, rating scale from -50 to 50), the participants declared on average to have been more creative during the lockdown period (mean=8.08+/-0.98, t(379)=8.26, p=2.10 - 15 ) (Figure 1A).

Additionally, at the end of the survey, participants were asked to freely report the five most creative productions they carried out during the lockdown. Based on ratings of four experts in creativity research, we assessed how much those productions were creative (RS, ranging from 0 to 25). RS mean was 7.74+/-0.21 across the 286 participants who responded to that question.

Creative personality traits were also assessed using the BFI-openness (BFI-O) questionnaire. The average score of our sample was 3.68+/-0.03.

The BFI-O scores, RS and SCC were all significantly pairwise correlated (r(BFI-O-RS)=0. 46, p=2.10 -16 , r(BFI-O-SCC)=0.21, p=3.10 -2 , r(SCC-RS)=0. 15, p=9.10 -3 ). Hence, as the RS might reflect both the creative personality traits and creativity changes during the lockdown, we tested whether BFI-O scores mediated the correlation between SCC and RS. The mediation analysis revealed that the correlation between SCC and RS was mediated by the BFI-O scores, as we observed a loss of significance of the direct path when including the mediator effect (linear regression of SCC against RS (without the mediator): 𝛃SCC-RS=0.68, p=9.10 -3 ; linear regression of SCC against RS and BFI-O (the mediator): 𝛃 * SCC-RS=0.37, p=0.2). The difference between 𝛃SCC-RS and 𝛃*SCC-RS was confirmed using bootstrapping (confidence interval=[0.01 0.62]).

Since our main aim was to assess the creativity change related to the lockdown, we decided to use the SCC score as our main variable of interest, as RS seems to reflect more the creative personality traits than the change in creativity during the lockdown period, and because baseline measurement of RS was not available.

53.1% of the variance. Figure 1B shows the correlation matrix between variables, and Table 2 shows the components loadings after rotation. The variables that cluster on the same component suggest that component 1 represents affective changes during the lockdown, component 2 represents changes in the available time, and component 3 interactions with other people.

We searched for correlations between the participant's components loadings and SCC. SCC correlated significantly with component 1 representing affective changes during lockdown (r=0. 28, p=1.10 -6 ) and with component 2 representing changes in time available (r=0.12, p=0.045), but not with component 3 representing interactions with other people (r=0.04, p=0.52) (Figure 1C). Individual correlations of each variable and SCC are provided in Supplementary Table S2. These results indicate that two main factors were associated with SCC during the lockdown period: affective changes and change in the available time. Restrictions in social interactions were not significantly associated with creativity changes.

As creativity could help emotionally cope with difficult situations, we explored whether creative personality traits could have impacted the subjective and affective experience of the lockdown, which influenced creativity changes. We tested for a mediation effect of component Regarding demographic and other categorical factors, there was no significant effect of gender (F(1, 378)=0.01, p=0.93), age (r=-0.02, p=0.69), education (rho=0.04, p=0.44), socioprofessional category (F(8, 350)=1.45, p=0.17), socio-professional domain (F(19,339)=0. 64, p=0.88), or residential area (F(2, 377)=0. 43, p=0.65) on SCC (see Table 1). Being at home (F(1, 378)=0, p=0.99), having a job related to COVID-19 (F(1, 378)=0, p=0.99), or working remotely (F(1, 378)=0. 56, p=0.46) had also no significant association with SCC.

Change in creative activities

In the survey, we qualitatively and quantitatively assessed the creativity change experienced during the lockdown. In total, 343 participants were presented with 28 activities (among them, 37 participants completed an older version of the questionnaire that included additional questions not considered here). All activities are reported in Supplementary Table S3. For each of them, participants were first asked to indicate whether they had performed the activity during the last five years (lockdown included). If so, they were asked whether they did it during the lockdown and how often (more or less than before), for which reasons (various reasons were proposed for negative and positive frequency changes), and how they valorized it. For each activity that was less performed during the confinement than before, the participants were asked whether they thought more or less about it during the lockdown.

Activities

We identified activities that were relevant at the participant level to further estimate a lockdown-related change. On average, participants engaged in 8.38+/-0.25 activities (out of the 28 proposed) during the previous five years (including the lockdown period). Three participants did not engage in any activity, and four participants engaged in more than 20 activities. All the activities were performed at least by 9.9% of the participants during the lockdown (Figure 2).

The activities of our list were consistent with the top-productions that the participants freely reported (see 'Top-production' section below).

Reasons and motives

To examine why each activity was more or less frequently performed during the lockdown than before, we analyzed the reasons provided by the participants for positive and negative changes separately (Figures 4A and 4C,respectively). Note that they could select several reasons for each activity during the survey. Across all activities, the frequency of the selected reasons for positive changes significantly differed (F(6,1722)=152, p=2.10 -98 with Greenhouse-Geisser correction; Figure 4B). The most frequently selected reasons for positive changes were: 1) having more free time ('More time', 60+/-2.3% of the activities carried out by each subject); free time significantly differed from each of the other reasons (pcorr<0.05 for all posthoc tests); 2) being inspired by the lockdown situation ('Inspired', 21.3+/-1.7%); and 3) having to solve lockdown/pandemic issues ('Problem solving', 20.8+/-1.6%). The frequency of those two last reasons did not statistically differ but was higher than the frequency of each of the remaining reasons (pcorr<0.05 for all corresponding post hoc tests). Additionally, 12.3+/-1.3% of the activities performed more often than before were carried out for work ('Work related'), and 11.1+/-1.3% for other undetermined reasons ('Others'). The frequencies of these last two reasons did not significantly differ from each other but were significantly higher than the frequency of each of the remaining low-rated top-productions were about 'learning something new', 'storing/classifying', 'reading', and 'projects/ideas'. Among the top-productions not covered by our list, the topproductions with a score higher than 2.5 (18 productions) were about ecology (n=3), artistic projects or ideas (n=4), and diverse multimedia creations (n=3).

To analyze the frequency of the productions among the participants, we recoded the name of the top-productions and selected the ones with a mean RS higher than 0.5. 'Cooking and recipes' were the most frequent realizations (reported by 31% and 19% of the participants), followed by 'Writing' (25%), 'Manufacturing' (19%), 'Gardening' (18%), and 'Sewing' (13%).

Discussion

Using an online survey, we aimed to estimate the impact on creativity of the lockdown in France due to the COVID-19 pandemic and to understand the potential factors leading to creativity changes. Our main a priori factors were the affective states, available time, personal conditions of confinement, and social isolation. Our survey used two main approaches to quantify individual creativity changes: subjective self-rated creativity changes (SCC), and activity-based measures including frequency, motives, encountered obstacles, and achievement. Our study documented the unprecedented situation we all had to face during the lockdown and shed new lights on potential factors that could have modulate individual creativity behavior.

Creativity during the lockdown

SCC analyses revealed that participants experienced being more creative during the lockdown than the period before, which is consistent with previous studies that reported an increase in everyday creativity during the lockdown (Karwowski et al., 2021;Mercier et al., 2020). Lockdown-related creativity changes using the activity-based questionnaire approach were also consistent with the SCC report. Notably, both the frequency score and the achievement score, correlated with SCC, indicating that SCC related to changes in creative activities and achievements. We also found a quadratic relationship between the amount of obstacles to overcome to achieve an activity assessed by activity-based questionnaire and SCC: individuals who declared having a decrease in their creativity had to face too many obstacles while individuals experiencing an increase in their creativity might have been stimulated by the amount of obstacles. This result may suggest that creative behaviors differed according to the perception or the nature of an obstacle. Individual factors that influence our creative behavior in facing obstacles remain to be explored. As the SCC came early in the survey, before asking participants about the activities they engaged in during the lockdown, it is unlikely that activitybased questions influenced our SCC measure [START_REF] Karwowski | Toward dynamizing the measurement of creative confidence beliefs[END_REF].

In addition, SCC correlated with the BFI-O score, which captures a basal creative personality trait (Jauk, Benedek, & Neubauer, 2014;Beaty et al., 2017), openness, and which was in the range previously observed [START_REF] Denissen | Motivational individual reaction norms underlying the Five-Factor model of personality : First steps towards a theory-based conceptual framework[END_REF]. The linear link between SCC and BFI-O indicates that individuals with higher openness to new experiences reported proportionally more positive self-rated creativity changes. Measures of creative confidence beliefs, such as self-rated creativity, classically correlate with openness (Karwowski & Lebuda, 2016), which is considered as a big five personality trait that impacts creative self-efficacy and self-concept.

We also assessed creativity during the lockdown using an external rating approach of the top-5 creative productions freely reported by the participants (RS). SCC correlated positively with this measure, indicating that the participants who reported a higher increase in their creativity also had higher creativity ratings of their top-productions. Interestingly, BFI-O mediated the link between SCC and RS, suggesting that creativity during the lockdown (i.e., external ratings of the participants' productions) depended on both a basal creative personality trait (BFI-O) and the subjective feeling of lockdown-related changes in creativity (SCC).

Overall, these findings indicate that our self-rated measure SCC relates to creative personality traits and several creativity measures based on real-life activities and achievements, in accordance with the model of primary and secondary creativity [START_REF] Runco | Primary and secondary creativity[END_REF] and can capture event-related changes in creativity. SCC may be an interesting measure of reallife creative changes that remains to be validated in future cognitive studies measuring changes with baseline comparison.

Activities carried out during the lockdown: quantitative and qualitative aspects

Our activity-based questionnaire offered an overview of the participants' engagement in creative activities during the lockdown and related changes using a list of 28 predefined activities. We also asked them to freely report their 5 most creative productions during the lockdown. Overall, 90% of the self-reported top-productions corresponded to activities included in our list, suggesting that our list was indeed representative of the activities performed during the lockdown period. 'Home rearrangement', 'Cooking and drinks recipes', and 'Garden or floral arrangement' were the three activities in which participants engaged the most. These creative activities were consistent with a pre-pandemic study that assessed the frequency of creative activities in several domains (Benedek et al., 2019).

The activity-based analysis revealed a global quantitative increase in carrying out creative activities during the lockdown, which is consistent with the increase in self-rated creativity shown by the SCC results. The activities with the highest increase in frequency were 'Cooking and drinks recipes', 'Sports program', 'Dance and choreography', 'Mutual aid initiatives', and 'Garden and floral arrangement'. This result echoes studies in other countries showing that individuals increased their cooking and baking activities [START_REF] Gerritsen | Seven weeks of homecooked meals : Changes to New Zealanders' grocery shopping, cooking and eating during the COVID-19 lockdown[END_REF]), physical training (Di Renzo et al., 2020) during the lockdown. For the explored activities, the creative achievement score of each subject was relatively low, indicating that our questionnaire assessed 'everyday creativity' rather than extraordinary achievements.

Potential reasons for the impact of the lockdown situation on creativity

We explored how several factors could be related to these creativity changes. No demographic factors correlated with the subjective change in creativity. However, using PCA, we found that the two first principal components of the lockdown conditions and subjective experience significantly correlated with SCC. The first component that we labeled 'affective change' included anxiety and stress, motivation, psychological pressure, mood, and to a lower extent, loneliness and physical constraints. The link between this first component and SCC is consistent with a series of studies showing that affective states such as positive mood and motivation are associated with higher creativity (Baas et al., 2008;[START_REF] Collins | Motivation and creativity[END_REF] and that intrinsic motives drive everyday creative activities (Benedek et al., 2019). Conversely, negative affective states hindered creativity in some individuals. Our survey shows that overall, our participants reported more negative subjective and affective experiences during this period than before. This finding converges with other studies conducted worldwide during the lockdown of Spring 2020 that showed that people experienced an increase in stress and depression symptoms in Italy [START_REF] Roma | A 2-Month Follow-Up Study of Psychological Distress among Italian People during the COVID-19 Lockdown[END_REF]), Spain (Ozamiz-Etxebarria, Dosil-Santamaria, Picaza-Gorrochategui, & Idoiaga-Mondragon, 2020), New Zealand (Every-Palmer et al., 2020), Kuwait [START_REF] Burhamah | The psychological burden of the COVID-19 pandemic and associated lockdown measures : Experience from 4000 participants[END_REF], and Croatia (Đogaš et al., 2020). Interestingly, we found a significant mediation effect of the first principal component (reflecting affective changes) on the relationships between BFI-O and SCC. This result suggests that higher openness may help to cope with difficult psychological situations during the lockdown, which in turn facilitated their creativity (Wu et al., 2019). Hence, creative traits and abilities may be essential to resilience.

The second principal component that correlated with SCC reflected the available time, including the number of working hours, free time, and to a lower extent, space per inhabitant.

An increase in free time was linked to higher creativity change. This finding is in agreement with studies that investigated the time pressure as hindering creativity (Amabile, Conti, Coon, Lazenby, & Herron, 1996;[START_REF] Amabile | Time pressure and creativity in organizations : A longitudinal field study[END_REF].

It is important to mention that given the correlational nature of the link between change in creativity and affective change, we cannot conclude about any causality effect: more positive affective states could trigger higher creativity, but the reverse could be true, or another unknown variable could mediate this correlation. However, the coherence of our findings based on two different sets of measures is to be highlighted. In addition to a global subjective report of their self-perceived emotions, free time, and creativity, the participants indicated the reasons why they achieved or gave up specific creative activities during the lockdown. The reasons for carrying out an activity more frequently were dominated by more free time, while the reasons for carrying out an activity less frequently were mainly related to concerns and worries. These reasons for doing or not an activity correlated with the individual component loadings of the two first principal components (reflecting affective state and free time) identified among the subjective experience factors that we collected. These results indicate strong consistency between the different parts of our questionnaire. Overall, the correlations of SCC with the reported affective and free time changes on the one hand, and the identified reasons for engaging more or less frequently in creative activities during the lockdown than before on the other, converge to the crucial role of emotions and free time on creativity during this period.

The exact nature and context of the affective factors remain to be clarified.

Limitations

One potential limitation of our study is that our sample is not fully representative of the French population. For instance, our sample had more women, with a high education level, and most of them could work remotely. It is also possible that individuals who participated in this survey were inherently interested in creativity or had more time for online surveys. Thus, the increase of creativity observed in this sample might not be replicable in other populations, such as individuals who could not work remotely or had no time to answer online surveys. In addition, the participants responded just after the end of the lockdown, their reports are thus retrospective and may lack accuracy or objectivity. Yet, it is worth mentioning that most surveys used questions regarding the actual lockdown period, with difficulties in measuring a valid baseline, while in our study, we focused on the change between before and during the lockdown.

Conclusion

The massive lockdown provoked by the COVID-19 pandemic gave an exceptional opportunity to investigate how various factors and exceptional situations could impact everyday creativity. Our study documents this unique situation regarding creativity, creativity enablers, and blockers based on the description of how individuals experienced this situation and how they adapted their everyday creative activities. Our subjective report of self-rated creativity and activity-based questionnaire provide converging results supporting an overall positive change in creativity during the lockdown period, which was mainly related to having more free time or opportunities. However, the changes were heterogeneous across participants, and when negative changes in self-rated creativity or creative activities were reported, they were mainly related to negative affective factors. A creative personality may have helped to cope with the negative emotions induced by the crisis, and to better express one's creativity. Those factors have thus to be considered when trying to enhance creativity in individuals and society. Our observations may provide interesting hypothesis to explore in future studies for a better understanding of the affective, emotional, and wellbeing dimensions that modulate our creativity, which could inspire political, societal or management decisions.

Table S3

List of the 28 activities for which participants had to respond a series of question. Original French sentence, English translation and short name are reported for each of the activity.

Table S4

Details of the presented options for reasons for change in the frequency of an activity, and details of the presented options of achievement reached for each activity. Original French sentence, English translation and short name are detailed. Ranking values were used to compute the achievement score.

Introduction

A creative production can be viewed both as the result of high-level control functions or top-down processes (including inhibitory control) and as the result of a relaxation of one's constraints or inhibitions, possibly favoring spontaneous associative thinking and bottom-up processes. These two views make opposite predictions regarding the role of the prefrontal cortex (PFC), that support controlled functions, in creativity. Recent cognitive models try to conciliate these views and con-sider spontaneous associative and controlled processes as complementary rather than paradoxical in the creative process [1][2][3][4][5]. With the development of network neuro-science based on functional connectivity, the neuroimaging of creative cognition in healthy subjects have started to clarify the multiple brain systems involved in associative and controlled creative processes, and to specify the place of the PFC in these systems. The lesion approach may be especially useful in complementing functional imaging toward a better understanding of the cerebral substrate of creativity, by identifying critical nodes in the diversity of brain networks reported in functional imaging, and precising their specific role in the creativity processes [6]. Surprisingly, very few lesion studies have investigated creative abilities in brain damaged patients [7-14,15*,16,17,18*] (Table 1). Focusing on the neuroscientific literature of creativity, especially coming from patients, this review will explore not only whether the PFC is critical for creative abilities, but more importantly what prefrontal systems are necessary for associative or controlled creative processes.

Recent cognitive and neuroimaging findings

The cognitive neuroscience of creativity provides abundant experimental evidence of the important role of PFC-dependent cognitive control functions in creative abilities. These functions include inhibition of prepotent responses [19][20][21], controlled retrieval [22][23][24] and working memory [25,26]. Neuroimaging data using task-related activation and functional connectivity in healthy subjects are consistent with this view. Several meta-analyses of creativity-related activation studies have reliably shown that lateral frontal and parietal areas are the most recruited set of regions in creativity tasks [27][28][29]. Functional connectivity analyses have highlighted the importance of the control-related networks including the frontoparietal network in creativity [30,31**]. The lateral PFC region may be especially recruited for inhibition of dominant responses [20], controlled retrieval [23,24], and selection or evaluation processes [32].

Aside from the deliberate control processes, creative thinking also involves more spontaneous associative processes. The role of associative thinking abilities in creativity may rely on the organization of associations between elements of one's semantic knowledge that determines the ability to generate and combine remote associations [33]. In support of this theory, psychological studies have shown that more creative people have more flexible semantic associations, allowing them to connect more distant concepts or words [34-36,37*,38**,39,40*]. Although the cerebral substrates of associative processing are not elucidated, recent evidence supports the role of the default mode network (DMN) in unconstrained associative thinking [40*] and more broadly in spontaneous cognition [41] including mind wandering [42,43] and contextual associations [44]. Several studies have shown that the morphometry and functional connectivity of DMN regions correlate with creative performance [45][46][47][48][49][50][51]. Consistent with the role of semantic associations in creativity, semantic memory regions are also important for creative abilities [11,27,28] and have both overlaps and functional interactions with the DMN that remain to be clarified [20,52,53].

Overall, the cognitive neuroscience of creativity now considers that creative abilities result from an interaction or a balance between associative and controlled processes [1][2][3][4][5].

Recent functional connectivity studies strongly support this view by demonstrating that two main net-works interact during creativity tasks: a lateral frontoparietal control network (FPCN), known to support control functions, and the DMN, that has been related to associative thinking and spontaneous cognition [31**,32,54,55*]. The interaction between these systems might be mediated by the Salience network [30] or by regions of these networks acting as connector hubs. The identification of large scale networks that involve different parts of the PFC is an important step in understanding the cerebral basis for creativity and offers new predictions regarding which creativity mechanisms can be impacted by specific lesion locations.

Critical role of the DMN and FPCN for creativity (Table 1)

To explore whether the DMN and FPCN are critical for creative processes, Bendetowicz et al. [17] performed a lesion study on patients with single focal frontal lesions. To assess creative abilities, the authors used the Combined Association Task, an adaptation of Mednick's Remote Associates Task (RAT), in which three unrelated cue words are presented, and the subject has to provide a solution word related to all three cue words. Because Mednick's theory predicts that associative processes are required during RAT-like tasks, the same patients performed a simple word-to-word association task. In this task, patients were instructed to provide the first word that came to mind in response to a given cue word and an unusual associate of the same cue word. The results showed that damage to the left FPCN impaired the performance on the RAT-like task, while damage to the DMN impaired the ability to generate unusual word associations. Hence, these results confirm recent functional connectivity findings [30,31**] by

showing the crucial role of both DMN and FPCN in creativity.

The critical role of the DMN in creativity is consistent with previous lesion studies that showed a deficit in both RAT [15*] and divergent thinking [12] tasks in patients with damage to the hippocampus, a DMN-related region. A rostromedial PFC damage, another region that is thought to belong to the DMN, has been associated with a divergent thinking impairment in Shamay-Tsoory et al.' s study [10], which highlighted difficulties in generating original ideas in these patients. However, this result was not replicated by Abraham et al., who did not observe significant deficits in divergent thinking in polar and/or orbitofrontal patients [11]. A medial PFC lesion also appears to impact mind wandering [56], a mental activity shown to be related to creativity abilities. In line with lesion studies, direct brain stimulation studies using transcranial direct-current stimulation (tDCS) have shown that stimulating the medial PFC could improve the remoteness of word associations during a word-generation task in certain conditions [57,58], and inhibiting this region could also decrease mind wandering [59].

According to Bendetowicz et al. [17], the DMN, and especially its medial PFC regions, may be critical for a particular thinking type or mode characterized by a spontaneous and associative spread of thoughts that is essential in RAT-like tasks. Voxel-based lesion deficit mapping analyses revealed that when a lesion affected the rostromedial prefrontal region, patients produced more common or typical word associates, with or without the instruction to think unusually, which is consistent with a 'rigidity' in semantic associations, and may explain that these patients also had poor creativity performance. The 'rigidity' of semantic associations that may results from a DMN lesion impacts the generation of unusual associations, which impairs remote thinking in both convergent and divergent tasks. This interpretation supports Mednick's hypothesis, only tested previously in healthy subjects. The organization of semantic associations also likely relates to semantic memory that has been shown critical for divergent thinking [8;but see Ref. 13]. However, the relationships between the DMN and semantic memory network for associative thinking remains to be clarified.

The critical role of the FPCN has been shown in several patient studies. In patients with a frontal neurodegenerative disease, De Souza et al. [9] showed that divergent thinking scores correlated with atrophy in prefrontal, temporal, and parietal regions that overlap with the FPCN. Rankin et al. [8,see also Ref. 16] reported that both frontal and temporal neurodegenerative diseases affected divergent thinking. In patients with focal lesions, a left rostrolateral PFC lesion has been associated with decreased performance on RAT-like tasks [17]. More caudal lateral PFC lesions as explored by Abraham et al. [11] were associated with decreased fluency and originality of ideas produced in divergent thinking tasks and temporoparietal lesions with an increased fixation on examples. Both lateral frontal and temporoparietal lesions impacted divergent thinking tasks and a creative imagery task [11]. These data suggest that several regions of the FPCN are essential to various creativity tasks. On the contrary, other findings suggested an increased originality of ideas after a left posterior parietotemporal lesion, and possibly a left inferior frontal lesion [10,see also Ref. 14], and better problem solving abilities after a lateral PFC lesion [7]. A few patient reports have described the emergence or improvement of creative artistic abilities after a frontotemporal disease ( [60,61] for reviews). Hence, patient studies provide inconsistent conclusions regarding the critical role of frontoparietal regions in creativity. Similarly, non-invasive brain stimulation studies targeting the left lateral prefrontal cortex have shown divergent effects on creative performance: both cathodal [62,63*,64*] and anodal [START_REF] Aberg | The "Creative Right Brain" Revisited: Individual Creativity and Associative Priming in the Right Hemisphere Relate to Hemispheric Asymmetries in Reward Brain Function[END_REF][START_REF] Kenett | Novel conceptual combination can dynamically reconfigure semantic memory networks[END_REF][START_REF] Karwowski | How Is Intelligence Test Performance Associated with Creative Achievement? A Meta-Analysis[END_REF][START_REF] Lubart | Creative potential and its measurement[END_REF] tDCS of the left lateral PFC, aiming to suppress or elicit the activity of this region respectively, have been reported to increase creative abilities. Overall, the few lesion and brain stimulation studies that have been conducted confirm the importance of FPCN regions for creativity, but some of them challenge the role of the left lateral PFC. This discrepancy may be explained by the combination of: firstly, a lack of precision regarding the exact location of lesions/stimulation and the networks impacted by them, secondly, a functional heterogeneity of prefrontal regions regarding creativity processes and thirdly, differences in the processes most required by the creativity tasks that have been used.

Specialization of the lateral PFC for distinct creative mechanisms (Figure 2)

The PFC appears to be the most consistently explored and/or reported region in lesion (and brain stimulation) studies, which may allow to examine whether a functional PFC organization for creativity can emerge. It is likely that patient and brain stimulation studies lack anatomical precision and target distinct areas of the PFC within or outside the FPCN. Except for Bendetowicz et al. [17], lesion studies on creativity did not use voxel-based methods to specify the location of the critical area and did not analyze which networks were affected by the lesion. By using these approaches [69*], Bendetowicz et al. [17] were able to identify a specific area that was critical for verbal creativity, and clarified to which net-works it was connected. In the left FPCN, the rostrolateral PFC region and its connections were critical for solving RAT-like problems (Figure 1). Yet, patients with a lesion in this region did not have any difficulties generating close or remote word associates, indicating that the left rostrolateral PFC plays a critical role in RAT-like tasks by affecting control rather than associative processes. The pivotal role of the rostrolateral PFC in RAT-like tasks parallels morphometry findings in healthy subjects, showing that the volume of the left rostrolateral PFC was associated with better performance in such task [START_REF] Newman | Modularity and community structure in networks[END_REF]. In addition, a meta-analysis of functional MRI studies showed that tasks involving the combination of remote elements activated the left rostrolateral PFC more than other creativity tasks [27]. Combined with the established of the left rostrolateral PFC in relational integration and multitasking, and consistent with the link between the RAT and relational reasoning [71-74,75*], these data suggest that the role of this region is to integrate the relationships between several pairs of words during RAT trials, possibly by coordinating or branching the associative and controlled processing.

In posterior areas, a functional specialization of distinct prefrontal subregions or FPCN subregions for creativity processes has been proposed [24] (Figure 2). For instance, ventral and dorsal prefrontal regions are proposed to play a role in the generative versus evaluative steps of the creative process, respectively. Although functional imaging studies have highlighted the key role of the left inferior frontal gyrus in creativity [20,23,27,40*,76,77*], several studies suggested that idea generation may improve when suppressing its activity. Brain stimulation studies that targeted the left inferior frontal gyrus showed that inhibiting this region improved idea generation in a divergent thinking task [62,64*,78,79*], especially if it was combined with a right inferior frontal stimulation, and suggested that the activity of the ventrolateral PFC is deleterious to the idea generation step. One hypothesis that has been put forward is that some of the controlled processes supported by the PFCN are impaired after a left ventrolateral PFC damage, releasing spontaneous associative processing and spontaneous flexibility. Relaxing the control on associative thinking may benefit the generation of original ideas in some tasks and conditions [START_REF] Karwowski | How Is Intelligence Test Performance Associated with Creative Achievement? A Meta-Analysis[END_REF][START_REF] Lubart | Creative potential and its measurement[END_REF]. However, this explanation questions the nature of the interaction between left inferior frontal gyrus and DMN which functional connectivity has been demonstrated to increase during idea generation and to correlate with creative abilities [31**,76,77*].

Alternatively, decreasing left lateral prefrontal activity may unfilter and increase the availability of bottom-up perceptual information processed in posterior occipitotemporal regions, which might be explained by the participation of lateral PFC subregions in other networks including the attentional networks [2,54,[START_REF] Abraham | Creative conceptual expansion: A combined fMRI replication and extension study to examine individual differences in creativity[END_REF]. Another hypothesis that has been proposed to explain the improvement of certain problem solving abilities after damage [7] or brain inhibitory stimulation [63*] of the left lateral PFC is that this region plays a role in sculpting the response space in a given situation, resulting in constraining the field of ideas directly available. Altering the function of the lateral PFC may release this constraining role of the context.

Although a constraining role of the left inferior frontal gyrus may be consistent with its critical role in contextual control [80], its negative influence on idea generation may seem to contradict a series of studies that have demonstrated the role of this region in the controlled retrieval of memory [23,53,[81][START_REF] Kan | Selection from perceptual and conceptual representations[END_REF][START_REF] Ralph | The neural and computational bases of semantic cognition[END_REF]. Specifically, patients with left inferior frontal lesion were shown to be impaired in retrieving distant word associates, suggesting an impairment in generating unusual associations or ideas [START_REF] Thompson-Schill | Verb generation in patients with focal frontal lesions: a neuropsychological test of neuroimaging findings[END_REF]. The potential limiting role of the lateral PFC in idea generation and originality also remains difficult to reconcile with patient studies that showed the crucial role of the inferior PFC in inhibition of dominant responses [START_REF] Volle | Dissociation between verbal response initiation and suppression after prefrontal lesions[END_REF][START_REF] Aron | Inhibition and the right inferior frontal cortex: one decade on[END_REF][START_REF] Robinson | Verbal suppression and strategy use: a role for the right lateral prefrontal cortex?[END_REF]. To date, two main hypotheses of pre-frontal organisation have been proposed to reconcile the beneficial and deleterious effects of caudal prefrontal lesions/brain stimulation on creativity. The first one con-siders that ventral and dorsal caudal PFC regions have opposite roles in the generative and evaluative steps of divergent thinking. The activity of the ventrolateral PFC may be deleterious to the generation step that requires less control processes [32,64*], while other prefrontal regions are important for the more controlled evaluation step [START_REF] Karwowski | How Is Intelligence Test Performance Associated with Creative Achievement? A Meta-Analysis[END_REF] or for the inhibition of constraints [20]. The second one is to consider a balance between left and right ventrolateral PFC rather than the role of each region separately [78]. For instance, the left and right ventrolateral PFC may have specific roles, in constraining the search according to the context and inhibiting the inappropriate or less interesting responses, respectively. Overall, the specialization of the caudal PFC for creativity processes according to an inferior-superior or a left-right axis remains to be tested (Figure 2).

Finally, a potential factor influencing the reported results is that different creativity tasks require a different balance between associative and controlled processes, so that altering controlled processes may or may not impact a given task [START_REF] Karwowski | How Is Intelligence Test Performance Associated with Creative Achievement? A Meta-Analysis[END_REF]. Existing tDCS studies may support this explanation. In fact, to our knowledge, all tDCS studies targeting the left lateral PFC with anodal stimulation showed improved performance in RAT tasks [START_REF] Aberg | The "Creative Right Brain" Revisited: Individual Creativity and Associative Priming in the Right Hemisphere Relate to Hemispheric Asymmetries in Reward Brain Function[END_REF][START_REF] Cerruti | Anodal transcranial direct current stimulation of the prefrontal cortex enhances complex verbal associative thought[END_REF][START_REF] Zmigrod | Stimulating creativity: modulation of convergent and divergent thinking by transcranial direct current stimulation (tDCS)[END_REF]. All tDCS studies targeting the same region with cathodal stimulation showed an improvement of divergent thinking tasks [62,64*,78,79*]. Only one study showed an improvement of divergent thinking after anodal stimulation of the left dorsolateral PFC, but it was conditioned by a priming instruction to think divergently [START_REF] Kenett | Novel conceptual combination can dynamically reconfigure semantic memory networks[END_REF], which may have involved more controlled processes than divergent thinking tasks usually require. These data suggest that originality in divergent thinking tasks may depend more on spontaneous associations computed by the DMN while RAT-like tasks additionally require the lateral PFC, likely for controlled processes. Problem solving tasks vary according to control demands [63*]. The reliance on controlled processes to solve the different creativity task may shape the performance observed after a prefrontal lesion.

Hence, the specific role of distinct prefrontal subregions in creativity processes remains to be clarified and integrated with findings from other fields of cognitive neurosciences.

Although the lesion approach may be especially useful to address this question, the conclusions that can be drawn from existing patient studies are limited by the fact that they rarely considered the functional and connective properties of the damaged areas and explored a relatively small number of patients. Precisely defining the lesion location anatomically is thus essential in future studies as close regions in frontal, temporal and parietal lobes are functionally heretogeneous and may belong to distinct networks, including the FPCN, DMN but also salience and attentional networks [START_REF] De Schotten | Rostro-caudal architecture of the frontal lobes in humans[END_REF]. The use of voxel-based lesion methods with larger sample size could improve the anatomical resolution of brain-behavior correlations, and is more comparable to functional imaging findings. In addition, connectivity approaches in patients are needed to identify the networks that are anatomically and functionally impacted by the lesion [START_REF] Kenett | The semantic distance task: Quantifying semantic distance with semantic network path length[END_REF] (or the brain intervention [START_REF] Chen | Causal interactions between fronto-parietal central executive and default-mode networks in humans[END_REF]). New anatomical and functional connectivity methods integrating network-based and lesion approaches in patients are promising avenues for future research on creativity [START_REF] Siegel | Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke[END_REF]. Such approaches may help to clarify how distinct networks and their interactions support distinct creativity mechanisms, to identify the critical hubs allowing these interactions, and, for instance, to disentangle the role of default mode, semantic, attention, and control networks in creativity.

Conclusions

Patient studies can provide important information to the neuroscience of creativity by identifying the critical net-works and pivotal nodes within the networks. The few lesion studies that have been performed confirmed that the integrity of both the DMN and the left FPCN is important for creative performance. In agreement with predictions from functional imaging in healthy subjects, the DMN appears critical for associative processing and the FPCN for controlled processes. Brain lesions affecting some regions of the left control-related network may imbalance the associative and controlled processes involved in creative thinking, affecting

PART 1 :

 1 Semantic and brain networks underlying creative abilities 1. Building individual semantic networks and exploring their relationships with creativity

  Kenett et al. (14) investigated the SemNets of groups of low and high creative individuals, based on free associations generated by both groups to a list of 96 cue words. They found that the SemNets of low creative individuals were less connected and more spread out compared to the SemNets of high creative individuals. However, estimating SemNets at the group level may obscure individual differences related to creativity. To address this issue,Benedek et al. (13) developed a method to estimate individual SemNets, based on word relatedness judgment ratings. Participants made relatedness judgments on the relationship between all possible pairs of 28 cue words, serving as a proxy for the organization of these words in an individuals' semantic memory. They demonstrated how individual-based SemNet metrics replicated the group-based findings ofKenett et al. (14), and were related to individual differences in divergent thinking scores (the most widely assessed component of creative thinking; 27). The findings ofBenedek et al. (

  All participants were French native speakers, right-handed, with normal or correctedto-normal vision. One hundred and one healthy participants (48 women) aged between 22 and 40 years (mean 25.6 years ± SD = 3.7) with formal years of education between 12 and 22 years (mean 16.6 years ± SD = 1.5) were recruited via the RISC platform (https://www.risc.cnrs.fr).

  2; 44 women) in behavioral analyses and 93 participants in the fMRI analyses (mean age 25.4 ± 3.4; 44 women). An approved French ethics committee approved the study. After being informed of the study, the participants signed a written consent form. They were paid 140 euros for their participation (that included an MRI session lasting almost 2 hours and 3 hours of cognitive tests) and reimbursed for transportation when relevant. Cognitive testing included tasks that are outside the scope of this article. General procedure Participants underwent a task-based fMRI session during which they performed the Relatedness Judgment Task (RJT). Several training tasks were conducted before acquiring the fMRI data, first outside the scanner, then in the scanner. The training included a motor training task to become familiar with giving responses using the MRI-compatible trackball on a visual scale in the RJT, and a task training to get familiar with the actual task. The task training was similar to

(

  ICAA) questionnaire(29) to assess the real-life creative activities and achievements across eight different creative domains (i.e., literature, music, art and crafts, creative cooking, sport, visual arts, performing arts, science and engineering). The creative activities (C-Act) score reflects the frequency in which participants engaged in various creative activities. Six different questions were posed for each domain, and participants reported the frequency with which they engaged in each activity during the last ten years, using a scale ranging from 0 (never) to 4 (more than ten times). The maximum score for each domain is 24 points. For each participant, the final domain-general score of C-Act was the sum of the creative activities across all activities of the eight different domains. The creative achievements (C-Ach) score estimated the level of achievement acquired in a creative domain. Ten different levels of achievement were included for each domain going from 0 (never engaged in this domain) to 10 (I have already sold some of my work in this domain). The maximum score for each domain is 55 points. For each participant, the final domain-general score of C-Ach was the sum of the scores across the eight different domains.

  multi-echo echo-planar imaging (EPI) sequences. No dummy scan was recorded during the acquisition; therefore, we did not discard any volume. Each run included 335 whole-brain volumes acquired with the following parameters: repetition time (TR) = 1,600 ms, echo times (TE) for echo 1 = 15.2 ms, echo 2 = 37.17 ms and echo 3 = 59.14 ms, flip angle = 73°, 54 slices, slice thickness = 2.50 mm, isotropic voxel size 2.5 mm, Ipat acceleration factor = 2, multi-band = 3 and interleaved slice ordering. After the EPI acquisitions, a T1-weighted structural image was acquired with the following parameters: TR = 2,300 ms, TE = 2.76 ms, flip angle = 9°, 192 sagittal slices with a 1 mm thickness, isotropic voxel size 1 mm, Ipat acceleration factor = 2 and interleaved slice order. A resting-state fMRI session of 15 minutes (570 volumes) followed with the same parameters of acquisition as the task runs.

  and independent component analysis (ICA). TEDANA then classifies the resulting components as BOLD or non-BOLD. The exclusion of the non-BOLD components allowed the removal of thermal and physiological noise such as the artefacts generated by the movements, respiration and cardiac activity. The resulting denoised data was co-registered on the T1-weighted structural image using the Statistical Parametric Mapping (SPM) 12 package running in Matlab (Matlab R2017b, The MathWorks, Inc., USA). We then normalized the data to the Montreal Neurological Institute (MNI) template brain, using the transformation matrix computed from the normalization of the T1-weighted structural image, performed with the default settings of the computational anatomy toolbox (CAT 12; http://dbm.neuro.uni-jena.de/cat/)(75) implemented in SPM 12. The resulting denoised and normalized images from the task-based fMRI data were then entered in a general linear model (GLM) in SPM to covary out the task-related signal from each run. In this analysis, we entered 24 motion parameters (standard motion parameters, first temporal derivatives, standard motion parameters squared and first temporal derivatives squared) and the onsets and durations of each task related events (reflection period, response period, inter trial interval, cross fixation periods and change of the cross-fixation color) as confounds that were regressed from the BOLD signal. We standardized and detrended the residuals of this model for each run and then concatenated the six runs, removing the rest periods between runs (six volumes in total). This final dataset composed of the six task-run residuals concatenated was used as input for the subsequent task-based functional connectivity analyses.Building task-based and resting-state functional connectivity matricesCalculation of the task-based and resting-state functional connectivity matrices for each participant was performed using Nilearn v0.3 (76) in Python 2.7 (https://ir.cwi.nl/pub/5008).

Figure 3D ).

 3D Figure 3D). These steps were conducted separately for each SemNet metric and each model network property. We built a single linear model combining the network metric of the positive and negative model networks of N-1 participants as predictors of a given SemNet metric. The mean FD was included in the model to deal with possible effects of the head motion related to fMRI acquisition on the CPM process. At each iteration, we computed the network metric of the positive and the negative model networks in the left-out participant. We used these values as predictors in the linear model to compute its predicted value of the SemNet metric tested.The final step evaluated the predictive model by performing a Spearman correlation between the predicted and the observed SemNet metric. The correlations near or below zero indicate model failure(41). Since we used within-data set cross-validation, for the significant

  Before the RJT task, participants performed a motor and task training outside the MRI scanner. The motor training was performed for the participants to become familiar with the trackball used as an fMRI response device. The training consisted of 25 trials that had the same structure and timing as in the actual task (Figure1A), but instead of word pairs, a number was presented on the screen. Participants were instructed to locate the proposed number on the visual scale by moving the slider using the trackball. Each training trial began with the display of a random number in the center of the screen along with a visual scale below going from 0 to 100. Numbers could have any of the values of the visual scale, and only the extreme values (0 and 100) were displayed on the visual scale. The stimuli were displayed for 4 seconds in total including a reflection period of 2 seconds, and a response period of 2 seconds. During reflection period, participants could visualize the position of the proposed number on the visual scale, but they couldn't move the slider yet. During the response period, the cursor appeared in the middle of the visual scale and the participants were allowed to move the slider on the visual scale to locate it on the position of the proposed number. Participants were asked to validate their response by clicking the left button of the trackball. After the validation, participants received a feedback on the screen indicating the position of the slider on the visual scale at the moment they validated their response. When participants did not validate their response, they received a feedback indicating that they were unable to complete the trial during the response period. At the end of each trial, an inter trial interval jittered from 0.3 to 0.7 seconds (interval = 0.05) occurred before the next trial.The task training was performed to familiarize the participants with the actual task. This training followed the same principles and timing than the actual task and only varied from it regarding the words used. Participants performed 15 trials. Each trial included the reflection period, the response period and an inter trial interval. Before starting the fMRI acquisition, the participants performed the motor and the task training inside the scanner. In addition, we ensured that all participants were familiar with the 35 words used in the RJT.S2: Selection of the verbal material of the RJT task.

  The selected verbal material contained a set of 35 nouns which resulted in 595 word pair combinations with semantic distances distributed as follow: 10% of 1 step, 18% of 2 steps, 28% of 3 steps, 26% of 4 steps, 15% of 5 steps and 3% of 6 steps. Some of the were polysemous and also belong to another grammatical category, such as verbs (conjugated form, N = 10), adjectives (N = 3), adjectives and verbs (N = 1), and adjectives and adverbs (N = 1). The resulting selection included an approximately balanced mixture of concrete and abstract words. marked by arrows. Nodes with degree k = 0 are not displayed. The highest degree nodes were found in the right hemisphere in the temporal pole (purple arrow, k = 65), medial prefrontal cortex (orange arrow, k = 64), extra-striate cortex (red arrow, k = 48), temporal parietal (green arrow, k = 44), parietal medial (light blue arrow, k = 40) and insula (light blue arrow, k = 37).

  creative activities and creative achievements, and we can only speculate on why WUN ASPL and WUN Q metrics predict C-Act and C-Ach differently. Creative activity and creative achievement may reflect less versus more complex forms of creative behavior: C-Act captures personal accomplishments whereas C-Ach captures publicly acknowledged accomplishment, and these differences may be captured by different properties of the SemNet. Overall, future studies should address these different points to better clarify how SemNet can explain specific processes of creativity differently.In support to our findings, recent studies exploring individual SemNets in relation to creativity using the task developed byBenedek et al. (2017) have shown consistent results(He et al., 2021;[START_REF] Yan | Left temporal pole contributes to creative thinking via an individual semantic network[END_REF].[START_REF] Yan | Left temporal pole contributes to creative thinking via an individual semantic network[END_REF] explored the relationships between brain gray matter volume, individual SemNet properties, and creativity. They showed a correlation between several SemNet metrics and creativity. Moreover, they reported a mediating role of the SemNet efficiency in the relationship between the gray matter volume of the left temporal pole and creativity. This finding also supports the role of SemNet structure in the relationship between brain correlates and creative abilities. Another study byHe et al. (2021) explored the relationships between SemNet organization, associative abilities, and creative ability in DT

PART 2 : 1 . 1 Introduction

 211 Brain connectivity patterns underlying flexible search in memory in relation to creative abilitiesIn collaboration with Dr. Mathias Benedek and Dr. Yoed Kenett The cognitive and neural correlates of clustering and switching components of memory search related to creative ability1.Previous research, including the studies reported in the first chapter of this thesis, strongly supports the role of individual differences in SemNet properties in creativity. More creative individuals show more connected, less modular, and shorter path lengths within the

  central cognitive components of memory search by assessing clustering and switching performance based on the number of responses within and between the different meanings. The clustering component was related to fluency and cluster size of responses and the switching component to the diversity of meaning and switches between them.

  Behavioral and MRI data from 93 healthy participants(43 women, mean age = 25.6 years, SD = 3.83) were collected in total. Data from seven participants were excluded due to a brain abnormality revealed by the MRI acquisition (n = 6), and for falling asleep during the experiment (n = 1). The final sample consisted of 86 participants (43 women, mean age = 25.5 years, SD = 3.48), who took part in a larger MRI study on creative thinking (Ovando-Tellez et al., 2021). All participants were French native speakers, right-handed, with normal or correctedto-normal vision and with no neurological disorder, cognitive disability or medication affecting the central nervous system. All gave a written consent and received a compensation for their participation. The study was approved by an approved French ethics committee.

ms and echo 3 =

 3 59.14 ms, flip angle = 73°, 54 slices, slice thickness = 2.50 mm, isotropic voxel size 2.5 mm, Ipat acceleration factor = 2, multi-band = 3 and interleaved slice ordering. In addition to functional imaging, a T1-weighted structural image was acquired using TR = 2,300 ms, TE = 2.76 ms, flip angle = 9°, 192 sagittal slices with a 1 mm thickness, isotropic voxel size 1 mm, Ipat acceleration factor = 2 and interleaved slice order. In the last part of the session, we acquired resting state fMRI data for 15 minutes (not analysed in the current study). No volume was discarded from any of the fMRI data since the recording did not contain dummy scans.

  which includes 200 ROIs of 2-mm dimensions distributed into 17 functional subnetworks distributed on eight main functional networks. We extracted and averaged the BOLD signal for each ROI and performed Pearson correlations pairwise. As a result, we obtained a 200 x 200 matrix for each participant corresponding to the functional connectivity network of each participant in which ROIs are the nodes and correlation coefficients the links. These matrices were Z-Fisher transformed and

  functional connectivity matrix (z-scored connectivity values between ROIs) that significantly correlated with each AmbiFlu component (threshold p < .01) either positively (the positive model network) or negatively (the negative model network) across participants. We used Spearman correlations to avoid the possible influence of outliers in the predictions. The ROI pairs thereby selected formed the positive and the negative model network for the fluency and flexibility components. In the second step, we estimated the connectivity strength in these model networks for each participant by summing the functional connectivity values of the selected ROI pairs, i.e., of the positive and the negative model brain network, separately. In the third step we built a linear model with the resulting individual connectivity strength in the positive and negative model networks as predictors and the AmbiFlu component as the outcome. To further ensure that the results are not biased by movement during fMRI, the mean FD (sum of the absolute values of the derivatives of the six realignment parameters) was included in the model. The fourth step was a leave-one-out validation. We applied iteratively the predictive linear model built on N-1 participants to the left-out participant. This allowed us to obtain a predictive value of the AmbiFlu component for each participant. The final step tested the prediction of the linear model by running Spearman correlations between the predicted and

Figure 2 .

 2 Figure 2. Correlation Matrix. Spearman correlations between AmbiFlu clustering and switching components and SemNet metrics, creativity scores and neuropsychological tests. Cold to hot colors code the Spearman correlation coefficient (rs). * p < .05; ** p < .01. All correlations p < .01 remained significant after FDR correction for multiple comparisons.

clustering

  and switching components differ in their relationships with semantic memory structure and executive control measures, confirming that they capture distinct semantic and/or executive processes involved in memory search. While both components correlate with category fluency, only the switching component relate to executive abilities, including letterfluency, and (although uncorrected for multiple comparisons) inhibition of automatic responses (stroop-interference), cognitive flexibility (TMT-shifting), and working memory (backwardspan). This finding is in line with previous research showing that switching between subcategories in fluency tasks classically correlates with frontal control functions (Abwender

  Clustering and switching measures related to divergent and convergent creativity tasks, respectively, and were predicted by distinct brain connectivity patterns. The switching component correlated with semantic memory structure and executive functions, and was predicted by brain functional connectivity between and within the DMN, ECN, Salience networks. This suggests that flexibility in memory search relies on interactions between spontaneous and controlled processes guiding the search. Contrary to our initial expectations, the clustering component did not correlate with semantic memory structure and its predictive connectivity patterns relied on interactions between ECN, salience and attentional regions. Thus, clustering was not limited to spontaneous associative processes, but also likely captured attentional focus allowing persistent memory search behaviour. Together these findings shed new light on the neurocognitive mechanisms allowing efficient and flexible search of semantic memory in support of creative cognition.

  memory search, as captured by the switching component, relies on interactions between spontaneous and controlled processes guiding the search. The clustering component captures controlled processes which are likely not reflected by the executive function measures we used in our study.These findings shed new light on the neurocognitive mechanisms allowing an efficient and flexible search of semantic memory to support creative cognition. Critically, a more efficient semantic memory organization as measured with the SemNet properties contributed to a more flexible retrieval that was related to the ability of combining remote associations. To better understand how semantic associations and search processes relying on a more (or less) flexible SemNet structure contribute to creativity, it is important to improve our knowledge of how concepts are organized in memory. Importantly, does the organization of the concepts in memory allows us to implicitly relate concepts? We address this question in the next study, exploring which are the different types of relationships that participate in the organization of semantic memory and allow them to be implicitly activated.

  demonstrated how a training in goal-derived category construction improves insight problem solving performance, suggesting the important role of categorization for this type of problem solving. Moreover, the effect of training in category construction on insight problem solving was also observed without participants being explicitly informed about the relevance of the training for the problemsolving tasks.

2. 1

 1 Participants: Forty subjects (20 women) with a mean age of 23 years old (± standard deviation (S.D.) = 1.31) participated in this study and performed the priming task and other cognitive tasks. One participant was excluded because she did not carry out the task until the end. Thirty-nine subjects were included in the behavioural analyses (n = 39, 19 women, mean age = 22.5 ± 1.3 years old). Electrophysiological data were recorded in a subgroup of 24 participants (13 women, mean age = 23 ± 1.19 years old). Subjects were French native speakers, right-handed, and all had a normal or corrected-tonormal vision. Participants had no medical history of neurological or psychiatric disorders, no cognitive impairment (Mini mental State test score > 28) and were free of any drug or psychotropic medication. The local ethical committee (Comité de Protection des Personnes

  programs "Investissements d'avenir" ANR-11-INBS-0006 and ANR-10-IAIHU-06. This work was supported by the "Fondation pour la Recherche Médicale" (FRM), [grant numbers FDM20150632801 and DEQ20150331725], and by the ENP-Chilean internships of the "École des Neurosciences de Paris Île-de-France" and Becas-Chile of ANID (CONICYT).

  Here, we add to previous studies by showing the critical role of semantic memory in creative activities and achievements in real life. These measures of creativity capture a wide range of creative aspects, from an individual's everyday creativity to the individual's highest achievements. This finding contributes to better understanding the creativity mechanisms underlying by SemNet properties since they have been mostly explored in relation to AUT tasks. Here we show that creativity mechanisms rooted in semantic memory are also critical to predicting individual differences in creative activities and achievements. Hence, individual SemNets properties allow exploring underlying cognitive mechanisms of creativity by revealing how different aspects of the semantic memory structure relate to creative behavior (He et al., 2021).

Figure - 21 Figure 42 Figure 43 Figure 47 Figure 51 Figure 55 Figure 57 Figure 61 FigureANEXESANEX- 1 :

 -21424347515557611 Figure -Framework based creativity tasks ……………………………….……………….. 21 Figure -Global activation likelihood estimation (ALE) meta-analysis of creativity .…….. 42 Figure -Single ALE meta-analysis on specific domains of creativity …………….……… 43 Figure -Brain regions and related functional networks involved in creativity ……….…... 47 Figure -Neural systems of semantic cognition based on the controlled semantic cognition framework ……………………………………………………………….………………..…. 51 Figure -Representation of the types of networks …………………………………...……. 55 Figure -Networks properties ……………………………………………………………... 57 Figure -Brain functional connectivity network representation ……………...…………… 59 Figure -Semantic network representation …………………………….…………….……. 61 Figure -Semantic networks in people with low and high creative abilities ……..……… 64 Figure -French semantic Network …………………………………..………………… 258 Figure -Relationships between RJT ratings and theoretical steps …………………..… 262

  (c) Available time considering the objective number of working hours per week (multiplechoice question with eight options ranging from 'not concerned' to 'more than 42 hours' with an increment of 7 hours per option) (VIII) and the feeling of a change in the amount of free time (II). (d) Affective changes assessed by asking participants their feeling of a change in mood, motivation, anxiety, and stress, and pressure (III).

1 (

 1 representing affective changes during lockdown) on the relationship between BFI-O and SCC. The results showed a significant partial mediation with a decrease in the direct path when including the mediator effect (linear regression of SCC against BFI-O (without the mediator): 𝛃SCC-BFI-O=0.62, p=3.10 -5 ; linear regression of SCC against PC1 and BFI-O: 𝛃*SCC-BFI-O=0.38, p=0.02). The difference between 𝛃SCC-BFI-O and 𝛃*SCC-BFI-O was confirmed using bootstrapping (confidence interval=[0.08 0.44]). Hence, individuals with higher openness may have felt more creative because they managed to have a more positive affective experience during the lockdown. The same mediation analysis conducted with the second component loadings (time available) did not reveal a significant mediation effect (confidence interval=[0 0.33]).

Figure 3 .

 3 Figure 3. Quantitative changes in performing creative activities during the lockdown period. A. Averaged proportions of changes across participants in performing activities. We show the activities with negative changes, positive changes, and no changes separately. The activities with negative changes were not done during the lockdown (dark red), were not done, but participants had ideas to do it (light red) or were done less frequently than before the lockdown (pink). The activities with positive changes were done more frequently (light green) or were new (dark green) during the lockdown. The activities with no changes were done as frequently (gray) as before the lockdown. B. Averaged proportion of changes across participants for negative changes (red), no changes (gray), and positive changes (green) during the lockdown period compared to the five last years. Each dot represents a participant, color boxes represent the upper and lower quartiles, and squares indicate the mean. Stars indicate post hoc tests with a pcorr<0.05. C. Participant's SCC as a function of the averaged frequency score of change (a dot per participant). The red line indicates a significant correlation between SCC and the mean frequency score of change (Pearson correlation, p<0.05).

  mis en place des jeux ludiques et/ou pédagogiques pour des enfants (loisirs ou école à la maison) I have set up fun and / or educational games for children (leisure or réalisé une sculpture, une mosaïque, une poterie ou de la céramique I made a sculpture, a mosaic, a pottery or a ceramic Original sculpture or alike J'ai créé un slogan ou un logo I created a slogan or logo Logo creation J'ai réalisé ou j'ai joué dans une vidéo, un montage, un court ou longmétrage, ou un film d'animation I made or acted in a video, a montage, a short or full-length film, inventé ou revisité une musique, une mélodie, un rythme ou les paroles d'une chanson I invented or revisited a music, a melody, a rhythm or the lyrics of a song Music and lyrics J'ai conçu un instrument de musique ou une manière de faire de la musique I designed a musical instrument or invented a way to make music Musical instrument J'ai inventé ou réfabriqué un objet ou un accessoire I made an object or an accessory Object making, craft J'ai écrit un projet innovant ou diversifié mon activité dans le cadre de mon travail I wrote an innovative project or diversified my activity as part of my work Professional project

  l'ai montré à personne I did not share it Not shared 2Je l'ai montré à mes co-confinés I shared it with my co-

. Brain functional connectivity patterns underlying semantic network properties in relation to creative behavior

	o To explore how individual SemNet properties relate to creative abilities
	assessed by different approaches
	2• In this study, we aim to explore the role of SemNet properties and its brain
	correlates in real-life creative behavior. To accomplish this aim, we have 4
	subgoals:
	o To build individual SemNets using the RJT task and to explore their
	network properties
	o To explore the correlations between individual SemNet properties and
	real-life creativity
	o Using the CPM approach to explore the brain functional networks
	underlying the SemNet properties in relation to creative activities and
	achievements in real life
	o To explore whether the relationships between creative abilities and brain
	functional connectivity is mediated by SemNet properties
	• In this study, we aim to explore the relationships between individual SemNet
	properties and creative abilities and behavior in order to replicate and extend
	previous findings. To accomplish this aim, we have 3 subgoals:
	o To develop the French version of the relatedness judgment task (RJT) -
	which allows building the individual SemNets -and validate it
	o To build individual SemNets based on the individual's relatedness
	judgments in the RJT and to explore their properties by means of network
	science methods

PART 2: Brain connectivity patterns underlying flexible search in memory in relation to creative abilities 1. The cognitive and neural correlates of clustering and switching components of memory search related to creative ability

	• In this study, we aim to explore the underlying brain correlates of search
	mechanisms in semantic memory and how they relate to semantic memory structure,
	executive functions, and creative abilities. To accomplish this aim, we have 3
	subgoals:
	o To identify clustering-and switching-related components of memory search
	using a free generation of associates task with ambiguous words
	o To explore the correlations between these components and individual
	SemNet properties, executive functions processes, and creative abilities
	o To identify the unique patterns of brain functional connectivity that predict
	these components using the CPM approach

PART 3: Exploring different types of relationships in the organization of the semantic memory. 1. Do we implicitly relate concepts?

	• In this study, we aim to explore different types of relationships that participate in the
	organization of semantic memory and how concepts are implicitly categorized using
	a double semantic priming paradigm. To accomplish this aim, we have 2 subgoals:
	o To explore whether the implicit retrieval of a thematic and taxonomic
	category name is facilitated by two semantically related words belonging to
	this category more than by a single exemplar of this category measured by
	the reaction time in a lexical decision task.

  analyzed separately for each theoretical distance, AJT relatedness judgement correlated with AUT_Originality for word pairs separated by 6 steps, ρ(19) = .55, p < .01, 5 steps, ρ(19) = .46, p < .05, 4 steps, ρ(19) = .46, p < .05, 3 steps, ρ(19) = .49, p < .05 and 2 steps, ρ(19) = .56,

p < .01. Participants judging theoretically distant word pairs (step >= 2) as more related produced more original ideas at the AUT task. AJT relatedness ratings for close word pairs (1 step apart) correlated positively with CAT_Solving, ρ(19)= .50, p < .05, and CAT_Close, ρ(19)= .55, p < .001: Participants judging theoretically close word pairs (1 step apart) as more related were better at combining word associates and solved more CAT trials. No statistical results survive FDR correction for multiple comparisons.

  In this model network, the highest degree nodes -nodes with highest number

	of connections (k; i.e., the number of functional connections) -belonged to the right
	hemisphere being part of the visual network (i.e., extra-striate inferior, k = 53), default mode
	network (i.e., medial prefrontal cortex, k =39), salience (i.e., insula, k = 31; parietal medial, k =
	28), temporoparietal (i.e., temporal-parietal; k =29) and limbic (temporal pole, k = 28) networks
	(Figure

  The selection of the RJT stimuli words used in our study is detailed inBernard et al. (26; a summary is given in the supplementary material SI S2). In brief, we first created a French SemNet, based on French verbal association norms (http://dictaverf.nsu.ru/dictlist), where the nodes represent the words,

Table 1 . Descriptive statistics of creativity scores and semantic network measures.

 1 Dataare shown for real-life creativity activities (C-Act) and achievements (C-Ach), and for SemNet metrics of weighted (WUN) and unweighted (UUN) networks.

		Mean	SD	Min	Max	Skewness	Kurtosis
	Creativity scores
	C-Act	47.894	21.695	13	102	0.428	-0.609
	C-Ach	74.638	42.249	1	207	0.744	0.507
	WUN metrics
	ASPL	0.021	0.004	0.015	0.037	1.713	3.353
	CC	0.363	0.096	0.142	0.628	0.274	0.267
	Q	0.122	0.058	0.032	0.319	1.037	1.436
	S	1.003	0.073	0.828	1.387	1.913	9.134
	UUN metrics
	ASPL	1.633	0.221	1.262	2.361	1.456	2.665
	CC	0.585	0.082	0.438	0.781	0.130	-0.658
	Q	0.178	0.064	0.058	0.392	0.919	1.387
	S	1.386	0.271	1.011	2.936	2.628	11.269

Note. ASPL= Average Shortest Path Length; CC = Clustering coefficient; Q = Modularity; S = Small-Worldness.

Table 2 . Relationship between individual semantic network metrics and creativity.

 2 The Spearman correlations between SemNet metrics and creativity scores are reported (rs for C-Act and C-Ach). In bold are the significant predictions of creativity from the SemNet properties after permutation testing shown in Figure2. * indicate correlations that reached significance after FDR correction for multiple comparisons.

	Creativity scores		C-Act		C-Ach
		rs	p	rs	p
	WUN metrics
	ASPL	-.276	.007*	-.208	.044
	CC	.165	.111	.201	.052
	Q	-.179	.085	-.295	.004*
	S	.234	.023	-.017	.868
	UUN metrics
	ASPL	-.125	.230	-.149	.152
	CC	.092	.378	.080	.441
	Q	-.281	.006*	-.287	.005*
	S	-.154	.139	-.219	.034

Table S1 . Relationship between individual semantic network metrics, creativity scores, and age, education, IQ and sex.

 S1 For age, education and IQ factors we ran Spearman correlations. We ran Mann-Whitney U non-parametric tests using sex as a group variable. *p < .05 **p <.010 ***p < .005. In bold are correlations that reached significance after FDR correction for multiple comparisons.

	Variables		age	education	IQ		sex
		rs	p	rs	p	rs	p	U	p
	Creativity scores
	ICAA_Act	-.021	.844	-.188	.070	-.065	.533	1084.5	.906
	ICAA_Ach	.064	.541	-.240*	.020	.122	.242	1055	.733
	WUN metrics
	ASPL	.054	.608	-.023	.825	-.091	.384	824*	.036
	CC	-.058	.580	.086	.408	.157	.130	761*	.010
	Q	.020	.846	-.027	.799	-.279**	.006	687***	.002
	S	-.117	.263	-.197	.057	-.192	.064	886	.105
	UUN metrics
	ASPL	.031	.766	-.103	.325	-.113	.279	785*	.017
	CC	.027	.796	.159	.125	.066	.528	865	.075
	Q	-.050	.635	.027	.798	-.184	.076	694***	.002
	S	.111	.287	-.016	.878	-.118	.258	854	.062

Table S2 . Relationship between individual semantic network metrics and creativity explored in 93 participants.

 S2 The Spearman correlations between SemNet metrics and creativity scores are reported (rs for C-Act and C-Ach) for 93 participants (i.e., excluding the participant who performed the RJT outside the scanner). In bold are the significant predictions of creativity from the SemNet properties after permutation testing. * indicate correlations that reached significance after FDR correction for multiple comparisons.

	Creativity scores		C-Act		C-Ach
		rs	p	rs	p
	WUN metrics
	ASPL	-.278	.007*	-.192	.065
	CC	.164	.116	.188	.072
	Q	-.179	.086	-.291	.005*
	S	.241	.020	-.001	.994
	UUN metrics
	ASPL	-.125	.233	-.133	.203
	CC	.089	.394	.062	.553
	Q	-.284	.006*	-.290	.005*
	S	-.155	.137	-.213	.041

Table S3 . Descriptive statistics of creativity scores for the eight domains explored by the ICAA questionnaire.

 S3 Data are shown for real-life creativity activities (C-Act) and achievements (C-Ach).

	SI Supplementary text
	Material and Methods.
	ICAA domains	mean	S.D.	Min Max Skewness Kurtosis
	C-Act
	Literature	6.979	4.333	0	21	0.508	0.199
	Music	5.521	5.900	0	24	1.235	0.691
	Arts and crafts	8.872	5.757	0	22	0.449	-0.65
	Creative cooking	9.830	5.847	0	24	0.299	-0.541
	Sports	3.394	4.266	0	24	1.950	5.160
	Visual arts	6.213	5.410	0	24	1.166	0.955
	Performing arts	3.277	2.942	0	11	0.867	-0.067
	Science and engineering 3.809	3.833	0	15	0.979	0.268
	C-Ach
	Literature	7.149	7.844	0	31	1.181	0.784
	Music	9.117	9.687	0	49	1.166	1.818
	Arts and crafts	8.851	9.880	0	55	2.467	7.398
	Creative cooking	7.596	5.138	0	25	1.069	1.928
	Sports	12.734 13.231 0	55	0.764	-0.286
	Visual arts	9.383	11.526 0	55	2.150	4.454
	Performing arts	9.160	10.055 0	47	1.267	1.406
	Science and engineering 10.649 11.212 0	46	1.086	0.858

S1: RJT motor and task training.

Table 1

 1

	. Principal Component Analysis (PCA). The PCA analysis performed on the five
	AmbiFlu measures, showing the loadings of the variables on the 2 principal components
	extracted from the data of the 86 participants. For each component, we also report the
	percentage of variance explained. Absolute values of regression coefficients higher than 0.5
	were considered important (bold font) in defining the principal components.
		Components
	AmbiFlu scores	1	2
	Total number of responses	.983	.339
	Rank of the first switch	.668	-.409
	Number of different meanings	-.093	.827
	Number of switches	.085	.937
	Biggest cluster size	.892	-.197
	Eigenvalues	2.599	1.549
	% of Variance	51.978	30.982
	Cumulative %	51.978	82.960

  /www.nitrc.org/projects/bnv/) in Matlab, and custom scripts in RStudio version1.3.1056.

							1.0
	(http://bisweb.yale.edu/connviewer),	BrainNet	viewer	(Xia	et	al.,	2013;
	http:/

  . The correlation matrix represents the number of links within the model network connecting 7 different brain lobes (total of links = 259). At the upper right side, a table with the percentage of links that belong to each functional network is shown.

	In summary, the main patterns of functional connectivity predicting clustering and
	switching components of AmbiFlu differed considerably. Higher clustering was predicted by
	connectivity between large scale brain networks, in particular between ECN, salience, DAN,
	and visual networks, but, notably, the clustering model network hardly included any
	connectivity within these networks. Higher switching was predicted by connectivity between
	the DMN, ECN, salience, DAN, somatomotor and visual networks; yet, contrary to the

clustering model network, links within the ECN and between the ECN and the DMN played a more important role in the switching model network, and DMN regions were generally much more involved. The salience network mostly related to the ECN in the clustering model but with the DMN in the switching model network. The temporal pole connected to salience network regions in the clustering model network but to DMN regions in the switching model network. Only the switching model network involved intra-ECN connectivity. Both model networks involved the visual and salience networks.

Table 2 . Spearman correlations between the five measures of the AmbiFlu task.

 2

	)

Table 3 . Descriptive statistics of creativity tasks (AUT and CAT), SemNet metrics and executive tests.

 3

		M	SD	Min	Max
	AUT scores
	AUT-fluency	23.163 11.584 7	87
	AUT-uniqueness	7.442 4.979 0	29
	AUT-ratings	1.710 0.425 0.57	2.67
	AUT-commonness	12.248 3.748 3.39	24.25
	CAT scores
	CAT-CR	47.1	11.6	13.0	73.0
	CAT-index	0.424 0.226 0	1.08
	CAT-eureka	69.3	19.9	0	100
	SemNet metrics
	WUN Eff	54.204 6.874 36.782 71.717
	WUN CC	0.364 0.098 0.142 0.628
	WUN Q	0.121 0.060 0.032 0.319
	UUN Eff	0.708 0.072 0.516 0.869
	UUN CC	0.586 0.082 0.438 0.781
	UUN Q	0.178 0.065 0.058 0.392
	Executive tests
	forward-span	11.244 2.275 6	16
	backward-span	9.430 2.324 6	16
	TMT-shifting	31.655 17.051 9.080 111.40
	category-fluency	39.035 8.589 22	61
	letter-fluency	21.361 6.473 4	35
	stroop-interference	25.143 11.019 2.810 65.990
	Note. WUN= Weighted Undirected Networks; UUN= Unweighted Undirected Networks;
	AUT = Alternative uses task; CAT = Combination of Associates task

  1,3 , Theophile Bieth 1 , Laurent Hugueville 3 , Yoan Ibrahim 1 , Ophelie Courbet 1 , Lionel Naccache 1 , Richard Levy 1,2 , Béatrice Garcin 1,4, + and Emmanuelle Volle

  1, +, *

Table 1 . Examples of the six different conditions used as stimuli.

 1 The table contains real trials used in the taxonomic session. Each condition included two primes and one target, and

		Prime 1	Prime 2	Target	Number of Trials
	RR-word	coffee (café)	beer (bière)	drink (boisson) 26 Trials
	RU-word	beer (bière)	pigeon (pigeon) drink (boisson) 26 Trials
	UR-word	boot (botte)	coffee (café)	drink (boisson) 26 Trials
	UU-word	copper (cuivre) sight (vue)	drink (boisson) 26 Trials
	RR-pseudo-word* coffee (café)	beer (bière)	asimum	13 Trials
	UU-pseudo-word sorbet (sorbet) sight (vue)	disseya	39 Trials

Table S4 .

 S4 Descriptive statistics of RTs. Mean, Standard Deviation (SD), Minimum (Min) and Maximum (Max) of the median RTs of all participants are presented in milliseconds. Data are shown for the two types of semantic associations and for the four conditions analyzed. The average between UR and RU conditions is included in the table asRUR.

		Taxonomic		Thematic
	Condition Mean SD	Min Max	Mean SD	Min Max
	RR	490 87	377 757	497	91	381 822
	UR	505 86	406 755	502	82	402 766
	RU	503 82	415 770	501	77	388 747
	UU	513 91	394 780	513	83	409 795
	RUR	504 79	413 763	502	79	395 757
	Supplementary

  contributed to better understand the relationship between associative abilities and SemNet properties, it is still unclear whether SemNet structure captures purely associative or spontaneous processes. The authors showed that SemNet metrics mediate the relationship between associative processes and creative abilities. However, what are the mechanisms captured by our SemNets based on relatedness judgments, and how they differ, or parallel associative processes captured by other associative fluency tasks is not fully understood. Then, whether the mechanisms capture by the SemNet metrics involve associative

  1,2 *, Stella Guiet 1 , Marcela Ovando-Tellez 1 , Emmanuelle Volle 1 Sorbonne Université, Institut du Cerveau -Paris Brain Institute -ICM, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France. 2. Neurology department, Hôpital de la Pitié Salpêtrière, AP-HP, F-75013, Paris, France Corresponding authors: lopez.alizee@gmail.com, emmavolle@gmail.com * Authors who contributed equally

	Affiliations :
	1. FrontLab, Acronyme: ConfiCrea
	Running title: Creativity during the first lockdown of the Covid-19 pandemic
	(In revision in The Journal of Creative Behavior)

Sorbonne University, FrontLab at Paris Brain Institute (ICM), INSERM, CNRS, 75013, Paris, France.

Faculty of Industrial Engineering and Management, Technion -Israel Institute of Technology, Haifa

Israel.3 Institute of Psychology, University of Graz, Graz, Austria.4 Sorbonne University, CENIR at Paris Brain Institute (ICM), INSERM, CNRS, 75013, Paris, France.5 Neurology department, Pitié-Salpêtrière hospital, AP-HP, F-75013, Paris, France.

ACKNOWLEDGEMENTS

I would like to thank everyone who has been alongside me during this long and important process in my professional life.

Acknowledgments

We thank D. Margulies, A. Lopez-Persem, F. De Vico Fallani, M. Chavez for advice and helpful discussion and commentary. We also thank the participants for making this work possible.

Funding

The research was supported by "Agence Nationale de la Recherche" [grant numbers ANR-19-CE37-0001-01] (EV, MBEN, YNK) and received infrastructure funding from the French programs "Investissements d'avenir" ANR-11-INBS-0006 (EV) and ANR-10-IAIHU-06 (EV).

This work was also funded by Becas-Chile of ANID-CONICYT (MOT). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

The research was supported by "Agence Nationale de la Recherche" [grant numbers ANR-19-CE37-0001-01] (EV, MBEN, YNK) and received infrastructure funding from the French programs "Investissements d'avenir" ANR-11-INBS-0006 (EV) and ANR-10-IAIHU-06 (EV).

This work was also funded by Becas-Chile of ANID-CONICYT (MOT). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank D. Margulies, A. Lopez-Persem, F. De Vico Fallani, M. Chavez for advice and helpful discussions. We also thank the participants for making this work possible.

Acknowledgment

We thank all the participants to the survey. EV is funded by the 'Agence Nationale de la Recherche' [grant numbers ANR-19-CE37-001-01], the 'Fondation pour la recherche medicale' [grant number DEQ20150331725]. The research also received funding from the program 'Investissements d'avenir' ANR-10-IAIHU-06. MOT is funded by Becas-Chile of ANID (CONICYT). We thank Baptiste Barbot for his helpful comments on the structure of the survey. Through thick and thin: changes in creativity during the first lockdown of the Covid-19 pandemic. Acknowledgements Emmanuelle Volle is supported by FRM grant DEQ20150331725 and program 'Investissements d'avenir' ANR-10-IAIHU-06. Marcela Ovando Tellez is supported by a CONICYT funding. Theophile Bieth was supported by a funding from 'Journe és de neurologie de langue franc aise'. References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: * of special interest ** of outstanding interes

Data and materials availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials, or are available at https://osf.io/eh8z7/?view_only=d6eece3debcd4faeac9eeb3423f407bd. All the data used in this study were collected on the PRISME and CENIR platforms at the Paris Brain Institute (ICM). Analyses were conducted using open softwares and toolboxes available online as described in the Materials and Methods section (SPM: https://www.fil.ion.ucl.ac.uk/spm/software/spm12/; AFNI: https://afni.nimh.nih.gov); Nilearn: https://nilearn.github.io/stable/index.html; TEDANA: https://tedana.readthedocs.io/en/stable/; CPM: https://www.nitrc.org/projects/bioimagesuite/; Network metrics computation: https://sites.google.com/site/bctnet/Home/functions).

Data and code availability statement

We developed a Matlab App that allows exploring the data collected in our survey. It is available here: https://osf.io/XBR78/. Anonymous data can be downloaded at the same address.

Chapter 2: Experimental studies

Author Contributions EV, YNK and MBEN designed the study. MOT, MBER and JB collected the data. MOT analyzed the data with contribution from BB, MBER, TB, JB, EV, and YNK. MOT wrote the first draft of the article. MOT, YNK, MBEN, BB and EV wrote and revised the manuscript. All authors revised and approved the manuscript.

Competing Interests

The authors declare no competing interests.

Supplementary Information

Marcela Ovando-Tellez 29))

The ICAA assesses creative activities and achievements across eight domains.

Creative activities (C-Act):

To explore the engagement of the participants in each creative domain, six activities were proposed for each domain, and participants were asked to indicate how often they have carried out this activity over the past 10 years (29). The response options were: Never, 1-2 times, 3-5 times, 6-10 times, more than 10 times. We provide one example of the activities proposed for each domain:

Literature: Wrote a short literary work (e.g., poem, short story).

Music: Wrote a piece of music.

Art and Crafts: Tinkered with a common, everyday object.

Creative cooking: Cooked an original dish.

Sports: Created new tricks/movement sequences requiring dexterity (e.g., juggling).

Visual arts: Created a photomontage or photo collection.

Performing arts: Performed in a play.

Science and Engineering: Wrote a scientific paper.

Creative achievements (C-Ach):

To explore the level of achievement in each creative domains, participants answered to these 10 premises, going from a more personal to a more public level of achievement:

(0) I have never been engaged in this domain

(1) I have tried this domain once

(2) I have already created at least one original work in this domain

(3) I have presented my original work in this domain to some friends (4) I have presented my original work in this domain to strangers (5) I have already taken classes to improve my skills in this domain (6) I have already published my original work in this domain (7) I have already participated in a contest in this domain (8) I have already won an award or prize for my original work in this domain (9) Media have already reported about my work in this domain (10) I have already sold some of my work in this domain.

switching components only partly aligned with the previously proposed alignment of clustering and switching with associative memory and executive control, respectively. Together these findings shed new light on the neurocognitive mechanisms allowing an efficient and flexible search of semantic memory to support creative cognition.

Supplementary Information

Marcela Ovando-Tellez

Abstract

Categorization -whether of objects, ideas, or events -is a cognitive process that is essential for human thinking, reasoning, and making sense of everyday experiences. Categorization abilities are typically measured by the Wechsler Adult Intelligence Scale (WAIS) similarity subtest, which consists of naming the shared category of two items (e.g., "How are beer and coffee alike"). Previous studies show that categorization, as measured by similarity tasks, requires executive control functions. However, other theories and studies indicate that semantic memory is organized into taxonomic and thematic categories that can be activated implicitly in semantic priming tasks. To explore whether categories can be primed during a similarity task, we developed a double semantic priming paradigm. We measured the priming effect of two primes on a target word that was taxonomically or thematically related to both primes (double priming)

or only one of them (single priming). Our results show a larger and additive priming effect in the double priming condition compared to the single priming condition, as measured by both response times and, more consistently, event-related potential. Our results support the view that taxonomic and thematic categorization can occur during a double priming task and contribute to improving our knowledge on the organization of semantic memory into categories. These findings show how abstract categories can be activated, which likely shapes the way we think and interact with our environment. Our study also provides a new cognitive tool that could be useful to understand the categorization difficulties of neurological patients.

Does adding beer to coffee enhance the activation of drinks? An ERP study of semantic category priming

Supplementary information

Marcela Ovando- The role of brain regions related to DMN, FPCN, SalN, was also highlighted in the prediction of creative behavior in real life in our previous study. Differently, they supported individual differences in creative behavior that were mediated by the modularity of the SemNet.

Here, these interactions supported search processes related to clustering and switching, where

SemNet efficiency uniquely supported switching process (not clustering) and thus, a more flexible semantic retrieval. Overall, individual's pattern of functional connectivity between DMN-FPCN-SalN support the flexibility of the SemNet structure and more flexible search in memory which are relevant for different aspects of creativity. Then, how the concepts are related in memory? To better understand the types of relationships that participate in the organization of semantic memory we performed the fourth study reported in this thesis.

How concepts relate in memory: Implicit access to the semantic memory information

In the third part of my thesis, we further explored processes related to accessing information in semantic memory. We examined the implicit retrieval of taxonomic and thematic category names. Addressing this question contributed to better understand how concepts are associated in memory without explicit instructions to relate or combine such elements.

Moreover, exploring the implicit access to memory information contributed to a deeper understanding of the types of relationships that participate in the organization of semantic memory and how semantically related concepts are associated with each other in memory.

The categorization process is critical as it is at the basis of our knowledge, allowing an efficient organization and retrieval of the concepts in memory (Cohen, 2005;Murphy, 2002).

We previously showed how semantic memory structure and search processes across memory allow more efficient and flexible retrieval of concepts from memory which have an important role in creativity. In contrast to fluency tasks such as the one we used in study 3, that require participant to name associates to a given word (or exemplars of a given category in category fluency tasks), in the semantic priming study we tested how the category name could be implicitly retrieved for two exemplars (for taxonomic) or associates (for thematic). The explicit equivalent to our task is the similarity task of the WAIS, which explores categorization and abstract thinking. Even if the initial goal of this study was to better understand the processes of the situation, participants reported that they were more creative during the lockdown than before. Positive changes were linked with more time availability, more motivation, or the need to solve a problem while negative changes were related to negative affective feelings or a lack of resources or opportunities. This study documents the effects of the first lockdown period on creativity and the factors that modulated it.

List of abreviations

Measures of creativity

We collected four different continuous variables related to participants' creativity.

The main measure was the SCC that aims to capture the self-perceived creativity change during the lockdown period. We asked participants how much they think their creativity changed positively or negatively during the lockdown as compared to before (IV). The participants responded using a continuous visual scale ranging from -50 (less creative during the lockdown)

to 50 (more creative during the lockdown). Hence, SCC relies on a self-rating of one's own creativity. Similar self-report scales or ratings measuring creativity belief, self-concept, and self-efficacy have been developed [START_REF] Beghetto | Creative self-efficacy : Correlates in middle and secondary students[END_REF][START_REF] Karwowski | Creative self-beliefs : Their nature, development, and correlates[END_REF][START_REF] Karwowski | Toward dynamizing the measurement of creative confidence beliefs[END_REF]Karwowski & Lebuda, 2016, 2017;[START_REF] Ng | A comparison of self-ratings and non-self-report measures of employee creativity[END_REF] and used in COVID-19-related studies (Mercier et al., 2020). In contrast to these previous measures, we asked participants to rate a change in their creativity related to a key event (i.e., the beginning of the lockdown). This allowed us to minimize recall bias and avoid the need of baseline measure that was difficult to obtain. To check the validity of our approach, we confronted this new creativity measure to more standard ones, including BFI-O and several activity-based measures described below.

We adapted a French-translated version of the Openness part of the Big Five Inventory (BFI-O) [START_REF] Plaisant | Validation par analyse factorielle du Big Five Inventory français (BFI-Fr). Analyse convergente avec le NEO-PI-R[END_REF]. This is an 11 questions questionnaire that evaluates the openness personality traits of participants (V). We chose the BFI-O because openness is the personality trait most associated with creativity [START_REF] Beaty | Brain networks of the imaginative mind : Dynamic functional connectivity of default and cognitive control networks relates to openness to experience[END_REF]Vartanian et al., 2018). The participants answered each of the 11 questions using a 5-point Likert scale, converted to individual scores ranging from -2 to 2. The BFI-O score was computed as the mean of the 11 individual responses recorded in the BFI-O.

In addition, we collected information about lockdown-related changes concerning participants' involvement in creative activities in an independent part of the survey (VI). We aimed to qualitatively and quantitatively assess the creativity changes induced by the lockdown on a list of 28 creative activities (see Supplementary Table S3). The 28 activities were selected based on existing items in other validated creativity questionnaires, including Inventory of Creative Activities and Achievements (ICAA) [START_REF] Diedrich | Assessment of Real-Life Creativity : The Inventory of Creative Activities and Achievements (ICAA)[END_REF], Creative Behavior Inventory (CBI) (Hocevar, 1979), and Runco Ideational Behavioral Scale (RIBS) [START_REF] Runco | The Incremental Validity of a Short Form of the Ideational Behavior Scale and Usefulness of Distractor, Contraindicative, and Lie Scales[END_REF]. For each activity, participants had to answer sequential questions about their involvement in this activity. The sequence of questions was conditional to their previous binomial, ordinal, or continuous. For binomial and categorical variables (such as being a woman or a man), one-way ANOVAs were used to test their effect on continuous variables (such as SCC). Correlation analyses were performed using Pearson coefficient for continuous variables and Spearman coefficient for ordinal variables (such as Education, Frequency change in activity, and RS). When relevant, the difference from zero was tested for significance at the group level using a one-sample two-tailed t-test.

To explore whether the relationship between RS and SCC could be mediated by creative personality traits (BFI-O), we ran a mediation analysis. BFI-O can be considered as a mediator if the direct path from RS to SCC is no longer significant when introducing the mediator BFI-O (𝛃*SCC-RS). Thus, the mediation analysis was estimated using the two following linear models:

Path significance (𝛃SCC-RS -𝛃*SCC-RS) was then assessed with a bootstrap test using 5,000 bootsamples.

We used the same procedure to test the mediation effects of the components loadings on the link between BFI-O and SCC.

When examining the factors that can be associated with SCC during the lockdown period, we first conducted a principal component analysis (PCA) to reduce the ten variables used to assess the living conditions and subjective experience of the lockdown situation into a smaller set of dimensions while retaining as much of the original information as possible. It also helped to deal with the problem of multicollinearity among our variables. We ran the PCA on our ten variables of interest with oblique rotation (direct oblimin) to rotate loadings and identify the variables contributing the most to each component, using the available complete data from 287 participants. Factors with eigenvalues over Kaiser's criterion of 1 were kept. Sampling adequacy for the analysis was assessed with the Kaiser-Meyer-Olkin measure (standard threshold=0.5).

Results

Demographics and lockdown situation

The first part of the survey was designed to collect information about the confinement circumstances of the participants (n=380). Overall, the majority of participants were confined frequency of those five main reasons did not statistically differ from each other (pcorr>0.05 for all posthoc tests). However, the frequency of each of these reasons differed significantly from the remaining reasons that were: not having ideas for this activity ('Lack of ideas', 3+/-0.8%), that this activity was performed only occasionally before and was not common for the participant ('Occasionally', 6.3+/-1.1%), or other undetermined reasons ('Others', 7.9+/-1.2%) (pcorr>0.05 for all posthoc tests).

To check the consistency of the participant's responses to the first part of the survey (global experience of the lockdown) and the second part (activity-based questions), we explored whether the activity-based reported reasons correlated with the global subjective experience of the lockdown measured in the first part of the survey. The difference between selecting 'Inspired' for positive reasons and 'Concerned and worries' or 'Did not feel like it' for negative reasons was correlated with Component 1 (reflecting affective experience) across individuals (r=0.13, p=0.03). Additionally, we found that the difference between selecting 'More time' for positive reasons and 'No time' for negative reasons was correlated with Component 2

(reflecting more free time) across individuals (r=0. 51, p=2.10 -19 ) (See Methods and Supplementary Figure S1).

Obstacles

For each activity carried out more frequently or as often as before the lockdown, participants were asked how many obstacles they had to overcome to conduct this activity (on a scale ranging from 0 to 100). The overall mean obstacle score was 15.44+/-0.79. The mean obstacle score was not significantly correlated to SCC (r=-0.04, p=0.49, n=331). However, using a polynomial fit of second-order, we identified a quadratic relationship between those two measures (𝜷quadr=4.10 -3 , p=5.10 -3 ) (Supplementary Figure S2), suggesting that the amount of encountered obstacle was high for individuals experiencing a large negative or positive SCC.

Creative achievements

In this survey, we estimated, for each participant, the level of creative achievements for the activities performed during the lockdown by adapting items of an achievement questionnaire (ICAA, part 2 [START_REF] Diedrich | Assessment of Real-Life Creativity : The Inventory of Creative Activities and Achievements (ICAA)[END_REF])) (Figure 5; see Supplementary Table S4 for details). For the activities carried out more frequently or as often during the lockdown as before, we asked the participants to report how they valorized their activities. The participants reported that for 53.98+/-1.80% of the activities they carried out, they shared the result with family,

Conflict of interest

The authors declare no conflict of interest.

Author contributions

All authors designed the online survey and collected the data. TB, MOT, EV and ALP performed the data analyses. ALP designed the open MatLab App. ALP, TB, MOT and EV wrote the manuscript. All the authors discussed the results and commented the manuscript.

Tables Table 1 Demographic data.

Supplementary Information

Alizée Lopez-Persem 1 *, Théophile Bieth Corresponding authors: lopez.alizee@gmail.com, emmavolle@gmail.com * Authors who contributed equally

Figure S3

Supplementary Figure S3. Schematic representation of the conditional sequence of questions proposed for each activity

Table S1

Additional demographic information, with statistical tests (one-factor ANOVA) on SCC. SCC mean and SEM are reported in the table. N indicates the number of participants in each category.

No statistical test was performed when one group represented less than 5% of the data (in italic). creative performance differently depending on which process is the most required by the task.

The lesion approach also helps to clarify the functional organization of the PFC for distinct creative processes, showing a medial-lateral dissociation in rostral PFC for associative versus controlled processes, and suggesting a rostrocaudal lateral specialization for distinct controlled processes.

The contribution of lesion studies to the neuroscience of creativity could be enhanced in the future by combining different measures of creativity with anatomical and functional connectivity methods to identify the networks impacted by the lesion.

Conflict of interest statement

Nothing declared. The authors implemented several methods in a freely available software to assess lesion-deficit mapping while considering the functional and structural remote effects of a brain damage.