Martine Theillère
email: martine.theillere@inserm.fr

Claude Bernard

Marcela Perrone-Bertolotti

Ossandón

Tomás

Andrea Brovelli

Catherine Gabaude

Rheims

Sylvain

Jean Lachaux

Philippe

DOCTORATE THESIS OF THE UNIVERSITY OF LYON Operated within

Keywords: Executive Network, iEEG, SEEG, High-Frequency Activity, Naturalistic Approach, Distraction, Dual-Tasking, Cortex, Bottleneck, Serialization, Incompatible Dynamics, Multiple Demand System, Change of Strategy, Anterior Insular Cortex

In our everyday lives, efficiency and ease crucially depend on our ability to focus on what we are doing at any given time. From a neuropsychological point of view, it means that we are constantly "on-task", and that our brain always engages resources that are both necessarily and sufficient for the task at hand: at the neural level, this is characterized by a fine balance between activations and inhibitions to ensure that only perceptual, cognitive and motor processes relevant for the task are active, at the exclusion of any other. Yet, practically, such ideal patterns almost never occur because we are almost constantly distracted, most obviously by events in the environment around us, but also by spontaneous thoughts and fruitless attempts to perform several tasks at the same time.

This thesis tries to reveal the deep reasons why distraction has such a profound, detrimental effect on attention and performance. Why does performance drop when we try to perform two difficult attention-demanding tasks at the same time? Why is it so hard to stay on task in a noisy environment? Those questions have already been debated for years, and modelled in many ways, but no study so far had conducted the investigation at the deepest level of the fine neural dynamics that supports our ability to focus. We used the most precise recordings of a living human brain -intracranial EEG recordings with millisecond and milimetric resolution -to examine in two experiments how external distractions and multi-tasking interfere with the optimal dynamics of a demanding, continuous attention task, and we addressed those questions in naturalistic settings, in the context of a direct social interaction to ensure our conclusions extend to real-life situations.

We found that the tight balance between excitation and inhibition is disrupted in key regions supporting attention and executive control for short periods of time, at the subsecond level, but sufficiently to knock the network off-balance and impair performance. Altogether, our results provide explanations for many of the failures of attention of our modern lives and pave the way for new techniques to avoid them.

Résumé Détaillé

L'attention est un processus dynamique qui implique l'activité de différentes structures qui s'adaptent à la fois à nos besoins et au monde qui nous entoure. Son action débute au niveau de la perception, pour influencer la propagation des signaux relatifs à notre environnement depuis les régions sensorielles vers celles chargées des processus plus complexes. Elle agit également de manière descendante, depuis les zones responsables des fonctions cognitives dites supérieures pour adapter la prise d'information et le traitement de celle-ci en fonction des objectifs que l'individu se fixe, que ce soit à partir de ses souvenirs et expériences passées ou des contraintes du moment. La confrontation de cette double action ascendante et descendante aboutit à la stabilisation provisoire d'une dynamique cérébrale globale activant certaines aires du cerveau tout en déactivant d'autres régions, jusqu'à la production d'une réponse comportementale satisfaisante, qui conditionnera le comportement futur grâce aux informations obtenues sur l'efficacité de cette réponse.

Toutefois, ce schéma relativement idéal peut être dérouté dans le contexte d'une tâche, par des évènements a priori non pertinents pour celle-ci, avec pour effet principal une déstabilisation de cette dynamique et une diminution des ressources cognitives disponibles pour cette tâche. L'objectif de cette thèse est d'étudier deux cas de figure fréquents amenant à une forme de déstabilisation de l'attention: les distractions environnementales et ce qu'il est convenu d'appeler le multitâche, c'està-dire la tentative de réaliser simultanément deux tâches.

Dans le cas des distractions provenant de l'environnement, mon travail s'attache à comprendre comment un stimulus naturaliste, du type de ceux auxquels nous pouvons tous être confrontés chaque jour, va interférer avec la dynamique neuronale optimale d'une tâche d'attention soutenue jusqu'à entrainer une baisse transitoire de la performance. Dans le cas du multitâche, mes recherches portent sur la manière dont cette même dynamique, lors de l'exécution de la même tâche d'attention soutenue, est parfois incompatible avec celle nécessaire à la réalisation d'une seconde tâche simultanée (une tâche de fluence verbale) au point de créer un effet de goulot d'étranglement cognitif qui force l'exécution séquentielle des deux tâches. Ces deux études s'appuient sur des enregistrements de l'activité cérébrale humaine à très haute résolution spatiale et temporelle (millimètre/milliseconde) obtenus à partir d'électrodes placées directement dans le cerveau (électroencéphalographie intracérébrale, ou iEEG) lors de la phase d'exploration pré chirurgicale de patients épileptiques résistants aux médicaments. Nous avons enregistré ces patients lors d'une tâche expérimentale conçue spécialement pour mesurer des décrochages brefs de l'engagement attentionnel, de l'ordre de la seconde, la tâche BLAST (Bron/Lyon Attention Stability Test), présentée de manière exhaustive dans ce document. Cette tâche a été réalisée dans trois conditions expérimentales : d'abord au calme sans distraction, puis à proximité immédiate d'un expérimentateur chargé de se conduire d'une manière distrayante (distractions « naturelles ») et/ou en situation de double-tâche, sous la contrainte de devoir chercher des noms de villes, d'animaux, de personnes commençant par une certaine lettre (tâche de fluence verbale).

La première originalité de ce travail est le recours à l'EEG intracérébrale : si de nombreuses études ont déjà été publiées sur l'effet des distractions et du multitâche sur l'attention, ces études ont privilégié des méthodes d'accès à l'activité cérébrale dites « non invasives » -telles que l'EEG, la MEG ou l'IRMf -qui ne permettent pas une étude fine de la dynamique spatio-temporelle du cerveau en action.

Même si l'iEEG a certaines limitations bien connues, liées notamment au fait d'étudier le cerveau de patients souffrant d'une maladie neurologique sévère, la compétence de l'équipe d'accueil concernant la manipulation de ces signaux garantit que des conclusions générales peuvent être obtenues de ces signaux.

La deuxième particularité de ce travail est de s'approcher de situations réalistes : présence de l'expérimentateur à côté du participant, distractions communes liées à son comportement … un nombre croissant d'études de neurosciences cognitives cherchent à étudier l'activité du cerveau humain dans des conditions rappelant la « vie quotidienne », afin de s'assurer que leurs conclusions s'étendent à la vie d'un être humain au-delà du cadre strict et limité d'une expérience menée dans un laboratoire. Ces études semblent indiquer que le cerveau humain est mieux adapté pour répondre à des scénarios complexes et enrichis qu'à des scénarios simplifiés. C'est un des autres avantages de l'iEEG que de bien s'adapter à des situations dont la valeur naturaliste est forte.

Dans ces conditions, l'étude de la situation de double-tâche révèle un certain nombre de populations neuronales cérébrales sources d'interférences directes et indirectes entre les deux tâches. Ces populations se distribuent dans les régions corticales suivantes : le gyrus/sulcus précentral, le cortex préfrontal dorsolatéral (dlPFC), le cortex temporal latéral (TLC), le cortex insulaire antérieur (AIC), le cortex cingulaire antérieur et l'aire motrice pré-supplémentaire (ACC/pre-SMA), et le cortex temporal basal (TBC).

L'analyse des performances comportementales montre clairement que l'exécution simultanée des deux tâches réduit les performances globales. Nos résultats en iEEG indiquent que les deux tâches nécessitent des ressources cognitives communes -c'est-à-dire l'activité simultanée des mêmes populations neuronales dans les régions précitées -qui ne peuvent être allouées en même temps, de sorte que le système exécutif force les deux tâches à être réalisées l'une après l'autre plutôt qu'en même temps, avec pour effet immédiat une diminution de la vitesse de réponse et une augmentation du nombre d'erreurs. L'analyse précise de la dynamique de l'activation neuronale de ces régions révèle que les deux tâches sont « dynamiquement incompatibles » -c'est-à-dire que le profil temporel d'activation nécessaire à chacune, même s'il n'est pas parfaitement continu, ne laisse pas suffisamment de temps disponible au développement à celui de l'autre, et réciproquement. Ceci dit, l'interférence semble dans la majorité des cas davantage due à une incompatibilité entre deux stratégies plutôt qu'entre les tâches elles-mêmes : par exemple, la plupart des régions enregistréeset notamment dans l'AIC et l'ACC, deux régions très impliquées dans le contrôle cognitif -l'activité neuronale est bien moindre pendant les essais les mieux réalisés -avec une réponse correcte et un temps de réaction court -que pendant les essais les moins bien réalisés. Ce résultat laisse penser que la tâche principale peut être réalisée avec la même efficacité en réduisant le contrôle cognitif et en libérant donc des ressources pour la seconde tâche. Dans le cas des autres régions, ces différences d'activité neuronale entre les essais les mieux et les moins bien réalisés est très révélatrice d'un changement de stratégie -abandon d'une stratégie d'encodage en mémoire verbal par exemple, lorsque celle-ci interfère avec la fluence verbale. Cela suggère que dans bien des cas, un changement subtil de stratégie cognitive permet de réduire les interférences entre deux tâches, même lorsque celles-ci ne sont pas automatisées. Ce type de changement de stratégie peut réduire l'effet de goulot d'étranglement au niveau central et l'incompatibilité dynamique entre les tâches. En conclusion, nous pensons qu'un contrôle cognitif excessif lors de l'exécution simultanée de deux tâches exigeantes peut nuire à leur exécution, et qu'il est possible d'obtenir une meilleure performance avec moins d'efforts.

L'étude sur l'effet des distractions environnementales révèle plusieurs populations neuronales recrutées spécifiquement pendant le traitement des distracteurs qui interfèrent avec le réseau nécessaire à la tâche BLAST. Dans la limite de notre échantillonnage spatiale, ces populations se trouvent dans trois régions principales : le cortex insulaire antérieur (AIC), le cortex cingulaire postérieur (PCC) et le cortex visuel extrastrié (EVC).

Après avoir analysé les résultats, nous avons remarqué que les distractions n'avaient pas un impact global sur la performance lors de la tâche prise dans son ensemble, mais plutôt de manière transitoire après chaque distraction. Une suractivation du système exécutif pourrait diminuer l'impact global des distractions sur le système attentionnel, pour maintenir la performance globale intacte. L'AIC et la PCC présentent des profils d'activité différents selon que les essais sont bien réussis ou non, ce qui suggère un effet de détournement des ressources cognitives limitées par les distracteurs menant à des réponses plus lentes, voire erronées. Le profil de réponse aux distracteurs de l'EVC suggère un effet d'ouverture par ces derniers du champ attentionnel visuel, qui devrait au contraire être resserré sur les stimuli présentés en champ visuel central pendant la tâche BLAST. L'un des effets des distracteurs serait donc de forcer l'ouverture de l'attention, et d'empêcher de centrer les ressources sur la zone visuelle pertinente.

Ces deux études combinées révèlent donc que la distraction, qu'elle soit due à des stimuli extérieurs impromptus ou à une tâche secondaire, bouleverse les équilibres dynamiques fins nécessaires à la réalisation correcte d'une tâche exigeante sur le plan attentionnel. Mais ces bouleversements peuvent être limités grâce à des stratégies adaptées, en particulier grâce à une recherche du niveau de contrôle cognitif minimal pour réaliser correctement cette tâche. Il est fort probable -comme semblent l'indiquer clairement nos données -que nous exerçons généralement un contrôle cognitif exagéré par rapport aux demandes réelles des tâches que nous avons à mener, avec probablement une fatigue trop importante.

Mots Clés

Réseau exécutif; iEEG; SEEG; activité à haute fréquence; approche naturaliste; distraction; double tâche; embouteillage; sérialisation; dynamique incompatible; système de demande multiple; changement de stratégie; cortex insulaire antérieur.

As a personal reflection, I must say that doing the doctorate has been one of the most rewarding and enjoyable experiences of my life. I realized this by talking to a friend a few weeks ago, as we were eating our lunches to get back to work. Talking about our lives, future projects and the experience of being a PhD student, he asked me if I would ever do it all over again, and to his uneasiness and astonishment, I answered yes. In fact, I think it took me 2 seconds, 3 tops, to give my answer. Later I forgot about it, until after work, I headed for the tram station. I usually hum some stupid song, or make a schedule of what I should do at home, but sometimes I get lost in my thoughts. So I went through my answer again, and not only did I realize that I would be repeating my whole doctoral experience again, but I would be improving a lot of things which I think I had left undone. It may sound a bit crazy, but the truth is that if I had all the money and time in the world I would do another doctorate without hesitation, and the reason is clear: I like science.

I have always said I started doing science out of selfless passion. In Chile, being a physician is almost a way of solving your life: you secure a good job, get a good salary, and become an important member of society. The truth is that I did not need to be a scientist; and yet here I am, almost at the end of my road and ready to start a new stage as a postdoc, once again away from my homeland and my beloved ones. Yet, I am so happy and grateful for the opportunity, not only to learn from tremendously bright and resourceful people, but also to finally mature and become someone who can contribute something to humanity. I want to start by thanking my tutor, Jean Philippe Lachaux, whom I consider not only a teacher but also a friend. I had the pleasure of spending 4 years and something by his side, and I realized that having him as my boss I hit the jackpot (he is probably one of the biggest reasons why I would do a PhD again). In spite of his frequent trips, and the fact that we could never agree on him coming to our house for dinner (which is still pending!), he never failed in his duty and always gave me help without hesitation. I promise that at some point I will learn to play ping-pong properly so that in the future, as they say in good Chilean, I will kick his ass (with kindness of course).

I would like to thank Marcela Perrone-Bertolotti and Tomás Ossandón, for accepting to review my thesis; and Andrea Brovelli, Catherine Gabaude and Sylvain Rheims for accepting to be part of my thesis commission. In addition, I would also like to thank Nadine Ravel and Julien Bastin, for being part of my CST commission and always providing me with good feedback and help if needed.

I want to send a love letter to my beloved DYCOG lab, where for some strange reason the coolest people on the planet converge. I send a big hug to Martine Theillere, whom I consider the official heart of the lab, and of course, to our director, Olivier Bertrand, who has always been there to help or to deliver a simple hello in the mornings. I would also like to thank each of the members of Eduwell, but especially I would like to send a warm greeting to the whole group of irresponsible people with whom I have or had the pleasure of working: Florian, Mathilde, Adrien, Benjamin, and finally Benoit, who now receives the torch. Besides, I would like to thank all my masochist friends from the laboratory; from those who are still working at DYCOG, to those who have taken new paths: Jelle, Kristien, Stefano, Enrico, Oussama, Arnaud, Prisca, Cony, Laurie-Anne, Marta, Pauline, Hesham, Rapha, Rasa, Francesca, Mélodie, Nicole, Roxi, Mani, Judith… I hope, with all my heart that we meet again to have some beers and good laughs.

I also send a big hug to all the friends I made along the way. Thank you very much for being there in the moments of loneliness, of sorrow, of joy or of simple boredom, and for giving me a good laugh.

Know that I treasure every good moment we spend in the depths of my heart, and wherever life takes us, you will always travel in my long-term memory.

I want to thank ANID -Becas Chile Doctorados en el Extranjero -for giving me the resources and facilities to meet this challenge. I honestly hope to be able to return to Chile and collaborate to form a more just and dignified society for all, giving back knowledge, and if life permits, forming people who feel the same love for science as I do. I would like to thank Pablo Billeke and Patricia Soto, for receiving me in Chile in the context of the ECOS Project. Also special thanks to the Graduate School of the University of Chile, for being the place that opened my eyes to the incredible and unembraceable world of science. I wish to express my gratitude to many of my teachers: Pedro Maldonado, José Luis Valdés, María de la Luz Aylwin, Ennio Vivaldi, but especially to Eugenio Rodríguez, for being a true inspiration and introducing me the bug of curiosity and mental restlessness. I want of course to thank my parents, Claudio and Mónica, and their infinite love and support. Know that the grandchildren will arrive... at some point. To my Soteli, who I miss very much and always think of her. To my sisters, Rosario and Maria Ignacia, who I would love to have here so that we could travel together, and go out to eat sushi and laugh at any nonsense. To my uncles, cousins, family and friends who, even if I make them lose their patience, always support me.

Finally, and most importantly, I want to thank my partner and sidekick, Mónica. Together we left our lives behind, and only came to France with our love. Here we grew, changed and improved together.

We had good and bad times, like every couple, but we managed to move forward, and I can safely say that today I am a better man thanks to her. Thank you for your selfless and unconditional love, sweetie. I love you with all my <3.

There is some much to do So wrapping up, you probably used a good chuck on the time available to you If this is our one shot of life, there is no reason not to have fun, and to live as happily as possible Bonus points if you make the life of people better. More bonus points if you help to build a galactic human empire Do the things that make you feel good; you get to decide whatever this means for you

Part I GENERAL INTRODUCTION

Objective of this Thesis

Behold a worthy sight, to which the God, turning his attention to his own work, may direct his gaze. Behold an equal thing, worthy of a God, a brave man matched in conflict with evil fortune.

-Lucius Annaeus Seneca

The human brain is constantly exposed to more sensory information than it can process. Attention focuses processing on the most relevant signals, as defined by current goals, habits and preferences.

The factors defining priority, and the focus of attention, often contradict themselves and result in unstable attention, as it can be observed when an individual is distracted by salient, external events during an attention-demanding task, or when trying to perform two such tasks at the same time.

In this thesis, my main objective is to understand the mechanisms by which conflicting goals or stimuli might impair attention selection and lead to suboptimal performance, thanks to a detailed analysis of the disruption of the large-scale neural dynamics of a well-defined attention-demanding task. I will particularly study the interferences caused by multi-tasking and by external distractions, and their effects of the neural activity measured at high spatial and temporal resolution with intracranial electroencephalography (iEEG). All studies will be done in conditions as naturalistic as possible, so that their conclusions can directly be applied to real-life situations.

"

Focused Attention

Any man who can drive safely while kissing a pretty girl is simply not giving the kiss the attention it deserves.

-Albert Einstein

The human brain is able to compute only a limited amount of information from the surrounding world, due to a lack of processing power. This limits for instance the ability to perceive consciously several simultaneous stimuli in rivalry conditions, or in rapid succession, a phenomenon known as the attentional blink. [START_REF] Raymond | Temporary Suppression of Visual Processing in an RSVP Task: An Attentional Blink?[END_REF] This lack of power is associated with a limited amount of cognitive resources, 2 which can be optimized by focusing them using attentional control. This would allow to perceive a target and to perform a goal-oriented decision as a task. 3 Incoming stimuli therefore compete for attention, to gain access in some cases to cognitive resources. Some stimuli have intrinsic qualities that make them stand out from the rest, known as saliency. 4 A stimulus with high saliency can attract attention automatically in a bottom-up way, and bypass attentional control. On the other hand, attentional control allows one to select a low saliency stimulus that is relevant for ongoing behavioral goals. Such control is implemented by higher cognitive areas capable of guiding attention in a top-down fashion. 5 Allocating attention to an exogenous target involves several brain systems and networks, distributed in both cortical and subcortical areas. [6][7][8] When attention focuses on an external stimulus, neural activity triggered by that stimulus ascends towards primary sensory areas, and then to sensory integration areas which build a more finished and abstract concept of the stimulus. [8][9][10] Finally, this information reaches amodal areas located in frontal and parietal parts of the brain, 11 where it is evaluated with respect to the objectives, previous experiences and preferences of the individual, to reach a decision regarding the fate of that stimulus (see Fig. 1). 12 In classic stimulus-response paradigms, the process culminates with the motor execution according to task rules. The whole process can be seen as a classification procedure which ultimate goal is to save cognition for events that are important and optimize perception for ongoing goals. 11,[START_REF] Corbetta | Voluntary orienting is dissociated from target detection in human posterior parietal cortex[END_REF] This procedure occurs practically in most daily life situations, such as reading a book without losing track of it.

"

Attend Condition Ignore Condition this whole process will be updated thanks to perceptual feedback due to online performance monitoring.

The orientation of attention is most often modelled through the action of two complementary networks, firstly conceptualized by Corbetta and Shulman 11 (see Fig. 2): a) the Ventral Attention Network (VAN), which allows the perception of relevant, or salient but irrelevant stimuli from the environment; 14 b) the Dorsal Attention Network (DAN), which allows the selection of sensory stimuli based on goals and expectations, and links them to appropriate motor responses. 14,15 Nowadays, it is widely accepted that both networks interact to determine where and to which target should be paid attention. 16 Frontal Gyri (MFG and IFG, respectively) (see Fig. 2). 14,15 The VAN essentially responds to events and objects which lie outside of the current focus of attention, and which properties are not immediately relevant to ongoing behavioral goals.

The DAN comprises the Frontal Eye Field (FEF) in the dorsal and posterior frontal lobe, and an area close to the Intraparietal Sulcus (IPS), including the Superior Parietal Lobe (SPL) (see Fig. 2). 14 In contrast to the VAN, the DAN mainly implements goal-driven attention, 14,15 guiding perception through top-down signals through dynamic interactions with bottom-up information to implement rapid control over both overt and covert shifts of attention. It is crucially involved in standard attentional search, when specific targets must be found which match behavioral goals.

Searching for the Filter

The DAN plays a central role in filtering out stimuli that are irrelevant for the task-at-hand. Historically, several lines of arguments have been proposed to identify how this filtering process operates, and importantly, at which stage of sensory processing.

In broad terms, a first hypothesis, heralded by Broadbent,17 suggests that filtering occurs in the early stages of sensory processing, to discard elements which properties do not match target-defining properties (i.e. the specific timber of an instrument you're specifically listening to in an orchestra).

Alternatively, a second view defended by Deutsch and Deutsch [START_REF] Deutsch | Attention: Some theoretical considerations[END_REF] among others suggested that perception includes an early, automatic and parallel stage during which all stimuli are processed independent of their relevance for the task. That early analysis could not be "turned off". According to that view, attentional filtering would occur at later stages of processing, including for instance semantic analysis; which would allow a selection based on meaning, among other high-level properties, and not solely basic physical properties of the stimulus, as in the early selection model.

Treisman [START_REF] Treisman | Monitoring and storage of irrelevant messages in selective attention[END_REF] then introduced a third possibility -or "thin" filter -in which irrelevant stimuli would not be blocked from the very start of sensory processing, but attenuated or weakened, in relation to their resemblance with task-relevant items: elements sharing some characteristics of the behavioral targets would be processed more extensively than very dissimilar stimuli.

In contrast with the previous views, which all suppose a common pool of resources shared by all incoming stimuli, Wickens [START_REF] Wickens | Multiple resources and performance prediction[END_REF] proposed that cognitive resources might in fact be parted into separate and independent reservoirs. The proposal predicts that it should be possible to process different stimuli simultaneously if they do not compete for the same reservoir of resources (e.g., visual and auditory stimuli), while stimuli of a same kind would interfere (e.g., only visual stimuli), and conflict with each other. [START_REF] Wickens | Multiple Resources and Mental Workload[END_REF] This proposal has been refined by several authors, such as Young and Stanton 22 who proposed that the size of these reservoirs might not be static, but change according to the features and demands of the task at hand.

Searching for a Target

The implementation of those ideas -and their relation to the DAN/VAN dichotomy -can be understood during the paradigmatic example of the visual search: searching for a predefined object among others. When searching for a target with particular features (e.g., a friend in a blue t-shirt and white cap), elements with physical characteristics markedly different from the target (e.g., people wearing other colors) cause little or no delay to the search process. [START_REF] Treisman | The perception of features and objects. Attention: Selection, awareness, and control: A tribute to Donald Broadbent[END_REF] This effect can be understood in the context of the Biased Competition Theory proposed by Desimone, [START_REF] Desimone | Neural mechanisms of selective visual attention[END_REF] which proposes that visual processing can be biased by prioritizing certain features of an object (e.g., blue or white color) or an item (e.g., a T-shirt or a cap) over others, later influencing attention and cognitive processing. [START_REF] Craighero | Action for perception: A motor-visual attentional effect[END_REF][START_REF] Craighero | Hand action preparation influences the responses to hand pictures[END_REF] Within that model, attention would facilitate or inhibit any perceptual process flexibly through the DAN, [START_REF] Woodman | Do the Contents of Visual Working Memory Automatically Influence Attentional Selection During Visual Search?[END_REF][START_REF] Han | Do the Contents of Working Memory Capture Attention? Yes, But Cognitive Control Matters[END_REF] so that the desired target could access into memory at the expense of irrelevant elements. [START_REF] Wolfe | Guided Search 4.0: Current Progress with a Model of Visual Search[END_REF] If the physical attributes of the target differ markedly from surrounding distractors, the search can be completed in a short time, regardless of the number of elements within the search array. The target is said to "pop-out" in a phenomenon known as "efficient search". [START_REF] Treisman | A feature-integration theory of attention[END_REF][START_REF] Treisman | Feature Analysis in Early Vision: Evidence From Search Asymmetries[END_REF] However, when the target is characterized by a combination of features, rather than a single distinctive one (e.g. the same friend in a blue shirt and a white cap in the middle of a crowd of people wearing either blue shirts of white caps) the search becomes sequential with a one-by-one inspection of all items matching some aspects of the target, through voluntary attention shifts until the target is found. [START_REF] Treisman | Similarity, and Integration of Features Between and Within Dimensions[END_REF] The search duration increases linearly with the number of elements in the search array, in a phenomenon known as inefficient search. [START_REF] Treisman | A feature-integration theory of attention[END_REF][START_REF] Treisman | Feature Analysis in Early Vision: Evidence From Search Asymmetries[END_REF] Efficient search would largely depend on a first stage of feature-level preattentive perception, mainly at the level of sensory cortices and adjacent areas, that would process all stimuli in parallel, while inefficient search would require a second sequential stage involving directed attention according to task demands, [START_REF] Treisman | A feature-integration theory of attention[END_REF] and fronto-parietal areas of the DAN orienting network. [START_REF] Marois | The Neural Fate of Consciously Perceived and Missed Events in the Attentional Blink[END_REF][START_REF] Kranczioch | Neural correlates of conscious perception in the attentional blink[END_REF] According to this view, the fate of incoming stimuli would crucially depend on the interface between pre-attentive and attentive processing, which might mark a turning point in the entry to conscious awareness.

Incidentally, a sensory stimulus could be processed and yet not be consciously perceived, before sequential top-down attentive processing. [START_REF] Dehaene | Cerebral mechanisms of word masking and unconscious repetition priming[END_REF][START_REF] Beck | Neural correlates of change detection and change blindness[END_REF] A finer view of that turning point reveals that sensory features perceived pre-attentively by the primary visual area (color, brightness, motion, and edge orientation) are integrated into first-or second-order visual areas as feature maps. [START_REF] Koch | Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry[END_REF] Subsequently, this information is normalized and compiled pre-attentively in a "binding" parallel process, integrating into second-, third-, or fourthorder visual areas, [START_REF] Treisman | The binding problem[END_REF] creating two-dimensional saliency maps. [START_REF] Egeth | Parallel versus serial processes in multidimensional stimulus discrimination[END_REF][START_REF] Itti | A model of saliency-based visual attention for rapid scene analysis[END_REF][START_REF] Zelinsky | The what, where, and why of priority maps and their interactions with visual working memory[END_REF] This information would then interact with higher sensory, associative, and lower cognitive areas, to create a "priority map", which highlights those elements that stand out most intensely in the saliency maps because of intrinsic physical features (e.g. a shining cap), [START_REF] Zelinsky | The what, where, and why of priority maps and their interactions with visual working memory[END_REF] but also those that possess characteristics defining the ongoing behavioral targets (what needs to be found) based on signals descending from higher cognitive areas. [START_REF] Fecteau | relevance, and firing: a priority map for target selection[END_REF] Visual attention would spontaneously shift in space according to the priority map, which implies that attention scan paths would be determined by a conjunction of bottom-up information and top-down signals.

If we consider a simple example, where an observer looks at someone jogging around a football field (see Fig. 3), the perceived information would first be processed automatically, leading to the perception of the colors of the subject's clothing and the background, their brightness, the speed at which the subject moves, and the spatial orientation of the surrounding objects. This information would be organized into specific feature maps, with each map providing important but discrete information about the scene. These feature maps would then be grouped into saliency maps, providing the observer with the ability to perceive the jogger's blue clothing and his speed against the background, standing out against other high saliency elements. Subsequently, this information would proceed from a pre-attentive to an attentive processing mechanism, organizing into a priority map that would contrast whether or not the perceived target matched the downstream control signal from higher cognitive areas (if the observer is trying to see whether he knows the person running, for instance). analysis and is used to detect noticeable locations at feature maps. Feature maps are then combined to make saliency maps. Then, they are combined with top-down information to make a priority map.

Finally, a motor decision is taken (i.e. a saccade). Image based on Veale et al. [START_REF] Veale | How is visual salience computed in the brain? Insights from behaviour, neurobiology and modeling[END_REF]

Attention and Eye-Movement

It follows that in most naturalistic conditions (i.e. with no explicit instructions to keep the gaze still), attention (in particular visual attention) and gaze are tightly coupled. Anatomical studies have revealed two visual pathways to control saccades, starting in the retina and ending in the extraocular muscles. [START_REF] Felleman | Distributed hierarchical processing in the primate cerebral cortex[END_REF][START_REF] Bogler | Decoding successive computational stages of saliency processing[END_REF] The first route, known as the cortical pathway, enters information through the Primary Visual Cortex (V1) via relays in the Lateral Geniculate Nucleus (LGN) of the dorsal thalamus. [START_REF] Felleman | Distributed hierarchical processing in the primate cerebral cortex[END_REF][START_REF] Bogler | Decoding successive computational stages of saliency processing[END_REF] According to the Dual Visual Stream model, V1 sends axons to higher visual areas such as V4 (part of the ventral visual stream) and IPS (part of the dorsal visual stream). 6,[START_REF] Goodale | Separate visual pathways for perception and action[END_REF] V4 and IPS project fibers to the superficial layers of the Superior Colliculus (sSC) and the FEF, respectively, which then project fibers to the deeper layers of the Superior Colliculus (dSC). [START_REF] Stein | The development of a dialogue between cortex and midbrain to integrate multisensory information[END_REF] From here, the information spreads to the brainstem, specifically to the eye movement control centers that manipulate the extraocular musculature. [START_REF] Felleman | Distributed hierarchical processing in the primate cerebral cortex[END_REF][START_REF] Stein | The development of a dialogue between cortex and midbrain to integrate multisensory information[END_REF][START_REF] Hikosaka | Visual and oculomotor functions of monkey substantia nigra pars reticulata. IV. Relation of substantia nigra to superior colliculus[END_REF] The second route, known as the subcortical pathway, begins in the retina and proceeds directly to the sSC, avoiding the LGN. [START_REF] Cowey | The projection of the fovea to the superior colliculus in rhesus monkeys[END_REF] In these layers, the sSC not only receives fibers from this subcortical pathway, but also from primary visual areas such as V1, [START_REF] Fries | Cortical projections to the superior colliculus in the macaque monkey: A retrograde study using horseradish peroxidase[END_REF][START_REF] Schiller | Response characteristics of single cells in the monkey superior colliculus following ablation or cooling of visual cortex[END_REF] others of greater complexity such as V2 and V4, and from the middle (MTG) and posterior inferior temporal (ITG) gyri. 53- 55 Then, the sSC projects fibers through the Inferior Pulvinar towards the IPS, [START_REF] Cowey | Retinal ganglion cells labelled from the pulvinar nucleus in macaque monkeys[END_REF] continuing then to the dSC and FEF, or alternatively sending fibers towards the dSC through internal connections, finally arriving at the brainstem (see Fig. 4). Given the strong interplay between visual attention and gaze position, one should not be surprised by the fact that those areas are essential components of the cortical and subcortical pathways that can be functionally categorized as feature, saliency or priority maps (see Fig. 5). Whether any of these regions implements more a feature, a saliency or a priority maps can be subtle, but the decision can be based on the fact that local neural activity is solely dependent upon sensory information (feature detection) or upon the response which must be associated with the stimuli (goal-related activity). It follows that neurons of feature maps are expected to react to the specific visual features of a stimulus rather than its global saliency, or goal-related activity (i.e., related to top-down control). Using that criteria, feature maps have been found primarily in the LGN and in V1. [START_REF] Gegenfurtner | Cortical mechanisms of colour vision[END_REF][START_REF] Livingstone | Segregation of form, color, movement, and depth: Anatomy, physiology, and perception[END_REF][START_REF] Betz | Dissociation between saliency signals and activity in early visual cortex[END_REF][START_REF] Yoshida | Residual attention guidance in blindsight monkeys watching complex natural scenes[END_REF] In contrast, neurons of a saliency map react selectively to the global saliency of a stimulus, rather than its specific visual features. They are not sensitive either to manipulation of the stimulus-goal relationship and must be organized along a bidimensional topographic map. These strict conditions are met mostly in the sSC and the Inferior Pulvinar of the subcortical pathway. [START_REF] Yoshida | Residual attention guidance in blindsight monkeys watching complex natural scenes[END_REF] The sSC activates more intensively when the perceived information from a target matches the cognitive signal, [START_REF] Wurtz | Organization of monkey superior colliculus: enhanced visual response of superficial layer cells[END_REF] but do not react strongly to particular visual features. [START_REF] Davidson | Selectivity for relative motion in the monkey superior colliculus[END_REF][START_REF] White | Color-Related Signals in the Primate Superior Colliculus[END_REF] The two regions seem to be related: neurons in the Inferior Pulvinar receive fibers from the sSC, [START_REF] Berman | Functional Identification of a Pulvinar Path from Superior Colliculus to Cortical Area MT[END_REF] presenting a similar type of selective activity. [START_REF] Berman | Signals Conveyed in the Pulvinar Pathway from Superior Colliculus to Cortical Area MT[END_REF] According to Veale and colleagues, [START_REF] Veale | How is visual salience computed in the brain? Insights from behaviour, neurobiology and modeling[END_REF] it is reasonable to think of the existence of a special type of priority map capable of detecting specific features and contrasting them with the cognitive signal, allowing active modulation (see Fig. 5). These "feature-specific priority maps" can be recognized as mid-level maps forming part of the cortical pathway, after areas that maintain feature and saliency maps, possibly involving V4 and IPS. [START_REF] Veale | How is visual salience computed in the brain? Insights from behaviour, neurobiology and modeling[END_REF] V4 receives fibers from lower visual areas, and would play a decisive role in selecting the next target to be pursued. [START_REF] Bogler | Orientation pop-out processing in human visual cortex[END_REF][START_REF] Mazer | Goal-related activity in V4 during free viewing visual search: Evidence for a ventral stream visual salience map[END_REF] It is strongly modulated downstream by the FEF, [START_REF] Moore | Selective gating of visual signals by microstimulation of frontal cortex[END_REF] highlighting behaviorally relevant stimuli, [START_REF] Ogawa | Neuronal dynamics of bottom-up and top-down processes in area V4 of macaque monkeys performing a visual search[END_REF] transmitting this information to FEF and downstream regions. On the other hand, the IPS responds to the bottom-up information before the top-down signal, constantly interacting with FEF which responds first to the top-down signal and then to the bottom-up information. [START_REF] Buschman | Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices[END_REF][START_REF] Ibos | A Functional Hierarchy within the Parietofrontal Network in Stimulus Selection and Attention Control[END_REF] Priority maps would constitute a final output of that early sensory processing stage, in relation with top-down biasing signals: they would contain neurons that activate in response to global saliency and not selectively to specific visual features, and integrate this general information to mental goals. Areas that most likely fulfil this function are the FEF [START_REF] Fernandes | Saliency and saccade encoding in the frontal eye field during natural scene search[END_REF][START_REF] Thompson | A visual salience map in the primate frontal eye field[END_REF] and the dSC. [START_REF] Zelinsky | The what, where, and why of priority maps and their interactions with visual working memory[END_REF][START_REF] Veale | How is visual salience computed in the brain? Insights from behaviour, neurobiology and modeling[END_REF] Note that this architecture is mostly based on, and biased towards, the visual modality. Few studies have searched for saliency maps in relation to other sensory modalities, such as audition. In contrast to the visual system, the auditory system does not seem to have a common metric to define saliency.

Although there are cases in which a sound really pops out, such as a fire alarm, in other cases separating someone's voice from the ambient noise already involves attention to separate targets from stimuli, which are not necessarily salient. This suggests that the interplay between a preattentive and an attentive stage of auditory processing, for instance, might not follow a more complex logic than for the visual modality. [START_REF] Kaya | Investigating bottom-up auditory attention[END_REF][START_REF] Pulvermüller | Language outside the focus of attention: The mismatch negativity as a tool for studying higher cognitive processes[END_REF] In particular, it seems more difficult to define map-based models for ascending auditory information.

Yet, a large number of models of auditory attention combining bottom-up and top-down processes have been proposed over the years, informed by numerous studies documenting neural modulation by attention [START_REF] Mesgarani | Selective cortical representation of attended speaker in multitalker speech perception[END_REF] at several stages of the auditory pathways, in primary auditory areas, as well as in higher auditory areas. [START_REF] Atiani | Emergent selectivity for task-relevant stimuli in higher-order auditory cortex[END_REF][START_REF] Ahveninen | Task-modulated 'what' and 'where' pathways in human auditory cortex[END_REF] These efforts have led to several models of auditory saliency. The earliest models were primarily inspired by the visual models, [START_REF] Kayser | Mechanisms for allocating auditory attention: An auditory saliency map[END_REF][START_REF] Kalinli | A saliency-based auditory attention model with applications to unsupervised prominent syllable detection in speech[END_REF][START_REF] Tsuchida | Auditory saliency using natural statistics[END_REF] but failed in most cases to capture essential aspects of the auditory modality. Later models based on the specificities of the auditory system operate on the premise that the system must determine whether a sequence of sounds comes from a single source and must be perceived as a single stream, or from multiple sources. [START_REF] Bregman | Auditory scene analysis: the perceptual organization of sound[END_REF][START_REF] Kondo | Auditory and visual scene analysis: an overview[END_REF] According to Bregman, the auditory system performs internal processes in which it organizes complex sequences of auditory signals into coherent streams, corresponding to the source of each sound, forming an auditory stream analysis. [START_REF] Bregman | Auditory scene analysis: the perceptual organization of sound[END_REF] Attention would then stick to sounds of the same source, while ignoring intervening sounds of alternate sources.

The proposed architecture implies that the auditory system first segregates the sound sequence according to its features (based on continuity and discontinuity in intensity, frequency, time and orientation) simultaneously and pre-attentively. These features would thus integrate into streams sequentially, [START_REF] Alain | Bottom-up and top-down influences on auditory scene analysis: Evidence from event-related brain potentials[END_REF][START_REF] Winkler | Event-related brain potentials reveal multiple stages in the perceptual organization of sound[END_REF][START_REF] Sussman | The role of attention in the formation of auditory streams[END_REF][START_REF] Cusack | Effects of location, frequency region, and time course of selective attention on auditory scene analysis[END_REF] the question of whether this occurs in a pre-attentive way or not being still debated. [START_REF] Sussman | The role of attention in the formation of auditory streams[END_REF][START_REF] Cusack | Effects of location, frequency region, and time course of selective attention on auditory scene analysis[END_REF][START_REF] Snyder | Effects of attention on neuroelectric correlates of auditory stream segregation[END_REF] As At the anatomical level, evidence suggest that pure and combined tone streams are already built up in the nuclei of the brainstem and in primary auditory areas, possibly in interaction with one another. [START_REF] Pressnitzer | Perceptual Organization of Sound Begins in the Auditory Periphery[END_REF][START_REF] Bidet-Caulet | Neurophysiological mechanisms involved in auditory perceptual organization[END_REF] After that initial stage, streams would reach the "core" of the primary auditory cortex, where neural population would increase the contrast between the target and background streams using a specific mechanism of frequency-suppression, making it easier to focus on a sound of interest. [START_REF] Pressnitzer | Perceptual Organization of Sound Begins in the Auditory Periphery[END_REF][START_REF] Bidet-Caulet | Neurophysiological mechanisms involved in auditory perceptual organization[END_REF] These neurons would have a high attentional and contextual dependence, [START_REF] Kaya | Modelling auditory attention[END_REF] probably playing the role of a feature-specific priority map.

Auditory information would then be analyzed separately along the two main pathways of the dual auditory network. [START_REF] Rauschecker | Mechanisms and streams for processing of 'what' and 'where' in auditory cortex[END_REF] One of these paths, called the antero-ventral stream, would provide a more abstract interpretation of the auditory information. Its lower sensory areas (the medial geniculate nucleus (MGN), the core and the IFG) would allow auditory switching between different streams depending on the features of the sound. In contrast, the second path, called postero-dorsal stream, would have the ability to direct attention towards a sound or speech anywhere in space. In addition, as for the visual modality, the MGN may possess a subcortical pathway capable of bypassing the auditory core, to interact directly with lower sensory areas such as the cerebellum or the caudate nucleus, and play a role in perceptual switching, detection of sound rhythmicity and novelty. [START_REF] Kashino | Functional brain networks underlying perceptual switching: Auditory streaming and verbal transformations[END_REF][START_REF] Thaut | Brain networks for integrative rhythm formation[END_REF][START_REF] Teki | Distinct neural substrates of duration-based and beat-based auditory timing[END_REF]

Attention and Memory

Attention and memory closely interact with each other, for instance, when a subject realizes that some particular sensory elements are important for what he needs to do, the characteristics of those elements will be stored as a memory trace, which later serves as a template for the task at hand, guiding attention in a top-down fashion and influencing decision-making and behavior. [START_REF] Nadel | Memory formation, consolidation and transformation[END_REF]98 Memory can be subdivided into several specific memory systems, [START_REF] Atkinson | Human memory: A proposed system and its control processes BT -Human memory: A proposed system and its control processes[END_REF][100][101][102][103] most of which fail beyond the scope of this thesis; this section will primarily focus on working memory, and its modeling by Baddeley. 104 Baddeley' model describes a specific type of limited-capacity cognitive resource called working memory (WM), [104][105][106] primarily used to encode and maintain items either drawn from the external environment 101 or from long-term memory. 107,108 WM serves as a dynamic workbench that allows the flexible use of information. 109 WM and attention strongly interact at the level of attentional templates, which characterize the properties of the items an individual should pay attention to and process in priority. This template represents in part the internal goals, previous experiences or preferences of each subject in relation to a particular objective or element. According to Monsell, 110 a template is initially acquired through instruction or trial and error, thus the more one practices a task, or the more recent its execution, the easier it becomes to recall it again and activate it into WM.

Attention Templates must be thought in relation to the task-sets, [111][112][113] which function as "activation protocols" that guide how higher cognitive areas must modulate the attentional system and other associated networks downstream, in order to achieve a particular goal and update in real-time. With the task-set, templates serve as a guide capable of keeping a goal "in mind", allowing higher cognitive areas to influence attentional orientation by sending a signal downstream that represents a preconceived idea, 114,115 against which external information processing upstream must be compared.

Theories differentiate between two states of the task-set [START_REF] Woodman | Do the Contents of Visual Working Memory Automatically Influence Attentional Selection During Visual Search?[END_REF]116,117 : an active one with a direct impact on attention, and another -accessory or passive -with no direct effect. 116 An active template can behave in a flexible way, facilitating the target selection process [START_REF] Woodman | Do the Contents of Visual Working Memory Automatically Influence Attentional Selection During Visual Search?[END_REF] through different degrees of activation or suppression that varies according to the task demand. [118][119][120] This means that each template varies its degree of focus, without necessarily following an on/off mechanism. Additionally, upstream information that enters the WM can change the code of the neuronal population dedicated to maintaining active templates regardless of their relevance, 121,122 behaving in a dynamic way that adapts to change over time.

Early attempts to localize task-sets found neural counterparts solely in the dorsolateral prefrontal cortex (dlPFC). 123,124 However, more recent studies estimates that task-set are also maintained in the ventrolateral prefrontal cortex (vlPFC) towards the IFJ, [125][126][127] with a variation related to sensory modality of the stimulus and memory trace. [128][129][130][131] Activity patterns in the lateral prefrontal cortex (LPFC) might contain and manipulate information related to templates currently in use, either in an active or passive state, 123,124,132,133 and contribute to the behavioral performance of new or repeated tasks, 134 but the distribution of task-related memory content and task-sets might follow a gradient of abstraction, starting in sensory areas that reflect low-level features, ascending to prefrontal regions for more complex features and response-related content. 135 Yet, the management of templates goes beyond pure LPFC-related activity and extends to additional cognitive areas, 136 creating a collaborative network. The LPFC itself performs several different types of cognitive functions, such as managing attention reorientation conflicts between the upstream information and downstream signal, novel task-learning, 137 decision-making, 138 task switching, 139 and conflict resolution, 140 but beyond the LPFC, other frontal and prefrontal areas, such as the Premotor Cortex, Anterior Cingulate Cortex (ACC) and Anterior Insular Cortex (AIC) for instance, are consistently activated across a wide variety of these cognitive tasks when control is required in any non-automated task, and when control must be adjusted at task level rather than at trial level. 141 As of today, there is still no consensus on how those areas organize into a coherent "executive network". Yet, one general idea seems to be consensual about that network: it is the source of a topdown regulation of how external stimuli are processed; and feeds back information about performance for future adjustments on lower brain areas. 14,15 It is also widely accepted that top-down control is associated with ACC activation, 14,15 which has even been postulated by some as a gatekeeper to consciousness. 12,142 Most established is the fact that the dorsal ACC plays a key role in the executive system 143 and activates in relation to motivation and reward-based decision-making. 144 One particular aspect of motivation, the ability to stay on task and resist the urge to shift to an alternative, 145 has received a lot of interest by researchers interested in behavioral flexibility and implemented in computational models of the ACC. 146 The dorsal ACC constantly communicates with the AIC, 147 providing fast and efficient connectivity for top-down control. Both areas harbor a specific type of neurons called Von Economo neurons, 148 which are a specific type of brain cell found only in higher apes and humans, elephants and big-brained cetaceans. Although speculative, one popular idea is that those neurons are important computational components of self-regulation and willpower so developed in those species.

Nowadays, the most important debate is on whether the executive network follows a single or a dual organization. Proponents of the single organization, known as Unified Control Theory, propose that the dorsal ACC plays a leading role as a key area, 149,150 while the dual organization, known as Dual Control Theory, favors a different view with two different and separate control networks, in which the role of the dorsal ACC is not so prominent. 147,151 According to the Unified Control Theory or Conflict Monitoring Theory, the dorsal ACC detects conflicts between incoming bottom-up information and top-down signals informing about behavioral goals, and makes the decision about whether the incoming signal should be processed further, with a report to the LPFC, which would in turn provide control over the orientation network, direct attention and influence perception. 149,150,152,153 Within that framework, anterior parts of the LPFC would receive the instruction first from the dorsal ACC, actively maintain memory templates and goal representations in a rather abstract and amodal fashion, and then influence downwards, 154,155 along a caudo-rostral hierarchical gradient, portions of the LPFC involved in more concrete representations of the taskset. [156][157][158] In contrast, the Dual Control Theory is reminiscent of other "dual-networks" such as the DAN/VAN network and suggests a division between a Fronto-Parietal network and a Cingulo-Opercular network. 141 Both networks would work independently, but none of them could by itself establish a direct and constant communication with other cognitive areas: it is the interplay between the two networks that would implement a parallel control following a rapid-adaptive/set-maintenance model, constantly receiving external feedback. 147 The Fronto-Parietal network would comprise the dlPFC, the dorsal Frontal Cortex (dFC), the Middle Cingulate Cortex (MCC), the IPS, the Inferior Parietal Lobe (IPL) and the Precuneus (see Fig. 6). 14,15 It would activate at task initiation, especially during the first presentation of a target or cue, during the switch to a new task, in response to errors and while adapting in real-time to task requirements. 14,15 In contrast, activity of the Cingulo-Opercular network would be stable during the task as long as the rules remain unchanged; it would most act as a background support for constant performance (an "energizer"). 14,15 It includes the dorsal ACC, the medial Superior Frontal Cortex (msFC), the area around the AIC and frontal operculum (AIC/fO), the anterior prefrontal cortex and the Anterior Thalamus.

Additionally, the cerebellum seems to act as a link between both control systems, carrying error information and triggering error related activity, making an indirect connection between the thalamus with the dlPFC and the IPL (see Fig. 6). 141 Another influential theory proposes instead the existence of a Multiple Demand (MD) System (see Fig. 7), 159 which brings together a number of frontal and parietal areas into a general supramodal control system. 160,161 Experimentally, all components of the MD system -the dlPFC (the Inferior Frontal Sulcus (IFS) and MFG), the AIC/fO, the precentral gyrus, the Supplementary Motor Area (SMA), the pre-Supplementary Motor Area (preSMA), the ACC and the IPS 162 -are active during executive control processes. 163,164 The network is conceptualized as a whole in which all components closely interact with each other and constantly exchange information. Its activity usually increases with the increasing difficulty of the task at hand, 162,165 starting by recruiting parietal and frontal posterior regions, and then expanding to anterior regions as demand and complexity rises. 160,166

Multitasking

The secret to multitasking is that it is not actually multitasking. It is just extreme focus and organization.

-Joss Whedon

Human beings are often inefficient at dividing their cognitive resources among multiple targets and performing multiple attention-demanding tasks, usually being able to execute only one task at a time. Early studies on divided attention videotaped subjects watching a basketball game while superimposed a hand-slapping game. 168 Subjects could successfully monitor one activity at a time while ignoring the other, but they had great difficulty trying to monitor both at once. Following these 3 " results, the researchers hypothesized that the subjects would likely show an improvement in both task performance after practice. 168 Other seminal studies have used multi-tasking paradigms asking participants to read short stories while typing a dictated text. 169 The researchers compared and contrasted the latency and accuracy of performance in each of the three conditions (i.e., both tasks separately and both together). As expected, the initial performance of doing both tasks at the same time turned out to be quite poor compared to tasks alone. However, researchers tested subjects after practicing the two tasks 5 days a week for almost 85 weeks in total. Eventually, subjects showed an improvement in reading speed, reading comprehension, and increased recognition of written words during dictation. Subjects' performance on both tasks improved to the level of matching the performance of each task performed individually. 169 Additionally, the authors observed that when the dictated words were semantically related (e.g., rhymed or formed a sentence), the subjects did not notice that relationship before training, but did so after repeated practice. 169 These results suggest that controlled tasks could be automated, and liberate cognitive resources for high-level processes.

However, Pashler 170 claimed that no task can be fully automated, and always involve an intentional and conscious component involving high level cognitive processes and some level of control.

According to Posner and Snyder,171 our limits in performing multiple tasks suggest that our ability for control is limited, opening up a completely different approach when studying divided attention.

Multitasking (MT) refers to situations when an individual performs two (or more) tasks involving the same cognitive processes at the same time. 170 This overlap prevents both tasks to be done independently of each other and with no interference, creating a situation of competition for shared limited resources. 172 This notion is important because, instead of just indicating structural limitations (e.g., two tasks requiring activation of the same brain area), it also indicates functional limitations (e.g., two tasks requiring activation of the same brain function). 173 As an example, a structural limitation would occur when we try to drive a car and look at a map at the same time, while a functional limitation would occur when a phonological representation is used to encode heard words while reading sentences aloud.

The most studied experimental paradigms for MT are the Task Switching paradigm and the Dual-Task (DT) paradigm. Although both share similar features, they have led to quite separate lines of research (i.e., empirical and theoretical developments processed more or less separately). 110,174 In the Task-Switching paradigm, the presentation of stimuli (i.e., S1 and S2) and their respective responses (i.e., R1 and R2) occur in a strictly sequential way (i.e., S1 -R1 -S2 -R2; or task 1 -task 2; or T1 -T2), whereas in a DT paradigm, the presentation of external stimuli occurs collectively before the response (i.e., S1 -S2 -R1 -R2) (see Fig. 9).

Task Switching

In a Task Switching paradigm, subjects are usually presented with a stimulus that allows them to take more than one action (responses); they are asked to choose one of these actions, execute it and then continue with the following stimulus. For example, if a screen presents a letter next to a number (e.g. G7), the first task (or T1) would be to answer whether the letter is a vowel or consonant; then the second task (or T2) would be to answer whether the number is odd or even. In such circumstances, successful performance would require maintenance of the currently relevant task and careful selection of the response (T1), along with sufficient flexibility to update the task-set for the next task (T2).

When the performance of both single tasks (e.g., T1-T1-T1-T1 or T2-T2-T2-T2) is compared with both switched tasks (alternating tasks; e.g., T1-T2-T1-T2) and the difference between them is calculated, the resulting value is known as switch cost. 175 According to Roger and Monsell, 111 the switch cost reflects the time it takes for the cognitive system to reconfigure the current task-set and replace it with the relevant parameters for the next task. It is important to mention that the concept of task-set in task-switching is lax and usually confused with the concept of attentional template, so we will be careful to use them under the definitions already provided.

Theoretically, switching between two tasks requires changing the attentional template (i.e., a full template reconfiguration), since the template applied in the previous trial is not necessarily valid for the next trial. In contrast, when the same task is maintained, no reconfiguration is needed, since the template applied in the previous trial is still valid for the following trial. Reconfiguring a template would lead the executive system to change the way systems are activated downstream, in a process called task-set reconfiguration. 111 This carries a reconfiguration cost, which can be reduced with enough preparation time before the target appears, for example by explicitly displaying a cue. 176 However, even with enough time, a small but consistent residual switch cost remains, 136 leading to the possibility that this process may have an inherent limitation associated with it.

Several studies have tried to manipulate the switch cost by creating mixed conditions, presenting trial sequences that allow both task repetition and switching within the same block (e.g., T1-T1-T2-T2). It then becomes possible to study repetitions and switches, and compare the settings at task-set level. Thus, subjects would not only need to switch activity between tasks, but would also need to maintain two passive templates at the same time in the WM.

However, when switches are mixed with repetitions in more complex ways (e.g., T1-T1-T2-T1-T2-T2), uncertainty increases. Therefore, part of the difficulty of mixed blocks would come from the lack of preparation that would affect all types of trials. 177 Even though performance would recover quickly after switching between tasks, responses would be slower than when executing only one task per block. 136,177,178 Because repeated trials benefit more from predicted sequences than switched trials, 179 the sum of their individual performances would produce a "global" switch cost known as a mixing cost. 180 Another way to manipulate the switch cost is through voluntary task switching: executive control processes regulate what particular behavior should be carried out when there are multiple responses or tasks to choose from for a given stimulus. 181 Using the example previously discussed (e.g., G7), it would become possible to answer whether the letter "G" corresponds to a vowel or a consonant using two different fingers of the left hand, as well as whether the number "7" is odd or even using two different fingers of the right hand.

Standard methods of task switching provide strict experimental control with factors such as setup time and transition, which allows for systematic investigation of parameters that influence switching processes. In contrast, voluntary task switching swaps control for an environment in which task selection becomes a component of behavior. The main feature of voluntary task switching is that the task to be performed can be selected by the participant from trial to trial, without being imposed by the experimenter. This situation is interesting because it echoes experimental results showing that the switch cost increases more when subjects are forced to switch between targets driven by an external source than when they select them freely from "an internal source". 182

Dual-Tasking

During a DT paradigm, two consecutive stimuli (i.e., S1 and S2) traditionally occur. Early DT studies used a simplistic stimulus-response (S-R) structure, 183 while later studies proposed a more complex and more naturalistic DT paradigm with several possible responses for each stimulus. 3 For example, an auditory stimulus could be one of two different sounds (i.e., S1 sounds like /yaya/ or /haha/) while a visual stimulus could present two different faces (i.e., S2 could be a male or female face). After choosing one of the possible responses for each stimulus (e.g., S1a and S2b), they must be executed as sequential motor tasks (i.e., R1a and R2b), by speaking aloud (i.e., /yaya/ for R1a) and by pressing a button (i.e., female face for R2b).

Both simple and complex DT paradigms require that participants perceive stimuli, either simultaneously or separated by a time interval, hold the information in memory, and chose a response among several while the two tasks keep going. In such designs, the processing of two simultaneous stimuli always comes with an associated cost, called DT cost. Although there is no clear explanation to justify its existence, the "dual task vs. single task" phenomenon correspond to a difference in performance between the two tasks performed individually (i.e., T1 or T2) vs. both combined (i.e., T1 and T2). 169 Results show that the performance of a single task is always be worse when it is executed in combination with another task than when it is executed alone. 170,184 It is widely accepted that the DT cost is due to a theoretical construct called the Psychological Refractory Period (PRP) 3 which can be described as a cognitive limitation at a central, response selection (RS) stage, 170 when a response must be associated to the incoming stimulus (see Fig. 10). experiences a delay or slack, delaying its R2 and affecting performance. Thus, RS2 cannot be completed before RS1. Taken from Pashler. 3 That limitation would prevent tasks from proceeding at the same time, while the peripheral stages (i.e., perception and motor response) could occur in parallel. During DT, one of the tasks would keep the RS busy, forcing the other into a standby state until central resources are available again. As such, when two relevant stimuli are shown (i.e., S1 and S2) with a variable time separation known as Stimulus Onset Asynchrony (SOA), 3 the cognitive processing of the first stimulus (i.e., RS1) would delay the cognitive processing of the second stimulus (i.e., RS2) and its response (i.e., R2) if the SOA is too short (see Fig. 10).

Since RS occurs in shared neuronal populations with limited resources, the task that is processed first (i.e., T1) would consume a large portion of those resources, preventing the processing of T2 and creating a Response Selection Bottleneck or RSB. 3,170,185,186 This RSB would only allow one task to occur at one time due to its rigid single channel structure, operating as an "all or nothing" mechanism. 187,188 Additionally, the longer the time required to complete RS1 (i.e., the higher the SOA between S1 and S2), the longer RS2 will be delayed. This would lead to a progressive deterioration of T2's performance without affecting T1's performance, resulting in the so-called PRP effect (see Fig. 10). 3 Additionally, manipulating the difficulty of RS2 (e.g., varying S-R compatibility, number of alternatives per response, or number of repeat responses) can lead to an increase in overall processing time in T2, independent of SOA and RSB, resulting in an additive pattern on the effects of SOA (see Fig. 11). 189,190 Therefore, at short SOA an interruption emerges in the Task 2 chain before the response selection stage. An increase of the processing time in the RS2 stage leads to an increase of the processing time in Task 2 in the hard compared to the easy condition independently on SOA. This results in an additive pattern of the effects of SOA and difficulty on the response times in Task 2. Adapted from Schubert et al. 190 Alternatively, Schumacher and colleagues 191 reported a sub-additive interaction in the overall processing time in T2, where manipulation of the difficulty of RS2 would lead to a smaller effect than mentioned above. They found that after a brief practice, the manipulation of S-R compatibility went from creating an additive effect to a sub-additive effect, finding a more important result when there is a shorter SOA than a longer one. They justified this finding by stating that the bottleneck is not located at the RS level, but at a later stage (i.e., at the motor response stage), so RS1 and RS2 can be processed in parallel. In this way and during a short SOA, an increase in T2 difficulty (i.e., an increase in RS2) would be absorbed by the cognitive motor delay, while at a long SOA there would be no cognitive delay, so the increase in RS2 difficulty would only increase T2 processing time (see Fig. 12).

Figure 12: The sub-additive effect of alternative bottlenecks. The response selection stages in Task 1 and Task 2 proceed in parallel and the bottleneck is located after the response selection stages (i.e., at the motor stages). An interruption emerges in the Task 2 chain after the response selection and before the motor stage. An increase of the processing time in Task 2 in the hard compared to the easy condition will be absorbed by the cognitive slack (the interruption) at short SOA. At long SOA there is no cognitive slack and, therefore, the additional processing time in the hard compared to the easy response selection condition will add to the processing time at long SOA. The resulting pattern represents a so-called sub-additive interaction of the difficulty and the SOA effects on the reaction times in Task 2. Adapted from Schubert et al. 190 These findings have led to the idea that the bottleneck may not be the result of a structural limitation, but rather a strategic one. 192 In a strategic bottleneck, the cost of the DT does not come from exceeding the processing capacity of a single channel, but from the ability to separate attention between two similar tasks. 193,194 Therefore, limited resources would be shared between tasks in different proportions (e.g., 80% for T1 and 20% for T2) and be managed by attentional control, depending on the instructions, priorities between tasks and value of the result, 194,195 allowing parallelism. 186,196,197 Thus, the greater the amount of shared resources, the greater the parallel processing.

Some authors have tried to explain why individuals try to perform several tasks simultaneously, despite the risk of interference between tasks and the performance decrement, and the main reason might be that this poor strategy may require less mental effort than that needed to implement strictly serial processing. Yet, serialization tends to be preferred, despite the excess effort needed to maintain a serial organization, whenever performance must be prioritized. 115,198 In fact, several studies support the view that parallelism is an illusion 199,200 and that "multi-taskers" in fact alternate rapidly between tasks. 194 How does practice facilitates multi-tasking, then? Studies on single tasking have shown that regular practice leads to reduced processing time in RS stages. 201 Additionally, neuroimaging findings suggest that this decrease is mirrored by practice-related cortical reorganization, allowing the implementation of faster and more direct neural connections between perceptual and motor regions. 202 This decrease in RS time would in theory reduce the DT cost, 203,204 shortening RS1 and RS2, both being scheduled in such a way that they avoid temporal overlap without achieving complete automatization (see Fig. Schubert et al. 190 Standard models of the RSB assume that central processing follows a predefined sequence of entry and processing: T1 goes through the bottleneck before T2, following a "first-come, first-served" principle. 3,17,188 In contrast, a more recent model extending the idea of serial processing has proposed an active task management system, 156,190,195 in which the executive network can adjust the order in which tasks are performed depending on the current goal, context, convenience or experiences, and reduce the DT cost. 206 The latest experimental results supports a mixed model, in which the shift between tasks is observed in long SOA situations, when time is available for strategic planning, while "first-come, first-served" is more prevalent when the SOA is short. 207 One limitation of the ability to multi-task might also relate to WM, and the ability to maintain an active memory trace of the different templates associated with each task. 208,209 However, much research needs to be done at this stage, to determine, for instance how many templates can remain in WM in a passive state. Recent studies suggest that only one passive template can be maintained at each time, 116,210,211 which must be continuously refreshed to avoid forgetting, 212 but other studies suggest that it is possible to maintain several active templates at the same time. 213,214 One point of debate is whether the memory trace of the template, at the neural level, must be continuous, in which case two templates could not be maintained simultaneously by the same neural population. 212 Recent DT studies suggest that during the evocation or maintenance of memories, templates maintenance is associated with non-continuous, intermittent activation states. Yet, most of the results available come from studies that average neural activity over time and trials, or across neurons in different sessions 215,216 : procedures that masks the precise neural dynamics, 217 and fail to solve the issue. Some studies have bypassed that technical limitation to show that template-related neural activity is typically sparse and compatible with the separated maintenance of different templates. 218,219 The fact that neurons are not continuously active 220 allows for the sequential rehearsal of multiple templates, [221][222][223][224] and multiplexing. 212,225 What is more established is that during DT paradigms, templates alternate between an active state and a latent or passive state, when the task must go to a stand-by mode. 101,104 This latent state, which is different from complete inactivation, allows the templates to be reused without going through an actual reconfiguration.

The reconfiguration may include changes in the criteria defining task-relevant stimuli (what must be attended), the overall goal of the task itself and the stimulus-response contingency (how to react to the stimuli). This is possible via a series of activations and inhibitions, to activate new task components and suppress components of the previous task. 110,226 The shift between tasks comes therefore with a cost, stronger when the two tasks share few components and the reconfiguration is massive. 175,227 Some authors, like Altmann and Gray 228 believe that the cost can be suppressed when participants are given enough time to reconfigure between tasks, while other, like Meiran 229 claim that the cost cannot be fully suppressed despite the efficiency of the endogenous preparation.

One central factor affecting the efficiency of the reconfiguration is the similarity between the tasks in the DT paradigm, as different tasks can rely on very similar templates, 110 for example if a subject were asked to provide names of flying animals first and the name of running animals. Although the two tasks differ, the difference is minor and the type of responses and cognitive processes that must be engaged is similar, making it possible to use the same template for different tasks and shift swiftly between tasks.

DT performance is complicated by the fact during a DT paradigm, higher cognitive areas involved in RS do not distinguish the type of information being processed. 3,101 This means that tasks with different sensory or motor modalities may experience interference at higher cognitive levels, 3,230,231 despite the fact that stimuli are auditory in one task and visual in the other, for instance. 232 This is supported by observations in which tasks that use different sensory and motor modalities end up interfering with each other. 233 One example of highly abstract and amodal region involved in RS is the LPFC. 205,234,235 A recent functional Magnetic Resonance Imaging (fMRI) and multivariate analysis pattern study mapped different cortical and subcortical areas during experiments of modality manipulation, finding that bilateral AIC and dlPFC contain significantly less information about sensory and/or motor modalities than other regions of interest. 232 Higher sensory and lower cognitive areas are more modality specific, 232 which means that information from distinct modalities can theoretically be processed in parallel at this level, 236,237 at least until it reaches amodal areas, mostly in the prefrontal cortex. 238 Purely amodal areas, mostly located in the most rostral part of the prefrontal cortex, are shared by most tasks that require greater executive control and constitute a RSB even when two tasks rely on different modalities. 3,170,236 According to Logan and Gordon,115 this intrinsic limitation caused by higher cognitive areas, supporting attentional control and WM, can be minimized by pre-organizing the order in which tasks are executed and use self-regulated serial processing between tasks. 237,239 According to Pashler,170 excessive interference should lead individuals to shift naturally from unsuccessful parallelism to serialization. Following that strategy, the executive system can pre-establish a DT order by scheduling attention in a serial way, 192,240 facilitating the transition from one task to the other while monitoring task performance. In that framework, executive control applies mostly to organize tasks in time, solve the interference, 241 and improve performance. 237,239 This is consistent with statements by Brass and Von Cramon, 242 who stated that those subjects who invest more cognitive resources in strategic task scheduling have better performance.

Most studies associate the scheduling process with the LPFC. 236,[243][244][245][246] In a recent fMRI study, Szameitat and colleagues 247 showed a stronger activity during multi-tasking in the LPFC bilaterally, specifically in the left MFG, IFS and Precentral Sulcus, and right anterior Superior Frontal Gyrus (SFG) and posterior IFS/MFG, revealing as well as other studies a more intense and extended activity towards the left hemisphere. [247][248][249] Yet, the bottleneck should not be thought as so localized : other areas were activated in relation to DT, including the posterior SFG, AIC, Premotor Cortices, pre-SMA, SMA, Precentral Gyri, ACC, Inferior Pulvinar extending to IPS and the medial Precuneus, ITG, posterior Occipital areas, Lingual and Fusiform gyri. [247][248][249] Therefore, DT performance decrement cannot be attributed to a single focal portion of the LPFC, but rather to a network whose activity is greater when two tasks are performed in a DT condition, than when the tasks are performed separately. 247 The LPFC is only one major node, actively involved to solve interferences between two tasks, as shown by Szameitat and colleagues 247 who found a significant negative correlation between activity related to the cost of DT and performance in two areas of the LPFC: the left and right IFS/MFG.

Besides the LPFC, other areas present a stronger activity during DT paradigms. Bush and colleagues 250 found that gray matter volumes in the ACC positively correlate with average performance during MT paradigms, probably playing a key role in cognitive, emotional 250 and MT processes. 251 By studying the modular organization of brain networks and their relationship to DT cost, Avalash and colleagues 252 found that greater network overlap between simple tasks and less modular flexibility during DT leads to a significant increase in interference. In addition, they found that greater flexibility in the areas of cingular and frontal control, along with less flexibility in right-sided nodes that include the mid-occipital and superior temporal gyri, support enhanced DT performance. Finally, they determined that the topological reconfiguration of each single somatosensorial network in adaptation to the DT condition is associated with less interference during DT. 253 These results suggest that the stability, flexibility and dynamic behavior of brain networks are important in determining the DT cost. 190,248,253,254 Thus, these networks adapt over time to task conditions, 255,256 with patterns that can change and become potentially relevant to cognitive and behavioral fluctuations. 257 These characteristics appear only when two different and relevant tasks are performed in parallel in a DT, and not when each task is executed individually. Therefore, the DT not only carries an inherent effort in its execution, but also adds an effort in the use of higher cognitive functions. These functions optimize the passage of information through the RSB, consequently improving the performance of the DT.

Although most of the research done on DT has focused on amodal RSB, there are other types of bottlenecks that relate to specific peripheral brain mechanisms for each modality, such as sensory perception (i.e., S1 and S2) and motor response (i.e., R1 and R2) phases.

One particular problem when stimuli of two tasks share the same sensory modality 3,187,188 is that attention must be divided between them, [START_REF] Marois | The Neural Fate of Consciously Perceived and Missed Events in the Attentional Blink[END_REF][START_REF] Kranczioch | Neural correlates of conscious perception in the attentional blink[END_REF] for example, if someone drives a car while trying to look at a map, the competition between the two tasks is obvious at the sensory level. Therefore, and as we have discussed previously, two tasks might interfere at the simple level of allocation of sensory attention. Strategic multiplexing is necessary to choose the most important information in a scene at any given time.

The same problem can obviously found at the response level, since the execution of two motor responses in parallel is not always possible (looking to the right and to the left at the same time). There is an obvious limitation of the motor system to execute two responses at the same time using the same effectors, or even to prepare two movements with the same muscles. 247 According to some studies, the execution of R1 delays the execution of R2 by several hundred milliseconds, which impairs DT in many cases. 189,247,258 DT is facilitated when the two tasks use different sensory and motor modalities, but specific combinations seem to be easier than others are. For example, a task with visual perception and manual execution combined with another task with auditory perception and vocal execution causes a lower performance drop than the reverse combination (i.e., visual-vocal and auditory-manual). This suggests that certain combinations produce less crosstalk between tasks, since there are "more natural" associations between certain types of processing. 259,260 This is interesting avenue of research for ergonomics and design.

Distraction

The moon, by her comparative proximity, and the constantly varying appearances produced by her several phases, has always occupied a considerable share of the attention of the inhabitants of the earth.

-Jules Verne

The ability to remain focused on an exogenous stimulus or endogenous goal while ignoring irrelevant surrounding elements is crucial for goal-directed behavior. Nevertheless, in certain cases mechanisms already discussed within the orientation network compare highly salient but irrelevant ascending information with template-defining descending signals, with the former bypassing early filters and capturing attention involuntarily.

Such processes, characteristic of distraction, have been promoted by evolution because of their value for survival. According to Lang, being able to reorient attention away from the requirements of the current behavioral mental goal is the result of natural selection, especially when reoriented towards those that threaten the life of the organism. 261 Yet the distraction process also occurs for irrelevant elements that do not represent a threat, which can then be as distracting as real physical threats. 262 We get distracted when the attention-grabbing effect of such stimuli is so strong that we cannot ignore them. 263 Stimuli can capture attention because of their particular physical salience: stimuli that are especially large, bright or noisy, or possess a special significance, such as a person's first name or the face of a relative, capture attention more easily. The capture of attention has positive effects as it ensures that potentially important external events do not go unnoticed, and allows when necessary a renewal of internal goals and an adjustment of behavior to the environment. However, this phenomenon also involves a decrease in performance for the task that was previously being performed. 264 Regarding the distraction process, while it is commonly accepted that attentional reorientation towards the distractor forces processing at the level of primary sensory areas, 265,266 the main debate 4 " questions whether the capture solely proceeds in a bottom-up fashion following a "stimulus-driven" tendency, or whether it is regulated and maybe enhanced by top-down effects following a "goaldriven" tendency. 267 Advocates of a purely bottom-up mechanism propose that the allocation of cognitive resources depends exclusively on transient bottom-up processes that mediate the attraction of attention, with no contribution from the prefrontal cortex. According to their view, attention would always reorient towards the most salient physical distractor (i.e., the one with the highest contrast relative to the background), independent of any constraint relative to the current behavioral goal (for instance, what item must be found within a search array). [268][269][270] In a visual search experiment, the sudden presentation of a salient singleton would attract attention independently of its resemblance with the target of the search, and increase reaction time (RT) and/or error rate (ER). 11,271 Other researchers have proposed that the capture by distracting stimuli is affected by the current task goal. [272][273][274] In a classic example of visual search, a cue is presented before each trial. This cue can be a spatially predictive or non-predictive singleton for the target position in the next trial. In the case of a predictive situation, the cue draws attention to the specific position within the visual field, facilitating the perception of the target, causing a spatial cueing effect and decreasing the RT of the task. This effect is only present when the cue matches the target and the actual goal, but the same does not occur with non-predictive cues, indicating that the capture of attention is affected by the current task. [272][273][274] Folk 272 concluded that this post-distraction attentional reorientation is not an inevitable consequence of saliency, but occurs because the salient stimuli partially matches current task-relevant attributes (what needs to be attended). In that view, attention would be drawn to those elements that match at least partially what is being searched. This debate has led to diametrically opposed interpretations regarding the phenomena of attention capture.

Current models try to reconcile conflicting views and assume that during the distraction by an external stimulus, attention capture results from the joint influence of factors driven by stimuli and ongoing goals. [START_REF] Wolfe | Guided Search 4.0: Current Progress with a Model of Visual Search[END_REF] Priority, as in priority maps, would be calculate from these respective influences. Therefore, the more salient a stimulus is, the higher its priority "value", 271 regardless of whether it is relevant or not for the task, but its resemblance to the attentional template would also increase that value. 275,276 In a study investigating the relationship between bottom-up and top-down processes, subjects were asked to recognize stimuli with different levels of saliency, while keeping different goals in mind that may or may not match the external stimulus just presented. It was found that when the bottom-up information and the top-down signal do not converge on the same target, regions of the VAN, specifically the IFJ and IPS, activate bilaterally, forcing the reorientation towards the most salient external target. In contrast, it was shown that when bottom-up information and the top-down signal converge on the same target regardless of its saliency, the same regions of the VAN activate together with other areas such as the AIC and the post-central and supramarginal gyri bilaterally. 277 According to this, the IFJ would act as an interface between VAN and DAN, mediating the control processes back and forth to the IPS, either acting as a circuit breaker for ongoing top-down processes, or representing the effort of the cognitive system to nullify salient but unimportant information, matching early theories about regulation between networks. 11,277 This communication between VAN and DAN supports the idea of a circular attention system.

At the same time, the neural activity of the DAN tends to increase and be maintained in the face of distractors of different saliency as well as without them. 278 This occurs especially in the FEF and in the SPL near the IPS, including also areas of the executive network such as the dlPFC and the ACC, supporting the idea that their activity is independent of the saliency of the distractor. 278,279 In addition, studies argue that signal-induced cognitive activity lasts longer than that induced by distraction, so that even in cases where distraction captures attention, the former eventually exhausts itself and attention is reoriented to the mental goal. 269,271,280 These findings serve to clarify that top-down control can access and affect attentional orientation away from a salient distractor. 279,281 According to Gaspelin and colleagues, top-down control can actively inhibit or suppress distractors if higher cognitive resources are used before reorienting attention. 4 Therefore, the attentional system protects itself from distraction via the executive network, cutting the inflow of information before priority and feature-specific priority maps, 282 as demonstrated in Event-related Potentials, 281,283 behavior and functional oculomotor studies. 282,284 The suppression of distracting stimuli has been largely studied in the visual modality. A first model states that the visual system inhibits only specific elements that must be known in advance. 285 Another model states that the system does not require any a priori information about the specific features of the element to be suppressed, and simply identifies distractors based on the fact that suppression is simply applied to elements with a high weight in the priority map, so that only the processing of elements matching the current goal is allowed. 4 Yet, several studies have shown that a distractor can be processed attentively even after such an active suppression. 286,287 One interpretation of that phenomenon involves WM: several studies claim that its capacity differs substantially across individuals, 106,288 and that lower WM would be associated with lower attentional control and a greater tendency to distraction. [289][290][291] Thus, low-capacity subjects may have greater difficulty in avoiding storage of irrelevant items in contrast to high-capacity subjects. 292 Additional studies have extended these results, showing that high-capacity individuals are able to resist the capture of attention by irrelevant elements, while low-capacity individuals are less likely to resist it. 293 However, Theeuwes and colleagues 294 proposed a variation of that explanation, claiming that all subjects have equal susceptibility to attentional reorientation, but that they vary in the time needed for them to recover. One argument in favor of their interpretation is that high and low WM capacity subjects experience similar effects of attentional reorientation, but that low-capacity subjects take longer to recover from distraction than high-capacity subjects. These results suggest a slow attentional detachment from a distractor may be due to poor attentional control associated with low WM capacity. 280

Intracranial EEG and HFA oscillations

The human brain has 100 billion neurons, each neuron connected to 10 thousand other neurons. Sitting on your shoulders is the most complicated object in the known universe.

-Michio Kaku

My experimental work relied on intracranial electroencephalography (iEEG) to measure brain activity.

EEG measures the voltage fluctuations resulting from the flow of ions within brain neurons, known as brain waves or neural oscillations. The main advantage of EEG is its high temporal resolution, at the millisecond level, and it used in many pathologies, such as epilepsy, sleep disorders, depth of anesthesia, coma, encephalopathies as well as to investigate the neural dynamics of normal cognition. 295 EEG can be recorded non-invasively at the scalp level, with surface electrodes (surface EEG), and intracranial (iEEG), as in the present thesis. The obvious advantage of scalp EEG is that it is noninvasive, 295 however, the skull, meninges and skin distort and blur the signal, which limits its spatial resolution to a few centimeters and prevent the recording of neural activity with frequencies higher than 30-50 Hz, 296 in particular the "high-gamma range" [50-150 Hz], which will be discussed extensively.

In contrast, iEEG has a much higher resolution (mm) than scalp EEG, 296 and can record from precise cortical regions at the gyrus/sulcus level. 297 It has been estimated that each recording site provides an average measure of the electrophysiological activity of about 200,000 to half a million brain cells. 298 Further, iEEG has a higher signal-to-noise ratio than EEG (about 100 times higher), 299 with less electromagnetic noise from the recording room, and little contamination by physiological noise of the heart signal, muscle contractions or skin potentials (e.g., skin cells on the scalp or ionic potential of sweat glands). 300 5 "

iEEG also provides a precise and unbiased measure of neural activity in a wide frequency range, 296 typically up to 300 Hz, that is only limited by the sampling frequency of the recording device (1024 Hz typically, in Lyon and Grenoble for instance). This allows the extraction of a larger frequency range than that obtainable with scalp EEG, ranging from low frequency activity such as delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), to low gamma (30-50 Hz) and high gamma (50-150 Hz), or

High-Frequency Activity (HFA). Other high frequencies such as ripples (150-250 Hz) and fast ripples (250-500 Hz), initially associated with memory consolidation during sleep, are beginning to be analyzed today in different cognitive studies with awake subjects. 300,301 The main limitation of iEEG is that it is used specifically for the presurgical exploration of drug-resistant epileptic patients; 302 as it is the only diagnostic method capable of delivering sufficient resolution to delineate epileptic zones and map functional areas, which must be spared by the resection. 296 Yet, iEEG responses to tasks, which are reproducible across patients with different kinds of epilepsy and different onset zones, are considered as valid for generalization to normal human brains. 297 iEEG can be measured using two different types of electrodes: subdural grids/strip (see Fig. 14: b,c,d) and depth electrodes (see Fig. 14: a, e, f). Both present important differences in data collection and diagnosis of epilepsy. Subdural electrodes are placed directly onto the exposed surface of the brain. They are useful to localize the onset of seizures and their spatial extent on the cortical surface and delineate the limit between epileptogenic zones and the functional cortex. However, subdural electrodes cannot capture epileptic foci of deeper origin such as the insula or the amygdalo-hippocampal complex. 303 On the other hand, depth electrodes are placed on the parenchymal brain or directly on brain injuries, being usually indicated when depth recordings are necessary for an accurate diagnosis. They provide sufficient resolution to localize epileptic activity at the gyral level, with an accuracy as good as that achieved by fMRI, in many cases surpassing it. 297 However, their main limitation is the limited spatial sampling and the difficulties in performing precise anatomical delineation between healthy and epileptic areas. 304,305 The insertion of these electrodes can be performed bilaterally, but their implantation must be done by a neurosurgeon with previous knowledge in neuronavigation. 306 European epilepsy centers mainly use stereotactically implanted depth electrodes for diagnosis (stereo-electroencephalography, or SEEG). The process of implantation is as follows.

1. The planning of the implantation requires a specific hypothesis about the epileptic lesions based on non-invasive evaluation tests, such as seizure semiology, neuroimaging and nuclear imaging. 307,308 2. After formulating a location hypothesis, the epileptic target is selected and reached with depth electrodes of different lengths and number of leads, depending on the specific region of the brain to be explored (usually 5 up to 15 depth electrodes, with 5 up to 18 leads per electrode). 300 Each electrode has a diameter of 0.8 mm, with leads measuring 2 mm wide and a distance between them of 1.5 mm. They are placed inside hollow plastic tubes that can be inserted into the brain tissue with a stiletto that provides the necessary rigidity. 306 Electrodes are usually made of stainless steel or platinum, with the latter material being more appealing as they are nonferromagnetic and therefore compatible with MRI. They are implanted using conventional stereotactic techniques according to the MNI coordinate system or each patient's own preimplantation images through drill holes, 309 and are usually left implanted for a period of 5 to 14 days (see Fig. 15). 310

3. The local accuracy of each lead is usually higher when each registration site is referred to its nearest neighbor (bipolar montage) than when a remote site is used as a reference for all sites (common reference). 297 4. Then follows a prolonged post-surgery examination with video recording and EEG information without anticonvulsant drugs, with the intention of recording epileptic seizures. During this phase, the cortex can be stimulated with electrical pulses that further assist with triangulation. 306 It is usual to record intracerebral activity with up to 128 deep electrode leads (up to 1024 Hz sampling rate and a 0.1-200 Hz bandpass filter). Clinical studies in epilepsy usually target frequency activity with a bandwidth between 0.5 and 70 Hz as the gold standard, 311 but this does not mean that epileptic activity cannot be found at much higher frequencies.

5. Next, imaging is used to complement the results obtained. As an example, synchronized oscillatory activity correlates especially well with the hemodynamic activity of post-implantation fMRI, thus the increase in signal power at a particular recording site reflects the local involvement of cortical tissue around the electrode. 312 6. Compiled data can then be used to differentiate normal from abnormal neural oscillations, triangulate the anatomical areas that produce these abnormal readings and subsequently resect epileptic foci within established surgical margins. 306 As stated above, HFA can be used as a powerful biomarker for most cognitive functions. Sustained HFA has for instance been associated with keeping representations in WM, 314-316 maintaining attentional templates with sustained and persistent activity of recurrent connected neurons in shortterm memory. 212 Such persistent activity would facilitate synaptic encoding in long-term memory, thanks to increases in frequency amplitude and subsequent synaptic plasticity. 317 This phenomenon has been observed in early sensory zones as well as in the extra-striatal visual cortex, and even in nonvisual areas, are involved in the maintenance of visual memory traces. 315,318 This suggests a mechanism of transmission or transformation between synchronous neuronal assemblies, ranging from sensory cortices to high-order cortical regions that may start even during stimuli presentation. 315 Therefore, HFA can trace neural processing related to perception, attentional orientation and maintenance of working and long-term memories, reflected in increases of amplitude, which are all highly relevant for attention. 319,320 Nevertheless, whether or not HFA represent a phenomenon that is intrinsic to cognition is still debatable.

Limitations of Intracranial EEG

While iEEG has its advantages, it also comes with limitations. One of them is that data are collected in patients suffering from epilepsy. Obvious guidelines must be followed, to exclude from the analysis, for instance, iEEG sites in the immediate vicinity of brain lesions or malformations and signals contaminated by epileptiform activity. Any sign of strong performance decrement due to the diseases must also lead to the exclusion of the patient. 302 Yet, if such issues are handled with sufficient care, iEEG remains a venue of choice to study the fine neural dynamics supporting human cognition, and it is now at the core of a very active research field in human neuroscience. 297,315,316,321,322 A second and somewhat underestimated limitation comes from the limited coverage of the brain surface. This makes the collection of data across different subjects difficult, as the structures chosen for the placement of invasive electrodes are determined for each subject individually and independently of the research objectives. 303 Because traditional group statistics are not possible, our laboratory has created a different approach based on three steps: 1) first we perform a statistical comparison between experimental conditions at each subject level, at each recorded site, to identify regions of interest with task-related neuronal responses. This would include multiple comparison corrections either by direct comparisons between two conditions or comparisons between a condition and a pre-stimulus resting baseline. 2) We then identify the brain regions of interest that show similar effects across several subjects. 3) Finally, we display the individual information for each region of interest, exposing the task-related effects on each patient's anatomy (see Fig. 16). 303,315,322

Software for Intracranial EEG Visualization

Part of what made the Macintosh great was that the people working on it were musicians, poets, and artists, and zoologists, and historians. They also happened to be the best computer scientists in the world. But if it hadn't been computer science, these people would have been doing amazing things in other fields.

-Steve Jobs HFA can also be monitored online thanks to the very high signal to noise ratio of iEEG, and our laboratory has created an online monitoring system of intracranial oscillatory activity called BrainTV, which extracts and displays in real-time the variations of several frequency bands -including HFA -, at each recording site in each particular subject. This allows visualizing the immediate effect of the behavior or tasks imposed on brain activity, with a high temporal and spatial resolution. 323 In addition, our laboratory has also created an off-line monitoring system which mirrors the BrainTV system -but allows the joint visualization of HFA and the video of the patient in his hospital room, while filmed for instance during an experiment (see Fig. 17). This software called BrainTV Replay is an off-line monitoring system that allows to extract real-time neural activity and transform it into a HFA envelope for every electrode. This information can be visualized jointly with behavioral responses,

recorded

Naturalistic Conditions

Human Nature is the only science of man; and yet has been hitherto the most neglected universe.

-David Hume

Most conventional paradigms use simple stimuli experiments during which the interaction of the participant with the external world is entirely computerized (for stimulus delivery and response monitoring). Whether the results of such experiments can be generalized to actual, real life situations is a subject of debate. An emerging research field is using stimuli with a greater richness and contextual weight, together with a technological development for a more open interaction, between the participant and his environment. 324 This naturalistic approach focuses on human cognition in ecological or naturalistic situations, 325 closer to everyday conditions than those simulated within four walls. 326 In addition, new evidence and theoretical considerations suggest that the brain may be strongly "tuned" to receive naturalistic stimuli over artificial ones, 327 making it very useful to investigate attention with a practical approach.

Naturalistic designs can be highly valuable to study questions related for instance to multi-tasking and distractions. In particular, the complexity, diversity and unpredictability of the distractions one might encounter in a natural environment, such as a room or an office, are much wider than what can be recreated using a computer. In addition, one might argue that the interaction with a real human being is more complex and taxing than a simulated situation.

In anticipation of the description of the tasks used in this thesis, we can propose that naturalistic distraction (i.e. distraction mimicking what is observed in our daily lives) 328 and its effect on attention could be studied by comparing the pattern of neural activity recorded in a participant performing an attention demanding task a) alone, in a quiet place, and b) with an experimenter sitting next to it, trying to create distraction. Even though the amount of distraction would be less controlled than if monitored by a computer, it would likely be much stronger because of the social interaction involved.

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7
This experimenter-participant interaction is interesting because it provides a new venue to study the neural underpinnings of multi-tasking and distraction, and its effect on attention; for instance by considering two conditions while the participant is already involved in a demanding attention task: 1) one in which the researcher actively asks questions to assess subjects and forces the participant into a secondary verbal fluency test to induce dual-tasking; 2) and another in which the researcher actively attempts to distract subjects by using human or artificially induced movements and/or sounds (e.g., an incoming cell phone call, paper sheet movements). The advantage of such situations is that it is reminiscent of real-life situations encountered by most of us.

One advantage of such designs is to put forward an essential component of modern life: social interaction, which strongly interacts with attention during most of our activities. 329 And social interactions are not easily reproduced by computerized designs, as shown by several studies regarding the neural networks that subserve the perception of communicative cues, such as direct gazes or indirect social gestures, which are more pronounced when delivered in vivo than, for example, through recorded video. 330 In most attention-related research, a software usually controls the delivery of relevant or irrelevant information. 211,236,247,331 Although the effect achieved may be much cleaner and more stable compared to that of a human being, its effect in influencing a subject's response is, at best, incomplete. This is because by excluding the human factor, we are leaving aside certain attributes that are impossible to achieve with a machine. However, probably the most important ignored characteristic of all is the attribution of a mental state to a second person. 324 When coming into contact with a human being, a large amount of cognitive resources are used to infer his/her mental state, creating representations of intentions and beliefs based on observable behavior, while trying to keep personal and second-person representations in mind simultaneously. 324 Human beings probably attribute a mental state to something that they believe possesses sufficient autonomy to surprise them beyond expectation. Therefore, they are not able to define exactly how someone might react to a particular situation, unlike a computer that has a certain amount of specific responses that do not vary.

Although the specific brain regions that are activated differ across the different paradigms, areas such as ventromedial Prefrontal Cortex (vmPFC), dorsomedial Prefrontal Cortex (dmPFC) and TPJ are consistently activated in the face of task-independent social interaction. 330,[332][333][334] Similarly, different studies have found responses with greater amplitude changes at occipital locations during social interaction than when interacting with pictures. 335 These considerations motivate a naturalistic approach to address questions about attention with high relevance in our daily lives, because the responses we expect from a neuroscientific inquiry should be applicable to daily life situations, which would not be the case with crude approximations of that reality in the experimental designs. In fact, this approach has been explored several times in neuroimaging, with the experimenter sitting next to the patient in fMRI studies for instance, making eye contact through the borehole of the machinery, 336 holding his hand, 337 or playing collaborative games. 338 Another factor that is often neglected in attention-related research is the inherent richness of a "complex stimulus" versus a "refined stimulus". To perceive complex stimuli requires at least attention, active understanding and integration across multiple time scales. 328 According to Sonkusare and colleagues, 328 sensory systems may be better tuned to receive complex rather than refined stimuli, evidenced by a better perception of natural images over abstract forms, or by the perception of human language over isolated sounds.

Apparently, complex stimuli have a weak dynamic stability, 339 matching that of the sensory systems, which are highly correlated with each other. 340 For example, a small part of a dynamic natural event, such as moving a door handle, can lead to a sudden change in a scene, such as a person entering through a door. These dynamic scene changes can lead to dynamic and unstable activities in areas of the nervous system as early as those on the periphery or in primary sensory areas. 341 Nevertheless, a drawback of using naturalistic paradigms is the lack of means to probe or measure the level of engagement that may vary across participants according to cultural background or personal preferences. Although is possible to alleviate this by using eye tracking, concurrent physiological recordings and post hoc questionnaires, the nature of the real-time engagement in the absence of action remains unclear. 328 Part III

BACKGROUND AND OBJECTIVES

Overview of our Research

Science is more than a body of knowledge. It is a way of thinking; a way of skeptically interrogating the universe with a fine understanding of human fallibility. If we are not able to ask skeptical questions, to interrogate those who tell us that something is true, to be skeptical of those in authority, then, we are up for grabs for the next charlatan (political or religious) who comes rambling along.

-Carl Sagan

Attention is a dynamic process that involves the activity of different structures that adapt both to our needs and to the world around us. Its process begins at the perceptual level, allowing us to detect elements of the environment by activating sensory areas that send signals to more complex brain regions. At the same time, higher cognitive areas select a mental goal based on memories, experiences and preferences of what to do in certain situations, and send a descending signal that is contrasted with the incoming information. This comparison process consumes a large part of the available cognitive resources, activating and suppressing sets of cortical areas, creating an activation sequence that ensures an adequate motor response. This response in turn allows for feedback on perception and modulation of future behavior.

However, this sustained pattern of attention can be diverted to stimuli that are irrelevant to the taskat-hand, temporarily reducing the amount of cognitive resources available for processing and execution. Among these irrelevant stimuli, the processing and execution of two tasks simultaneously may lower the ability to deliver fast and accurate responses to both activities, while environmental distractions may inadvertently redirect the focus of attention away from the task-at-hand. Therefore,

"

the main objective of this thesis was to study how a primary task that requires continuous attention can be affected by an additional task or distractions, both at the behavioral and neural levels.

Hence, in the case of the dual task, our main objective was to understand how the execution of two simultaneous tasks decreases its performance, using cognitive resources from common brain areas, causing a bottleneck and forcing serialization. In the case of distraction, our main objective was to understand how an externally induced naturalistic stimulus affects sustained attention, consuming part of the resources allocated on a specific task and disturbs its underlying neural architecture.

To do this, we studied drug-resistant epileptic patients with intracranial electrodes (iEEG) who performed a baseline task called BLAST (Bron/Lyon Attention Stability Test; more details below). Then, the same patients performed BLAST under two new conditions: one in which they had to perform an additional but equally important task; and another in which someone tried to distract them using visual or auditory stimuli. Despite the existence of an extensive literature related to attention control in multitasking situations and distraction, these usually privilege work with non-invasive methods, such as scalp EEG, magnetoencephalography (MEG) or fMRI, but none of these methods is capable of providing anatomical and temporal information as precise as the iEEG. On the other hand, work with epileptic subjects is not without its problems, but our team has the necessary experience to collect and process this information in the best possible way, supported by protocols and results published in high impact journals on numerous occasions.

Furthermore, current trends in neuroscience advocate experimental settings with greater ecological validity, since the human brain seems to be tuned to respond better to complex and enriched scenarios than to simplified ones. This is why we have decided to conduct our experiments using iEEG, specifically SEEG, respecting as much as possible the ecological value in data collection.

Part IV EXPERIMENTAL RESULTS

Study No. 1

Identification of specific neural populations forming a bottleneck that prevents multi-tasking and two possible solutions to reduce interference between attention-demanding tasks Diego Mac-Auliffe 1 2 , Benoit Chatard 1 , Mathilde Petton 1 , Anne-Claire Croizé 3 4 , Florian Sipp 1 , Benjamin Bontemps 1 , Adrien Gannerie 1 , Olivier Bertrand 1, Sylvain Rheims 1 5 , Philippe Kahane 3 4 6 , Jean-Philippe Lachaux

Introduction

Some tasks do not go well together. For example, you might find it difficult to remember your passenger's spouse name while driving in hazardous conditions; because processing external stimuli interferes with the retrieval of information from long-term memory. The interference between attention-demanding tasks 228 has been extensively studied using the dual-task paradigm, 3 in which participants must respond to two separate sets of stimuli according to two distinct sets of rules. The main observation is that performance is lower when the tasks are performed together than when they are performed separately, in a single-task condition, 170,184 even when the stimuli and responses of the two tasks involve distinct sensory and motor systems. 238,342,343 The performance decrement from the single-to the dual-task (DT) condition has been called the DT cost. 169 The current interpretation of the DT cost is that tasks compete for resources at a stage intermediate between perception and response production, devoted to response selection 196 according to the taskrules. 14 This competition has been theorized in the Response Selection Bottleneck (RSB) model, 3,170,186 in which any task that is not fully automatized requires the participation of a common set of brain regions: the RSB. The RSB could not process two tasks at the same time and would therefore impose their sequential -rather than parallel -execution. 188,344 A direct prediction of the RSB model is that any continuous task should prevent the concurrent performance of a secondary task, if none of the two is fully automatized. 345 In practice however, such complete exclusion is rarely observed because very few tasks require the response selection process to be truly continuous. For instance, under normal driving conditions, eye-tracking studies have shown that attentive sampling of environmental stimuli and response selection can occur only once every second, with a minimal impact on performance. 346 It leaves some time for a secondary task, such as recovering specific information from long-term memory, 347 at least when driving is easy.

It follows that despite the constraint imposed by the RSB, the ability to perform a secondary task T2 during a "continuous" primary task T1 might depend primarily on the existence and duration of recurrent "gaps" during T1, defined as time-intervals with no requirement for the RSB. In other words, the two tasks might be performed "simultaneously" provided that they used the RSB alternatively, in which case they could be said to be "dynamically compatible", because the dynamics of RSB recruitment by the two tasks would be compatible.

The primary goal of the present study was to find neural evidence for dynamic compatibility (or incompatibility) between two tasks and turn that theoretical concept into an observable phenomenon. It required: a) a DT paradigm combining a primary "continuous" task T1 with a secondary task T2; and b) the identification of neural populations involved in both T1 and T2 (the RSB) as well as c) a time-resolved measure of their activation by T1 to identity possible "gaps" and their duration in relation to T2.

Time-resolved recordings of specific neural populations can be performed in epilepsy patients implanted with intracranial EEG electrodes (iEEG). 300,305 We recorded twelve patients in a DT paradigm reminiscent of our introductory example: participants were asked to perform a verbal fluency test (T2) 348,349 while processing and responding quasi-continuously to external visual stimuli (T1: a rapid succession of short visual search tasks). 167 We identified a set of cortical regions involved in perception, language and control processes during T1 and T2 -the bottlenecks -which dynamics of activation was incompatible between the two tasks. However, our data also revealed the neural implementation of clear strategy changes to avoid bottlenecks, including the diminution of an excessive cognitive control. Altogether, our study provides novel insights regarding the impossibility (and possibility) of multi-tasking based on the large-scale dynamics of cortical activations and deactivations.

Materials and Methods

Stimuli and Tasks

BLAST (T1) -see annex N°1

The continuous attention task was adapted from the BLAST paradigm published by our group 167 to detect transient failures of executive attention. In short, BLAST repeatedly asks participants to find a target letter (the Target) in a subsequent two-by-two square array of four letters (the Array), with new letters every trial (Target and Array) (see Fig. 19). Each trial starts with the central presentation of the Target for 200 ms, followed by a mask (# sign) for 500 ms, until the presentation of the Array which remains on screen until the manual response, or for a maximal duration of 3000 ms. The next trial starts after a 800 ms pause, with no visual or auditory feedback of any kind. The global instruction was to avoid errors and to keep a steady and reasonably fast pace, with an explicit analogy to car-drivers who avoid accidents at all costs, but nevertheless move forward at a decent speed. BLAST was first performed by participants in a single-task (ST) condition, with no exogenous distractions, and then in a dual-task (DT) condition, simultaneously with a Verbal Fluency Test (see below). 348

Verbal Fluency Test (T2)

The Verbal Fluency Test (VFT) is primarily used in clinical settings as a diagnostic tool, 351 or to assess cognitive functions in patients suffering from Alzheimer Disease or schizophrenia. 349,352,353 It provides a good evaluation of the ability to retrieve lexical knowledge 354,355 and more generally, of executive control. 356,357 In its most typical versions, participants are asked to provide as many words of a given semantic category (semantic VFT) or starting with a given letter (letter or phonemic VFT), as possible in a given time. 357 VFT performance relies heavily on language and executive processes 358 and at the neural level, on the left Inferior Frontal Gyrus (IFG, including Broca's area), the dorsolateral Prefrontal Cortex (PFC), the premotor cortex, and the right cerebellum. 359 To maximize the demand on the central executive system, we combined the two VFT variants into one task: a letter was chosen at the beginning of the experiment and participants had to provide names of a given category (animals, names of people, towns, tools …) starting with that letter. Category names were given by an experimenter sitting in the same room as the participant, in its remote peripheral field (see Fig. 20). The timing of the VFT was not computerized so that the switch to a new category occurred as soon as the participant showed obvious signs s/he was running out of answers -which was better appreciated by an experimenter than by a software. Apart from providing new category names, the experimenter remained still and silent throughout the experiment. VFT performance was analyzed offline from a video recording of the session. To mimic naturalistic conditions, participants received no instruction to prioritize BLAST or the VFT.

In addition, this study makes use of several datasets recorded in separate sessions while participants performed short functional localizers designed to identify iEEG sites involved in visual perception, auditory perception, semantic and phonological processing, verbal and visuo-spatial working memory as well as visual attention respectively. 279,360 Although those data cannot be fully described in this manuscript (see Annexes N°2), we will refer to them each time they provide important insights regarding the function supported by the cortical sites of interest for our study.

Behavioral Analysis

Our DT design came with two advantages: a) the experimental situation resembled naturalistic conditions (when asked a question that requires an access to long-term memory, people must rarely

give their answer within a precise, computerized time-limit, besides TV shows and video games), 325 and b) it let participants choose the optimal time-windows to carry out the secondary VFT task T2. The downside was that it was difficult to assess precisely when participants engaged in T2. The only periods when participants were surely performing T2 was just before they provided an answer. They might have engaged in T2 at other times covertly, but we chose to study the impact of T2 on BLAST performance and T2-related neural processes in the windows preceding overt verbal responses.

One remaining issue is to estimate the duration of such windows: since the cognitive processes leading to the verbal responses are covert, there is no way to assess their duration and to specify a window during which participants are surely engaged in response selection for the VFT. Yet, a reasonable estimate could be derived from single-unit recordings performed during free-recall. Gelbard-Sagiv and collaborators 361 observed that the activity of individual neurons in the human hippocampus increased about 1500 ms before the onset of the verbal report, when they recalled video clips they had seen recently and when a neuron with a strong response to that clip was being recorded. Since activity of interfered with T1 at the behavioral level. Of course, participants might have engaged covertly in T2

during "T2-free windows", but it is our best possible detection of T2-free periods and any misidentification would simply make our analysis too conservative. A straightforward randomization procedure was then designed to statistically estimate the impact of T2 on performance: a) first, the average reaction time (RT+) and accuracy (i.e. % or correct BLAST responses, AC+) were calculated across all T2-windows (N windows for N VFT verbal responses); b) the same procedure was performed on a random selection of N T2-free windows (excluding any window within 2s of a response) to compute a mean reaction time and accuracy value (RT-and AC-) for that selection; c) that last procedure was repeated 10,000 times to generate a distribution of 10,000 surrogate RT-and ACvalues, to which RT+ and AC+ were compared, indicating of whether T2 had a significant negative impact on T1's reaction time and accuracy.

Electrophysiological Analysis

High-Frequency Activity between 50 Hz and 150 Hz (HFA [50-150] hereinafter, also termed 'high-gamma' by some authors) was extracted from iEEG time-series following our usual procedure. That procedure converts raw signals into time-series of neural activity which approximates neural spiking activity at the population level 303 and correlates tightly with behavior, even in real-time 362 and at the single-trials level. 279

Bipolar HFA Extraction

The data were recorded with a standard 128-channels acquisition system (Micromed, Treviso, Italy), bandpass filtered online from 0.1 to 200 Hz and sampled at 512 Hz in all patients. At the time of acquisition, the data were recorded using a reference electrode located in white matter, and the signal in each recording site was subsequently re-referenced to an adjacent channel on the same electrode (bipolar montage). A bipolar montage reduces signal artifacts common to adjacent electrode contacts (line-noise and distant physiological artifacts) and improves the spatial resolution of the recording to a few millimeters 297,303 -slightly from different from subdural grid electrocorticography 303 -by cancelling out effects of distant sources that spread equally to both adjacent sites through volume conduction. It might complicate functional connectivity analysis based on phase estimation, 363 but no such analysis was performed here.

The frequency band of interest [50-150 Hz] was defined from preliminary time-frequency (TF)

analyses of the iEEG data using wavelets, 364 performed with an in-house software package for electrophysiological signal analyses (ELAN) developed at INSERM U1028, Lyon, France, 365 and from previous studies by our group. 303 Raw data was extracted by using a homemade Matlab scripts (the Mathworks, Inc.) and transformed into HFA trial-matrixes with the following procedure. 279,366 Continuous iEEG signals were first bandpass-filtered in multiple successive 10 Hz wide frequency bands (e.g. 10 bands from [50-60 Hz] to [140-150 Hz]) using a zero phase shift no causal finite impulse filter with 0.5 Hz roll-off. The envelope of each bandpass-filtered signal was then computed with a standard Hilbert transform (see Fig. 22), 367 then down-sampled 64 Hz and divided by its means across the entire recording session and multiplied by 100, to express each value as a percentage (%) of that mean (normalization).

HFA Binarization

An additional analysis of HFA trial-matrix was specifically developed to discriminate between "silent" vs "active" time-windows for any given recording site and to identify possible gaps left by T1 during which neurons could engage into T2. While a single-neuron can be said to be silent when it fires no action potential, the concept is difficult to transpose at the population level. In the specific context of this study, the best approximation for such "quiescent" state was during the baseline period of T1

(immediately before the onset of the Target). However, since T2 could potentially generate some intrinsic neural activity -this is our working hypothesis -we had to select episodes of relatively "pure" T1 activity.

For this reason, and considering the impact of T2 on behavioral performance -anticipating the results of the behavioral analysis provided in the results section -the baseline was chosen from the best 50% trials of T1 (the 50% fastest correct trials). Our best estimate of the "neutral" or "quiescent" state of a given neural population recorded with iEEG, was therefore the activity recorded in the best 50% T1 trials during a period (200 ms) immediately preceding the Target onset (shown in the white frame of Figure 24). The median of all HFA values measured during those windows -called Vmed -was computed for every iEEG site, and used to binarize the HFA signal: assigning a state 'on' or 'off' to every time-sample of the experiment. Figure 24 and 25 illustrate that simple procedure, which was performed independently for each iEEG site. The end-result is a binary raster-plot, which reveals the impact of T1 and T2 on the local neural activity in the ST and DT conditions (see Fig. 25 and26). Several statistical procedures were then applied to identify periods with enhanced activity due to T1 or to T2, and to study the interaction between the two.

Identification of T2-related neural responses

Binarized HFA signals were also used to identify iEEG sites in which T2 had an effect on the local neural activity. Using the same logic as for the behavioral analysis, a T2-window of 2s was defined for each of the N verbal response of the participants. Then, the percentage of 'on' samples was calculated across N T2-windows (PCT+), and for N randomly chosen T2-free windows (PCT-), repeating the surrogate procedure 10,000 times to create a distribution of 10,000 surrogate PCT-values, to which PCT+ was compared to obtain a p-value (and apply a statistical threshold of p<0.05, Bonferroni-corrected for multiple comparisons, as FDR correction could not be applied here; see Fig. 27). In the Results section, iEEG sites identified with that procedure are said to have an "abnormally dense activity" during T2.

Detection of sites of interference between T2 and T1

To identify iEEG sites in which T2 interfered with T1, the analysis was based on the observation that T1 trials repeat roughly every 2 seconds: 800 ms of fixation followed by 200 ms of Target presentation, a 500 ms mask and roughly 500 ms before the earliest button presses (see Fig. 19). Given that convenient value, similar to the duration of T2-windows, an estimate of the typical density of 'on' states during optimal T1 performance could be estimated from the fastest correct trials, by calculating the proportion of 'on' sites in a [-800 ms : 1200 ms] surrounding the Target onset (see Fig. 28). Any evidence that the density of 'on' state during T2-windows was greater than during "high performance"

T1 windows was indicative of a possible interference between the two tasks at the neural level, with an excess of neural activity due to T2. The actual comparison used a Wilcoxon sign-rank test to compare between the density of 'on' states measured in the N T2-windows (N density values ranging between 0 and 1, i.e. one density value for each T2-window) and the density of 'on' states measured in the N T1 windows corresponding to the N fastest correct trials. The result was an "Interference detection test" which identified all iEEG sites with a higher density of 'on' states during T2-windows (p<0.05, Bonferroni correction). Comparison of T1-related activity between the ST and DT conditions. Finally, a similar procedure was used to test whether the activity of specific regions involved in executive control varied between the beginning and the end of the ST session of BLAST. The motivation was to test for a possible automatization of BLAST throughout that initial session (see results). The analysis was as previously described, except that the comparison was performed between the first and last 20% trials (50 trials for each group).

Correlation Analysis

For every pair of iEEG sites, and for every VFT verbal response, we considered a 10s window centered on the response [-6000 : 4000 ms; response = 0 ms] and calculated the correlation coefficient (R+)

between the two HFA trial-matrixes for that window. R+ was compared to a population of 10,000 surrogate correlation coefficients created by repeating the following procedure 10,000 times: a) consider two random iEEG sites separated by more than 30 mm and for each of them, a 10s window around a verbal response (with no temporal overlap between the two windows), then b) compute the correlation coefficient R-between the corresponding HFA signal, c) repeat for i = 1 to 10,000. The initial R+ coefficient was considered to be significant if greater than all surrogate R-values (p < 0.0001, corresponding to a conservative Bonferroni correction). This procedure was used to identify VFT responses with a significant correlation between two iEEG sites.

Anatomical Display

The precise anatomical location of the electrodes (and their MNI coordinates) were obtained by aligning each patient's pre and post-implantation MRIs using the NUTMEG toolbox 368 and IntrAnat, 369 a specific toolbox interfacing with the BrainVisa software (IntrAnat Electrodes, GIN INSERM U1216, Grenoble, available at https://f-tract.eu/index.php/softwares/). Participants' behavior was recorded and inspected using an in-house software -BrainTV Replay -while anatomical representations were made using HiBoP, also developed by our group and available here (https://github.com/hbp-HiBoP/HiBoP/releases/tag/2.2.3a).

BrainTV Replay is an updated version of our original BrainTV software. 360,370,371 It is designed to visualize a "replay" of the entire experimental session for any participant, with a simultaneous display of the video, the behavioral responses and HFA activity measured for all iEEG sites (see Fig.

Results

Behavioral Analysis

Our main assumption was that high performance in T1 (i.e. fast reaction times, few errors) requires a specific dynamic pattern of cortical activations and that T2 might interfere with that pattern in the DT condition to decrease performance. Accordingly, the T1 response selection process should be delayed (slower reaction time) or cancelled (the participant would either not respond or take a bet with a 50% chance of failure) when performing T2 concurrently.

The results of the "ST behavioral analysis" (see Fig. 30) largely confirmed that prediction at the global performance level: overall, reaction times were slower and accuracy lower in the DT than in the ST condition (with a significant increase in reaction time in 11 of the 12 participants). The more detailed "DT behavioral analysis" also revealed that T1 performance in the DT condition was impaired when participants actively searched for T2 answers (all patients had either a significantly slower reaction time or a lower accuracy in periods immediately preceding verbal responses, as exemplified in Figure 31). Overall, the behavioral data obtained in the ST and DT conditions largely confirmed the difficulty for patients to perform both tasks at the same time, which motivated the subsequent search for interferences between the two tasks at the neural level.

Electrophysiological Analysis

Marked differences were observed between T1 activation patterns in the ST and DT conditions. As an illustrative example and an introduction to our analysis strategy, Figure 32 shows the response in the left Precentral Gyrus to all T1 trials in the two conditions, sorted by reaction time and accuracy. Besides the higher error rate in the DT situation, the most visible difference is the presence of sustained blocks of activation in DT, which are largely absent in ST and incompatible with the fine temporal structure of the neural activation pattern supporting T2 (as visible for the fastest correct trials).

To identify cortical sites with a similar effect, we ran the interference detection procedure described earlier to detect iEEG sites with increased HFA before T2 verbal responses ("T2-windows"), as compared to other time periods of the DT session, including the best T1 trials. We found that the optimal T1 electrophysiological response was disrupted in DT in six major cortical areas: the left Dorso-Lateral Prefrontal Cortex (DLPFC); the Lateral Temporal Cortex (LTC); the Anterior Insula bilaterally (AI), the junction of the Anterior Cingulate Cortex and the Pre-Supplementary Motor Area (ACC/preSMA), the Precentral Gyrus/Sulcus (Precentral cluster), and the Basal Temporal Cortex (BTC). We now discuss each cluster separately.

Precentral Cluster

In the Precentral cluster, we found effects in the Precentral Gyrus (1 LH, 1 RH, 2 patients) and in the Precentral Sulcus (2LH, 1 RH, 3 patients) with similar neural responses in all sites. The optimal T1

response -for the fastest correct trials in the ST condition -was characterized by two separate phases of activation, in response to the Target and throughout the visual search process, until the manual response (see Fig. 32). In the DT condition, a strong and sustained activation was mostly present during the bad trials (i.e. slowest reaction time and/or higher error rate). Recordings from the same sites during additional tasks (the functional localizers) revealed a strong activation during a rhyming task emphasizing phonological processing, and during a verbal working memory task (data not shown). 360 These are strong indications that our electrodes sampled a region specialized in covert speech and verbal rehearsal. That might explain the local interference between T1 and T2, because the participants seem to have used a verbal strategy to encode the T1 Target, while the search for specific words in the latter also requires a covert linguistic production. The observed disturbance could most likely be interpreted as direct evidence of a specialized bottleneck, involving a specific cognitive component shared by the two tasks. Yet, we also found clear evidence for a strategy change during T1

for some patients, who seem to have used a non-verbal strategy during the best trials, with a strong attenuation of the double activation peak (see Fig. 32, again). dlPFC Cluster

Two types of effects could be distinguished in the lateral prefrontal cortex. In the Inferior Frontal Sulcus immediately adjacent to Broca's pars triangularis, a double activation peak was observed in three sites (LH, 3 patients) while participants encoded the Target and searched the Array (see Fig. 33), very much as it was observed in the Precentral cluster. In all three sites, the interference in the DT condition produced a response attenuation for the fastest correct trials, and a contamination by T2 during the slower and/or incorrect trials, characterized by a sustained activation. In separate verbal localizers, that sub-cluster was particularly active during semantic and phonological processing, as well as during the retrieval of items stored in verbal working memory. This is consistent with a role in manipulating verbal working memory, information as required by T2 and T1 when the strategy is verbal. The An interference pattern was also observed at three other sites more anterior in the DLPFC, specifically in the Middle Frontal Gyrus above the anterior pars triangularis (LH, 3 patients, see Fig. 34). There, T1related activity was different from the previous pattern, with no response during the Target presentation and an activation by the end of the Array presentation extending to the short inter-trial pause. The role of that sub-region in both tasks is less easy to understand, but the corresponding sites were selectively activated by language and verbal working memory localizers. These observations were mirrored by responses in the Paracingulate Sulcus (one site in each hemisphere, 2 patients, see Fig. 36) although the activity rose more progressively throughout T1 trials.

Data from the functional localizers revealed a similar increase in all tasks, emphasizing executive control (visual search, working memory). As often seen before, the neural activation during the best T1 trials (i.e. 50% fastest correct trials) was weaker or even absent in the DT conditions, compared to the ST condition. That specific result should catch our attention and will be discussed further, considering that this specific anatomical cluster is believed to support general executive control, rather than specific cognitive processes: it suggests the possibility of a general change of approach to cope with the DT situation, much more global than a simple strategy change from a verbal to a visual encoding of the Target, for instance.

Basal Temporal Cluster

Finally, an interference pattern was also observed in three sites around the Fusiform Gyrus (2 in the LH, 1 in the RH) (see Fig. 38). Interestingly, all three sites were located in subregions of the visual cortex specific to precise object categories, as revealed by responses to a functional localizer showing several categories of pictures. Both sites in the left hemisphere were located in the Visual Word Form Area (selective to letter strings), while the right hemisphere site was most efficiently activated by face stimuli (but still responsive to letter strings). The activation of these sites during T2 was not unexpected, since participants could rely on mental imagery to remember specific faces, find names starting with the given letter, or visualize spelling. Still, it is quite remarkable that such activity could be detected online, revealing in real-time the strategy used by the participant. Incidentally, T2-related activities turned out to be stronger in the right hemisphere when the participant was searching for people's names vs. other categories, such as fruits.

Network Analysis (and the possibility of indirect interference)

Our visualization software, BrainTV Replay, allows for the joint review of HFA trial-matrixes of two sites, as superimposed curves synchronized with video recordings, which is extremely convenient to detect high-correlation episodes visually (see Fig. 39). During the preliminary visual review of the data, we were immediately struck by extremely high correlations between HFA time fluctuations of specific sites distant from each other. This motivated a quantitative search for significant functional coupling between the sites reported above, when several clusters could be recorded in the same patient. The analysis revealed patterns of very high and significative correlation episodes for specific verbal responses and cortical areas (as illustrated between the left Precentral cortex and the left BTC in Figure 39a); however, few of them were systematic enough to be reported. The most reproducible correlation was observed in two patients (P3 and P11), between the left and right Anterior Insula, where we found significant correlations between both hemispheres for more than half of T2's verbal responses (13 out of 16 for P3, and 17 out of 31 for P11) (see Fig. 39b for an example). Although limited, our results

showed that "the" central bottleneck should rather be thought as a network of bottlenecks, where tasks interact dynamically with each other. This also raises the possibility that a secondary task like T2 could also interfere indirectly with T1, by recruiting cortical regions that do not belong to the T1 network, but that are coupled to some of its components and interact with T1 through that interaction.

Fine Tuning of Executive Control

To conclude, we specifically investigated the differential involvement of the executive control network in the ST and DT conditions. As reported above, some sites in the DLPFC, the AI and the ACC/preSMA were less active during the best trials of BLAST in the DT than in the ST condition, as recapitulated in Figure 40. The figure reveals a significant difference between the density of 'on' states in the two conditions, when considering the same number of trials with the fastest correct responses (see methods). In some sites situated in essential nodes of a network supporting cognitive control, an attention-demanding task such as BLAST evoked almost no response in the DT condition, which comes as a surprise. We reasoned that participants might have automatized BLAST through the ST session, which always came before the DT session; in which case an attenuation of the response to BLAST should already be visible within the ST session. To test that hypothesis, we tested whether activity in the executive network was lower at the end than at the beginning of the ST session. For all sites discussed in this section, we measured the density of 'on' states for each trial, in a critical [900 : 1200 ms] window relative to the Target onset, and compared the values obtained for the first 20% of the trials (50 trials at the beginning of the ST session) and the last 20%. We found no significant difference except for one site in the ACC where activity was greater by the end of the experiment. It is therefore unlikely that the reduced response of the executive control network in the DT condition was caused by the automatization of BLAST. It is most likely due to an effective reduction of control with no performance decrement, which suggests that participants exerted more control in the ST condition than strictly necessary.

Discussion

Our main objective was to observe and understand the interference between two attentiondemanding tasks at the neural level. To the best of our knowledge, that had never been done with direct electrophysiological recordings that can reveal the recruitment of a focal neural group by a cognitive task at a subsecond temporal scale. Inspired by the Response-Selection Bottleneck theoretical framework, 3 we searched for specific neural populations that the two tasks would have to activate at the exact same time, that is, a situation of dynamic incompatibility that would exclude dualtasking de facto. Although several fMRI studies have already identified potential bottlenecks, 232,235,236,263,374 their temporal resolution was not sufficient to demonstrate that two tasks are strictly mutually exclusive, with no possibility for a fast-multiplexing process giving the false impression that they are performed simultaneously. 375 Also, fMRI suffers from the fact that the BOLD signal is an indirect measure of neural activity: for instance, the observation that two tasks cause an increase of the BOLD signal in the same voxel does not fully explain why the tasks interfere, because that effect could reflect the activation of two different neighbor neuronal populations that do not compete with each other (see Fig. 41). 375,376 A true bottleneck is a specific neuronal population that is required by the two tasks at the exact same time, and it can only be identified with focal recordings of that population with a very high temporal precision. iEEG is the only measure of human brain activity with the desired spatio-temporal precision. 300 We hypothesized that the simultaneous performance of two attention-demanding tasks T1 and T2 might rely on temporal gaps in the dynamic pattern of neural activations induced by those tasks, e.g. transient time-windows within T1, during which neural activity returns to baseline level and can be used for T2. We reasoned that if such gaps were long enough, it might be possible for neural populations to participate in T2, thanks to a time-sharing mechanism that would enable dualtasking. 194 We identified gaps in several cortical sites active during T1 but they were usually relatively short -around one second -and not long enough to allow the recall process required by T2, as it had been evaluated by direct neural recordings during a free-recall task similar to the VFT. 361 In principle, that observation formally excludes any form of parallelism between the two tasks we present, which are quite representative of naturalistic tasks involving a simultaneous attention towards external stimuli and internal representations. Although some authors have proposed that parallelism might be possible in some instances, 169,377 several arguments go against that hypothesis. 203,378,379 In short, we propose that our two tasks are "dynamically incompatible": one must be stopped for the other to unfold. Indeed, the participants were mostly confused and frustrated in the DT condition, and reported after the experiment that they found it very difficult to perform the two tasks simultaneously.

We specifically found that the two tasks co-activated iEEG sites in virtually all the regions of the Multiple Demand System (MDS) 159 : in the DLPFC (in the Inferior Frontal Sulcus and the Middle Frontal Gyrus), the Anterior Insula, the Precentral Gyrus, and the ACC/preSMA; the remaining component, the Intraparietal Sulcus, is rarely sampled by stereotactic EEG electrodes. 162 The MDS is a broad domaingeneral and process-general network involved in a large variety of demanding cognitive tasks, 162,246,380 such as BLAST and VFT. Therefore, it is not surprising that it constitutes a strong bottleneck preventing efficient dual performance. What we show is that quasi-continuous tasks such as BLAST create a situation of dynamical incompatibility in the MDS with tasks that require an access to long-term memory.

We also found clear evidence that cortical regions of the MDS shared by the two tasks do not work in isolation from each other: activity was strongly coupled across large distances, from instance in the Anterior Insula bilaterally. It follows that the central bottleneck should always be considered as a distributed network: interference between tasks is due to a network of bottlenecks, which strongly interact with each other.

Besides the MDS, we also found process-specific bottlenecks in the language network and in high-level visual areas such as the Visual Word Form Area or the Fusiform Face Area. Interestingly, those bottlenecks do not fall into one of the usual categories of the DT literature at central 14,159 or peripheral levels. [START_REF] Veale | How is visual salience computed in the brain? Insights from behaviour, neurobiology and modeling[END_REF][START_REF] Diederich | Are patients with Parkinson's disease blind to blindsight?[END_REF] Peripheral bottlenecks usually refer to sensory or motor systems which interfere when two tasks involve sensory stimuli of the same modality (e.g. simultaneous sounds) or overt responses of the same type (e.g. left index movements). Yet, BLAST and the VFT create a competition that is mainly between overt and covert representations: perceiving letters on a screen vs. mentally imagining how a word is spelled. Therefore, our study provides clear evidence that any combination of tasks that involves a competition between sensory perception and imagery should be incompatible, because they rely on common high-level sensory areas (intermediate between peripheral and central bottlenecks). 235 9.5.1 Bypassing the Bottleneck(s)?

Our data revealed two possible mechanisms to reduce interference in a dual-task condition and although they were found for a specific combination of tasks, we believe our findings might extend to more general situations. First, we found evidence for clear strategy changes for the BLAST task between the ST and DT conditions. During BLAST, the target can be encoded, and to some degree searched for, using either verbal or visuo-spatial working memory (rehearsing the letter with covert speech or taking a visual 'snapshot'). In some participants, their strategy could be deduced from the activity of regions supporting phonological processes, such as the lower Precentral Gyrus, when they were sampled by iEEG electrodes. Our data revealed striking instances where those regions withdrew from the BLAST task for the most successful trials in the DT session. Considering the fact that the VFT task heavily relies on verbal processes, 358 it seems like an efficient way of coping with that specific combination of tasks. Interestingly, that effect was not observed for the least successful trials, suggesting that the participants might have found it difficult to stabilize such an alternate and possibly less natural strategy.

We observed similar effects in the MDS, which is much more surprising. The Anterior Insula, the Anterior Cingulate Cortex, the Pre-Supplementary Motor Area and the Inferior Frontal Gyrus are part of a network which activity is not believed to depend on the type of strategy used. A recent article summarizing a number of findings on executive processes concluded that they form the core of the executive Multi-Demand Network (core eMDN), in charge of "executive processing and cognitive control by initiating and maintaining cognitive sets, coordination behavioral responses and guiding behavior in general". 165 Beyond differences in terminology and delineation -core eMDN, MDS or saliency network -these regions are believed to support general cognitive control rather than a particular strategy. BLAST is a demanding task designed to prevent automatization 167 which activates the core eMDN consistently, at least in the ST condition. Therefore, we did not expect that BLAST could be performed efficiently by some participants with virtually no activation of the core eMDN, especially during the best trials in DT.

One might argue that the core eMDN might primarily activate in the DT condition when participants try to perform both tasks at the same time, because that situation requires more executive control 141,144,147 -but the core eMDN is also active when BLAST is performed in isolation.

Alternatively, one might remind that the AI and ACC have also been associated with errormonitoring 146,[381][382][383] which could explain their strong activation during the worst trials of BLAST in the DT condition. However, that suggestion is incompatible with the fact that those regions are systematically active in the ST condition, even for successful trials. One remaining explanation is that participants might have automatized BLAST during the ST session, and therefore exert less executive control during the DT session. Yet, BLAST is so repetitive and long -with 250 trials -that signs of automatization should already be visible in the ST condition when comparing the beginning and the end of the experiment. This is not the case.

We believe that our data rather support the following scenario: in the DT condition and consciously or not, participants sometimes shift cognitive control away from BLAST and towards the VFT, with no impact on BLAST performance, quite surprisingly. It follows that BLAST can be performed fast and accurately with less control (i.e. less activation of the eMDN), which provides an unexpected way out of the central bottleneck: performing one of the two tasks "as if" it was fully automatized.

Since cognitive control has been associated with mental fatigue, one might then wonder why participants do not use that strategy also in the ST condition. One possible explanation is that the need for control is so deep that they must be forced to do so by a secondary task: forced to "let go" simply because they are unable to control everything. The implication of that finding is rather profound, as it suggests that we might be exerting too much cognitive control, and therefore too much mental effort, on tasks that could be done almost effortlessly. This conclusion is reminiscent of a study that used the attentional blink phenomenon to show that control participants, compared to meditation experts, over-process task-relevant stimuli 384 -too much effort, again. More generally, it should remind us that focused attention should be dissociated from mental effort, as clearly demonstrated by multiple insightful contributions in a seminal book on that topic. 385

Introduction

When performing a behavioral task, executive control ensures the optimal performance during attention-demanding tasks -as characterized by stable, fast and accurate behavioral responses. 246,274 This allows the brain to engage specifically in the perceptual, cognitive and motor processes relevant to the task at the exclusion of any other that might sidetrack limited cognitive resources. [START_REF] Wolfe | Guided Search 4.0: Current Progress with a Model of Visual Search[END_REF] Distraction occurs when executive control fails to filter out task-irrelevant processes, 262,263 which might imply excessive processing of external events (external distraction), 331 or task-unrelated spontaneous cognition (internal distraction). 386,387 Although detrimental to performance, 264 the capture of attention by stimuli irrelevant to the task at hand has obvious evolutionary advantages:

without a defense mechanism to quickly reorient attention towards potentially dangerous stimuli, such as the approach of a predator, our species would most likely have gone extinct. 261 Nowadays, the same mechanism allows the driver to notice an approaching ambulance even when focused on the cars just in front. Focus and distraction should therefore be thought as two complementary mechanisms, which compensate the excess and pitfalls of each other. 261 The neural implementation of the distraction by external stimuli has been extensively studied 263,277,281 and there is now general agreement that the reorientation of attention towards unexpected stimuli outside of the context of the current task is mediated by the VAN, comprising part of the Orienting Network. 14,16 This VAN would act as a circuit breaker 277 while a DAN comprising primarily the FEF and the IPS would ensure that attention stays on task-relevant information. 4 Recently, our team published a study showing that a region at the interplay between the two networks, in the IFS, might quickly analyze incoming sensory events in relation to ongoing task goals and decide whether they should be processed attentively or discarded. 12 The current study examines the effect of external distraction on performance. While it is expected that the reorientation of attention away from critical information should impair performance, the exact impact of external distraction on the large-scale networks subserving task performance has yet to be documented. In particular, it remains to be determined how irrelevant external events cause a deviation of such networks from their optimal dynamics, and cause performance decrement.

The main reason for this lack of understanding might be simply be technical: because attention must quickly reorient towards distractors, the effect of such events occurs at the subsecond scale to disrupt the cortical dynamics, and non-invasive neuroimaging techniques lack the temporal resolution to reveal such events. 375 In contrast, iEEG has become increasingly popular to study the fine dynamics of the large-scale networks subtending human cognition, with a very high precision both in time (ms)

and in space (mm). 371 We used iEEG to study the effect of naturalistic external distractions on the dynamic neural architecture of an attention-demanding task, called BLAST (Bron/Lyon Attention Stability Test). BLAST requires that participants perform a paradigmatic working memory/visual search task updated every two seconds, so that the level of attention allocated to the task can be monitored continuously. Seven patients implanted with intracranial electrodes accessing the main attention networks performed BLAST in a distracting environment, with an experimenter sitting next to them to generate frequent naturalistic distractions (sneezing, moving, dropping objects ...) as could occur in a real working environment.

We found that distractions caused an interference with the optimal neural dynamics of the BLAST network in three major regions: in the medial parietal lobe, the anterior insula and the extrastriate cortex. Our results suggest that distraction primarily acts by preventing the narrowing of attention onto the task physical domain -the region of space that should be prioritized for optimal information extraction and by disrupting executive control.

Stimuli and Task

BLAST (T1) -see annex N°1

The attention-demanding task was adapted from the BLAST paradigm published by our group. 167 It repeatedly ask participants to find a target letter (the Target) in a subsequent two-by-two square array of four letters (the Array), with new letters every trial (Target and Array) (see Fig. 19). Each trial starts with the presentation of the Target for 200 ms, followed by a mask (#) for 500 ms (also known as the encoding phase), then being replaced by the Array, which remains on screen until the manual response (also known as the visual search phase). The next trial starts after a pause of 800 ms, with no feedback on performance and no auditory stimulus.

During iEEG recordings, BLAST was performed on a PC using Presentation® software (Version 18.0, Neurobehavioral Systems, Inc., Berkeley, CA) and synchronized with the EEG acquisition system. The letters were presented foveally in black on a light gray background, and participants pressed a button on a gamepad with their preferred/non-preferred index finger to indicate if the Target was absent/present in the Array, respectively. The trials sequence was pseudorandomized, with the same number of trials for « Target present » and « Target absent », and performance was measured on a total of 250 trials, for a total duration around eight minutes (depending on each participant's reaction times).

The global instruction was based on establishing a constant and reasonably fast pace, avoiding errors (with an explicit analogy to car-drivers who avoid accidents at all costs, but nevertheless move forward at a decent speed). BLAST was first performed by participants in an isolated room without exogenous distractions as a single task (ST) condition, and then with exogenous distractions delivered randomly (ST-DIST, see below).

Distraction (DIST)

To determine the effect of distractions on BLAST, both were combined as follows: while each participant performed BLAST, an experimenter sat within its peripheral visual field (see Fig. 20)

distracting it with movements and/or sounds caused directly (e.g., an arm stretch or a question aimed at finding out if the task is too difficult) or indirectly (e.g., by moving the pages of a notebook or ringing a cell phone). Because an experimenter provided distractions, it was possible to tailor their frequency to periods of silence with enough distance to avoid keeping participants constantly distracted, by choosing the type of distraction at random. Thus, there were periods within ST-DIST in which the participant was not being distracted (DIST-freeW, see below) and others where it was actively being distracted (DIST-W, see below). BLAST performance was analyzed offline from a video recording of the session. Participants were not given any instruction regarding distractions.

Behavioral Analysis

Our design came with two main advantages: A) the experimental set-up simulated naturalistic conditions (DIST are delivered without prior cues, with a combination of modality and intensity capable of unintentionally surprising the participants), and B) it allowed us to study what happens to the participants who are effectively distracted.

In general, it was simple to specify at what point participants started being susceptible to DIST (i.e., immediately after delivery). However, even now we are not aware of any research with iEEG that explicitly indicates how long its effect lasts at the brain level on a task that requires constant attention.

Although there are studies that speak of the activation of the DMN with iEEG (a network usually related to distraction), the majority focused on distractions of endogenous 279 but not of exogenous origin. According to the Gatekeeper theory, 12 a distracting stimulus that captures attention involuntarily must first be perceived pre-attentionally and then disturb the task-set imposed by a previously executed task, leading to a decline in its performance. For this, the cortical activity must be maintained at least over 250 milliseconds to speak of a distractor capable of overcoming this second "attentional filter". Additionally, we granted enough time to participants to experience the distractor and delay their response or make a mistake, finally opting for a 2 seconds post-distraction window (i.e. DIST-W), thus considering the total development of a distraction.

Therefore, it was possible to study the effect of distraction on T1 performance in the following ways:

A) comparing the overall performance (i.e., RT Reaction Time and ER Error Rate) between ST and ST-DIST ("ST Behavioral Analysis") conditions; B) in the ST-DIST condition, comparing behavioral performance between an "N" number of DIST-W ("N" being variable among participants) and "other" windows in which the participant was not being distracted, delivering responses to T1 ("DIST Behavioral Analysis"). Both analyses were performed on each participant individually.

For the DIST Behavioral Analysis, all continuous time-segments free of any distraction and longer than 2s were divided into consecutive non-overlapping 2s windows called DIST-freeW (surrogate data), excluding any window within 2s of a distraction. Then for the "N" DIST-W, the average RT and ER were calculated across all T1 responses within the DIST-W and compared to a distribution of 10,000 RT' and ER' values, where RT' and ER' are repeatedly calculated from a randomly selected set of 2-seconds DIST-freeW. We used a T-value with a threshold of p=0.05 to assess whether distractions had a punctual effect on RT or ER.

Electrophysiological Analysis

Bipolar HFA Extraction

We followed exactly the same procedure of data collection described in our previous study by extracting raw data using bipolar derivation, taking a frequency band of interest [50 : 150 Hz], dividing the signal in segments of 10 Hz each, converting them into envelopes using the Hilbert transform, and averaging them using the standard method of our team.

HFA Binarization

HFA were binarized for each channel, following our previous approach with the intention to maintain consistency between studies. The procedure was the following one: a neutral baseline was approximated (with little or no influence of T1 or DIST related processes) from the HFA measured during the pre-stimulus (a 200 ms immediately preceding T1's Target onset) for the "best" trials; that is, correct T1 trials with the fastest reaction time (in that case, the 50% fastest correct trials). The median value was calculated over all samples within the baseline of those trials (P x B samples, where P is the number of samples within the [-200 : 0 ms] window and B the number of "best" trials), serving as a threshold Tmed to define "on" vs "off" states: a sample was considered "on" if HFA is above that threshold. This procedure converted the original HFA trial-matrix for the entire experiment into a binary signal, allowing computing the density of "on" samples for any time-window.

Statistical Analysis

Identification of iEEG Sites Responsive to T1

To select sites "directly" responsive or "indirectly" coupled to DIST, we initially considered only those sites whose HFA was also modulated by T1. These were identified based on their 50% "best" T1 trials with a simple Wilcoxon sign-ranked test, comparing the distribution of "on" and "off" samples (0s and 1s) at a given latency after the Target presentation with the distributions of "on" and "off" samples at the baseline of the same trials, between [-200 : 0 ms]. T1-responsive sites were characterized either by a significant increase or by decrease of "on" states at certain latencies induced by T1.

Identification of iEEG Sites Responsive to DIST

The procedure to identify sites responsive to DIST was based on the comparison between the density of "on" samples in the 2s windows following the distractions (DIST-W), and the density of "on" samples in 2s windows centered on the 50% best T1 trials (T1-BEST-W). More precisely, "N" DIST events were identified in the video, and for each of them, a proportion of "on" samples was computed within a [0 : 2000 ms] window (with 0 ms indicating the onset of the distraction), and compared with the proportion of "on" samples computed within a [-800 ms : 1200 ms] window surrounding T1 trials (with 0 ms indicates the onset of the Target) using a Kruskal-Wallis comparison. Any significant difference between the two density distributions was considered an indicative that distractions cause an excess of "on samples" in DIST-W relative to T1-BEST-W, and a sign that DIST possibly interferes over T1.

Identification of iEEG Sites Coupled to DIST

This procedure was motivated by the visual inspection of the data that revealed that in some participants there was a striking correlation between the HFA in the auditory cortex triggered by auditory DIST and other specific sites. Given that most DIST were associated with sounds, we considered such sites as responsive to DIST, and investigated whether some of them supported T1related processes, in which case external sounds would affect T1-related processes indirectly through, or in relation with, the auditory cortex.

We considered for each participant one site particularly responsive to DIST sounds, from the correlation of its HFA with the envelope of the sound track of the video. Each site was used as a seed to calculate the correlation coefficient "R" between its smoothed HFA trial-matrix and all other iEEG sites (smoothing was performed by convolution with a 2500 ms square window). Each R correlation coefficient was compared with a distribution of 10,000 correlation coefficients R'i (i = 1 to 10,000) calculated between random pairs of iEEG sites, making sure that one of the two signals was flipped in time. The comparison with the surrogate provided a p-value and we considered as significant any Rvalue higher than 0.05 divided by the number of iEEG sites (Bonferroni correction). Both detection procedures complemented each other: the first may not have detected sites that respond strongly to visual DIST (e.g., motion), while the second may not have detected sites that respond primarily to auditory DIST.

HFA and Performance

We hypothesized that if the activation of some sites by DIST interferes with T1, the activity should be significantly different between the best and the worse T1 trials. To detect such effects, the 20% best T1 trials (50 fastest correct trials) and the 20% worst T1 trials (including 50 misses, errors, and slowest correct trials) were defined and compared by their HFA measured in a critical [0-1200 ms] window using a Kruskal-Wallis comparison.

10.4 Results

Behavioral Analysis

Overall, the global performance was not affected by DIST, indicating that the effect of distractions on ST-DIST was transient (see Fig. 42). Additionally, the analysis comparing the detailed performance between DIST-W and DIST-freeW revealed in some participants a significant slowing down of RT (one participant) or ER (three participants) after DIST.

Electrophysiological Analysis

The aim of our analysis was to detect sites that were both responsive to DIST and to T1 (and therefore, are part of the T1 network). All such sites are possible locations where DIST could interfere with optimal T1 performance. Overall, only three cortical clusters displayed a reproducible response to DIST in more than one participant, apart from the auditory cortex (we do not include it because it does not support T1-related process and cannot be a site of interference). These clusters included the Posterior Cingulate Cortex (PCC) and Precuneus (both in the posterior medial wall), the Anterior Insular Cortex (AIC) and the Extrastriate Visual Cortex (EVC). Additionally, two T1-related regions showed particularly clear effects for DIST, but since they were found each in a single participant, they are presented as anecdotic results by the end of this section. We now discuss each cluster separately.

Anterior Insular Cortex (AIC)

Two participants had electrodes passing through the right AIC. One of the two sites increased its activity during DIST-W (see Fig. 43, top half), while the other was detected via a positive correlation with the auditory cortex (see Fig. 43, bottom half). In line with our previous description of the T1 network, both sites were activated during the visual search phase (between the Array presentation and the manual response), and after errors. This activity is usually interpreted as a neural correlate of goal-oriented behavior, 151,388 and its response to distracting events as the necessity for increased control when attention is drawn away from T1 by DIST. An alternate interpretation recognizes the AIC is a key component of the salience network, 389 reacting to potentially important incoming stimuli.

However, the lack of response to T1 Targets does not fit with this interpretation, given that Targets are highly relevant stimuli in the present context. One might argue that the AIC reacts only to unpredicted stimuli, but again, this is hard to reconcile with the activity observed during T1's visual search phase.

PCC/Precuneus

Two participants showed a very similar response pattern in the medial region within the lower parietal cortex: one in the left hemisphere (see Fig. 44, top half) and the other in the right hemisphere (see activity in the auditory cortex driven by DIST sounds (mostly speech, matching with its anatomical localization as part of the language network). The site was active during T1's visual search phase -and to a less extent, during T1's encoding phase -and might participate in a verbal strategy ("rehearing" the letter which must be found until it is detected in the array) considering the role of the IFG in verbal working memory. Activation by DIST sounds might correspond to a similar verbal process triggered by speech, in which case one would expect interference with T1. It is very unlikely that this response is specific to that particular participant, since it matches the function described in the literature for the IFG, thereby clarifying that it would be desirable to have a larger pool of participants.

Discussion

Our main objective was to demonstrate how a salient exogenous distraction affects the execution of a task that demands constant attention, and how it can disrupt its functioning by consuming available cognitive resources. For this, we tried to find specific neuronal populations recruited during distraction that could hinder the execution of the primary task. Based on current models of attention in which target selection depends on the combined influence of exogenous stimulus-driven factors and endogenous goal-driven objectives, we attempted to deliver distractors salient enough to redirect attention by surpassing the cognitive control delivered by the executive network. We addressed these issues by using a naturalistic strategy and recording with iEEG, in order to provide a direct way to measure neural activity with high temporal and spatial accuracy. 300 We reasoned that a stimulus that is distracting enough to attract attention unintentionally should activate cortical areas used by the main task that interrupt its proper processing.

We identified those electrodes that presented a longer or denser high-gamma band activity during distraction than during the best BLAST trials, labelling them in different cortical regions. Subsequently, we relied on responsive auditory electrodes to identify by coupling other cortical areas related to overactivation during auditory distractions. Through both methods, we were able to create a widely distributed interference network in the cerebral cortex. Below, we will discuss each area where these electrodes are found and their function.

Anterior Insular Cortex

Thanks to our analysis, we were able to find two electrodes positioned on the right AIC. The AIC, along with other frontal and prefrontal areas, is consistently activated through a variety of non-automated cognitive tasks that demand control. As already mentioned, it participates in maintaining top-down control, guiding goal-oriented behavior 151,388 and increasing its activation during mental effort, thus justifying the increase of HFA during incorrect responses, especially on the right side. [391][392][393] The AIC is part of a saliency network within the Cingulo-Opercular Network, 14,15,389,394,395 setting the presence of new and salient events in a transitory way 396 or relevant to behavior in a continuous way. Within the possibilities of our electrodes, we found no evidence of transient activity during BLAST Target presentation or the encoding phase, but we did find evidence of sustained activity during the memory recall and visual search phase.

According to Cai, 397 the AIC detects increased sensory demands for cognitive control by mediating the salience of a target with the executive network, and then triggers and maintains the necessary actions for goal-directed behavior. According to our results, distraction would not hinder BLAST's encoding phase, but instead affect its evocation from memory and subsequent visual search, forcing a sustained response in the most difficult and wrong tests. In addition, the AIC tends to activate continuously during the appearance of salient elements, but focusing on maintaining cognitive control over exogenous stimuli, 394 participating also in the detection of mistakes 382 and activating itself with greater intensity against those perceived consciously. 381

PCC/Precuneus

We were able to find two electrodes on the boundary between the PCC and the Precuneus. Both areas are part of the DMN, with the PCC experiencing increments of HFA during momentary lapses of attention 398 and task independent thoughts, 399 and suppressions during cognitively demanding tasks, 400,401 being able to predict its performance according to the degree of suppression. 402,403 This turns the DMN with the PCC into an essential network for interactions with the outside world, coinciding with our bilateral findings, characterized by high gamma band suppression in the visual search phase in most trials.

Although the PCC is typically associated with the evocation of autobiographical memories, forward planning and unrestricted rest, it also seems to take a direct role in controlling the balance between an internal and external focus of attention. 404 Thus, the PCC would participate in social cognitive processing, especially when it comes to attributing value to a socially relevant everyday situation. 405 Furthermore, there is evidence that an external auditory distraction may affect the execution of a visual task of high cognitive load, with the PCC experiencing an increase in HFA around or above the baseline. 406 These situations partially explain the decrease or absence of suppression experienced by our electrodes in the worst trials, which additionally correlates with an increase in HFA in the primary auditory cortex. This means that distracting sounds could increase the neural activity in the PCC and Precuneus, affecting the performance of BLAST.

Visual Cortices

Those electrodes located in visual areas (two in the right Extrastriate Visual Cortex and one in the right Basal Temporal Cortex) belong to different parts of the visual system and the Dual Visual Stream. [START_REF] Goodale | Separate visual pathways for perception and action[END_REF] The first two locate specifically in the calcarine sulcus and form part of the Dorsal Visual Stream, which extends from V1 to the intraparietal sulcus, sensing motion information, optic flow and self-motion of the perceived elements. 407,408 Its most elementary information is collected in V1 foveally, and sent subsequently to secondary visual areas that expand the surrounding visual field, possibly creating rudimentary directional maps 409 that are sent towards the middle temporal gyrus, where the size of the receptive field is increased. 410 This matches with our findings as there is no activation during the appearance of the BLAST Target, but there is during the expansion of the visual field to search for the

Array. An alternative (or complementary) explanation comes from more updated theories about the Dual Visual Stream that divide it into a lateral part, related to the recognition of object movement and self-motion, and a medial part, related to the continuous monitoring of the location of objects in space, reaching-grasping and vision-for-action processes. 411 This also matches well with our findings, as our extrastriatal electrodes are placed in medial areas, being activated before the button is pressed, and when facing distracting movements of the experimenter in the peripheral visual field.

Moreover, our electrode in the Basal Temporal Cortex is part of the Ventral Visual Stream, specifically located in the lingual gyrus forming part of V1 and possibly V2. According to its location, it should contribute in the sense of major form-processing changes, such as fixing primordial contour-based object representation, and minor color-processing changes. 412 Its HFA pattern remains inactive in most of BLAST but increases around the button-press. Since this electrode possibly forms part of the periphery of the retinotopic map, it could experience a lack of activation during almost all trials due to a visual narrowing, caused by the attention deposited on a foveal element while trying to ignore all surrounding elements. 413 Additionally, the increase in HFA after the button-press could be due to a visual field reset, which attempts to encompass a bird's eye view image of the scene including peripheral visual elements, becoming more noticeable when the participant makes mistakes.

Frontal Cortex

Finally, our inferior front gyrus electrode experiences an increase in HFA during the BLAST visual search phase, as well as during speech-related distractions. This is probably due to its participation in the auditory processing network 414 as well as in the overlaying reading network related to language production, word phonological recording, 415 and verbal working memory, activating the phonological loop and forcing a constant rehearsal of the exposed letter. 104 To summarize, high saliency distractions can interrupt the processing of tasks that require constant attention, both directly by consuming cognitive resources necessary for the task, as well as indirectly by disturbing the functioning of areas do not take part of the task, but affect other associated functions. Thus, distractions not only affect areas that are currently in use, but extend beyond them, causing an overall effect that can take someone completely out of focus. This expanded perturbation fits with a vision of cooperative work, with an orchestrated participation between the different brain systems, leading to a harmonic result that can be unbalanced by a foreign event.

Although the extraction of information from epileptic patients presents a risk for data contamination, 300 our team has been able to create a set of strict rules that minimize these effects. 371 Additionally, and despite the low number of electrodes with significant activity, the use of iEEG with a very high signal-to-noise ratio allows us to obtain statistically significant results with high test-retest reliability between trials, allowing us to draw conclusions even from two electrodes positioned in the same cortical zone. 371 The use of naturalistic conditions makes the data ecologically valid and more consistent with what can usually be found in nature, but at the same time adds uncontrolled variables that could lead to results that are more difficult to reproduce. Despite that they may complicate the control of the experimental protocol, evidence suggests that they cause a greater reaction in participants than those delivered by a computer. 325 That might be because humans often assign mental states to second persons during social interactions due to the uncertainty of their behavior. 324 This makes sense, considering that movement or speech perception triggers several of our electrodes. Furthermore, human brains may be better calibrated to receive ecologically loaded stimuli than artificial ones 327 that are more complex than refined or simplified ones. 328 Despite this, the use of ecologically loaded distractors makes them difficult to standardize because of how noticeable they can be and because of their inherent social burden. 329 For this reason, we believe that in the future it would be interesting to complement our results with physiological and eye activity recordings.

Part V GENERAL DISCUSSION

Dual-Tasking

At the behavioral level, performing two tasks at the same time (BLAST and VFT) led to a performance decrease globally, in line with the general consensus on multi-tasking that any attempt to perform two tasks simultaneously -with no specific training -leads to a drop in performance, compared to the execution of each task in isolation. 3 Additionally, in DT condition, all participants had a lower performance (accuracy and/or speed) in BLAST while searching for answers for the other task (VFT).

Our hypothesis, that we could validate, was that the two tasks used common cognitive resources in specific cortical areas that worked as bottlenecks. 170,393 The executive system partially solves the bottleneck issue by forcing a serial -rather than parallelexecution of the tasks. 198 Once the decision to engage in one of the two tasks has been made, the executive system exerts a top-down influence on regions that must be prioritized for the task, configuring what is called the task-set. 111 Yet, this alternation is possible only if each one of the two tasks can be put on hold sufficiently long to perform the other one, what we called "dynamic compatibility" between two tasks: it implies that none of the two tasks requires truly continuous attention. To test whether BLAST and VFT were dynamically compatible, we identified neural populations (i.e. iEEG sites) activated by the two tasks and checked the duration of their activation.

We organized those populations into functional clusters or Regions of Interest: the Precentral Gyrus/Sulcus; the dlPFC; the Lateral Temporal Cortex (LTC); a cluster combining the AIC, the ACC and the Pre-Supplementary Motor Area; and the Basal Temporal Cortex. Dynamical incompatibility was established for any of those neural populations when the VFT triggered a sustained increase in neural activity longer than the longest silent (i.e. inactive) period during BLAST trials.

Additionally, we searched for neural populations causing indirect interference between the two tasks.

Those were characterized by a) a strong reactivity to BLAST (i.e. a participation in the BLAST network) and b) a significant correlation with neural populations activated by VFT. In other words, such populations are not directly active during the VFT, but the correlation suggests that they can be driven by a coherent activity in the VFT network, which might create an interference with BLAST, given that those populations are reactive to BLAST. We found one such candidate in the ACC/preSMA, correlated with the AIC (two patients), indicating that indirect interference might happen, in addition to more direct bottlenecks. This is because the activation of a task-set involves the participation of multiple neural networks, including some not directly involved in the attentional network, such as the DMN or WM, which could be activated by another task and interrupt the processing of BLAST.

The comparison of the neural responses in the ST and DT conditions brought more insight on how the two tasks might interact. In the Precentral cluster, we found that the response to BLAST differed between the ST condition, and the best and the worst trials of the DT condition. The ST response was characterized by a double peak, while the best trials of DT showed in contrast a suppression of activity, and an increase selective to the visual search phase in the worst trials. Additional data collected in other tasks in the same participants (functional localizers) showed that this region participates in verbal/phonological tasks, in line with the classic functional interpretation of that area. 279,416 Participants would then use a verbal strategy to perform BLAST, at least in the ST condition, interacting with VFT that has a strong verbal component. However, the lack of activation in the best trials of DT suggest that participants change their strategy to avoid that interference and to minimize the impact on BLAST, at least when they can. In contrast, the failure to shift to a "less verbal" strategy during the worse trials of DT would lead to a drop of BLAST performance. A strategy change seems like a natural way out of the bottleneck problem.

In the dlPFC cluster, half of the electrodes showed exactly the same response as the Precentral Cluster, in a region that has been also associated with language processes, including verbal working memory. 416,417 The remaining sites were located in the Middle Frontal Gyrus with an increased activity during the visual search phase, both in the ST and DT conditions. Recordings during the functional localizers revealed a sensitivity to semantic and phonological processing, as well as verbal working memory, 104 and their activity during BLAST (and VFT) might be interpreted as signs of a verbal strategy.

Again, the interference between BLAST and VFT should be thought as occurring not between tasks, but between strategies.

Responses in the Lateral Temporal Cortex confirm this interpretation: we found that in both hemispheres, neural activity was increased in the Middle Temporal Gyrus during the visual search phase of BLAST, in both the ST and DT conditions. Again, functional localizers revealed that this region reacts well to tasks involving verbal working memory and language BLAST and VFT. 418,419 The Cluster formed by AIC, ACC and preSMA point to a somewhat richer understanding of multitasking. Sites in the AIC showed a sustained activity increase during the visual search, for all trials in the ST condition, while that increase was much weaker in the DT condition for the fastest trials, and stronger in the slowest, more difficult trials. This difference between ST and DT could be due to the role of the AIC within the salience network, 389 but more likely in relation to cognitive control, 151,383,391,397,420 when errors become aware. 381,382 The implication is that the AIC might act as a central bottleneck, 393 preventing simultaneous processing of VFT and BLAST. 235 Nevertheless, the lack of activation in the best trials of the DT condition, during BLAST, is intriguing, as it suggests that, again, the bottleneck might be avoided via a change of strategy: a strategy that would reduce executive control to a minimum, during BLAST, would still be compatible with high performance while allowing VFT.

The same pattern was observed in the ACC/preSMA, and the implications are similar. Such similarity makes sense, considering that the ACC/preSMA and the AIC belong to the same functional networks for executive control (the cingulo-opercular network) and for salience detection. 389 The ACC receives direct inputs from the AIC, 393,397 to update the internal task-set models in response to stimuli considered as potentially relevant. 383,396 However, we could observe a slight difference of the response patterns of the AIC and ACC, with a sustained activity after the button-press in the AIC only. One interpretation is that the ACC is not a conflict detector like the AIC, [421][422][423] but participates in rewardbased decision making, receiving feedback from the AIC to decide how much cognitive control to use to control task execution, 146 in turn justifying the increase in cognitive activity in more difficult trials.

The cluster in the Basal Temporal Cortex was sampled by three iEEG sites, two of them in the Visual Word Form Area (in the left hemisphere), and one in the Fusiform Face Area (right hemisphere). There, activity increased during both the encoding and visual search phase, both in the ST and DT conditions.

Functional localizers revealed, expectedly, that the VWFA processed character strings, 424 and it worth noticing that they were also active when participants searched for names starting with a given letter (VFT), which suggests that the VWFA is active as soon as writing is involved, either exogenously or endogenously: the VWFA would therefore constitute a local, peripheral bottleneck specific of the BLAST-VFT interaction, that would lay intermediate between a pure 'sensory' bottleneck and a central, amodal, bottleneck. This conclusion was reinforced by our observations in the FFA, which was particularly active while searching for names of people, argues for the existence of several level bottlenecks ranging from purely sensory or purely motor to central, rather than the classic dichotomy emphasizing only those extremes.

Altogether, our results confirm that the interference between two tasks performed simultaneously is largely due to the difficulty of sharing common cognitive resources. However, we went far beyond that interpretation by identifying the neural substrates of such resources. We proved that they correspond to the activation of a set of cortical regions supporting higher cognitive functions (as cognitive control in the cingulo-opercular network) as well as 'low-level' functions at the perceptual level (visual processing and visual imagery of letters, for instance, in the VWFA). We found that two tasks can be said to be truly incompatible because they relied on specific dynamics of activation that exclude each other, but that the interference should be thought of as occurring between strategies rather than between tasks. Surprisingly, even central bottlenecks considered as indispensable components of tasks requiring executive control, in the ACC and the AIC, can be avoided by strategies reducing the amount of control of one of the two tasks, this might be the most important result of this thesis: we might apply too much control over what we are doing, while high performance could be obtained with less. This echoes frequent observations by elite athletes and coaches that excessive control over routine actions might be detrimental to performance.

BLAST and Distractions

At the behavioral level, external distractions (DIST) did not significantly impair the global performance of the participants; yet reaction times and/or error rates were transiently impaired by distracting events. The emerging picture is that the executive system overactivates in response to distractions to decrease globally their impact and keep the performance intact. [START_REF] Wolfe | Guided Search 4.0: Current Progress with a Model of Visual Search[END_REF]281 At the cortical level, we identified three main regions of interest responsive to external distractions: During BLAST, the AIC was especially active during the visual search phase (between the display of the Array and the button-press). This is somewhat consistent with the literature, which predicts that the AIC should be transiently activated when attention redirects towards novel, salient stimuli, 396 and longer during attentive processing of stimuli that are behaviorally relevant. 151,383 We therefore expected that sustained increase of activity of the AIC during the visual search phase that was even greater as the task became more difficult due to distractions and the need to exert a stronger cognitive control. 391 We also expected the strong activity of the AIC after erroneous responses, because this region is known to be involved in conscious error detection 381,382 and coordinates sensory inputs to domain-specific control processes. 392,425 Altogether, our observations fit with the general idea that the AIC, together with the ACC, the Amygdala and the IFG, 395 form a saliency network, 389,394 in which the AIC performs a type of analysis/interpretation of the stimulus, and the ACC updates the task-set models in response to stimuli that are considered relevant. 383 It is worth mentioning that the increase of activity in the right AIC reaches a plateau faster than the left AIC, 391,393 because that saturation has been proposed to be one of the main "limiting" factors of amodal central processing.

The PCC/Precuneus is a major node of the Default Mode Network (DMN). We found a gradual increase of neural activity during the visual search of BLAST, starting with a marked suppression in the easier trials, shifting to a positive increase in the harder trials (longer reaction times). Major theories regarding the role of the PCC predict that its activity should be transiently suppressed by salient stimuli 402 and during attentive processing of external stimuli. 403 The main cognitive processes associated with that region are the recovery of autobiographical memories, planning for the future, 386,426 as well as the regulation of the focus of attention, 427,428 and the control of the balance between internally and externally directed thought. 404,429 This matches our findings, as the PCC should have its activity suppressed during the best trials, and increased during poor performance, since distractions alter the execution of tasks that require constant attention. In addition, the activity of PCC electrodes was correlated to that of primary auditory areas (i.e., reaction to auditory distractions), matching the findings of a study by Smucny and colleagues,406 in which PCC activity increased after auditory distractions during a visual task. In addition, the fact that most of these auditory distractions had a social origin is consistent with the fact that the PCC has been associated with social cognition, 405 especially when it comes to attributing value to a socially relevant daily auditory situation. 430 Sites in EVC increased their activity exclusively during the visual search phase, which suggests that they do not encode foveal information, but rather more peripheral visual information. Their anatomical location suggest that they belong to the Dorsal Visual Stream, with a preference for motion-related information 407,408 and peripheral visual activity, 409,410 in line with our observations that those iEEG sites were particularly reactive to visual -not auditory -distractions. If we try to be even more precise, our sites would lie in the medial portion of the Dorsal Visual Stream and support most specifically the identification of object movement in three-dimensional spaces, gripping range, and vision-for-action. 411 In addition to the previous regions of interest, we found isolated effects of distractions in the Basal Temporal Cortex and in the Inferior Frontal Gyrus, both in the right hemisphere. The BTC site showed no activity during the target encoding and the visual search, possibly participating in peripheral visual perception only. Its activity was correlated to fluctuations in the primary auditory cortex caused by distracting sounds, possibly marking a multi-sensory expansion of the visual attention field. This would be consistent with the results of another study by our group (see Annex N°4) 390 showing that peripheral parts of the visual cortex are inhibited during the attentive processing of central visual stimuli (the effect we report in this thesis would be the symmetric of that narrowing effect). The IFG showed a slight response during the encoding of the target followed by large increase during the visual search phase. Due to its anatomical situation, this pattern was expected in relation to verbal working memory and the phonological loop. 104,418,419 That recruitment of the IFG would depend upon the strategy used by the participants: performing BLAST using verbal working memory would lead to interference when the language network is activated by social distractions.

Future Improvements

Arguably, I recommend that new data be collected from a larger group of patients, for a denser mapping of the cortical mantle and a higher number of reproducible observations. I would also recommend a finer-level analysis of the impact of the type of search in the VFT paradigm (searching for names of tools vs animals, for instance), or the distractions, to dissociate for instance the effect of visual vs. auditory distractions in the DIST experiment. Since strategy seems to be a key -yet uncontrolled -factor affecting the ability to multi-task, future studies should probably suggest some possible strategies to the patients and contrast their impact on performance, or at least use verbal reports techniques to collect data about those strategies. Other possible improvements include: a)

providing a feedback after each response in BLAST (to identify areas processing feedback information) and b) include additional measures to complement results, such as IQ tests; 393 and c) use of eye-tracker for a more careful monitoring of the executive network supporting cognitive control. 391

General Conclusions

In conclusion, we believe that attention control is necessary to efficiently direct our limited cognitive resources to specific tasks in a selective way. Although there are control mechanisms to preserve these resources, certain circumstances, such as additional tasks or distractions side-track these resources and impair the execution of the primary task, either directly by disrupting the activity of key functional regions involved in that task, or indirectly by activating associated regions interacting closely with those key regions. In short, we believe that attention is essential for the efficient interaction with the environment, and for adapted and timely behavioral decisions based on our internal objectives, experiences and personal preferences.

Figure 1 :

 1 Figure 1: Endogenous attention orientation mixes with exogenous attention orientation ..................

Figure 2 :

 2 Figure 2: The Dorsal and Ventral Attention Networks .........................................................................

Figure 3 :

 3 Figure 3: Major steps of the Visual Saliency Model ..............................................................................

Figure 4 :

 4 Figure 4: Cortical and subcortical pathways for vision .........................................................................

Figure 5 :

 5 Figure 5: Possible routes for saliency computation ..............................................................................

Figure 6 :

 6 Figure 6: The Executive Network ...........................................................................................................

Figure 7 :

 7 Figure 7: The Multiple Demand System ................................................................................................

Figure 8 :

 8 Figure 8: Comparison between an on-task and off-task performance .................................................

Figure 9 :

 9 Figure 9: Comparison between Task-Switching and Dual-Tasking .......................................................

Figure 10 :

 10 Figure 10: The PRP phenomenon ..........................................................................................................

Figure 11 :

 11 Figure 11: The additive effect of the RSB ..............................................................................................

Figure 12 :

 12 Figure 12: The sub-additive effect of alternative bottlenecks...............................................................

Figure 13 :

 13 Figure 13: Possible alternatives to reduce the bottleneck ....................................................................

Figure 14 :

 14 Figure 14: Intracranial electroencephalography (iEEG) ........................................................................

Figure 15 :

 15 Figure 15: Depth electrodes placed strategically under neuronavigation guidance ............................

Figure 16 :

 16 Figure 16: Data collection using SEEG across patients .........................................................................

Figure 17 :

 17 Figure 17: BrainTV Replay .....................................................................................................................

Figure 18 :

 18 Figure 18: HiBoP ....................................................................................................................................

Figure 19 :

 19 Figure 19: Schematic depiction of BLAST ..............................................................................................

Figure 20 :

 20 Figure 20: Dual-Task set-up ...................................................................................................................

Figure 21 :

 21 Figure 21: Behavioral analysis during the ST ........................................................................................

Figure 22 :

 22 Figure 22: Signal processing using Hilbert ............................................................................................

Figure 23 :

 23 Figure 23: HFA trial matrixes ................................................................................................................

Figure 24 :

 24 Figure 24: Pre-binarized matrix .............................................................................................................

Figure 25 :

 25 Figure 25: Binarized DT matrix ............................................................................................................

Figure 26 :

 26 Figure 26: Binarized ST matrix ............................................................................................................

Figure 27 :

 27 Figure 27: Identification of interference during no-T2 windows .........................................................

Figure 28 :

 28 Figure 28: Identification of interference during T1 .............................................................................

Figure 29 :

 29 Figure 29: Entry point of iEEG depth-electrodes across all participants .............................................

Figure 30 :

 30 Figure 30: Mean reaction time during T1 for all participants .............................................................

Figure 31 :

 31 Figure 31: HFA signal measured during a verbal response .................................................................

Figure 32 :

 32 Figure 32: Interference in the Precentral Cluster ................................................................................

Figure 33 :

 33 Figure 33: Interference in the Inferior Frontal Gyrus/Sulcus ...............................................................

Figure 34 :

 34 Figure 34: Interference in the Dorsolateral Prefrontal Cortex .............................................................

Figure 35 :

 35 Figure 35: Interference in the Anterior Insula .....................................................................................

Figure 36 :

 36 Figure 36: Interference in the Anterior Cingulate Cortex and the Pre-Supplementary Motor Area ...

Figure 37 :

 37 Figure 37: Interference in the Lateral Temporal Cortex ......................................................................

Figure 38 :

 38 Figure 38: Interference in the Basal Temporal Cortex ........................................................................

Figure 39 :

 39 Figure 39: Correlation between bottlenecks .......................................................................................

Figure 40 :

 40 Figure 40: Interference control in the executive network and the visual network .............................

Figure 41 :

 41 Figure 41: BOLD signal limitations ......................................................................................................

Figure 42 :

 42 Figure 42: Mean reaction time during T1 for all participants .............................................................

Figure 43 :

 43 Figure 43: Binarized electrodes in the right AIC during the ST + DIST condition ................................

Figure 44 :

 44 Figure 44: Binarized electrodes in the bilateral PCC/Precuneus during the ST + DIST condition. .......

Figure 45 :

 45 Figure 45: Binarized electrodes in the right EVC during the ST + DIST condition. ...............................

Figure 46 :

 46 Figure 46: Binarized electrode in the right Inferior Frontal Gyrus during the ST + DIST condition. .....

Figure 47 :

 47 Figure 47: Binarized electrode in the right Basal Temporal Cortex during the ST + DIST condition. ...

Figure 1 :

 1 Figure 1: Endogenous attention orientation mixes with exogenous attention orientation. This image shows the cortical dynamic during an Attentive Reading Task, contrasting an attend condition on the left vs an ignore condition on the right at 213, 323 and 418 ms after the stimulus onset, with a divergent increase of activity in red in the attend condition after 300 ms. This helps to illustrate how exogenous and endogenous factors combine until the response selection process and motor execution. Finally,

Figure 2 :

 2 Figure 2: The Dorsal and Ventral Attention Networks. Taken from Petersen and Posner.14

Figure 3 :

 3 Figure 3: Major steps of the Visual Saliency Model. Visual information is analyzed in parallel at feature

Figure 4 :

 4 Figure 4: Cortical and subcortical pathways for vision. The majority of fibers (thick arrow) originatefrom the retina and projects to the primary V1 in the occipital lobe via LGN. From there, it divides into the dorsal and ventral visual streams. However, a minority of fibers originating in the retina take a direct route into sSC and IP, bypassing V1. Taken from Diederich et al.[START_REF] Diederich | Are patients with Parkinson's disease blind to blindsight?[END_REF]

Figure 5 :

 5 Figure 5: Possible routes for saliency computation. (a) Columns indicate recognition of visual features or not, and rows indicate if maps contain information about behavioral goals or not. (b) Cortical (black arrows) and subcortical (grey arrows) pathways. A white arrow indicates lack of clarity in transferred information from the initial to the following point. LIP in macaques is roughly the same area as IPS in human beings. PI mean Inferior Pulvinar. Taken from Veale et al. 43

Figure 6 :

 6 Figure 6: The Executive Network. Top half: Distinct cingulo-opercular (black) and fronto-parietal (yellow) control networks, in addition to cerebellar regions (blue circles) are shown on an inflated surface rendering of the human brain. Bottom half: Black lines indicate strong resting state functional connections between brain regions. The thickness of the lines indicates the relative connection strength. For each region (circle), the central color indicates to which network it belongs. The outer color indicates the predominant control signal type of each region (red = set-maintenance; blue = errorrelated and yellow = start cue-related).The cingulo-opercular and fronto-parietal networks are not directly connected to each other but each network is connected to the cerebellar error-network through regions that also carry error information (the Thalamus, dlPFC and IPL). Taken fromDosenbach et al. 141

Figure 7 :

 7 Figure 7: The Multiple Demand System. This system is responsible for managing goal-directed attention, as well as managing stimulus-directed attention when exogenous stimuli match endogenous objectives. Taken from Duncan et al. 159

  These limitations are central to a debate with broad social significance, such as the use of cell phones while driving, among other similar examples (see Fig.8).

Figure 8 :

 8 Figure 8: Comparison between an on-task and off-task performance. On the left panel, is possible to observe the performance of a participant executing an attention-demanding task, with each blue dot signalling the response to a specific trial, with fast and accurate responses. On the right panel, it is possible to observe the performance of the same participant off-task, with slower and less accurate responses, forcing mistakes marked as red crosses. This probably happens because the participant engages in task-irrelevant cognitive processes. Taken from Petton et al (2019).167

Figure 9 :

 9 Figure 9: Comparison between Task-Switching and Dual-Tasking. As previously mentioned, in the Task-Switching the presentation of the stimuli and their respective responses occur in a strictly sequential way (i.e. stimulus-response), while in the DT paradigm, the presentation of external stimuli occurs before the response. Adapted from Strobach et al (2018).174

Figure 10 :

 10 Figure 10: The PRP phenomenon. During this, both stimuli appear at the same or different times. The time difference between stimuli presentation is called SOA. After perceiving S1, information enters cognitive areas, allowing a RS1 and R1. Despite that S2 can be perceived overlapped with S1, RS2

Figure 11 :

 11 Figure 11: The additive effect of the RSB. The bottleneck is located at the response selection stages.

  13).203,205

Figure 13 :

 13 Figure 13: Possible alternatives to reduce the bottleneck. Panel A: Hypothetical processing durations in Task 1, Task 2 and the single task at the beginning of practice. Panel B: hypothetical processing durations in Task 1, Task 2 and the single task at the end of practice. Illustrated is the situation of a latent bottleneck: An unequal practice-related reduction of the duration of the processing stages in Task 1 and Task 2 has caused that the bottleneck stages (RS) in Task 1 and Task 2 do not temporally overlap anymore. In that case, dual-task costs vanish after practice as can be inferred by the comparison of RT2 and RT in panel B. Taken fromSchubert et al. 190

Figure 14 :

 14 Figure 14: Intracranial electroencephalography (iEEG). a), e) and f) are examples of depth electrodes (SEEG); b), c) and d) are examples of subdural electrodes (ECoG). Taken from Enatsu and Mikuni. 296

Figure 15 :

 15 Figure 15: A post-implant skull X-ray showing multiple depth electrodes placed strategically under neuronavigation guidance. The targets are predetermined on the basis of presurgical evaluation. In this individual, the electrodes were placed in bilateral hippocampal formations, orbitofrontal gyri and cingulate gyri. Some of the depth electrodes were secured with an anchor to minimize postimplantation movements (arrows). The fainter electrodes ends represent the tails of the depth electrodes that connects to amplifier and are located over the scalp (arrowheads). Taken from Shah et al. 313

Figure 16 :

 16 Figure 16: Data collection using SEEG across patients. a) Shows the reaction of an electrode in a particular patient under different experimental conditions; b) and c) show triangulation of anatomical areas in different group levels, according to amplitude in a certain frequency; d) organize functional areas per region. Taken from Jerbi et al.303

6 "Figure 17 :

 617 Figure 17: BrainTV ReplayAdditionally, data extracted from BrainTV Replay can be fed along into a software called Human Intracranial Brain Observations Player or HiBoP (see Fig.18). HiBoP allows to unify the anatomical and functional information of a great number of electrodes, allowing its visualization offline, by creating a structural build of a 3D model or mesh for each patient, from MRI pre and post implantation information and electrode depth coordinates and plots iEEG signals on top of it, in a four-dimensional brain visualization. This software also allows to visualize the activity of several depth electrodes for several subjects simultaneously on a common mesh.

Figure 18 :

 18 Figure 18: HiBoP. Picture of the Human Intracranial Brain Observation Player.

1 1-

 1 Inserm U1028, CNRS UMR 5292, DYCOG Team, Lyon Neuroscience Research Center

9. 3 . 1

 31 ParticipantsIntracranial EEG recordings (iEEG) were collected in twelve patients candidates for epilepsy surgery at the Epilepsy Departments of the Grenoble and Lyon Neurological Hospitals. Eleven to fifteen semirigid, multi-lead electrodes were stereotactically implanted in each patient (stereotactic EEG -SEEG, a special type of iEEG).350 Each electrode had a diameter of 0.8 mm and, depending on the target structure, consisted of 10 to 15 contact leads 2 mm wide and 1.5 mm apart (i.e. 3.5 mm center-tocenter, DIXI Medical Instruments). Selection of sites to implant was entirely based on clinical purposes, with no reference to the present experimental protocol. All electrodes showing traces of epileptiform activity were excluded from the present study (visual inspection by the clinical team). All participants were native French speakers with normal or corrected-to-normal vision, and had given their written informed consent; all experimental procedures were approved by the Institutional Review Board and by the National French Science Ethical Committee.

Figure 19 :

 19 Figure19: Schematic depiction of BLAST (T1). Each trial starts with the presentation of a Target, followed by a mask (#). Thus, this 700 ms window is known as encoding phase. The mask is then replaced by the Array, which remains on screen until the manual response. Thus, this window of variable length is known as visual search phase. Adapted fromPetton et al. 167

Figure 20 :

 20 Figure 20: Dual-Task set-up: the experimenter is standing or sitting at the bedside, in the peripheral visual field of the participant while the participant is performing BLAST (T1) -the verbal interaction is limited to a strict minimum, necessary for the VFT (T2).

Figure 22 :

 22 Figure 22: Signal processing. Because the extraction of a wide frequency band causes the spectral power to decrease at higher frequencies, the use of a Hilbert transform compensates for this decrease by creating ten successive envelopes of 10 Hz each, and averaging them all into a single High Frequency Activity (HFA) envelope over the entire session for each electrode, to then create HFA trial-matrixes.Taken fromVidal et al. 360

Figure 23 :

 23 Figure 23: HFA trial matrix. The creation of a HFA trial-matrix was centered on each BLAST stimulus and then were averaged together. Thus, all 250 coloured horizontal lines that forms the trial-matrix correspond to a single trial with normalized amplitude fluctuations according to the bar at the right, with each black dot marking a button press, sorting responses by reaction time. Additionally, the first vertical white line marks the Target presentation, while the second white line marks the Array presentation. In those trial matrixes that included additional conditions to BLAST, we marked with solitary black dots those answers associated to the additional task, or to the onset of a distraction.

Figure 24 (

 24 Figure 24 (top left): An example of neural response recorded in the left Precentral Gyrus during T1 in the DT condition, across all trials. Every row of the matrix corresponds to one trial, and HFA is colorcoded as a function of time (x-axis). Trials are sorted by reaction time (black line) for correct and incorrect responses (in the lower and upper part of the matrix, respectively). HFA is expressed as a percentage of the average HFA value for that particular iEEG site, across the entire recording. Large black dots indicate verbal responses to T2. The white frame indicates HFA values used to estimate a quiescent baseline state (the prestimulus baseline during the 50 fastest correct trials of T1). The median of all HFA values within that white frame, Vmed, was used to create the binarized version of the matrix shown in Figure 25.

Figure 25 (

 25 Figure 25 (top right): Binarized version of the same matrix (DT condition). By definition, 'off' samples in white (resp. 'on' samples, in red) have an HFA value below (resp. above) Vmed. During the baseline, the signal goes through a noisy succession of 'on' and 'off' states in equal number (at least, and by definition, for the best 50% trials); at other latencies, horizontal cyan lines indicate latencies with an abnormally high (or low) proportion of 'on' samples across the 50% fastest correct trials (see methods for details). By construction, cyan lines indicate time-windows during which the neural population is not available for T2.

Figure 26 (

 26 Figure 26 (bottom): Binarized trial matrix for the same iEEG site as in Figure 25 but in the ST condition.The comparison between Figure25and 26 is illustrative of a strong interference between T1 and T2.

Figure 27 :

 27 Figure 27: Identification of interference during no-T2 windows. Identification of 'on' samples between 'N' T2W [-2000 : 0 ms] and 10.000 random surrogates not related to T2 (i.e. T2-freeW), and statistical comparison (p<0.05) with a Bonferroni correction.

Figure 28 :

 28 Figure 28: Identification of interference during T1. Identification of 'on' samples between 'N' T2W [-2000 : 0 ms] and 50% "best" T1 trials [-800 : 1200 ms], and statistical comparison by using a Wilcoxon signed-rank test (p<0.05) with a Bonferroni correction.

  The direct comparison of HFA signals recorded in separate sessions can be biased by line-noise variations, even if a bipolar montage reduces such contamination. By comparing the binarized HFA trial-matrixes recorded in the ST and DT sessions, we reduced that bias: our analysis compared the proportion of 'on' values in the M best trials of the ST and DT conditions for every sample within a [-1000 : 3000 ms] interval surrounding target onset (T = 0 ms), where M was set to half the number of correct trials in the DT session (i.e. considering the 50% fastest correct trials of BLAST to minimize the effect of T2). A Wilcoxon test was used to compare for each sample the distributions of M activity values (0's or 1's) for the ST and DT conditions (p<0.05, with a False-Discovery Rate correction for multiple-comparisons).

17

 17

Figure 29 :

 29 Figure 29: Entry point of iEEG depth-electrodes across all participants. Projected onto the MNI-single participant brain template.

Figure 30 :

 30 Figure 30: Mean reaction time during T1 for all participants. In the ST (teal) and DT (magenta) conditions (a star sign indicates a significant difference between the two conditions, two-sample t-test; p<0,05). Accuracy is indicated in white (percentage of correct responses).

Figure 31 :

 31 Figure 31: Example of HFA signal measured during a verbal response ("un coq" or "a rooster"). Values above Vmed ('on' samples) are shown in red, 'off' samples are shown in blue. Several of our analyses measured the proportion of 'on' samples with T2-windows, in the 2 seconds before the response (vertical frame, in blue). T1 performance is color-coded for three consecutive trials (two hits in green, one miss in gray), and the height of the colored rectangles is proportional to reaction time ('min' = minimum reaction time across the entire experiment, same for 'max').

Figure 32 :

 32 Figure 32: Interference in the Precentral Cluster. Sites with an interference between T1 and T2 are displayed at their precise anatomical location onto a 3D representation of the participant's brain. The matrix representation reproduces the display of Figures 24, 25 and 26, for the single-task and dualtask conditions for one of the sites. Patients id [Px] and iEEG site names, from left to right and top to bottom: R'9 [P4]; S'9 [P5] (matrix displayed); M9 [P2]; R9 [P1].

  response attenuation in the best trials of the DT condition would again indicate to a shift towards a different strategy, in which the Target would be encoded as a visual template -possibly -rather than in a verbal, phonological form.111

Figure 33 :

 33 Figure 33: Interference in the Inferior Frontal Gyrus/Sulcus. Sites with an interference between T1 and T2 are displayed at their precise anatomical location onto a 3D representation of the participant's

Figure 34 :

 34 Figure 34: Interference in the Dorsolateral Prefrontal Cortex. Sites with an interference between T1 and T2 are displayed at their precise anatomical location onto a 3D representation of the participant's brain. The matrix representation reproduces the display of Figures 24, 25 and 26, for the single-task and dual-task conditions for one of the sites. Patients id [Px] and iEEG site names, from left to right and top to bottom: F'9 [P4] (matrix displayed); Y'13 [P4]; G'12 [P9].

Figure 36 :

 36 Figure 36: Interference in the Anterior Cingulate Cortex and the Pre-Supplementary Motor Area. Sites with an interference between T1 and T2 are displayed at their precise anatomical location onto a 3D representation of the participant's brain. The matrix representation reproduces the display of Figures 24, 25 and 26, for the single-task and dual-task conditions for one of the sites. Patients id [Px] and iEEG site names, from left to right and top to bottom: S2 [P7]; Z'2 [P5] (matrix displayed).

Figure 38 :

 38 Figure 38: Interference in the Basal Temporal Cortex. Sites with an interference between T1 and T2 are displayed at their precise anatomical location onto a 3D representation of the participant's brain. The matrix representation reproduces the display of Figures 24, 25 and 26, for the single-task and dualtask conditions for one of the sites. Patients id [Px] and iEEG site names, from left to right and top to bottom: E'7 [P4]; F6 [P1] (matrix displayed); L'3 [P5].

Figure 39 :

 39 Figure 39: Correlation between bottlenecks. A) BrainTV display showing an episode of very high and sustained correlation between two bottlenecks, one in the Precentral Cluster and the other in the Basal Temporal Cluster. Curves correspond to HFA activity measured in the last 10 or 20 seconds before a verbal VFT response, as indicated in the figure. B) Same representation showing a robust correlation between two sites in the left and right Anterior Insulae

Figure 40 :

 40 Figure 40: Interference control in the executive network and the visual network. Comparison of BLASTrelated activity in the ST and DT conditions for six sites (five of them supporting executive control in the anterior insula, DLPFC and ACC/preSMA, the last one supporting visual processes in the inferior temporal gyrus). For each site, BLAST-related neural activity is represented as a binarized matrix for the ST (top) and DT (middle) conditions. The curves at the bottom correspond to the average of those matrix for the M fastest correct trials, where M equals half the number of correct trials in the DT condition. Horizontal lines indicate samples with a significant difference between the DT and ST curves (black), or a significant increase relative to the [-200:0 ms] pre-Target baseline, for the ST and DT conditions respectively (in teal and magenta) (Kruskal-Wallis test, with a False-Discovery Rate correction for multiple comparisons). From left to right and top to bottom, iEEG sites and [Patients id] are : X'9 [P3]; X7 [P3]; E4 [P7]; K'10 [P5]; Z'2 [P5]; E'7 [P4]

Figure 41 :

 41 Figure41: BOLD signal limitations. There is a large number of studies based on fMRI in which it is very difficult to define if during a dual task condition, the increment of activity in a common voxel occurs for both tasks at the same time, or activates for each task separately in time. This is because the BOLD signal only show an indirect measure of the neural activity, with a limited temporal definition that does not allow for a distinction in the range of milliseconds. Taken fromSzameitat et al. 374

Figure 42 :

 42 Figure 42: Mean reaction time during T1 for all participants. In the ST (teal) and ST-DIST (magenta) conditions (a star sign indicates a significant difference between the two conditions, two-sample t-test; p<0,05). Error rate is indicated in white.

Figure 43 :

 43 Figure 43: Binarized electrodes in the right AIC during the ST + DIST condition. Left half: precise location of each sites on each patient's individual anatomy. Right half: each spectrogram corresponds to its respective brain at the side. Their construction is the same as the one explained in Figure 24 before binarizing. Each one shows HFA amplitude changes between electrophysiological responses to BLAST. Top right: the dark blue rectangle on the top marks encoding phase (between Target and Array presentation), and the light blue line marks visual search phase (between Array and Button-press). The white horizontal line divides between right (trial on the bottom) and wrong trials (trials on top).

Fig. 44 ,

 44 Fig.44, bottom half). Activity during T1 was characterized in both sites by a neural suppression relative to the pre-stimulus baseline level (suppression of HFA). Interestingly, the comparison between the best and worst trials of T1 (see Material and Methods -HFA and Performance) revealed that the suppression was reduced or absent in the worst trials, which should be considered in relation to the fact that both sites were positively correlated with the auditory cortex. Consequently, DIST sounds driving activity in the auditory cortex might increase neural activity in the PCC/Precuneus, which must be suppressed for optimal T1 performance. This might cause a performance decrement when an attention-demanding task is performed in a noisy environment.

Figure 44 :

 44 Figure 44: Binarized electrodes in the bilateral Posterior Cingulate Cortex/Precuneus during the ST + DIST condition.

Figure 46 :

 46 Figure 46: Binarized electrode in the right Inferior Frontal Gyrus during the ST + DIST condition.

Figure 47 :

 47 Figure 47: Binarized electrode in the right Basal Temporal Cortex during the ST + DIST condition.

  the right AIC (rAIC), the bilateral Posterior Cingulate Cortex/Precuneus (PCC/Precuneus), and the right Extrastriatal Visual Cortex (rEVC). All three ROIs showed reproducible responses across patients. They were completed by two other regions, in which a strong reaction to exogenous distractions was observed, although in one patient only, but these are worth mentioning: the right Temporal Basal Cortex (rBTC), and the other in the right Inferior Frontal Gyrus (rIFG).

Table of Illustrations

 of

  Selection of sites to implant was entirely based on clinical purposes, with no reference to the present experimental protocol. All electrodes showing traces of epileptiform activity were excluded from the present study (visual inspection by the clinical team). All participants were native French speakers with normal vision, and gave written informed consent; all experimental procedures were approved by the Institutional Review Board and by the National French Science Ethical Committee.

	10.3 Materials and Methods
	10.3.1 Participants
	Intracranial EEG recordings were obtained from seven neurosurgical patients with intractable epilepsy
	at the Epilepsy Department of Grenoble Neurological Hospital (Grenoble, France) and Epilepsy
	Department of Lyon Neurological Hospital (Lyon, France). Eleven to fifteen semi-rigid, multi-lead
	electrodes were stereotactically implanted in each patient (stereotactic EEG -SEEG). The SEEG
	electrodes had a diameter of 0.8 mm and, depending on the target structure, consisted of 10 to 15
	contact leads 2 mm wide and 1.5 mm apart (i.e. 3.5 mm center-to-center, DIXI Medical Instruments).

12

  Annex N°3 -Table I: MNI-coordinatesFor each site displayed in the figures with the anatomical labelization provided by the Freesurfer software (corrected when obviously inaccurate) (https://surfer.nmr.mgh.harvard.edu)

	P9 P11	G'12 Q'2	-45 -40	41 25	14 14	Middle Frontal Gyrus Inferior Frontal Sulcus	16
	P11	X5	30	20	-6	Anterior Insula
	P11	X'9	-30	23	5	Anterior Insula
	P11	C'12	-60	-37	-12	Middle Temporal Gyrus
	Patient id P12	iEEG site X7	x 33	y 17	z 10	Freesurfer labelization Anterior Insula
	P1 P12	R9 A9	67 52	0 -3	10 -20	Subcentral gyrus Middle Temporal Gyrus
	P1	F6	41	-50	-13	Lateral Occipito-Temporal Sulcus
	P2	M9	38	8	33	Inferior Frontal Sulcus
	P3	X7	34	25	2	Anterior Insula
	P3	X'9	-30	23	5	Anterior Insula
	P4	R'9	-61	7	16	Precentral Gyrus
	P4	G'13	-45	33	21	Inferior Frontal Sulcus
	P4	F'9	-28	44	30	Middle Frontal Sulcus
	P4	Y'13	-31	50	27	Middle Frontal Gyrus
	P4	E'7	-50	-37	-21	Inferior Temporal Gyrus
	P5	S'9	-41	0	40	Precentral Sulcus
	P5	K'10	-42	35	12	Inferior Frontal Sulcus
	P5	Z'2	-11	19	46	Anterior Cingulate Gyrus/Sulcus
	P5	L'3	-36	-52	-11	Lateral Occipito-Temporal Sulcus
	P6	D'7	-55	-42	8	Superior Temporal Sulcus
	P7	E4	41	17	4	Anterior Insula
	P7	S2	11	16	52	Anterior Cingulate Gyrus/Sulcus
						203

Acknowledgements Acknowledgements ........................................................................................................................... Table of Contents .............................................................................................................................. Lexicon .............................................................................................................................................. Table of Illustrations ......................................................................................................................... Part I -GENERAL INTRODUCTION ........................................................................................................

This thesis has been funded by the Agencia Nacional de Investigación y Desarrollo (ANID, ex CONICYT) -BECAS CHILE DOCTORADOS EN EL EXTRANJERO, 2015 -F72160215

Table of Contents Laboratoire .....

Part II METHODOLOGY

Abstract

The execution of a continuous attention-demanding task limits the ability to perform a secondary task concurrently, due to resource limitations at the response selection stage, in what is commonly referred to as a "central bottleneck". The current understanding is that such resources correspond to processing time of specific neural populations necessary for the two tasks. Yet, our understanding of why and how interference occurs is still very limited, mainly because of a virtual lack of data in humans, measuring the dynamics of shared neural ensembles in a dual-task condition. For instance, only direct recordings of well-identified neural populations can reveal a situation in which the time courses of neural recruitment by two tasks are mutually exclusive, what could be called a dynamical incompatibility preventing multiplexing. Such recordings can be obtained in drug-resistant epilepsy patients, implanted with intracerebral electrodes (iEEG). We recorded 12 patients while they performed a sustained attention-task (repeated visual search tasks) and a verbal fluency task in a dualtask paradigm. We found that the two tasks interfered in seven cortical regions involved in imagery and language-related processes and cognitive control. Although the neural activity induced by the attention task was not strictly continuous, gaps were not sufficient for neurons to engage concurrently in the fluency task (dynamical incompatibility). Interference was reduced by strategy changes, and most importantly, through a reduction of cognitive control with no performance decrement. Our results suggest that multi-tasking abilities are partially impaired by a tendency to exert more control that strictly necessary.

individual neurons are thought to reflect the access to long-term memory in situations of recall, we considered specifically the 2000 ms period preceding immediately each verbal response as a wise choice to study T2-related processes (in the following, such windows will be called "T2-windows").

Once T2 windows were defined, it became possible to evaluate if T2 interfered with T1 at the behavioral level, not only: a) by comparing of course the global performance (i.e. reaction time and accuracy) between the ST and DT conditions (this analysis is referred to as "ST behavioral analysis" in the results section) (see Fig. 21), but more importantly and precisely, within the DT condition, b) by comparing performance during T2-windows and during windows with no verbal response (referred to as, "DT behavioral analysis"). Both behavioral analyses were performed in every participant, extracting the information individually, averaging and comparing by using a T-test with a p-value <0.05. The "DT behavioral analysis" was performed as follows: in addition to the T2-windows (one window for every verbal response), all continuous time-segments free of any T2 responses and longer than 2s were divided into consecutive non-overlapping 2s windows called "T2-free windows". Any difference in performance between T2 and T2-free windows was considered as a strong indication that T2

AIC and ACC/preSMA Cluster

In the Anterior Insula, we found six sites (2 in the LH, 2 patients, and 4 in the RH, 4 patients) with a deviation from the optimal T1 dynamics in the DT condition (see Fig. 35). The T1 response pattern was reproducible across sites, with a sustained activation during the display of the Array, time-locked to the response. T2 also produced sustained activations, clearly incompatible with the T1 activation pattern. Both left and right hemisphere sites were activated by a verbal working memory task and by tasks emphasizing semantic and phonological processing.

Lateral Temporal Cluster

Three sites activated by T1 in the Middle Temporal Gyrus were also reactive to T2 (see Fig. 37, two sites in the left hemisphere and one in the right hemisphere in two patients). The activation patterns were reminiscent of the Precentral cluster, both for T1 and for the functional localizers, with strong and selective responses to conditions emphasizing verbal processing. We suggest that this cluster might support verbal processes shared by T2 and T1 under a specific verbal strategy. The sharp reduction in activity in the best T1 trials of the DT condition could again be explained by a shift to a non-verbal strategy to reduce interference. Once again, the amount of interference between T1 and T2 seems to depend more on the strategy used rather than on the tasks themselves.

Study No. 2

How environmental distractions interfere with behavioral performance during attention-demanding tasks Diego Mac-Auliffe 1 2 , Benoit Chatard 1 , Mathilde Petton 1 , Anne-Claire Croizé 3 4 , Florian Sipp 1 , Benjamin Bontemps 1 , Adrien Gannerie 1 , Olivier Bertrand 1, Sylvain Rheims 1 5 , Philippe Kahane 3 4 6 , Jean-Philippe Lachaux [START_REF] Raymond | Temporary Suppression of Visual Processing in an RSVP Task: An Attentional Blink?[END_REF] 1 -Inserm U1028, CNRS UMR 5292, DYCOG Team, Lyon Neuroscience Research Center, Lyon, France As more and more working environments lack spatial separations between co-workers ('open-space'), distraction by environmental sounds and events has become a major concern for productivity-seeking professionals, who fear a negative impact on attention and performance. The objective of the present study was to understand how environmental distractions affect neural activity during attentiondemanding tasks. Participants were asked to perform a novel continuous attention task specially designed to detect transient loss of focus due to external conditions (BLAST, a series of short visual search tasks, repeated every 2 seconds). Because distraction is primarily social in working environment, participants were distracted by an experimenter sitting next to them throughout the task, following a predefined series of naturalistic events (sneezing, dropping a pen...) carefully spaced in time. Distractions were identified offline on a video of the experiment to analyze their effect on the large-scale dynamics of the BLAST network, measured with milimetric and millisecond resolution with intracranial EEG (iEEG). IEEG measured in seven participants revealed that external distractions counteract and prevent the suppression of activation in the medial parietal lobe, which is normally inhibited during optimal BLAST performance.

Extrastriate Visual Cortex (EVC)

Two participants displayed a response to distractions in the visual cortex, specifically in the calcarine sulcus (see Fig. 45). Quite expectedly, both electrodes showed a strong activity during T1's visual search phase but not to the appearance of the Target, which might indicate a peripheral receptive field. Careful inspection of the video revealed clear responses to movements of the experimenter, consistent with a processing of peripheral visual events. Processing of lateral visual stimuli might disrupt normal neural processing during the visual search phase and slow it down.

Single-Case Observations

Two notable observations caught our attention, despite the fact that they were found in isolated participants. In the case of Figure 46, its activity in the right IFG was positively correlated with the

Summary of the Results

Attention allows cognitive resources to be focused on a particular behavioral goal. Those resources can be diverted away by external or endogenous distractions, imposing a greater load on cognitive control mechanisms to minimize performance decrease. The central objective of this thesis was to study how a primary attention-demanding task can be affected by additional tasks or distractions, at both behavioral and neural levels. That task had been created by our team -the Bron/Lyon Attention Stability Test, or BLAST 167 -to track transient fluctuations of attention and to measure the ability to stay on task.

BLAST was performed by participants in three separate conditions: first in a quiet environment, with no external stimuli (the Single-Task, or ST condition); then while asked to perform a Verbal Fluency Test (VFT) of equal importance during BLAST (Dual-Tasking, or DT); and finally, in a distracting environment, with an experimenter sitting nearby (ST + DIST). These last two conditions were intended to increase the attentional load (ST), affecting performance (i.e., BLAST speed and accuracy) and BLAST-related neural activity. Deviation from the optimal BLAST cortical dynamics could be observed by comparing the DT and the ST + DIST conditions with the ST conditions, using intracranial electroencephalography (iEEG).

In the DT condition, we searched for cortical sites which activity was modulated by BLAST and by the VFT to identify sources of interference between the two tasks. We then organized those sites into functional Regions of Interest. In the ST + DIST condition, we identified iEEG recording sites responsive to external distractions among those activated or suppressed by BLAST. The identification procedure relied on a) a simple statistical detection of neural activity increases after distracting events (visual and auditory), and b) a correlation analysis with sites in the auditory cortex activated by distracting sounds, to reveal cortical regions driven by auditory inputs. Both methods led to the identification of the cortical regions involved in BLAST that were affected by the distractions or the additional task.

11 Altogether, we believe we have fulfilled our initial objective: we have identified several key regions where external distractions interfere at the neural level with our attention-demanding task, and provided a plausible reason for the transient decrement of performance they generate. Among those regions, the AIC, which integrates the salience network 389 and the cingulo-opercular network, 14 together with the PCC/Precuneus, a key node of the DMN, 403 might harbor limited cognitive resources susceptible to be used by distractors, possibly in interaction with each other. 396,431 Other regions like the EVC, that react to distractors, could also generate interference, but most likely under the control of higher cognitive areas in charge of executive control.

Weaknesses in our Study

Of course, our studies come with some limitations that should be acknowledged. Firstly, the relatively small number of participants prevented a large and exhaustive sampling of our main Regions of Interest. It is possible that we identified only some of the regions involved in the distraction phenomenon, yet, the areas we could identify, even if some were sampled in a single patient, matched the main regions associated with multi-tasking and exogenous distraction. One might also complain that our conclusions were drawn from patients suffering from a several neurological condition, but iEEG cognitive research has now gone for almost three decades, and multiple guidelines have been provided to maximize the generalizability of intracranial observations (among which, the consistency of the results across patients, or the adequacy established principles of the brain functional architecture). 371 Nevertheless, a larger population of patients and a denser cortical sampling across individuals would certainly have led to more robust conclusions. Another potential source of concern is that the naturalistic conditions used in our paradigms could not provide a perfect control over the experimental conditions, with some obvious variability in the timing and nature of the endogenous or exogenous events, obviously. Yet, we believe that our observations are sufficiently robust and facilitated by the high signal-to-noise ration of iEEG signals, which allows visible effects to be identified at the single-trial level, without the need to average across a large number of strictly identical stimuli. ATT: each patient must recognize if a gray "T" appears at random in the upper or lower half of a 12x12 array. In the easy version, the letter "T" is surrounded by black letters "L" ("T" pop-out), while in the difficult version, the letter "T" is surrounded by grey letters "L" ("T" search), and press a button in a gamepad.

MOTO: each patient must execute different movements under command within each possibility in the Y-axis.

VEBM: each patient must identify whether a particular letter is within a string of six characters. Each string string has six slots, of which 2, 4 or 6 slots can be alphabet letters (easy, moderate or hard trials respectively). The string is shown (encode), to be replaced by a momentary fix and then by a single letter, which must be confirmed or not by the patient as part of the string (recall).

VISM: each patient must identify if a particular dot is part of a 4x4 checkboard dotted with 2, 4 or 6 random black dots (easy, moderate or hard trial respectively). The dotted checkboard is shown (encode), to be replaced by a momentary fix and then by a checkboard with a single dot, which must be confirmed or not by the patient as part of the dotted checkboard (recall).

VISU: each patient must observe images with different topics within each possibility delivered in the Y-axis, and identify the fruits by pressing a button on a gamepad.