Doctorat De

Universite De L'

Marie Pierre

Marwa El

Soutenue Le

Dr Julie Grezes

DR Directrice De Thèse

Dr Valentin Wyart

Co-Directeur De Thèse

Cr Pr Philippe Schyns

Professor Dr Floris Rapporteur

Rapporteur De Lange

Dr Mathias Pessiglione

Dr Nathalie George

Examinatrice, DR Pr Beatrice De Gelder

Professor Dr Jérémie Mattout

Julie Grèzes

THESE

Keywords: Magnetoencephalography fMRI: functional Magnetic Resonance Imagery RDM: Random dot motion P100: Positive evoked potential at 100 ms at occipital electrodes Positive starting at 300 ms at parietal electrodes Centro-parietal positivity in EEG signals. OFA: Occipital Face Area FG: Fusiform Gyrus STS: Superior Temporal Sulcus FFA: Fusiform Face Area Dorso-Lateral Prefrontal Cortex FEF: Frontal Eye Field anxiety, threat, emotion, decision-making, computational model , results and discussion): approx. 4600

Efficient detection and reaction to negative signals in the environment is essential for survival. In social situations, these signals are often ambiguous and can imply different levels of threat for the observer, thereby making their recognition susceptible to contextual cues -such as gaze direction when judging facial displays of emotion.

However, the mechanisms underlying such contextual effects remain poorly understood. By computational modeling of human behavior and electrical brain activity, we demonstrate that gaze direction enhances the perceptual sensitivity to threatsignaling emotions -anger paired with direct gaze, and fear paired with averted gaze. This effect arises simultaneously in ventral face-selective and dorsal motor cortices as a function of individual anxiety at 200ms following face presentation, and does not reflect increased attention to threat-signaling emotions. These findings reveal that threat tunes neural processing in fast, selective, yet attention-independent fashion in both sensory and motor systems, for different adaptive purposes.

Table of contents

First and foremost, I would like to thank my supervisor Julie Grèzes. Through your enthusiastic approach, you gave me the taste for research from the very first months we worked together. You taught me all the basics of a scientific approach, how to address questions, operate them, interpret findings and write papers, by challenging me at various levels, always pushing me further in my thoughts, and guiding me at the appropriate moments. I cannot thank you enough for all your support, for you trust, and for giving me so many opportunities to present my work abroad and to collaborate in numerous projects. Working with you was above all a real pleasure on a daily basis.

I am extremely grateful to my co-supervisor Valentin Wyart. Thank you for sharing your positive enthusiasm and your prolific ideas, and for communicating your scientific rigour and expertise with great pedagogy, things seem so simple when you explain them! I consider myself very lucky to have worked with you along this road.

Thanks to Etienne Koechlin for welcoming me in the LNC. Thanks to Laura Chadufeau and Marine Magne for making life so easy at the lab. Thanks to all the members of the LNC for the pleasant atmosphere at work every day. Particular thanks to Anne-Do Lodeho, for being so helpful and being a very good listener. Special thanks to Marianna, Marion and Florence for your friendship and support. Marion it was great to have you on my side in the writing process, your optimism and positive advices really helped.

Thanks to the social team (past and current), my lab 'family': Guillaume GuiGui, thanks for pushing me to work in the social team in the first place, discussions with you were and always are very enjoyable moments. Michèle MichMich, thanks for your constant support and encouragement, and for always bringing laughter into the lab. Lou, thanks for the very pleasant moments of collaboration, discussions, and laughs. Terry Chérie, thanks for wonderful advices, those we aim to follow. Mathias and Amelie, it was always nice to see you when you popped in the lab. Christina and Hannah, your presence brought a positive dynamism into the team, and it is really nice working with you too, thanks for your trust. Thanks to all those who helped with my experiments, Lena, Florie, Helène, Lydia, and Flavie. Thanks to Lise, for a nice year of collaboration. Thanks to Lucile, for the great push you gave me at the very beginning and for being there all along. Thanks to Laurence Conty and Coralie Chevallier, it was great and fruitful to collaborate with you.

And a particular IMMENSE thanks to Emma querida, who shared almost every minute I spent in the lab since the beginning, thanks for the laughs, the hugs, the advices, the complicity, the friendship... You were there every single day, always in my perspective, listening to any (really any) thing I had to say at any moment, thanks for your impressive patience and for always cheering me up. Thank you for making work a warm place.

Thanks to all the friends that were around all these years, since the cogmaster, Antoine, Aureliane, and Juliane, since the 'licence', Lea and Stephanie, and since childhood Maria, Mia and Mira.

Turning to family, I warmly thank my parents to whom I dedicate this PhD, you both are my cognitive and emotional strengths and drives. Thanks a million Diala for your continual friendship, and for just being there all these years, making Paris our home. Thanks to Azza, when you told me that doing a PhD was 'a life experience', I didn't realize how much you were right until now, thanks for being there with all your encouraging and helpful words despite the oceans between us. Thanks to Birendra, the discussions with you are always very pleasant, and your comments very helpful. Thanks to my little niece Meera, you made the writing process such an easy one by your simple day-to-day presence. Thanks to Hyam, for being a family in Paris, and such a good listener.

Thanks to my best friend Alizee, and yes ! we did it ! we realized our undergraduate dream, the one we shared since the very first day we met at Jussieu, 'étudier les neurosciences'. We really walked along this road together, from the biology undergrad at UPMC when we already travelled to Gif sur Yvette to listen to incomprehensible neuroscience conferences, to the Cogmaster when oh finally the brain was the main topic, to our PhDs, sharing ALL the good and bad moments -I cannot even imagine how the past years would have been without you and our weekly cafe lea.

Finally, thanks to Kleanthi, well, for so many things, everything, and I am unable to write everything down. Thank you for encouraging and listening to my work and ideas and for all our intense discussions around science. Thank you for knowing exactly how to handle every difficult moment I went through and for your perpetual optimism, you keep me going.

"...expression in itself, or the language of the emotion, as it has sometimes been called, is certainly of importance for the welfare of mankind"

Charles [START_REF] Darwin | The expression of the emotions in man and animals[END_REF] The expression of the emotions in man and animals. London: John Murray. 1st edition.

Abstract

Everyday social decisions require the combination of multiple sources of information and therefore build upon abundant contextual elements such as the social cues of emitters (e.g., gaze direction, emotion, gesture), the attentional focus of observers, their mood and their past experience. The work conducted during this Ph.D. (including three main studies in healthy human subjects) aimed at characterizing the cognitive and neural mechanisms of contextual influences in social settings.

In the first Electroencephalography (EEG) study, evoked potential and source reconstruction analysis revealed that unattended social signals are integrated in the human brain within 170 ms after stimulus onset as long as they are contextually relevant to the observer. However, selective attention increased the contribution to this integration of neural regions specific to the attended feature. In the second study, model-based behavioral and single-trial EEG analyses demonstrated that the mechanisms underlying the integration of multiple social cues from faces implicate an enhancement of the perceptual sensitivity to observer-relevant threat signals. This increased sensitivity to threat-signaling emotions emerged in ventral temporal and dorsal motor regions in parallel, starting 200 ms after stimulus onset until response. Moreover, individual characteristics of the participants, here their anxiety, influenced the regions where threat-signaling emotions were selectively encoded in a doubly dissociable fashion: preferentially in face selective temporal regions for low anxious individuals but in motor regions for high anxious individuals, consistently with the adaptive role of anxiety in action preparation under threat. Finally, in the third study, model-based behavioral and pupillometric analyses showed that experimentally induced priors as well as participants' idiosyncratic priors about the emotion (mood) of individual identities -i.e., identity/emotion associations, modulated decision biases rather than the sensitivity to displayed emotions. This was reflected in pupillary responses, which were increased to effortful decisions against biases on unexpected stimuli.

While co-emitted social cues interact by boosting bottom-up processing of relevant threat signals, prior experience enacts as a top-down contextual factor biasing decisions toward expected options, and attention and individual traits modulate the relative contribution to social processing of relevant neural regions. Altogether, these findings shed light on the distinct cognitive mechanisms underlying the influence of different contextual factors during perceptual decisions in social settings.

Résumé

Les décisions que nous prenons au quotidien nécessitent le traitement de plusieurs sources d'information, et dépendent par conséquent de nombreux éléments contextuels tels que les indices sociaux provenant d'un émetteur (par exemple, le regard, l'émotion et le geste), ainsi que le centre de l'attention, l'humeur et l'expérience passée de l'observateur. Le travail réalisé durant cette thèse (qui inclue trois expériences principales chez des sujets humains sains) a eu pour but de caractériser les mécanismes cognitifs et neuraux sous-tendant l'impact de ces éléments contextuels sur la prise de décision dans un environnement social.

La première étude d'électroencéphalographie (EEG), basée sur des analyses de potentiels évoqués et de reconstruction de source, a montré que les indices sociaux sont intégrés dans le cerveau dès 170 ms après l'apparition du stimulus, en fonction de leur pertinence pour l'observateur et indépendamment de l'attention qui leur était portée. Toutefois, l'attention augmente l'implication des régions cérébrales spécifiques au traitement de l'indice social sur lequel était portée l'attention. Grâce à des analyses comportementales et EEG basées sur la modélisation, la deuxième étude a démontré que les mécanismes qui sous-tendent l'intégration de différents indices sociaux extraits d'un visage induisent une augmentation de la sensibilité perceptive aux signaux de menaces pertinents pour l'observateur. Une telle augmentation de la sensibilité perceptive aux signaux de menace est apparue en parallèle au sein de régions temporales ventrales et motrices dorsales, dès 200 ms après l'apparition du stimulus. De plus, les analyses ont révélé que certaines caractéristiques propres à l'observateur, ici son niveau d'anxiété, ont influencé les régions dans lesquelles les signaux de menaces étaient représentés: chez les sujets peu anxieux, cette représentation s'est produite préférentiellement dans les régions temporales propres à l'encodage du visage, tandis qu'elle a impliqué les régions motrices chez les sujets plus anxieux, en accord avec la fonction adaptative de l'anxiété pour la préparation à l'action face à la menace. Enfin, en conjuguant modélisation comportementale et enregistrements pupillaires, la troisième étude a montré que les aprioris induits par l'expérimentateur ainsi que les aprioris propres à chaque individu concernant l'émotion exprimée par une identité particulière, influencent le biais décisionnel plutôt que la sensibilité à l'émotion. Cette influence a été mise en évidence par une augmentation des signaux pupillaires en réponse aux stimuli inattendus.

En conclusion, alors que les indices sociaux émis simultanément interagissent en renforçant le traitement ascendant 'bottom-up' des signaux pertinents de menace, l'expérience apriori agit comme un facteur contextuel descendant 'topdown' qui biaise les décisions vers les options attendues, tandis que l'attention et les caractéristiques de l'observateur modulent la contribution relative de régions pertinentes dans le traitement de stimuli sociaux. Les résultats pris dans leur ensemble mettent en lumière les différents mécanismes cognitifs qui sous-tendent l'influence de facteurs contextuels distincts lors de la prise de décision perceptive dans un environnement social.

INTRODUCTION

Humans are animals that are distinguished by their very elaborate social interactions. Every day, we communicate with several individuals, and the ability to correctly read and interpret social cues from others is critical for proper social integration and survival. When such abilities are modified, individuals suffer from social dysfunctioning reflected in psychiatric disorders often associated with altered social behaviors such as clinical anxiety disorders, autism spectrum disorders and schizophrenia. Investigating the functioning of the human brain when dealing with social situations to establish models of healthy brain functioning is a key step toward a better understanding of what exactly is different in such disorders. The domain of social cognition has expanded through the last years, and different levels of social processing have been extensively explored, from low level social perception to more complex collective decisions and 'theory of mind' judgments.

The work conducted in my Ph.D. focused on the visual processing of social information and how perceptual decisions are formed based on this information. In social contexts, perceptual decisions represent the first step in social interactions and often occur in an implicit fashion. For example, if you perceive a social individual as dominant because of visual characteristics in his/her face, you might avoid that person without explicitly forming the decision that he/she looks dominant. However, understanding what actually led you to (implicitly or not) judge that person as dominant (i.e., which visual features) and therefore avoid him/her, is a crucial step to understanding the reasons/causes for your behavior. The ecological theory of perception extended to social perception [START_REF] Mcarthur | Toward an ecological theory of social perception[END_REF] indeed proposes that perception itself is adaptive -as "the external word must provide information to guide biologically and socially functional behaviors". This theory emphasizes the necessity of studying social perception to comprehend social functioning. Therefore, understanding the neural mechanisms of how simple perceptual decisions are constructed based on social stimuli can bring important insights as to how individuals behave adaptively in social contexts. Importantly, social environments are rich with abundant contextual information, as social cues are never perceived in isolation. In addition, perceivers themselves represent a source of variability (in terms of their internal state -e.g., anxious or not) as to how the social environment is perceived. Taking into account the scenery and the characteristics of the emitter and perceiver is therefore pivotal when investigating social perception.

In cognitive neuroscience, taking advantage of the powerful advantage of modeling when trying to understand the underlying computations of brain functioning is more and more put forward. However, computational modeling has been used more frequently outside of the field of social cognition, in domains such as decisionmaking and visual perception. As the computational approach provides a compelling tool to better understand the mechanisms of brain functioning, most of the work conducted in this Ph.D. took advantage of behavioral and neural modeling to characterize the way individuals behave and perceive social stimuli. For this reason, the literature that investigated perceptual decision-making in the non-social domain and the leading models that attempt to explain non-social decision-making will be reviewed, as well as the influence of context and priors on these decisions. The introduction will be organized as follow: first, I will attempt to summarize the work that has been conducted in the field of social perception in the particular case of facial and emotion perception; second I will describe the findings of the studies that addressed contextual influence in emotion/face perception; third I will review the studies of perceptual decision-making in the non-social domain; fourth I will depict the influence of priors and contexts in the non-social domain and finally discuss the importance of bridging the gap between the social and non-social domains to address the question of perceptual decision-making on stimuli of emotional and social significance.

1-Social perception: the case of faces and facial expressions 1.1-Why the face and what are facial expressions of emotions?

The world 'social' derives from the latin 'socii' or 'socialis' which refers to partners, comrades, allies. Although there is no single definition of the word 'social', it clearly refers to interactions with others. In the human species, when you think of other individuals, the first thing that comes to mind is most probably their face. We identify others by their faces, we decode their intentions and moods in their facial reactions.

It is therefore not surprising that research on facial perception is very prominent in the domain of social perception. Faces probably represent the category of visual stimuli that is most salient biologically and socially in humans. Emotional signals are primarily expressed through faces. Charles Darwin has been a pioneer in the development of the idea proposing that facial expressions have an evolutionary role, serving as communication signals [START_REF] Darwin | The expression of the emotions in man and animals[END_REF]. In his book 'The Expression of the Emotions in Man and Animal' the parallel is drawn between animals and humans both expressing emotions in similar ways and using them as signals for adaptive purposes. This idea was later developed by Jaak Panksepp who defended that emotions stem from the challenges species have to face in the environment driving behaviors for survival [START_REF] Panksepp | Affective neuroscience: The foundations of human and animal emotions[END_REF][START_REF] Panksepp | Affective consciousness: Core emotional feelings in animals and humans[END_REF]. Further corroborating this idea, emotions are suggested to represent brain circuits that contribute to behaviors related to survival functions (see review/perspective by LeDoux, 2012). Appraisal theories of emotions [START_REF] Frijda | The emotions[END_REF][START_REF] Scherer | On the nature and function of emotion: A component process approach[END_REF] suggest that motivations and intentions are inherent to the emotion emergence process. In this framework, it becomes important to take into account the behavioral relevance of an expressed emotion. Within evolutionary accounts of emotions, they have been defined by their role of possessing the adaptive function of adjusting relevant behaviors [START_REF] Nesse | Evolutionary explanations of emotions[END_REF]:

"The emotions are specialized modes of operation shaped by natural selection to adjust the physiological, psychological, and behavioral parameters of the organism in ways that increase its capacity and tendency to respond adaptively to the threats and opportunities characteristic of specific kinds of situations."

Why is this important for emotion perception? Just as objects in the environment provide action possibilities (Gibson, 1979), social stimuli induce action tendencies in observers, and thereby their perception is already influenced by their functional significance [START_REF] Mcarthur | Toward an ecological theory of social perception[END_REF]. If emotions have the role of driving adaptive behaviors, do these behaviors motivate their perception? Enhanced processing of socially significant stimuli is intuitive within this framework and greatly depends on their emotional implication [START_REF] Compton | The interface between emotion and attention: a review of evidence from psychology and neuroscience[END_REF]. For example, fear, anger, snakes and fires can be considered of high emotional significance as they signal that the environment emanates threat, but also dominance and aggressiveness that would require adaptive action responses [START_REF] Mcarthur | Toward an ecological theory of social perception[END_REF]. This implies that the perception of faces and facial expressions, among other socially meaningful stimuli, is already influenced by their behavioral significance.

Influential work of Ekman and colleagues (Ekman,1972, Ekman andFriesen 1978) emphasizes the existence of innate muscle programs allowing facial expressions to be recognized universally. This research concentrated on prototypical expressions of what have been referred to since as the basic emotions: Anger, Fear, Disgust, Surprise, Happiness and Sadness (Figure 1). Although using 'prototypical' emotional facial expressions is a matter of discussion as they are very rarely encountered in real life situations [START_REF] Hess | Darwin and emotion expression[END_REF], the idea of basic emotions governs today most of the research on emotion perception. Ekman further proposed that the expression of basic emotions is the product of combinations of a set of facial action units (Facial Action coding system FACS, Ekman and Friesen, 1978). For example, the combination of lowering eyebrows (AU 4), raising the upper eyelid (AU 5), tightening the eyelid (AU 7) and pressing and tightening the lips (AU23 and AU24) mediated by specific facial muscles (such as the corrugator for the brow lowerer) give rise to an angry expression. This suggests that a hardwired program of emotional expressions exists, and justifies the recognition of basic emotions across cultures (Ekman and Friesen, 1971). This view has been repeatedly challenged since (e.g., [START_REF] Ortony | What's basic about basic emotions?[END_REF][START_REF] Barrett | Of Mice and Men: Natural Kinds of Emotions in the Mammalian Brain? A Response to Panksepp and Izard[END_REF]. Nev-ertheless, most of the existing faces databases include the 6 basic emotions and a neutral condition (NimStim, Tottenham et al., 2009, KDEF, Lundqvist et al. 1998) and some of them are based on the FACS (Radboud database, Langner et al., 2010, JACFEE/JACNEUF Matsumoto & Ekman 1988).

Even though the recognition of emotional expressions seems to be highly accurate with prototypical expressions, backward masking that is a way to reduce the detectability of visual stimuli in general through short time presentations reduces this ability in emotion recognition [START_REF] Sweeny | Detecting and categorizing fleeting emotions in faces[END_REF]. Importantly, this diminished recognition is especially noticeable if the categorization concerns emotions that share characteristics, for example negative emotions such as fear and anger [START_REF] Sweeny | Detecting and categorizing fleeting emotions in faces[END_REF]. Such observations question the basic component of visual features pushing individuals to recognize a face as emotional and which emotion particularly it is expressing, i.e. why is it more difficult to categorize anger and fear expressions and what are the visual features that allow their discrimination? It therefore seems important to integrate visual and dynamic components of facial expressions, and using finer methodological analyses can open a window into the mechanisms of face/emotion perception. Recently, to identify how emotion signals are transmitted over time, face stimuli were created by a random combination of action units and categorized by participants on the basis of the 6 basic emotions [START_REF] Jack | Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time[END_REF].

Using Bayesian classifiers and information theory, findings revealed a first early stage of emotion processing where only four basic emotional expressions were perceptually represented as a function of their behavioral significance: happy, sad, fear/surprise and disgust/anger. This first stage is suggested to involve biologically rooted signals such as raising the upper lid in both fear and surprise to enhance sensory processing in possible approaching danger situations (fearful and surprise expressions can signal danger in the near environment). A second late stage of information processing is marked by diagnostic information for each emotion, dissociating the 4 categories into the socially ingrained 6 basic emotions. These findings are in line with the ecological theory of perception and appraisal theories as they show that early perceptual recognition of emotion is based on the biological relevance of transmitted signals. As later stages dissociated the basic emotions, these results also reconcile both accounts stated above by shedding light on the dynamics of the signal transmission: 1) behavioral/biological relevance as inherent to the emotion recognition process, involving an early stage of processing, 2) hardwired programs for each emotion, involving a later stage of processing.

1.2-How are faces and facial expressions represented in the brain?

The question of how faces are perceived can be meticulously addressed through data-driven methods that map facial information into their subjective perception. Reverse correlation techniques indeed help characterizing the precise contribution of different visual information in the perception mechanism. These methods are successful in identifying the precise features of the face that help determining its belonging to a specific social or emotional category. The idea behind is to present visual inputs embedded in noise while participants make specific judgments that are used to reconstruct classification images showing which visual input drove the decisions.

For example, using this method to visualize trustworthiness and dominance social traits, the social judgment was reported to involve visual information from the mouth, eye, eyebrow and hair (Dotsch and Todorov, 2012). Furthermore, when judging the gender of a face, information from eyes, eyebrows and mouth regions was necessary for accurate categorization, whereas judging if a face is happy or neutral only required information from the mouth [START_REF] Gosselin | Bubbles: a technique to reveal the use of information in recognition tasks[END_REF].

In this last study, the specific method used, referred to as the "Bubbles" technique, consists in adding noise to faces by creating random Gaussian windows revealing different portions of the face. Afterwards, participants' responses mapped into correct and incorrect trials depending on the task are used to create classification images representing diagnostic information processed by observers' brain to correctly perform the task. Different facial features were involved in the mapping of emotions when using Bubbles to categorize basic emotions: for example, while recognition of fear requires information from the eyes, happiness recognition requires information from the mouth [START_REF] Smith | Transmitting and decoding facial expressions[END_REF]. In Figure 2, the diagnostic information that differed significantly from an emotion to the other (uncorrelated across emotions) proves that the brain transmits information about different emotional ex-pressions with minimal overlap possibly to optimize transmission as interpreted by the authors. These diagnostic features trigger increased attention even during a gender categorization task as eyes were more attended in fearful faces and mouths were more attended in happy facial expressions [START_REF] Scheller | Diagnostic features of emotional expressions are processed preferentially[END_REF].

The modulation of spatial frequency conveyed by the faces reveals that different frequency bands contribute differentially to the recognition of emotions (Smith and Schyns, 2009). As it is adaptive to recognize information signaling threat even if it is ambiguous or far away, one can imagine that emotions signaling threat are transmitted through low spatial frequencies that only provide coarse information. Accordingly, low spatial frequencies in fearful stimuli increase the activity of emotion selective brain regions [START_REF] Vuilleumier | Distinct spatial frequency sensitivities for processing faces and emotional expressions[END_REF] and brain waves [START_REF] Vlamings | Is the early modulation of brain activity by fearful facial expressions primarily mediated by coarse low spatial frequency information?[END_REF]. However, reverse correlation techniques during emotion recognition (Smith and Schyns, 2009) reveal that happiness and surprise are conveyed by low spatial frequency bands rather than fear and anger (two specific signals of threat).

To conclude, perceptual features constituting facial expression contribute differently to the representation of emotions and understanding how they do so sheds light on the role of the transmission of these signals.

1.3-When are faces and facial expressions represented in the brain?

Time-resolved techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) offer the possibility to determine when the visual information is encoded in the brain at the level of the millisecond. These techniques, combined with reverse correlation methods, provide a window into the algorithms of information processing in the brain [START_REF] Schyns | Information processing algorithms in the brain[END_REF]. Before going into the results of this powerful combination, let us first take a step back and look at primary EEG/MEG studies which investigated emotion and face processing.

Emotional expressions, particularly fearful expressions, trigger increased brain responses within 100 ms after stimulus onset on central electrodes [START_REF] Pourtois | Electrophysiological correlates of rapid spatial orienting towards fearful faces[END_REF], fronto-central electrodes [START_REF] Eimer | An ERP study on the time course of emotional face processing[END_REF], and in the amygdala [START_REF] Luo | Neural dynamics for facial threat processing as revealed by gamma band synchronization using MEG[END_REF][START_REF] Hung | Unattended emotional faces elicit early lateralized amygdala-frontal and fusiform activations[END_REF][START_REF] Liu | Emotion separation is completed early and it depends on visual field presentation[END_REF]. While this early involvement in the processing of fear can be interpreted as increased attentional resources toward threatening stimuli (Vuilleumier and Pourtois, 2007), this claim is contradicted by the fact that other emotions including joy [START_REF] Dubal | Human Brain Spots Emotion in Non Humanoid Robots[END_REF], anger, disgust, sadness and surprise [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF] show enhanced electrical positivity at occipital electrodes P100 when compared with neutral expressions. At 170 ms after stimulus onset, a negative occipito-temporal activity, the N170, responds specifically to perceived faces [START_REF] George | Electrophysiological correlates of facial decision: insights from upright and upside-down Mooney-face perception[END_REF]. This face selective N170 is also influenced by facial expressions as it is increased for fear as compared to other basic emotions [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF], peaks earlier for fearful expressions as compared to other basic expressions [START_REF] Schyns | Dynamics of visual information integration in the brain for categorizing facial expressions[END_REF] and varies as a function of the emotional intensity of angry, disgusted and fearful facial expressions [START_REF] Sprengelmeyer | Event related potentials and the perception of intensity in facial expressions[END_REF]. After 300 ms, positive electrical activity at centro-parietal electrodes is modulated by negative [START_REF] Schupp | The facilitated processing of threatening faces: an ERP analysis[END_REF] and positive emotions as compared to neutral also for emotional scenes [START_REF] Cuthbert | Brain potentials in affective picture processing: covariation with autonomic arousal and affective report[END_REF][START_REF] Liu | Neural substrate of the late positive potential in emotional processing[END_REF].

While these studies show that emotional information modulates brain activity as soon as 100 ms after stimulus onset, they fail to characterize the specific contribution of visual information in the emotion recognition process over time. By combining electrical brain recordings with reverse correlation methods, the curvature and the peak of the N170 were related to information processing showing that visual information is integrated through a pattern [START_REF] Schyns | Dynamics of visual information integration in the brain for categorizing facial expressions[END_REF]. First, visual representation started by the eyes and went downward to the nose and mouth, thus explaining the earliest peak of the N170 to fearful expression (diagnostic information is in the eyes). Second, the peak of the N170 actually coincided with the time where the diagnostic information for each emotion was integrated (e.g., nose for disgust, mouth for happiness). Using MEG, this type of analyses is transferred from single electrode (occipito-temporal for the N170) to the source level during an emotion categorization as well as gender categorization (Smith et al., 2009). In addition to showing that maximal visual information is processed 170 ms after stimulus onset (corresponding to the N170), this study revealed different patterns of integration dependent on the task at this same latency. After 300 ms, only task specific information was represented in cortical activity, (for example, mouth information if categorizing happy faces), consistently with a decision related signal at this later latency.

While in the last two sections, we addressed how and when facial expressions are visually represented in the brain, the next section will deal with the neural sources of emotion processing.

1.4-Where are faces and facial expressions represented in the brain?

Functional magnetic resonance imagery (fMRI) that has a great spatial resolution, allows for the isolation of brain regions involved in face processing. The most influential model in face processing is the model of the distributed human neural system for face perception [START_REF] Haxby | The distributed human neural system for face perception[END_REF] suggesting that after structural encoding of visual features in occipital areas (occipital face area OFA), invariant aspects of faces such as identity are processed in the fusiform gyrus (FG) while variant aspect of faces such as gaze and emotion are processed in the posterior superior temporal sulcus (pSTS) (Figure 3). According to this model, it is only after the visual analysis in the 'core system' which comprises the OFA, FFA and STS, that facial information will be integrated with other dimensions such as attention and emotion in the 'extended system'. As will be described next, evidence from the literature clearly highlights the role of FG and STS in face processing. Other core regions of face processing are also often reported, including the amygdala (AMG), the insula and the pulvinar for emotion processing, but also the orbitofrontal cortex, and the medial prefrontal cortex for inference processes (theory of mind). Here I will concentrate on the areas involved in the sensory processing of faces and facial expressions: the FG and STS. Then I will also describe the role of the AMG because of its involvement in threat and social relevance processing, and finally draw attention to the less acknowledged involvement of motor regions in emotion/threat processing.

The Fusiform Gyrus (FG)

The FG responds preferentially to faces shown in different viewpoints when compared to houses, objects animals and other body parts [START_REF] Kanwisher | The fusiform face area: a module in human extrastriate cortex specialized for face perception[END_REF]. The specific region in the FG responding to faces is referred to as the fusiform face area (FFA). Even when subliminally presented, the identification and detection of faces was associated with modulation in FFA while the identification of other subliminally presented objects such as houses, guitars and cars modulated other regions in the ventral temporal cortex [START_REF] Grill-Spector | The fusiform face area subserves face perception, not generic within-category identification[END_REF]. FG has been persistently shown to be the source of face-selective N170 mentioned earlier [START_REF] Itier | Inversion and contrast polarity reversal affect both encoding and recognition processes of unfamiliar faces: a repetition study using ERPs[END_REF][START_REF] Rossion | Early lateralization and orientation tuning for face, word, and object processing in the visual cortex[END_REF][START_REF] Conty | When eye creates the contact! ERP evidence for early dissociation between direct and averted gaze motion processing[END_REF]. In accordance with the core system model, the FG seems to be specialized in identity and familiarity processing [START_REF] George | Contrast polarity and face recognition in the human fusiform gyrus[END_REF][START_REF] Rotshtein | Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain[END_REF]. The FG also responds to emotional expressions, preferentially to high spatial frequencies [START_REF] Vuilleumier | Distinct spatial frequency sensitivities for processing faces and emotional expressions[END_REF].

The superior temporal sulcus (STS)

The STS responds to variant aspects of the face such as moving eyes and moving mouth [START_REF] Puce | Temporal cortex activation in humans viewing eye and mouth movements[END_REF]. The STS seems to have a general role in the perception of movement (even from other body parts), and its activation to static face and body is interpreted as a role in the detection of implied motion and stimuli signaling actions (Allison et al., 2000). Accordingly to its role in the encoding of variable features in the faces, the STS also co-varies with the emotional intensity of a face [START_REF] Said | Graded representations of emotional expressions in the left superior temporal sulcus[END_REF] and is involved in continuous, rather than categorical, representations of facial expressions [START_REF] Harris | Morphing between expressions dissociates continuous from categorical representations of facial expression in the human brain[END_REF]. In a recent review about the field of social neuroscience, the pSTS is suggested to have an integrative role in social processing as it is involved in three key neural systems: social perception, action observation and theory of mind [START_REF] Yang | An integrative neural model of social perception, action observation, and theory of mind[END_REF]. In line with the integrative role of the STS occurring at multiple levels of social processing, single neuron recordings in monkeys show that the STS encodes different types of social cues including identity in a hierarchical manner [START_REF] Morin | Hierarchical Encoding of Social Cues in Primate Inferior Temporal Cortex[END_REF]. Therefore the thorough dissociation between invariant and variant features of the face is questionable, and the fact that STS and FFA inter-act during face processing [START_REF] Baseler | Neural responses to expression and gaze in the posterior superior temporal sulcus interact with facial identity[END_REF] rather suggests that these regions act in concert to process socially relevant stimuli.

The amygdala (AMG)

The AMG is undoubtedly a central structure in emotion perception, its role in threat detection being essentially put forward. Indeed, AMG activity to fearful expressions when compared with neutral or positively valenced stimuli is increased (e.g., Whalen et al., 2004). Moreover, a patient that has a bilateral AMG damage (patient SM) is unable to recognize static fearful expressions [START_REF] Adolphs | Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala[END_REF]. Accordingly, in the monkey brain, AMG responds more strongly to threat expressions as compared to neutral and appeasing [START_REF] Hoffman | Facial-expression and gaze-selective responses in the monkey amygdala[END_REF].

However, although threatening expressions are processed within 100 ms after stimulus onset in the AMG [START_REF] Luo | Neural dynamics for facial threat processing as revealed by gamma band synchronization using MEG[END_REF][START_REF] Hung | Unattended emotional faces elicit early lateralized amygdala-frontal and fusiform activations[END_REF][START_REF] Liu | Emotion separation is completed early and it depends on visual field presentation[END_REF], there is no clear evidence that this processing occurs earlier than other emotions or uniquely involves a subcortical pathway [START_REF] Palermo | Are you always on my mind? A review of how face perception and attention interact[END_REF]. Indeed, many studies point toward the involvement of the AMG in the perception of all emotions including positively valenced [START_REF] Breiter | Response and habituation of the human amygdala during visual processing of facial expression[END_REF][START_REF] Winston | Common and distinct neural responses during direct and incidental processing of multiple facial emotions[END_REF].

The role of the AMG as a "relevance detector" comprising but not restricted to threat detection has been proposed [START_REF] Sander | The human amygdala: an evolved system for relevance detection[END_REF] and could reconcile the different findings. In contrast to continuous representations of emotions in STS, the AMG categorically represents emotion expressions including happiness, disgust, fear and anger [START_REF] Harris | Morphing between expressions dissociates continuous from categorical representations of facial expression in the human brain[END_REF], in accordance with the role of the AMG in the detection of relevant information (here, which emotion is expressed, independently of its intensity). The sensory processing of emotions of both appease and threat in facial expression selective temporal regions was impaired in monkeys with damaged AMG, in contrast to intact processing of neutral faces [START_REF] Hadj-Bouziane | Amygdala lesions disrupt modulation of functional MRI activity evoked by facial expression in the monkey inferior temporal cortex[END_REF].

Once more, this finding highlights the involvement of the AMG in relevance detection by showing that it influences the sensory processing of salient social signals. Finally, the impaired recognition of threat in patient SM seems to be restricted to static facial expressions of fear. Indeed, when patient SM with bilateral AMG damage is instruct-ed to pay attention to the eyes to recognize fearful expressions (diagnostic information in fear recognition), she is able to do the task accurately [START_REF] Adolphs | A mechanism for impaired fear recognition after amygdala damage[END_REF]. This shows that it is not the recognition of fear emotion per se that is impaired but rather the allocation of attention to the relevant feature of the face, consistently with the role of the AMG in orienting to salient information. Moreover, patient S.M is perfectly able to recognize fearful expressions from static and dynamic body expressions [START_REF] Atkinson | Spared ability to recognise fear from static and moving whole-body cues following bilateral amygdala damage[END_REF], highlighting the possible involvement of somatosensory regions bypassing the amygdala in threat processing.

Motor-related regions

Although the implication of motor related regions is not yet firmly rooted in emotion processing, its implication in threat perception in tandem with the AMG is suggested [START_REF] Grèzes | Limbic to Motor Interactions during Social Perception[END_REF]. This stems from experimental evidence showing that premotor regions are co-activated with the AMG during the perception of threatening emotions of anger and fear conveyed by body expressions [START_REF] De Gelder | Fear fosters flight: a mechanism for fear contagion when perceiving emotion expressed by a whole body[END_REF][START_REF] Grèzes | Perceiving fear in dynamic body expressions[END_REF][START_REF] Pichon | Two different faces of threat. Comparing the neural systems for recognizing fear and anger in dynamic body expressions[END_REF][START_REF] Pichon | Threat prompts defensive brain responses independently of attentional control[END_REF]. The involvement of motor regions in emotion perception extends to natural emotional scenes and facial expressions. Indeed, disrupting the motor cortex using TMS impaired emotion categorization of anger and fear expressions specifically, and not happy expressions (Balconi andBortolotti, 2012, 2013). Moreover, the perception of natural emotional scenes engages the motor cortex at very early stages (150 ms after stimulus onset) when the emotional valence of the scene is negative (Borgomaneri et al., 2014). Finally, evidence for a structural connection between amygdala and motor regions (Grèzes et al., 2014) supports the existence of a strong link between emotion and motor systems both representing threat signals to prepare individuals for action in threatening contexts (Ohman and Mineka, 2001;Frijda, 2009).

Networks of regions rather than specialized regions in emotion representation?

Recent meta-analyses [START_REF] Lindquist | The brain basis of emotion: a meta-analytic review[END_REF][START_REF] Wager | A Bayesian model of category-specific emotional brain responses[END_REF] argue for a constructionist approach entailing the involvement of a general brain network rather than discrete regions in the representation of emotions. They support the activation of general networks for different categories of emotions. Importantly and together with other meta-analyses and reviews on emotion processing (e.g [START_REF] Fusar-Poli | Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies[END_REF][START_REF] George | The Facial Expression of Emotions[END_REF], these integrative studies pinpoint the numerous areas of the brain involved in emotion perception, sometimes shared between different emotions or emotion modalities (faces/scenes/bodies), sometimes distinct, and also highly influenced by experimental settings and task demands. It therefore becomes crucial to not only assess where the emotion is represented in the brain but rather understand the mechanisms of such representation, in terms of networks such as suggested here, or detailed information processing such as described in the previous 2 sections.

Chapter 1 conclusion

Research on the perception of face and facial expressions, stimuli of high biological and social significance, has been extensively investigated in the last years showing the involvement of a broad brain network in facial processing starting from 100 ms after stimulus onset. Although the idea of facial expressions perceived in categories of basic emotions dominates the field of affective neuroscience, recent evidence points toward the perception of emotions as a function of their significance in fewer categories. Importantly, social significance is also provided by the abundant contextual factors present in social settings, therefore rendering the consideration of these factors essential when studying social perception.

2-Contextual effects in social perception

While many of the studies cited in the previous section investigated the processing of emotional faces without manipulating the context, it is crucial to take into account the different contextual factors that can interact with emotion perception. It is obvious that facial expressions encountered in our everyday life do not appear on their own, but are rather always embedded in a certain setting. Moreover, the perceiver who has to judge a certain emotion has specific characteristics and past experiences that might influence the way he/she will judge and react to facial expressions. The nature of the contextual information therefore varies from signals provided by the environment to signals coming from the sender of the emotional expression, to signals characteristic of the observer. In this section I will review some of the papers that have manipulated such contingent elements to emotions. First, I will present the contextual elements that are characteristic of the observer/perceiver such as his/her traits or origins, second the contextual elements related to the environment, and finally the contextual modulators belonging to the emitter such as features and body.

2.1-Contextual elements characteristic of the observer

When we think of an observer (represented by the participants in the studies we conduct), we can think of all the possible states we can be in depending on the day, on how much we slept, on our general character, on our knowledge of the word, on our past experience etc. It seems more than obvious that the person we are and our personal experiences -shape our perception of the world. This is particularly true for social perception as successful interactions with other people sometimes require social efforts that might be altered as a function of personal factors. The first example that comes to mind when mentioning personal difference is that of psychiatric disorders as social skills alteration is one the most prominent deficit in these disorders. As this literature is very broad (see review [START_REF] Fett | Social neuroscience in psychiatry: unravelling the neural mechanisms of social dysfunction[END_REF] and involves a large number psychiatric disorders (such as autism, anxiety, schizophrenia, compulsive obsessive disorder, depression…), I will only give a few examples demonstrating the strong impact of individual differences on social perception. Psychiatric dysfunctions are known to lie on a continuum that explains individual differences even in healthy individuals [START_REF] Kosslyn | Bridging psychology and biology. The analysis of individuals in groups[END_REF]. A useful way to investigate these individual differences is provided by personality scales that have long been used in experimental psychology and neuroscience and that clearly demonstrate differences in social perception within healthy individuals.

A vast number of studies show that highly anxious individuals (on personality scales, the most common for anxiety being the State-Trait Anxiety Inventory STAI [START_REF] Spielberger | Manual for the State-Trait Anxiety Inventory[END_REF])) and clinically anxious patients are strongly biased toward the interpretation of events and stimuli as threatening (for meta-analytic study see [START_REF] Bar-Haim | Threatrelated attentional bias in anxious and nonanxious individuals: a meta-analytic study[END_REF]. In addition, this bias is reflected in the brain by an increased activation of the AMG in highly anxious people when they are in a threatening context (e.g [START_REF] Bishop | State anxiety modulation of the amygdala response to unattended threat-related stimuli[END_REF]) (Figure 4). AMG activation to fearful vs neutral faces correlated with anxiety scores. Further, attention modulated less strongly this activation in high anxious individuals, showing that they encode threatening emotions even if their attention is not engaged in the processing of facial expressions. Another example of AMG modulation by individual trait concerns extroversion and the perception of happy faces: the more individuals were extroverted and therefore susceptible to positive mood, the greater was AMG activity in response to happy faces, specifically in contrast to neutral faces [START_REF] Canli | Neuroimaging of emotion and personality: scientific evidence and ethical considerations[END_REF]. These findings therefore suggest that the ways emotional expressions are encoded in the brain are susceptible to individual traits.

Individual traits linked to psychiatric disorders aside, another example of individual elements influencing social perception is that of race: the perception of faces from different races is influenced by the perceiver's own race as reflected in an increased AMG to dissimilar race and is related to the implicit measure of race bias [START_REF] Phelps | Performance on indirect measures of race evaluation predicts amygdala activation[END_REF]. Moreover, N170 EEG response to faces from a different race as compared to similar race was increased [START_REF] Ofan | Seeing race: N170 responses to race and their relation to automatic racial attitudes and controlled processing[END_REF] and finally activity in FG only predicted the race of faces of dissimilar race in people who showed an important implicit race bias [START_REF] Brosch | Implicit race bias decreases the similarity of neural representations of black and white faces[END_REF].

More generally, the belonging to a group can greatly influence the way outgroup and in-group individuals are perceived. Group belonging can be manipulated experimentally quite easily, by using minimal group paradigms (Tajfel, 1970) for example, where people are assigned to groups on the basis of trivial criteria. Belonging to a group can change how the participant perceives in-group as compared to outgroup members relatively automatically as it is independent of the task. This is reflected in an increased activity in face selective regions such as the AMG and the FG to in-group members [START_REF] Van Bavel | The neural substrates of in-group bias[END_REF], 2011). Behaviorally, in-group members are perceived as more trustworthy, caring and intelligent as compared to out-group members (e.g [START_REF] Ratner | Visualizing minimal ingroup and outgroup faces: implications for impressions, attitudes, and behavior[END_REF].

Finally, cultural differences influence the perception of facial expressions as Eastern and Western cultures differ in the way they represent basic emotions (Jack et al., 2012a(Jack et al., , 2012b)). For example, contrarily to Westerners, Easterners do not represent the 6 basic emotions with their distinct action unit set and they seem to be particularly attentive to eye region in the decoding of expressive information.

To conclude on this part, personality traits have a role in changing the way social stimuli are perceived, mainly by biasing interpretations toward what is considered relevant to a certain individual (i.e. threat for an anxious person), and enhancing the processing of such relevant social inputs. Although internal elements of the perceiver were considered in a separate section, all the contextual information that will be discussed in forthcoming sections cannot be dissociated from their significance to the observer.

2.2-Contextual signals related to the environment

Social environments are complex settings that include a variety of elements which can influence our perception of people. It is actually well acknowledged that any visual feature is influenced by the surrounding context [START_REF] Bar | Visual objects in context[END_REF], as there is no ecological situation where an object is perceived on its own. It is also important to keep in mind that the observer might have previous experience in a given environment, such as the people encountered just before, or the elements observed preceding a specific categorization, or the specific aims driving his/her behavior in this setting.

A clear example of how contextual settings change the categorization of emotional expressions comes from studies that manipulated the congruency between a facial expression and the concomitant scene. They show that when emotional faces (disgust, happiness, fear) are embedded in emotionally congruent scenes (Figure 5), their emotion categorization is facilitated even under high load (Righart and de Gelder, 2008a). This is also reflected in the brain where scene information and emotions seem to be combined as early as 170 ms after stimulus onset, as the evoked potential N170 was larger for fearful faces in a fearful context both when the task was explicit and implicit to the emotion (Righart andde Gelder, 2006, 2008b). The integration between emotional scene and emotion information involved the fusiform gyrus (Van den Stock et al., 2014). While the last mentioned studies manipulated the congruency of concurrent scene and face, the effect of positive and negative context induced by short videos on the rating of subsequent images of facial expressions was also investigated [START_REF] Mobbs | The Kuleshov Effect: the influence of contextual framing on emotional attributions[END_REF]. In line with the previous studies, congruency effects were evidenced in biased ratings: positive ratings for happy faces increased in positive contexts, and negative ratings for fearful faces increased in negative contexts (Figure 6). Interactions in the AMG, the FG and temporal pole, reflected these effects, with increased activity to fearful vs neutral preceded by negative context as compared to neutral context (interaction in the AMG shown in Figure 6b).

Aside from natural contexts, social settings often involve more than one person, and therefore other faces present in the setting can also influence the appraisal of emotional expressions. A study that investigated that issue showed that the presence of a fearful face in the periphery enhances the recognition of anger, however only an angry face looking at a fearful face increases the recognition of a fearful face [START_REF] Mumenthaler | Social appraisal influences recognition of emotions[END_REF]. As fearful faces signal potential danger in the surrounding, they can increase the perception of other elements in the scene (Phelps et al., 2006). However in the case of anger, this emotion signals a threat only if it is looking at its target, which explains why the fearful face is better recognized only if looked at. The distinction between these two emotions and their association with gaze direction will further be discussed in forthcoming sections.

Just like faces concurrent with a to-be-judged face can change the categorization of emotional expressions, previously encountered faces or judgments on one type of emotion before another can influence decisions about facial expressions. It has been demonstrated since early studies that judging an emotion before judging another strongly shifts decision about the second [START_REF] Russell | Relativity in the perception of emotion in facial expressions[END_REF]. For example, a neutral face will be perceived as sadder if the decision about that neutral face was preceded by the judgement of a happy face. The idea defended by the authors is that the perception of one emotion will displace your perception of the next one in the opposite direction. A more recent MEG study also highlights the effect of preceding facial expressions by showing that participants were more likely to categorize an ambiguous face as "fearful" if it is preceded by an ambiguous rather than a prototypical fearful face [START_REF] Hsu | The neural mechanism underlying the effects of preceding contexts on current categorization decisions[END_REF]. This effect was coupled with increased suppression of alpha range in frontal electrodes 120 ms before the onset of an ambiguous fearful face when the preceding context consisted of ambiguous fearful faces. Also in line with these findings, however during the judgment of faces but not facial expressions, the perception of facial identities seems to be highly modified by the preceding encountered identities (Apps and Tsakiris, 2013;Liberman et al., 2014). In an fMRI study (Apps and Tsakiris, 2013), contextual familiarity to faces measured as a direct effect of identities seen on previous trials predicted participants choice and this effect was mediated by the STS that tracks moment to moment fluctuations. View-independent familiarity that updated the familiarity of each identity (how many times the same identity was seen throughout the experiment) also influenced decision and co-varied with fusiform face area activity, consistent with the role of the FG in detecting static information in the face such as identity. In another study (Liberman et al., 2014), again with neutral faces only, participants were asked to adjust the identity of a face to a target stimulus. Results showed that the perceptual error is highly influenced by the preceding trial, biasing responses toward the identity seen in the previous trial. This effect survives changes in the viewpoint of the identity but also task modality (if forced choice and not adjustment, but also if no required response at the previous trial) suggesting that the perception of faces is serially dependent. The authors argue that this serially dependent perception of visual objects might serve for visual stability decreasing the neural computations needed to identify objects over short periods of time. This might be useful to our daily interactions with other individuals that constantly move by changing their facial expressions, head orientations, and body movements. The described results of the last two studies converge by showing that viewing independent identities encountered over short periods of time highly predicts the perception of subsequent faces, and therefore that face perception is remarkably influenced by preceding context.

Information about others can also constitute a contextual element during their perception/judgment. For example, faces paired with the sound "stupid" elicit increased skin conductance [START_REF] Iidaka | Transient neural activation in human amygdala involved in aversive conditioning of face and voice[END_REF]. Faces associated with an insult toward an observer trigger increased activity in the AMG [START_REF] Davis | Regional response differences across the human amygdaloid complex during social conditioning[END_REF]. Similarly, when specific behaviors were attributed to identities, those assigned a negative behavior triggered increased activity in STS (Todorov et al., 2007). Furthermore, within negative behaviors, disgusting versus aggressive behaviors triggered increased insula activity independently of explicit memory.

Together, the findings provided in this section demonstrate that signals present in the environment are part of facial perception even though not relevant to the task a participant is engaged in. As our actions and intents in real life situations are most often interrupted or unwarily altered by all sorts of external stimuli such as backgrounds, people and events, these results describe how they possibly do so and shed light on the neural basis of these indisputable influences.

2.3-Contextual signals from the emitter

Let's now consider the contextual elements intertwined with the central element to be perceived, the emitter. These elements can be within-face features such as dynamics in the face or gaze direction, or within-sender features such as body postures and voice [START_REF] Wieser | Faces in context: a review and systematization of contextual influences on affective face processing[END_REF]. When we perceive people every day, we probably perceive them holistically taking into account their movements, their voice etc rather than focusing on specific features. Therefore, all the visual and non-visual signals from an emitter should be combined to form a social signal that has a meaning to the observer (Figure 7).

Within face

Perceptual determinants within the face can highly influence perceptual or social judgments about faces (Todorov et al., 2015). Many examples can illustrate this influence such as baby-faced looking individuals judged as weak and submissive (Zebrowitz and Montepare, 2008), masculine faces and angry faces perceived as more dominant and angry faces perceived as more aggressive. Reverse correlation methods can be helpful to visualize how people expect a face in terms of a certain characteristic. When using this technique to visualize the internal representation of trustworthy and dominant individuals (Dotsch and Todorov, 2012), mouth, eye, eyebrows and hair regions mostly influenced decisions. The resulting classification images clearly show how people have a bias in representing a certain characteristic which implies that certain traits in the face will influence their judgment of people as dominant or trustworthy (Figure 8). Gaze is the most explored cue when faces are processed [START_REF] Yarbus | Eye Movements and Vision[END_REF][START_REF] Pelphrey | Visual scanning of faces in autism[END_REF][START_REF] Spezio | Amygdala damage impairs eye contact during conversations with real people[END_REF] and therefore undoubtedly processed and very likely influencing judgments about faces. This part about gaze as a contextual cue will be elaborated as most of the experiments in this Ph.D. included gaze as a contextual factor and addressed its influence on emotion categorization.

When faces are neutral, direct gaze seems to be advantageously processed, as even 4 months old infants show an increased processing of faces with direct gaze as compared to averted gaze 290 ms after face onset [START_REF] Farroni | Eye contact detection in humans from birth[END_REF]. In adults, faces with a direct gaze trigger enhanced N170 as compared to averted gaze, and this effect involves the medial prefrontal cortex, the orbito-frontal cortex and the STS [START_REF] Conty | When eye creates the contact! ERP evidence for early dissociation between direct and averted gaze motion processing[END_REF]. In addition, under uncertainty, individuals expect a direct gaze as they are biased toward the interpretation of an ambiguous gaze direction as directed toward them [START_REF] Mareschal | Humans Have an Expectation That Gaze Is Directed Toward Them[END_REF]. When perceiving a face, as long as it is neutral, direct gaze seems to be most relevant [START_REF] Senju | Direct gaze captures visuospatial attention[END_REF]. This can be explained by the signal provided by a direct gaze, a signal of a potential social interaction. Infants who already process direct gaze advantageously might do that as they know they will learn from their parents or other adults who interact with them by looking at them in the eye. This strong sensitivity to direct gaze has been suggested to come from the fact that it is an ostensive signal, used as communicative tool since birth [START_REF] Senju | Gaze Following in Human Infants Depends on Communicative Signals[END_REF].

When faces express emotions, gaze direction can impact the perception of the emotion. A happy person with a direct gaze toward an observer probably wishes engaging an interaction with the observer. Therefore in the case of happy expressions, direct gaze should be processed more easily. The shared-signal theory (Adams and Kleck, 2005) posits that emotions sharing an approach tendency similarly to direct gaze should be better processed when accompanied by a direct gaze. This involves emotion of happiness, just mentioned, but also anger that reflects the motivation of the expresser to engage with the perceiver for a fight for example. Contrarily, emotions such as fear and sadness that rather share with averted gaze avoidance tendencies, should be better perceived with an averted gaze. This theory is corroborated by increased emotional ratings and faster reaction times for angry and happy expressions displaying a direct gaze and for fearful and sad expressions with an averted gaze, but also increased ratings on anger and happiness scales for direct gaze neutral stimuli and on sadness and fear scales for averted gaze neutral faces (Adams andKleck, 2003, 2005). Participants also rated feeling more repulsed and more anxious when they saw an angry face looking at them as compared to an angry person looking away, more repulsed for averted fear as compared to direct, and more anxious for a an averted gaze sad face as compared to direct gaze (Hess et al., 2007). The latter results suggest that the effects are linked to the feelings of the observer. When angry, fearful and happy expressions were manipulated on schematic faces controlling for action units, ratings of anger and fear and the correspond-ing emotional intensity were again increased respectively for direct and averted gaze while no effect of gaze emerged for happiness (Sander et al., 2007) (Figure 9). The authors argue that this effect stems from appraisal theories of emotion suggesting that direct anger and averted fear both signal a threat to the observer: while an angry person gazing at an observer makes the observer the target of the threat, a fearful face gazing at the environment away from the observer might signal a nearby threat in the environment. Therefore, observers perceive more anger in direct anger and more fear in averted fear because their evaluation of the displays as being relevant to them (here threatening).

Reciprocally, when judging gaze direction, anger and happiness trigger in observers the tendency to judge stimuli as looking at them [START_REF] Lobmaier | Emotional expression modulates perceived gaze direction[END_REF][START_REF] Adams | Influence of emotional expression on the processing of gaze direction[END_REF][START_REF] Ewbank | Why are you angry with me? Facial expressions of threat influence perception of gaze direction[END_REF] The interaction between emotions of fear and anger and gaze direction was also evidenced when judging gaze, with faster reaction times for direct anger and averted fear [START_REF] Adams | Influence of emotional expression on the processing of gaze direction[END_REF].

Although the results are not always very clear, and for this purpose summarized in table 1, the processing of direct anger and averted fear seems to be facilitated quite consistently, and this appears to be the case since childhood [START_REF] Akechi | The effect of gaze direction on the processing of facial expressions in children with autism spectrum disorder: an ERP study[END_REF][START_REF] Rhodes | Facial Expressions of Threat Influence Perceived Gaze Direction in 8 Year-Olds[END_REF].

The neural basis of the interaction between emotion and gaze cues has also been investigated mostly during implicit tasks, drawing attention away from the social cues of interest. The activity in the AMG consistently varies as a function of this interaction during gender categorization [START_REF] Adams | Effects of gaze on amygdala sensitivity to anger and fear faces[END_REF]Sato et al., 2004[START_REF] Sato | Amygdala integrates emotional expression and gaze direction in response to dynamic facial expressions[END_REF], passive viewing [START_REF] Hadjikhani | Pointing with the eyes: the role of gaze in communicating danger[END_REF] and emotional intensity rating (N'Diaye et al., 2009). Its activity varies as a function of stimulus duration, responding more strongly to signals of threat (direct anger and averted fear) when faces are presented for a short time (300 ms), and more strongly to threat ambiguity (averted anger and direct fear) if stimuli are presented for 1 second (Adams et al., 2012). Other brain areas such as FG, medial prefrontal cortex (mPFC) and hypothalamus have been reported in integrating gaze and emotion (see table 1). Moreover, when the relevance related to threat signals was parametrically manipulated by adding a third social cue to gaze and angry emotion, pointing gesture, the premotor cortex mediated the integration of social cues as a function of their relevance to the observer (Conty et al., 2012). This integration was revealed within 200 ms after stimulus onset. The involvement of the premotor at 200 ms implied an increased motor preparation to socially relevant stimuli. Only one other study in children had shown a similar early signature of social cues interaction [START_REF] Akechi | The effect of gaze direction on the processing of facial expressions in children with autism spectrum disorder: an ERP study[END_REF] as the N170 was enhanced for averted fear and direct anger as compared to direct fear and averted anger. In contrast, another EEG study found a later interaction between emotion and gaze, around 300-400 ms after stimulus onset, and argued for a first stage of independent processing followed by an interaction (Klucharev and Sams, 2004). Interestingly, even 7 months infants showed an increased negativity (Nc) to direct as compared to averted anger [START_REF] Hoehl | Neural processing of eye gaze and threat-related emotional facial expressions in infancy[END_REF] which suggests that the interactions between gaze and emotion might arise very early on, because of their important social significance.

Study

Within sender

Elements within sender can influence the perception of an individual such as the body posture. The categorization of facial expressions of anger, fear and disgust is facilitated when they are matched congruently with body posture (such as a fearful face with a withdrawal-like posture) [START_REF] Meeren | Rapid perceptual integration of facial expression and emotional body language[END_REF]Aviezer et al., 2011). This is reflected in brain activity as soon as 100 ms after stimulus onset, as the evoked potential P100 in occipital electrodes was larger for incongruent as compared to congruent displays [START_REF] Meeren | Rapid perceptual integration of facial expression and emotional body language[END_REF] (Figure 10). Body congruency with voice prosody also influences the recognition of emotion in voice by biasing judgments toward concurrently presented body expression (Van den [START_REF] Van Den Stock | Body expressions influence recognition of emotions in the face and voice[END_REF]. Voice tonality interacts with facial expressions by biasing interpretation of either facial expression or voice toward task-irrelevant simultaneous voice and facial expression respectively [START_REF] De Gelder | The perception of emotions by ear and by eye[END_REF]. A neural signature of this interaction is found in the AMG and FG where activity is increased for congruent fearful faces and voice [START_REF] Dolan | Crossmodal binding of fear in voice and face[END_REF]. In the same vein, laughter increases the rated intensity of happy facial expressions [START_REF] Sherman | Laughter exaggerates happy and sad faces depending on visual context[END_REF] while screams increase the rating of fearful stimuli [START_REF] Müller | Incongruence effects in crossmodal emotional integration[END_REF].

To conclude, concurrent signals from the emitter coming from different modalities interact in the way he/she is processed independently from attentional demands. All the cited studies experimentally prove that when a person is perceived, all the different signals emitted by this person will be combined into a significant social signal.

Chapter 2 conclusion

Signals from the emitter, from the environment and elements encountered in the environment over short periods of time strikingly change the way social stimuli such as faces and facial expression are perceived and categorized. However, there is no unequivocal picture of how all these elements influence perception. Face selective areas involved in the perception of decontextualized faces (STS, FG) are often reported to be involved in theses influences. Together all these studies provide evidence to the idea that facial expressions result from underlying appraisals of the social context in which the emotion is expressed [START_REF] Hess | Darwin and emotion expression[END_REF].

In the last two chapters, we saw that the perception of emotional expressions and the influence of contextual factors on emotion processing have been extensively

investigated. However, the mechanisms underlying the processing of emotional expressions and the influence of context on their processing remain poorly understood.

Indeed, while previous studies could show that emotions in specific contexts modulate behaviors as well as relevant brain regions and brain waves, they did not characterize these influences. One way to understand the computational and neural mechanisms driving these influences is to inform analyses with models capable of explaining the observed effects. The following chapters will thus be devoted to the field of perceptual decision-making that uses the powerful advantage of modeling when trying to understand the underlying computations of brain functioning, as most of the work conducted in this Ph.D. took advantage of behavioral and neural modeling to characterize the way individuals behave and perceive social stimuli.

3-Perceptual decision-making

On a day-to-day basis, we take innumerable decisions consciously or unconsciously.

Some of these decisions are formed based on sensory evidence, such as whether the traffic light turned green (to continue your way), how close is the sound of the police siren (to be careful if it is close) and what color is the sky today (to avoid the rain). This type of decision is referred to as 'perceptual decision-making', as decisions are taken based on perceptual information. This Ph.D. focused on social perceptual decisions which represent a form of perceptual decisions based on socially meaningful stimuli, here emotional expressions. Perceptual decisions have been studied using psychophysical/quantitative methods which led to influential theoretical models, some of which will be described below.

3.1-Principal decision-making models

Signal detection theory (SDT)

In everyday life, we often face situations where the sensory information around us is ambiguous, and therefore requires some effort to decide what it is and how to react accordingly. This might happen on a rainy day where rain obstructs our way back home or in an unfamiliar place such as a new city where some elements are more difficult to recognize, or in a big crowd where it becomes harder to identify your friends etc. The Signal Detection theory (Green and Swets, 1966) 11a). The prime hypothesis underlying signal detection theory is that decisions are formed on the basis of a noisy variable which reflects the presence or absence of the signal and corrupted by additive Gaussian noise. Decisions are hypothesized to be taken by comparing the value of the noisy decision variable to a fixed, criterial value: if the decision variable exceeds this value, then the stimulus is classified as containing the signal; otherwise, it is classified as not containing the signal. As the (Gaussian) distribution probabilities of noise and signal overlap (Figure 11b), subjects will necessarily make some mistakes when deciding whether the signal is present or absent. Their behavior can be explained by two parameters: the discriminability or sensitivity parameter d', and the criterion c. The sensitivity d' corresponds to the signal-to-noise ratio of the decision variable and thus depends on the degree of overlap between the distributions of the signal and noise. In practice, it can be calculated by subtracting z corrected false alarms from hits. The sensitivity parameter represents how well the decision maker is capable of extracting the sensory information from noise. The less the distributions overlap, the higher will be the d', and the better the person can po-tentially discriminate noise from signal. The second parameter c represents individuals' decision criterion and can be set at any point on the decision axis to account for idiosyncratic biases in the decision-maker (e.g., conservativeness in reporting the signal): as indicated above, if the signal falls to the left of the criterion, the participant will respond 'NO', if the signal falls at the right of the criterion, he/she will respond 'YES'. Importantly, the criterion can differ from an individual to another: for example conservative decision-makers will tend to shift their criterion to the right (high criterion) needing more evidence to answer YES and thereby increasing misses while reducing false alarms. Liberal decision-makers on the contrary will shift their criterion to the left (low criterion) and respond 'yes' much more often on the cost of making more false alarms. In some cases, decision biases can have useful features. A famous example given to illustrate these differences in decision biases is the one of the radiologist who has to detect a tumour. Some doctors (liberal) will prefer to say that there is a tumour even if uncertain although they increase the rate of false alarms and therefore increase incorrect diagnosis, but it might be better to do a mistake in that case rather than missing a tumour that would likely kill the patient eventually.

Signal detection theory can also be applied to two alternative forced choice tasks, for example in a visual pattern where dots move right or left: the perceptual information to be tracked here would be the movement of the dots, and the decision bias or criterion would be the tendency of each observer to choose more often right or left. In this framework, psychometric functions can describe participants' behavior under uncertainty, by applying general linear models that assign a sensitivity parameter to decision (multiplicative by the sensory evidence), and a decision bias that is an additive parameter depicting the tendency to answer one option over the other. If

x represents the gradual evidence from option 1 to option 2, such as noise to signal, or left to right, the probability of choosing option 2 (such as 'yes' over 'no' in a detection task or 'right' over 'left' in a 2 options tasks) can be described as follow:

P(option2)= Ф(w*x + b)
where P(option2) corresponds to the probability of choosing option 2 over option 1, Ф to the cumulative normal function, w to the multiplicative gain assigned to the sensory evidence (proportional to the sensitivity parameter d' in the original formulation of signal detection theory) for option 2 response, x to the evidence in favor of option 2 response and b to an additive response bias toward option 2 choice (corresponding to the criterion c in the original formulation of the theory).

Extensions of SDT: sequential-sampling models

While the above described models do not take into account reaction times, extensions of the SDT do and are useful to have a more complete understanding of decision behavior (Gold and Shadlen, 2007), by describing speed accuracy trade-offs for example. These models, including the drift-diffusion model and the race model, portray the decision over time, assuming that there is an integration of sensory information in time until a decision is made.

The drift-diffusion model (Ratcliff, 1978) proposes that sensory evidence is accumulated until a threshold is reached, at which point the response is selected.

The slope of evidence accumulation is equivalent to the sensitivity parameter of SDT and represents the quality of the encoding of the visual information. The threshold at which a response is given corresponds to the criterion, as it changes as a function of individuals: liberal individuals will have a low threshold to respond, and thus need to accumulate less evidence to respond. The race model differs from the drift diffusion model because it considers that two independent populations of neurons will accumulate evidence toward one or the other option. In the drift diffusion model, the symmetric random walk shows accumulated evidence for either hypotheses H1 or H2 whereas in the race model, while evidence accumulates for H1, it decreases for H2 (Figure 12). Neural evidence that corroborate these theoretical accounts of decision-making both in monkeys and humans contributed to their influential impact in the field of decisionmaking.

Evidence from monkey studies indeed show that sensory areas respond to sensory evidence by firing as a function of evidence phasically after stimulus onset while 'accumulator' regions rather fire gradually until response is provided. An example is given in Figure 13 [START_REF] Freedman | A comparison of primate prefrontal and inferior temporal cortices during visual categorization[END_REF]: while monkeys categorized morphed pictures of cats and dogs, recordings in the inferior temporal cortex showed a transient neural firing after stimulus onset specific to the evidence to dog/cat stimuli. In contrast, higher-function areas computing the decision, here the prefrontal cortex (PFC), fired in an accumulative fashion, increasing gradually their activity until response.

Apart from the PFC, considerable work has focused on a region of the monkey parietal cortex, the posterior parietal cortex (LIP) which shows several properties of evidence accumulation during perceptual decision-making (e.g., [START_REF] Shadlen | Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey[END_REF]. Neural evidence confirming this model has also been provided in an fMRI experiment in humans involving a face/house categorization task [START_REF] Heekeren | A general mechanism for perceptual decision-making in the human brain[END_REF]). The available sensory information was degraded by noise added to the images and authors reasoned that two conditions should be fulfilled by higher level cortical regions that integrate decisions: they should show greatest activity for higher perceptual evidence and their activity should be correlated with the difference be- respond either face or house respectively. In the first example (blue), the accumulation process is much faster as the evidence is clear, and the slope is steeper as compared to the second example where the face is degraded (red) and the accumulation takes more time to reach the face response. This study therefore extends the drift diffusion model to more complex decisions in humans and suggests that the DLPFC is responsible for accumulation processes.

3.2-Neural findings in perceptual decision-making

Above, I described the most influential models in perceptual decision-making and illustrated with a few examples from monkey and human studies. Here I will elaborate on detailed recent findings in perceptual decision-making to undertake exposing its underlying neural systems (see extensive reviews, Gold and Shadlen, 2007;Heekeren et al., 2008). While the two examples of stimuli I gave above were house/face and animal morphing, the most common stimuli used in studies that tackled the neural mechanisms of perceptual decision-making are either random dot moving stimuli (RDM) or gabor patches (gratings tilted to the right or left with different contrast levels). These are highly controlled stimuli that can easily be used in both monkey and human studies with different response modalities, such as button press or saccade initiation to respond.

Accumulation of sensory evidence

The accumulation of sensory evidence in the brain, that substantiates the driftdiffusion model, has been frequently reported in perceptual decision-making studies.

It involves various brain regions in humans encompassing both sensory and domaingeneral structures that will be described below.

As reported above, during the categorization of face/house stimuli, evidence accumulation was computed by the DLPFC, suggesting that this area could play the role of a high order decision area in humans [START_REF] Heekeren | A general mechanism for perceptual decision-making in the human brain[END_REF]. The DLPFC involvement in human perceptual decisions was replicated when the task involved the categorization of the direction of RDM and shown to be independent of response modality (saccade initiation or button press) [START_REF] Heekeren | Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality[END_REF]. The idea that sensory information is integrated in associative processing areas for decision formation has also been corroborated during an object recognition task where parietal and frontal regions represented domain-general regions whose activity mirrored that of a decision accumulator until response [START_REF] Ploran | High quality but limited quantity perceptual evidence produces neural accumulation in frontal and parietal cortex[END_REF]. Furthermore, when decisions are decoupled from motor response, perceptual choices are decoded in parietal and visual cortices [START_REF] Hebart | Human visual and parietal cortex encode visual choices independent of motor plans[END_REF].

The combination of EEG and fMRI analysis during a car/face categorization task [START_REF] Philiastides | Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram[END_REF][START_REF] Philiastides | EEG-informed fMRI reveals spatiotemporal characteristics of perceptual decision making[END_REF] evidenced the time course of decision processes (Figure 15): first, early visual perception of face/car stimuli took place in sensory areas such as FFA and the superior temporal gyrus (STG), then at 220 ms task difficulty modulated brain responses in regions such as the anterior cingulate cortex (ACC) and DLPFC, and finally after 300ms, the lateral occipital cortex (LOC) varied with sensory evidence showing that there is a perceptual persistence in post sensory phase, and the ventro-lateral prefrontal cortex (VLPFC) was involved in uncertainty processing. Furthermore, this last component has been shown to be qualitatively different from the early perceptual encoding component as it is sensitive to task demands and reflects post sensory processes directly providing decision evidence [START_REF] Ratcliff | Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG[END_REF]. The difficulty component at 220 ms was however called into question by evidence showing that it is highly dependent on the addition of phase noise to the stimuli thereby increasing their sensory pro-cessing [START_REF] Bankó | Dissociating the effect of noise on sensory processing and overall decision difficulty[END_REF]. On the contrary, when overall task difficulty was manipulated by rendering the stimuli ambiguous with morphing techniques, this component was not affected. Nevertheless, these studies importantly show that sensory regions encode evidence strength along with non-sensory associative regions. Recently, it has been proposed that the evidence accumulating process involves a domain general component that tracks the decision variable (O'Connell et al., 2012) independently from sensory modality and task. In this study, the flickering of a gradual visual annulus was varied and participants were requested to detect the fading of the stimulus with a key press. In addition to a sensory specific occipital component, a motor selective component and a centro-parietal positivity (CPP) co-varied with the evidence to response. This centro-parietal positivity tracked evidence even when the sensory modality was changed to auditory and the task changed to a mental count of targets. On the contrary, when the visual information was made irrelevant, the CPP did not track the evidence, implying its role in the tracking of only goal-relevant sensory information.

In a different study that used RDM, the CPP showed a buildup rate tracking the sensory evidence to motion coherence (Figure 16) and reflected a bound in the decision process as it stopped building up at the moment of the decision [START_REF] Kelly | Internal and External Influences on the Rate of Sensory Evidence Accumulation in the Human Brain[END_REF]. Once again, these observations validate evidence accumulation processes and the reach of a threshold in the decision-making procedure. In addition, this component shared all the properties of the parietal evoked potential peaking around 300 ms, the P300, which led to the conclusion that the CPP, and its event-related potential ERP-equivalent P300, constitute a domain general decision variable independent from the motor effector system [START_REF] Kelly | The neural processes underlying perceptual decision making in humans: Recent progress and future directions[END_REF].

Perception/Action in perceptual decision-making

While traditional views of decision-making suppose that the decision process is serial, first requiring the perception of the stimulus on which the decision evidence is based and later the motor-specific selection of the decision, recent studies highlight the involvement of action related areas in the formation of the decision starting very early on. The boundaries between perceptual, decision related and motor systems seem to be less strong than initially considered. Single neuron recordings in monkeys show that motor related regions are involved in the decision formation before the motor execution per se. For example, the frontal eye field (FEF) that controls eye movements, encodes both decision and saccade related information when monkeys select with their eyes whether random dots are moving right or left [START_REF] Gold | The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands[END_REF]. Likewise, neurons in the premotor cortex coded the entire cascade of perceptual decision-making from sensory processing to motor outcomes during a vibrotactile discrimination task [START_REF] Hernández | Temporal evolution of a decision-making process in medial premotor cortex[END_REF][START_REF] Romo | Neuronal correlates of a perceptual decision in ventral premotor cortex[END_REF]. This is replicated in humans where response selective regions contribute to the decision formation during perceptual tasks, such as the involvement of the FEF in decision formation during an oculomotor decision task [START_REF] Heinen | An oculomotor decision process revealed by functional magnetic resonance imaging[END_REF]. However, it has been argued that an effector-general as opposed to an effector-specific mechanism sustains perceptual decision-making: during decisions on random dot movements, evidence accumulation regions including central regions such as FEF and precentral sulcus [START_REF] Liu | Neural correlates of evidence accumulation in a perceptual decision task[END_REF] were involved independently of task modality (saccadic movement or key press) and foreknowledge of the modality to use (cue before or after the visual stimulus).

Although there seem to be regions integrating evidence independently from motor planning, it does not exclude the fact that motor regions, including effector specific regions, are clearly involved in the decision formation process. During a signal detection task that required human subjects to detect movement coherence in random dot movements, effector selective measures built up choice predictive activity starting long before button press (Donner et al., 2009). Effector selective activity was computed by subtracting time frequency decompositions in the alpha-beta bands (8-36Hz) and gamma bands (64-100Hz) from the ipsilateral to the contralateral hemisphere of the hand used to respond (motor lateralization measure). Subjects were requested to use their left and right hands to provide their yes/no responses. This allowed computing when the motor lateralization started to predict choice after stimulus onset. While beta bands are suppressed in the hemisphere contralateral to the response, gamma bands are enhanced. This results in opposed lateralization for beta and gamma bands such as shown in Figure 17in the motor/premotor cortex. Generally, lateralization in both bands was able to predict the choice better than chance (marked by the dashed line on the figure) during stimulus presentation (shown for 2 seconds). The prediction was similar for hits and false alarms, as well as for misses and correct rejections, approving the idea of different populations of neurons building up for yes or no responses rather than one popula-tion, in which case the motor preparation should have started building toward yes responses before going to no responses during a miss. Further, the lateralization measures correlated with the temporal evidence in motion detection area MT (middle temporal area), suggesting that the evidence from sensory areas is integrated into response in motor effector selective structures. Further evidence pinpoints the role of motor beta band activity as a response preparation signal as it co-varied with response updates (i.e., response preparation increments provided by discrete pieces of sensory evidence provided sequentially) during a multisample categorization task (Wyart et al., 2012a). It also had an additive influence on decision-making, as it predicted decision bias rather than the multiplicative sensory evidence. The methods used to draw these conclusions will be described in further details in following sections, as they were applied in the work conducted in this Ph.D. (see Part 2, Experiment 2).

To conclude, growing evidence of a parallel functioning between motor and perceptual systems substantiates the viewpoint of interactive systems rather than segregated serial processing during decision-making [START_REF] Cisek | Neural Mechanisms for Interacting with a World Full of Action Choices[END_REF]. Of course, this does not preclude the importance of having abstract decision mechanisms in effector-independent regions when the motor action is decoupled from choice, as many studies have shown (e.g., [START_REF] Heekeren | Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality[END_REF][START_REF] Liu | Neural correlates of evidence accumulation in a perceptual decision task[END_REF][START_REF] Hebart | Human visual and parietal cortex encode visual choices independent of motor plans[END_REF]. One way to reconcile the findings supporting the two views (decision in effector related vs common regions) is to consider the distributed consensus model [START_REF] Cisek | Making decisions through a distributed consensus[END_REF] suggesting that the brain has different types of representations including one for abstract decisions and one related to action selection.

Decisions could thus possibly form through simultaneous implications of associative and action-specific brain regions, through a multi-level distributed mechanism. Both representations are necessary depending on whether decisions need a rapid motor reaction in which case the early involvement of motor-related regions might serve to facilitate actions, or whether abstract decisions have to be taken in which case associative domain-general regions could be sufficient to map the decision process.

Along these lines, the brain might have evolved to deal with the two types of decisions by concurrently mapping both representations; however how it did so remains an open question.

Neuromodulatory systems in decision-making: the role of the coeruleusnorepinephrine system

Although the role of neuromodulators in the brainstem has been traditionally assigned to basic functions such as reward and arousal, a growing number of recent studies are suggesting their involvement in more complex functions such as decision-making (Aston-Jones and Cohen, 2005). Specifically, mostly substantiated by monkey studies, the Locus Coeruleus-Norepinephrine system (LC-NE) is proposed to have a crucial role in decision-making. According to two modes of activity, LC activity facilitates behaviors increasing performance on decision tasks (phasic activity)

and drives the search of alternative behaviors when needed (tonic activity) (Aston-Jones and Cohen, 2005). In fact, phasic firing in LC monkey neurons is observed after a target stimulus but not a distractor therefore responding in a task specific manner while tonic phases are observed when distractibility appears in animals' behaviors. At the level of human studies, the decision-related P300 provides a window into the implication of norepinephrenic systems in decision-making [START_REF] Kelly | The neural processes underlying perceptual decision making in humans: Recent progress and future directions[END_REF]. The P300 is suggested to reflect activity in the LC-NE system that populations of neurons one for "yes" response, one for "no" response accumulate evidence toward opposite thresholds. If conservative people start at a point further away from "yes", then a stronger neural boost in gain from visual areas is needed to choose the "yes" response and could be reflected in increased pupil dilation .

Chapter 3 conclusion

Extensive research on the processes underlying decision-making shows that it relies on a set of sensory, associative and motor regions as well as neuromodulatory systems, all seeming to interact rather than working serially and independently.

A very recent study in monkeys corroborates this view by showing that sensorimotor decisions are mediated by a flow of information in a network involving visual, temporal, parietal and motor regions [START_REF] Siegel | Cortical information flow during flexible sensorimotor decisions[END_REF]. This allows the communication between fronto-parietal networks with sensory visual areas and motor-initiating regions to initiate flexible context-dependent behaviors. Findings thus converge toward the view that sensory, associative and motor regions are concurrently rather than successively involved during the formation of decisions. As suggested earlier, the brain might have evolved in such a way that it is capable of dealing with different sorts of decisions (abstract and action-driven), by forming these decisions in a broad network encompassing sensory, motor and associative regions.

4-Contextual effects in perceptual decision-making

During perceptual tasks, manipulating prior information and participants' expectation is feasible by adding explicit cues that bias toward a specific response. This has been the question of many perceptual tasks, as stated in section 2, it is indisputable that day to day decisions are influenced by prior information -in particular when the available sensory evidence to reach a decision is noisy or ambiguous. Hence understanding the mechanisms of contextual prior influences is essential to understand decision-making as a whole. In non-social perceptual decision studies, explicit expectation or attentional cues are typically provided before the decision relevant information and are processed consciously by the participant. Contrarily to social stimuli described in section 2, non-social stimuli are not typically encountered in daily situations and thus not particularly associated to specific settings or elements present in natural environments. If we take the example of random dots, there is no reason to think that a background with a blue sky or a grey sky will change the decision toward left or right motion movement. Similarly, the stimulus per se does not contain additional features (such as the face that contains many elements: gaze, mouth, etc) that will influence the left/right choice. Therefore what can be modulated is either cues provided before the stimulus that predict the upcoming visual information (also referred to as probability cues) or task relevant information such as the attentional focus or the behavioral relevance of a stimulus.

4.1-Expectation/probability cues

Contextual information can be manipulated by providing cues at the beginning of trials or blocks that will predict the upcoming stimulus with a certain probability. If participants integrate these cues in their decisions although the choice concerns an independent visual target, then it would signify that expectations bias the choices.

Normative (ideal) Bayesian accounts of decision-making suggest that conditionally independent sources of information, such as expectation cues and a target stimulus, should be combined additively into a 'posterior' belief about the judgment of the target [START_REF] Griffiths | Optimal predictions in everyday cognition[END_REF][START_REF] Oaksford | Bayesian Rationality: The Probabilistic Approach to Human Reasoning[END_REF], and this would be reflected in SDT terms as a change in the decision bias. As will be described below, while some studies confirm this statement, recent studies also suggest that expectation cues can change the sensitivity to the visual information. Target brain regions reflecting these effects are those described in the previous section as being involved in the decision process.

To start with, the DLPFC, which has been identified as a decision accumulator region in humans, also takes into account prior information. Expectation was manipulated during a task where participants had to judge whether a patch of dots was expanding or constricting (Rahnev et al., 2011a). A shaped cue (triangles and squares) was provided at each trial before the target stimulus and could either be predictive (75% of the trials valid, 25% invalid) or non-predictive (neutral cues) of the forthcoming stimulus, subjects were fully aware of the design and instructed to take the cue into account for their decisions. Subjects performed better at the task when provided with a valid cue and poorer for an invalid cue as compared to neutral. This expectation information was reflected in DLPFC activity and effective connectivity between sensory regions and DLPFC was also increased when participants had prior expectations. DLPFC reflected individual criterion adjustments that were shifted toward the expected option. To conclude on this study, in addition to being a target region that accumulates evidence during decision-making, the DLPFC incorporates prior expectations into the decision process.

Moreover, the motor system seems to be involved in the integration of priors during decisions. In a random dot task, a similar expectation manipulation was used where predictive (words "left" or "right", 75% of trials valid and 25% invalid) and nonpredictive (word "neutral") cues were presented before the motion stimulus (de [START_REF] Lange | Prestimulus oscillatory activity over motor cortex reflects perceptual expectations[END_REF]. Here the motor lateralization described previously (Donner et al. 2009) was computed separately for expected and unexpected conditions and locked to the cue presentation to see whether pre-stimulus activity is modified by expectation cues. Motor preparation signals were biased by the expectation as they started building up toward the congruent answer based on the expected cue even when the final choice was the opposite response (Figure 20). Interestingly, even in neutral cue trials, participants showed biases in the pre-stimulus interval that were partly explained by their preceding responses. Behavioral biases showed the same expectation modulations and correlated with the neural effect. Finally and strikingly with regards to the Bayesian view described above, prior expectations influence the very early processing of visual stimuli. Indeed, in Bayesian terms, expectations should enact as additive sources of information which should be combined, but should not a priori interact, with the processing of the sensory evidence. Effects of expectation were reflected in an interesting dissociation within vis-ual area V1 in a study where an auditory cue predicted the orientation of an upcoming visual graded stimuli (Kok et al., 2012): while expected stimuli triggered less activity in V1 (expectation suppression [START_REF] Summerfield | Neural repetition suppression reflects fulfilled perceptual expectations[END_REF]), they were better decoded in this same area using multivariate pattern analysis (MVPA). Expected stimuli being better represented in visual areas, the authors concluded that expectations sharpen the representation of visual information in primary sensory areas. Using a forward modeling approach to check whether reconstructed visual information is influenced by expectations, the same authors showed in a subsequent study [START_REF] Kok | Prior expectations bias sensory representations in visual cortex[END_REF] that prior expectations can modify what is represented in early visual areas. Further substantiating the early effects of prior probability on visual processing, providing information about the probability of stimulus occurrence at the beginning of the block increased sensitivity to weak signals by increasing the baseline activity of threshold-saturating signal-selective units -rendering them more sensitive to weak signals (Wyart et al., 2012b).

Altogether, the findings presented in the last section show that prior expectations are incorporated into the decision process in several brain regions including primary visual areas, domain-general decision areas such as DLPFC, and motor selective regions. Moreover, not only do expectations bias decisions toward expected stimuli by shifting the decision criterion, as predicted by conventional signal detection theory and Bayesian formulations of decision-making, they can also sharpen the sensory representation of expected stimuli.

4.2-Attention/relevance cues

Operating context in perceptual decision-making studies was most commonly implemented by explicitly shifting the attention of participants to specific information (either in space or among visual features), thereby biasing the processing of one source of sensory information over the other. The results of these studies generally converge by showing a multiplicative boost in the sensory processing of the relevant information (i.e. attended).

Since early days, manipulating attention in a perceptual task showed that providing participants with a cue that predicts the spatial location of the target stimulus enhances the perceptual sensitivity to that stimulus (H S Bashinski, 1980). As

shown in numerous studies since then, attention increases sensitivity to visual inputs by both decreasing the external noise and enhancing the sensory gain of relevant information (see review [START_REF] Carrasco | Covert attention increases contrast sensitivity: Psychophysical, neurophysiological and neuroimaging studies[END_REF]. Because paying attention to everything in our surroundings is impossible given the limited attentional resources at our disposal, the visual system adapts by increasing visual salience to relevant information.

Single neuron recordings in monkeys validate these statements by showing that attention heightens contrast discrimination of attended stimuli in visual area V4 [START_REF] Reynolds | Attention increases sensitivity of V4 neurons[END_REF]. Similarly, a multiplicative enhancement of V4 monkey neurons ability to discriminate orientation was observed [START_REF] Mcadams | Effects of attention on the reliability of individual neurons in monkey visual cortex[END_REF].

Task information is also first encoded in early sensory cortices before reaching frontal and parietal regions and finally spreading to other regions from the sensorimotor pathway [START_REF] Siegel | Cortical information flow during flexible sensorimotor decisions[END_REF]. In humans, task information increases the sensory processing of the attended visual stimulus. As an illustration, during a perceptual categorization task of either face (gender discrimination) or house stimuli (1 or 2 houses), providing a cue that determined whether the houses or faces will appear not only increased performance at the decision stage but also anticipatory BOLD activity in object-selective areas-FFA for faces, PPA for houses [START_REF] Esterman | Perceptual expectation evokes category-selective cortical activity[END_REF].

When examining the timing of attentional effects, the early processing of sensory information is enhanced to attended as compared to unattended stimuli at 250 ms. However, the first evident attentional filter only occurs around 500 ms in parietal regions, when the visual information is converted into associative decision signals (Wyart et al., 2015). At this point only task relevant information is encoded. Another study also showed an influence of relevance cues predicting the forthcoming signal within each trial later in the process at the decision moment, by increasing the sensitivity to strong signals through the suppression of the internal noise corrupting the decision process (Wyart et al., 2012b). It is also suggested that this attention-triggered reduction in internal noise can indirectly modulate the decision bias by diminishing the probability of reaching the criterion -which reduces the probability of a false alarm (Rahnev et al., 2011b).

Although it is acknowledged that attention/relevance cues sharpen the sensory processing of attended/relevant information, evidence from the later mentioned studies show that attention also influences later stages of the decision process: the conversion of sensory signals into decision-relevant evidence, and the apparent decision criterion.

Chapter 4 conclusion

In the perceptual decision-making domain, contextual cues consist of either varying expectation cues or relevance cues. The use of model based analyses for studying expectation-or relevance-dependent effects on decision-making has allowed for the conclusion to be drawn that both types of manipulations influence sensitivity to perceptual information as well as the decision criterion depending on the experimental design and tasks used. These influences involve sensory, motor and more domaingeneral regions in the human brain. Extending this model-based approach to contextual effects in the case of social perceptual decisions could thus potentially enable a fine characterization of how decisions are formed on stimuli of both biological and social significance.

5-Informing contextual effects in emotion perception with decision models

In the last chapters, I separately presented the perception of socially-meaningful stimuli and perceptual decision-making on non-social displays and the impact of contextual information on decisions. Naturally, all social tasks involving biologically rele-vant stimuli need at their core an adequate processing of the sensory evidence provided by the stimuli. Although countless studies investigated social perception, fewer studies employed methods used in the perceptual decision-making domain (modelbased analysis) to address the mechanisms underlying social perception. This partly stems from the fact that social stimuli are harder to control in terms of exact visual components, when compared to highly controlled stimuli such as Gabor patterns or random-dot kinematograms where the experimenter is fully aware of each pixel in the image. Of course, recent studies that used sophisticated methods such as "reverse-correlation" approaches overcame this limitation (e.g., [START_REF] Gosselin | Bubbles: a technique to reveal the use of information in recognition tasks[END_REF]Dotsch and Todorov, 2012). As stated in previous chapters, these studies elegantly dissociate different elements of the face that contribute to perceptual decisions on faces. However, they do not integrate decision theoretical models into the emotion processing mechanism. Using a model-based framework, both behaviorally and neurally, helps clarify the mechanisms mediating perceptual decisions on social displays. In perceptual decision studies, the parametric control over perceptual evidence (how much the Gabor pattern is tilted to the right or left, or the degree of coherence of the random-dot pattern) allows using generative models of choice to predict the performance of human participants given the amount of sensory evidence provided by the stimulus. The way we adopted in this Ph.D. to parametrically control socially-meaningful information when addressing emotion perception is a morphing technique: indeed it permits varying emotional expressions from ambiguous to prototypical displays, thereby rendering the stimuli more ecological and importantly perceptually more difficult to classify as the perceptual information is progressively degraded.

The This last study combined behavioral modeling with pupillometric measures to investigate how identity/emotion associations (i.e., an identity that is angry most of the time) can modify emotion categorization.

Part 1: Attention as a contextual factor during the perception of social cues (Experiment 1)

In chapter 2 section 3, we saw that emotion and gaze cues are perceived interactively by observers, and that this interaction depends on the significance of the combined signal. Table 1 shows that different types of tasks were used in different studies, these tasks varying from gender categorization to emotion categorization to gaze categorization tasks. Although the findings taken together show that emotion/gaze interactions are persistent across tasks, the regions integrating these social cues and the time at which they occur seem to be variable. This can be due to differences in experimental design and task demands. The question of the influence of the task was only directly addressed in emotion studies that did not vary gaze direction but used both explicit and implicit tasks regarding the emotion (e.g., which emotion, or gender categorization). These investigations show that emotional displays are detected at early stages even if irrelevant to the task (e.g [START_REF] Holmes | Attention modulates the processing of emotional expression triggered by foveal faces[END_REF]. Task modulation occurs later, [START_REF] Krolak-Salmon | Processing of facial emotional expression: spatio-temporal data as assessed by scalp event-related potentials[END_REF][START_REF] Hajcak | Attending to affect: appraisal strategies modulate the electrocortical response to arousing pictures[END_REF][START_REF] Holmes | Attention modulates the processing of emotional expression triggered by foveal faces[END_REF][START_REF] Van Strien | The late positive potential and explicit versus implicit processing of facial valence[END_REF][START_REF] Rellecke | Does processing of emotional facial expressions depend on intention? Time-resolved evidence from event-related brain potentials[END_REF], and often involved the P300 [START_REF] Hajcak | Attending to affect: appraisal strategies modulate the electrocortical response to arousing pictures[END_REF][START_REF] Van Strien | The late positive potential and explicit versus implicit processing of facial valence[END_REF]. Here we wanted to address whether the combination of social cues including emotion but not emotional displays alone, is affected by directing attention to one of the social cues manipulated. Importantly, as social cues that consisted of emotion, gaze and gesture were shown to be combined at 200 ms in the PM cortex (Conty et al., 2012) during gaze categorization, our main question in Experiment 1 was whether the timing and source of the integration would vary as a function of the attended cue. .

The main message of Experiment 1 was that social signals are integrated in the human brain within 170 ms after stimulus onset as long as they are contextually relevant to the observer, even if not relevant to the task at hand. Selective attention to social cues however modulated the relative contribution of feature selective regions:

while emotion-related regions were mainly involved during emotion judgments, the dorsal pathway was preferentially recruited during gaze direction judgments. Attention thus enhances the weight of social sensory processing in regions specific to the attended cue. The dissociation between the timing and the regions involved is interesting in regards to the nature of the stimuli used: indeed, while the rapid detection of relevant social information (here threatening) is essential for proper adaptive reactions and survival, the contribution of different brain regions to this integration can be variable.

Classical EEG analyses and source reconstruction showed that there is a neural marker at 170 ms that varies parametrically as a function of relevance to the observers integrating emotion, gaze and gesture independently from attentional demands. While the findings of this study replicate and highlight the fact that different social cues mutually influence each other when social signals are perceived, they do not provide the mechanisms by which they do so. Moreover, although gesture was included in the design in addition to gaze and emotion, to parametrically modulate the relevance of the cues to the observers, only anger emotion was manipulated. As described in chapter 2 section 3, the interaction between emotion and gaze would be more noticeable if both fear and anger are put together, as gaze direction has an opposite effect on their perception (facilitated perception of anger with a direct gaze and fear with an averted gaze). To address these last two issues, the next experi- on previous studies which reported differences in categorization performance between conditions of interest but could not explain how and why these differences occurred. To be able to apply quantitative models of decision-making, we created 'morphed' emotional expressions in order to parametrically control the amount of evidence toward an emotion or another.

Studies exploring perceptual decisions on ambiguous emotional expressions used conventional analyses described in Figure 21a, by relating either stimulus to choice using psychophysics, stimulus to neural activity using regression analyses, or neural activity to choice using 'decoding' techniques. Importantly, none of these studies manipulated contextual information such as gaze direction. For example, multivariate pattern analyses on morphs between fearful and angry avatar facial emotions revealed that the right STS sustains categorical representations of emotional expressions whereas the left STS carries graded representations [START_REF] Said | Graded representations of emotional expressions in the left superior temporal sulcus[END_REF]. In other studies using decoding techniques (Pessoa andPadmala, 2005, 2007), choice-related activity was identified in the prefrontal cortex and insula during a nearthreshold fear detection task, and a broader network including temporal and parietal regions using morphs from fear to disgust (Pessoa andPadmala, 2005, 2007;[START_REF] Thielscher | Neural correlates of perceptual choice and decision making during fear-disgust discrimination[END_REF]. However, choice-related activity was computed by considering neutral face trials only (a choice decoding method often referred to as of 'constant stimulus'), and distinguished from stimulus-related activity which was computed for morphs at the highest emotional intensity only. Thus, the intermediate level of morphs that are most informative for the understanding of how the brain uses in-creasing evidence to make decisions on ambiguous emotional stimuli were not exploited. This work will be presented in the form of a manuscript that is now under review, but I will first report the results of pretests performed to calibrate the stimuli that were less detailed in the manuscript, as these stimuli will be used in all the forthcoming experiments.

Stimuli and pretest calibration results

Stimuli consisted of 36 identities (18 females) adapted from the Radboud Faces Database (Langner et al., 2010). They varied in terms of displayed emotion (neutral, angry or fearful expressions) and gaze direction (direct toward the participant or averted 45° to the left or right). Using Adobe Photoshop CS5.1 (Adobe Systems, San

Jose CA), faces were modified to remove any visible hair, resized and repositioned so that eyes, nose and mouth appeared within the same circumference. All images were converted to greyscale and cropped into a 280 x 406 pixel oval centered within a 628 x 429 pixel black rectangle.

To vary the intensity of emotional expressions, faces were morphed from neutral to angry expressions and from neutral to fearful expression using FantaMorph (www.fantamorph.com). At first, 7 levels of morphs from neutral to angry expressions and from neutral to fearful expressions (separately for direct and averted gaze stimuli) were created using a simple linear morphing transformation. This resulted in 30 conditions for each identity: 7 levels of morphs * 2 emotions * 2 gaze directions = 28 + 2 'neutral' (emotionless stimuli) with direct and averted gaze.

To calibrate the intensity ratings of anger and fear emotions, a pre-test was performed on 19 subjects (9 females, mean age, 24.7 ± 0.9 years). We considered essential to have the same degree of uncertainty at all degrees of emotion intensity for both angry and fearful stimuli. All stimuli were presented for 250 ms and participants rated the emotional intensity perceived on a continuous scale from "not at all intense" to "very intense" using a mouse device (with a maximum of 3 seconds to respond). A main effect of emotion (F (1,18) =50.5, p<0.001) on perceived intensity ratings was observed, fearful stimuli being judged as more intense than angry stimuli.

To adjust for this difference between emotions, we linearized the curve of perceived intensities of anger as a function of the levels of the morphs (Figure 22a). We then extracted the percentage values of morphs of angry and fearful faces needed such as the perceived intensities fall into this linearized curve. New morphs were correspondingly regenerated using FantaMorph, and another test was carried out on 10 new subjects (4 females, mean age 24.1±1.9) to validate the method used. As expected, perceived intensities of the adjusted stimuli did not vary between emotions anymore (p>0.9) (Figure 22b).

Introduction

Perceptual decisions rely on the combination of weak and/or ambiguous samples of sensory evidence. The accuracy of this decision process is particularly important for the interpretation of negative signals, which require rapid and adaptive responses. In the social domain, identifying the emotional state of a conspecific -e.g., is he/she angry or afraid? -rarely depends solely on facial features, which are usually ambiguous and can imply different levels of threat for the observer. Surrounding cues, such as gaze direction and body posture, are known to act as contextual information during emotion recognition (Righart and de Gelder, 2008;[START_REF] Barrett | Context Is Routinely Encoded During Emotion Perception[END_REF]Aviezer et al., 2011).Specifically, the detection of anger represents an immediate threat for the observer when paired with a direct gaze; by contrast, it is when paired with an averted gaze that fear marks the presence (and possibly the localization) of a threat in the environment (Sander et al., 2007). These threat-signaling combinations of gaze direction and emotion have been shown to be better recognized and rated as more intense than other combinations (Adams andKleck, 2003, 2005;Graham and LaBar, 2007;Sander et al., 2007;[START_REF] Bindemann | How do eye gaze and facial expression interact?[END_REF]N'Diaye et al., 2009), and this as a function of anxiety level of the individuals (Ewbank et al., 2010). However, the computational mechanisms underlying the prioritization of threat-signaling information remain unspecified.

Classical theories distinguish two classes of mechanisms by which contextual information such as gaze direction could influence the recognition of negative emotions. Gaze direction could bias the interpretation of negative facial expressions in favor of the emotion signaling higher threat in this context -anger for direct gaze, fear for averted gaze. In signal detection theoretical terms (Green and Swets, 1966;Macmillan and Creelman, 2004), this effect would correspond to a additive shift of the decision criterion as a function of gaze direction. However, gaze direction could also increase the perceptual sensitivity to the facial features diagnostic of the emotion signaling higher threat. In contrast to the first account, this effect would correspond to a multiplicative boost of threat-signaling cues in the decision process. While the two accounts predict similar effects of gaze direction on the recognition of threat-signaling emotions, a bias effect would be maximal for neutral (emotionless) expressions, whereas a sensitivity effect would be maximal at low emotion strengths.

Here we arbitrated between these two competing accounts by recording hu-

Results

Behavior

Participants were presented at each trial with a face expressing fear or anger of varying emotion strength and had to categorize the displayed emotion (Fig. 1). Crucially, direction of gaze (direct or averted) was manipulated independently of the displayed emotion in a completely implicit fashion, as it was never mentioned to the subjects nor relevant to the emotion categorization task. Nevertheless, in addition to an expected increase in categorization performance with emotion strength (F 6,138 = 187.3, p < 0.001), gaze direction strongly interacted with the displayed emotion on performance (F 1,23 = 21.2, p < 0.001). Facial displays of anger were better categorized when paired with a direct gaze (t 23 = 4.3, p < 0.001), whereas expressions of fear were better categorized when paired with an averted gaze (t 23 = -3.4, p < 0.01; Fig. 2a). Because these combinations of gaze and emotion are associated with higher threat for the observer (Sander et al., 2007), they will be labeled as THREAT+ as opposed to THREAT-combinations (anger paired with averted gaze, and fear paired with direct gaze).

To characterize the mechanism underlying the improved recognition of threatsignaling emotions, we fitted participants' behavior using a family of nested models of choice which hypothesize that decisions are formed on the basis of a noisy comparison of the displayed emotion to a criterion (see Methods). We compared quantitatively two variants of the model which could account for the improved recognition of THREAT+ combinations: 1. a first variant in which gaze direction biases the decision criterion in favor of the emotion signaling higher threat, and 2. a second variant in which gaze direction enhances the sensitivity to the emotion signaling higher threat.

Bayesian model selection revealed that a sensitivity enhancement for THREAT+ combinations explained decisively better the behavioral data than a criterion shift (Bayes Factor ≈ 10 8 , p exc > 0.74). As expected, maximum-likelihood sensitivity estimates extracted from the winning model were significantly increased for THREAT+ combinations of gaze and emotion (t 23 = 3.9, p < 0.001; Fig. 2bc).

Enhanced neural encoding of threat-signaling emotions

To validate the finding of enhanced sensitivity to threat-signaling emotions, and to identify its neural substrates, we then investigated how facial expressions modulated scalp EEG activity recorded during the emotion categorization task. Instead of computing event-related averages, we relied on a parametric regression-based approach consisting in regressing single-trial EEG signals against the strength of the displayed emotion at each electrode and time point following the presentation of the face (Wyart et al., 2012a(Wyart et al., , 2015)).The resulting time course at each electrode represents the degree to which EEG activity 'encodes' (co-varies with) the emotion strength provided by morphed facial features.

Parameter estimates of the regression slope revealed significant correlations between emotion strength and EEG activity peaking initially around 290 ms following face presentation at temporal (t-test against zero, t 23 = -12.7, p < 0.001) and frontal electrodes (t 23 = 8.7, p < 0.001), and then around 500 ms and at response time at centro-parietal (t 23 = 10.2, p < 0.001) and frontal electrodes (t 23 = -7.9, p < 0.001) (Fig. 3abc). Time points and electrodes where parameter estimates diverge significantly from zero indicate neural encoding of emotion information. The strength of this neural encoding -indexed by the amplitude of the parameter estimate -provides a measure of the neural sensitivity to emotion information.

To test for a neural signature of the increased sensitivity to threat-signaling emotions, we compared parameter estimates extracted separately for THREAT+ and THREAT-combinations of gaze and emotion. This contrast revealed increased parameter estimates for THREAT+ combinations first at 170 ms at temporal (paired ttest, t 23 = -2.5, p < 0.05) and frontal electrodes (t 23 = 2.2, p < 0.05), and then later at 500 ms and at response time at centro-parietal (t 23 = 2.2, p < 0.05) and frontal electrodes (t 23 = -2.4, p < 0.05) (Fig. 3abc). This finding indicates that the neural gain of emotion encoding was enhanced at these time points and electrodes for threatsignaling emotions. Interestingly, THREAT+ combinations were not associated with increased event-related averages at classical peak latencies (P1, N170, P2, P3: all t 23 < 1.95, p > 0.07). We computed the cortical sources of this enhanced encoding of threat-signaling emotions by performing the same regression approach to minimumnorm current estimates distributed across the cortical surface. Parameter estimates at time points of interest (where differences between THREAT+ and THREAT-combina-tions were observed) were then contrasted between the two conditions (see Material and Methods). Increases in regression slopes for THREAT+ combinations shifted from ventral visual areas selective to facial expressions of emotion (fusiform gyrus and superior temporal sulcus) around 180 ms, to associative brain regions encompassing parietal, temporal and frontal cortices (superior and middle temporal, temporal pole, and orbitofrontal cortices) at 500 ms, and then to sensorimotor regions around response onset (dorsal central, parietal and frontal regions) (Fig. 3dfe).

These neural effects converge with behavioral modeling in favor of a sustained enhancement of perceptual sensitivity to threat-signaling emotions, starting 180 ms following face presentation and lasting until after response onset. Additional evidence supports our hypothesis that enhancements in neural sensitivity to THREAT+ combinations are specifically linked to an increase in implied threat for these combinations of gaze and emotion. A separate group of participants rated the identities used in the emotion categorization task in terms of perceived threat and trustworthiness (see Methods), and the group-level ratings for each identity were regressed against single-trial EEG signals as additional regressors. This regression showed that perceived threat, but not trustworthiness, correlated significantly with temporal and centro-parietal EEG activity at 500 ms following face presentation, in the same direction as the contrast between THREAT+ and THREAT-combinations (threat: t 23 > 3.6, p < 0.01; trustworthiness: t 23 < 0.7, p > 0.48).

Attention-independent enhancement of neural processing by threat

Analyses of the neural data have so far confirmed the hypothesis that contextual gaze information affects emotion categorization by increasing the perceptual sensitivity to threat-signaling emotions. Such an effect could be mediated by increased top-down attention to threat-signaling emotions -i.e., THREAT+ combinations. To test this possibility, we explored whether residual fluctuations in single-trial EEG signals unexplained by variations in emotion strength (measured by the previous regressions) modulated the accuracy of the subsequent categorical decision -i.e., the perceptual sensitivity to the displayed emotion. This approach is reminiscent of 'choice probability' measures applied in electrophysiology to measure correlations between neural activity and choice behavior [START_REF] Britten | A relationship between behavioral choice and the visual responses of neurons in macaque MT[END_REF][START_REF] Shadlen | A computational analysis of the relationship between neuronal and behavioral responses to visual motion[END_REF][START_REF] Parker | Sense and the single neuron: probing the physiology of perception[END_REF]-by estimating how much fluctuations in recorded neural signals are 'read out' by the subsequent decision (Wyart et al., 2012a(Wyart et al., , 2015)). Stimulusindependent improvements in neural-choice correlations have been classically interpreted as increases in 'read-out' weights -i.e., increased top-down attention to these neural signals (Nienborg andCumming, 2009, 2010). Here, an increased neural modulation of choice for THREAT+ conditions could indicate an increase in top-down attention to threat-signaling emotions, which could in turn explain the observed increase in perceptual and neural sensitivity to these combinations of gaze and emotion.

In practice, we entered EEG residuals from the previous regression against emotion strength as an additional 'mediation' predictor of choice -as means to test whether these neural signals co-vary with perceptual sensitivity (see Methods). The time course and spatial distribution of this neural modulation of perceptual sensitivity followed qualitatively the neural encoding of emotion strength (Fig. 4abc), with a negative temporal component peaking at 270 ms (t 23 = -4.2, p < 0.001), followed by a positive centro-parietal one peaking around 600 ms (t 23 = 8.0, p < 0.001) and then at response time (t 23 = 7.6, p < 0.001). We used Bayesian model selection to confirm that EEG residuals co-varied multiplicatively with the perceptual sensitivity of the subsequent decision, not additively as a bias in emotion strength, both at temporal (Bayes factor ≈ 10 3.4 , p exc = 0.79) and centro-parietal electrodes (Bayes factor ≈ 10 8.9 , p exc = 0.99). Critically, no difference in modulation strength was observed between THREAT+ and THREAT-combinations (temporal: t 23 = -0.4, p > 0.5; centroparietal: t 23 = 0.1, p > 0.5). To determine whether this absence of significant difference is due to a genuine absence of effect (rather than a lack of statistical sensitivity), we computed Bayes factors under the same parametric assumptions as conventional statistics (see Methods). We obtained Bayes factors lower than 10 -4 at temporal and centro-parietal electrodes, indicative of no increase in 'read-out' weights for THREAT+ conditions. This null effect suggests that the observed enhancement in perceptual and neural sensitivity to these threat-signaling combinations of gaze and emotion is not triggered indirectly by an increase in top-down attention in these conditions.

Early neural encoding of threat-signaling emotions in motor preparation

We reasoned that threat could impact not only the neural representation of the displayed emotion in visual and associative cortices, but also the preparation of the upcoming response in effector-selective structures (Conty et al., 2012). To measure response-preparatory signals in the neural data, we computed spectral power in the mu and beta frequency bands (8-32 Hz) (Donner et al., 2009;de Lange et al., 2013).

The contrast between left-handed and right-handed responses at response time identified lateral central electrodes, associated with focal sources in motor cortex (Fig. 5a). Subtracting contralateral from ipsilateral signals relative to the hand assigned to the 'fear' response (counterbalanced across participants) provided a motor lateralization index whose sign predicts significantly the upcoming choice (anger or fear) from 360 ms before response onset (paired t-test, t 23 = 4.6, p < 0.001; Fig. 5b).

We applied the previous neural encoding approach by regressing this motor lateralization index against the signed emotion strength (from 0 for a neutral expression, to ±7 for an intense anger/fear expression) on a trial-by-trial basis. Parameter estimates of the regression slope diverged significantly from zero from 400 ms after stimulus onset (t-test against zero, t 23 = 5.1, p < 0.001) and at response time (t 23 = 5.2, p < 0.001) -reflecting stronger response preparation to stronger (i.e., more diagnostic) emotions. Computing regression slopes separately for THREAT+ and THREAT-combinations revealed that THREAT+ combinations produced a stronger encoding of emotion strength in motor preparation late at response onset (t 23 = 2.9, p < 0.01), but also early around 200 ms following face presentation (t 23 = 3.2, p < 0.01).

While THREAT-combinations of gaze and emotion were not associated with significant neural encoding in motor preparation until 440 ms following face presentation (t 23 < 0.8, p > 0.4), THREAT+ combinations resulted in significant neural encoding between 100 and 320 ms (t 23 = 3.2, p < 0.01; Fig. 5c).

To determine whether this early neural encoding of threat-signaling emotions in motor preparation influences the speed of subsequent responses, we recomputed and compared regression parameters estimated separately for fast and slow responses to THREAT+ combinations, on the basis of a median split of response times informed by emotion strength. This comparison revealed a single, gradual neural encoding of emotion strength in motor preparation preceding fast, but not slow responses, arising as early as 150 ms (at a threshold p-value of 0.05) following the presentation of the face (difference in encoding onset between fast and slow responses, jackknifed [START_REF] Kiesel | Measurement of ERP latency differences: a comparison of single-participant and jackknife-based scoring methods[END_REF] t 23 = 5.2, p < 0.001; Fig. 6a). This effect indicates that the early neural encoding of THREAT+ combinations in motor preparation is characteristic of efficient (fast) responses. We verified that this latency shift in neural encoding was selective of motor preparation signals, by performing the same comparison on the neural encoding of emotion strength at centro-parietal electrodes.

This contrast revealed only a difference in peak amplitude, not onset latency, between fast and slow responses (peak amplitude: t 23 = 5.1, p < 0.001; onset latency: jackknifed t 23 = -1.3, p > 0.2; Fig. 6b).

Finally, we performed neural-choice correlations analyses to assess whether the early neural encoding of threat-signaling emotions in motor preparation influences not only the speed, but also the content (anger or fear) of subsequent responses. Across conditions, the neural 'mediation' analysis described above revealed that stimulus-independent fluctuations in motor lateralization index co-vary as an additive choice bias in the upcoming response from 400 ms following face presentation (t 23 = 2.9, p < 0.01). In contrast to fluctuations in temporal and centroparietal activity, the impact of variability in motor lateralization on emotion categorization was better described as an additive choice bias rather than a change in perceptual sensitivity (Bayes factor ≈ 10 36.4 , p exc = 0.98) -consistent with its hypothesized role as a motor representation of the decision variable (Donner et al., 2009;de Lange et al., 2013). No difference in modulation strength was observed between THREAT+ and THREAT-combinations (t 23 < 1.6, p > 0.1; Fig. 5d). Critically, even when considering THREAT+ combinations alone, residual variability in motor lateralization measured between 100 and 320 ms (where the neural encoding of threat-signaling emotions was significant) did not bias significantly the upcoming choice (t 23 < 1.4, p > 0.17). This null effect was supported by Bayesian model selection which identified a genuine absence of neural-choice correlation as the most likely account of the data (Bayes factor ≈ 10 2.3 , p exc = 0.96). This finding indicates that the early neural encoding of threat-signaling emotions in motor preparation occurs earlier than the formation of the upcoming choice.

Anxiety-dependent neural encoding of threat-signaling emotions

In the general population, anxiety has been classically associated with an oversensitivity to threat signals in social conditions. Here, we assessed whether the enhanced neural processing of threat-signaling emotions in temporal and motor regions covaried with the level of anxiety in our participants. For this purpose, we measured anxiety at the beginning of the experiment using the Spielberger State-Trait Anxiety Inventory (STAI). We analyzed the effect of anxiety on the behavioral and neural data in two complementary ways: 1. by splitting the participants in two equally-sized groups based on their measured anxiety, and 2. by correlating neural encoding parameters estimated at the level of individual participants with their measured anxiety.

Surprisingly, we found no effect of 'anxiety on overall measures of performance (t 11 < 0.05, p > 0.9), nor on the difference between THREAT+ and THREAT-combinations of gaze and emotion (F 1,22 < 0.4, p > 0.5).

Nevertheless, the absence of effect of anxiety at the behavioral level was accompanied by a compensatory double dissociation in the neural data. Indeed, state anxiety influenced significantly the neural encoding of emotion strength at temporal electrodes between 200 and 400 ms following face presentation (median split, interaction: F 1,22 = 10.8, p < 0.01; Fig. 7a): high-anxious observers showed no difference in neural encoding between THREAT+ and THREAT-combinations (THREAT+ : t 11 = -6.5, p < 0.001; THREAT-: t 11 = -6.2, p < 0.001, difference: t 22 = 1.1, p = 0.28), whereas low-anxious observers encoded exclusively THREAT+ combinations in the same neural activity (THREAT+: t 11 = -6.9, p < 0.001 ; THREAT-: t 11 = -0.63, p = 0.53 ; difference:

t 22 = -3.8, p < 0.01).
A parametric assessment of the relationship between state anxiety and the difference in neural encoding between THREAT+ and THREAT-combinations proved to be significant (r = 0.58, d.f. = 22, p < 0.01; Fig. 7a). In other words, high anxiety was associated with a significant and indifferent neural encoding of negative emotions, whether threat-signaling or not, in ventral face-selective regions.

Interestingly, at the early time window (200 ms) where only THREAT+ combinations were encoded in motor signals, a reverse pattern was observed: only high anxious individuals showed a significant encoding at this latency (interaction between between-subject state anxiety and gaze pairing F 1,22 = 4, p = 0.05; Fig. 7b). The more the individuals were anxious, the more they encoded observer-relevant threat signals in motor systems (correlation between parameter estimates for threat + conditions and state anxiety r = 0.52, d.f. = 22, p < 0.01). Moreover, the encoding of THREAT+ combinations correlated with high anxious behavioral sensitivity to THREAT+ (r = 0.66, d.f. = 10, p = 0.01; Fig. 7b), whereas it did not for low anxious individuals (r = -0.42, d.f. = 10, p > 0.16, significant difference between correlation coefficients p < 0.01). To sum up, while high anxious individuals process all threat signals equivalently in face selective regions, they selectively encode threat signals that are relevant to them in motor specific systems, and this encoding reflects their behavioral sensitivity to threat-signaling emotions.

Discussion

Accurate decoding of emotions in others, especially negative ones, conveys adaptive advantages in social environments. Although typical social interactions do not require an explicit categorization of the emotion expressed by others, a precise understanding of the neural mechanisms involved in emotion recognition provides important information regarding how the human brain processes socially meaningful signals. And while past work has uncovered the neural correlates of perceptual decisions (Gold and Shadlen, 2007;Heekeren et al., 2008), only few studies have addressed the issue of how such decisions are formed on the basis of socially relevant stimuli such as facial displays of emotion. As in most perceptual categorization tasks, we manipulated the ambiguity of sensory evidence -here, using controlled morphs between angry or fearful expressions and neutral ones. But owing to the social nature of our stimuli, we could simultaneously and implicitly manipulate the contextual significance of the displayed emotion in terms of implied threat for the observer, using gaze direction, and apply a model-guided approach to characterize the neural prioritization of threat-signaling information in electrical brain signals.

Gaze direction, which acts as a contextual cue in our emotion categorization task, differs from contextual cues found in perceptual decision-making studies which are typically provided hundreds of milliseconds before the decision-relevant stimulus (Rahnev et al., 2011;Kok et al., 2012;Wyart et al., 2012b;de Lange et al., 2013).

Here, as in many social situations, contextual cues can co-occur with the decisionrelevant stimulus -a property which strongly constrains their impact on stimulus processing. Moreover, the meaning of contextual cues (e.g., attention or expectation cues) used in perceptual decision-making studies is usually instructed explicitly, and thus processed explicitly by the participants during task execution (Kok et al., 2012;Wyart et al., 2012b). Here, by contrast, gaze direction is irrelevant for the emotion categorization task, and thus does not need to be processed explicitly. Despite these two differences with other contextual cues, we show that gaze direction tunes the neural processing of emotion information from 200 ms following stimulus onset until response in sensory, associative and motor circuits of the human brain.

Previous observations of increased subjective ratings and improved recognition of angry expressions paired with a direct gaze and fearful expressions paired with an averted gaze have been interpreted in terms of a contextual evaluation of the displayed emotion during its processing [START_REF] Adams | Effects of gaze on amygdala sensitivity to anger and fear faces[END_REF]Sander et al., 2007;Adams et al., 2012). In particular, 'appraisal' theories (Sander et al., 2007) emphasize that an angry expression paired with a direct gaze can be interpreted as behaviorally 'relevant' to the observer as being the target of a verbal or physical assault, whereas a fearful expression looking aside from the observer might signal a source of danger in the immediate vicinity of the observer. However, the mechanisms which instantiate the proposed contextual evaluation of emotions as a function of their implied threat for the observer have remained unclear. Gaze direction could either bias the perceived emotion towards its most relevant (threat-signaling) interpretation -i.e., anger when paired with direct gaze, or fear when paired with averted gaze, or increase the sensitivity to the most relevant emotion. The present study answers directly this issue by showing, both behaviorally (by comparing quantitative fits of the two effects to the behavioral data) and neurally (by regressing brain signals against emotion strength), that the improved recognition accuracy for threat-signaling emotions corresponds to a selective neural enhancement of perceptual sensitivity to these combinations of gaze and emotion.

Emotion information modulated EEG signals at centro-parietal electrodes from 500 ms following face presentation until response execution, a finding in accordance with the 'supramodal' signature of perceptual integration reported in previous studies (O'Connell et al., 2012;Wyart et al., 2012a). This centro-parietal positivity has been proposed to encode a 'domain-general' decision variable, as it varies with the strength of sensory evidence for both visual and auditory decisions, independently from the associated response (O'Connell et al., 2012). Here, the same centro-parietal positivity was found to increase with the emotion strength of facial expressionswhich indexes the decision variable in our emotion categorization task. Importantly, the strength of this relationship was enhanced for threat-signaling emotions. This improved neural representation of threatening combinations of gaze and emotion cannot be explained by increased attentional or surprise responses, since the centro-parietal 'P3' potential, previously reported to vary as a function of attentional resources (Johnson, 1988) and surprise (Mars et al., 2008), was not increased in response to threat-signaling emotions. Moreover, we could also rule out the possibility that this enhanced neural encoding is triggered indirectly by an increase in selective attention, which should have been associated with an improved 'decoding' of participants' decisions from their underlying neural signals (Nienborg andCumming, 2009, 2010;Wyart et al., 2015). We therefore hypothesize that the enhanced neural processing of threat-signaling emotions proceeds in an attention-independent, bottomup fashion.

Earlier contextual modulations of emotion processing were also observed in ventral face-selective areas from 170 ms following face presentation. While these findings contradict a 'two-stage' view according to which emotion and gaze information would be processed independently during the first hundreds of millisecond (Vuilleumier and Pourtois, 2007) before being integrated as a function of their significance to the observer (Klucharev and Sams, 2004), they are in agreement with recent findings (Conty et al., 2012;[START_REF] El Zein | Selective attention effects on early integration of social signals: same timing, modulated neural sources[END_REF] of early interactions between emotion and gaze information on N170 and P200 components. At these early laten- ones (Balconi andBortolotti, 2012, 2013). Moreover, the perception of natural scenes engages the motor cortex at very early latencies only when the emotional valence of the scene is negative (Borgomaneri et al., 2014). Taken together, these findings support a strong connection between emotion and motor circuits (Grèzes et al., 2014) enabling the brain to react swiftly and efficiently to threat signals (Ohman and Mineka, 2001;Frijda, 2009). Our findings build on these earlier observations by showing that the brain encodes parametrically the strength of threat signals in motor cortex in parallel to their representation in face-selective, sensory regions.

Finally, our data reveal a clear functional dissociation between face-and effector-selective regions as a function of individual anxiety. The enhanced sensitivity to threat-signaling emotions in face-selective temporal cortex is driven by lowanxious observers, whereas the early enhancement measured in motor cortex is only found in high-anxious observers. The observation that high-anxious individuals encode all negative emotions as equally (and strongly) salient in face-selective regions is consistent with earlier reports of an 'hyper-vigilance' to potentially threatening signals in these individuals (Bishop, 2007;[START_REF] Cisler | Mechanisms of attentional biases towards threat in anxiety disorders: An integrative review[END_REF], and with their tendency to interpret ambiguous stimuli as threatening [START_REF] Beck | Anxiety Disorders and Phobias: A Cognitive Perspective[END_REF] -both associated with amygdala hyperactivity (Bishop, 2007;Etkin and Wager, 2007). Nevertheless, our findings reveal that high-anxious individuals are capable of encoding threat signals in a selective fashion in motor cortex. Consistent with the idea of a compensatory mechanism, the distinct neural enhancements of temporal and motor activity found in low-and high-anxious individuals lead to similar behavioral improvements in terms of perceptual sensitivity to threat signals. Together, this pattern of findings suggests that anxiety increases the relative contribution of the motor pathway during the processing of negative social signals, in accordance with the adaptive function of anxiety in detecting efficiently and reacting swiftly to threats in the environment (Bateson et al., 2011).

By applying theoretical models of decision-making to socially-relevant stimuli, we were able to characterize the neural and computational mechanisms underlying the integration and interpretation of facial cues in the implicit context of threat. Evolutionary pressure might have shaped the human brain to prioritize threat signals in parallel in sensory and motor systems [START_REF] Darwin | The Expression of the Emotions in Man and Animals[END_REF]LeDoux, 2012).

Such prioritization -found to proceed in a fast, selective, yet attention-independent fashion -could increase perceptual sensitivity to other features of the sensory environment (Phelps et al., 2006) to enable rapid and adaptive responses in complex, multidimensional situations of danger.

Methods

Subjects

Twenty four healthy subjects (12 females; mean age, 22.7 ± 0.7 years) participated in the EEG experiment. All participants were right-handed, with a normal vision and had no neurological or psychiatric history. They provided written informed consent according to institutional guidelines of the local research ethics committee (Declaration of Helsinki) and were paid for their participation.

Stimuli

Stimuli consisted of 36 identities (18 females) adapted from the RadBoud Faces Database (Langner et al., 2010) that varied in emotion (neutral, angry or fearful expressions) and gaze direction (direct toward the participant or averted 45° to the left or right). Using Adobe Photoshop CS5.1 (Adobe Systems, San Jose CA), faces were modified to remove any visible hair, resized and repositioned so that eyes, nose and mouth appeared within the same circumference. All images were converted to greyscale and cropped into a 280 x 406 pixel oval centered within a 628 x 429 pixel black rectangle.

To vary the intensity of emotional expressions, faces were morphed from neutral to angry expressions and from neutral to fearful expression using FantaMorph (Abrosoft http://www.fantamorph.com/). At first, we created 7 levels of morphs from neutral to angry expressions and from neutral to fearful expressions (separately for direct and averted gaze stimuli) using a simple linear morphing transformation. This resulted in 30 conditions for each identity: 7 levels of morphs * 2 emotions * 2 gaze directions = 28 and 2 neutral stimuli with direct and averted gaze. We then calibrated the morphing between angry and fearful expressions by performing an intensity rating pre-test of the emotional expressions and adjusting the morphs based on the results. 19 subjects (9 females, mean age, 24.7 ± 0.9 years) were presented with the facial expressions for 250 ms and rated the emotional intensity perceived on a continuous scale from "not at all intense" to "very intense" using a mouse device (with a maximum of 3 seconds to respond). We adjusted for differences between emotions by linearizing the mean curves of judged intensities and creating corresponding morphs, that were validated on 10 new subjects (4 females, mean age 24.1±1.9). To summarize, the stimuli comprise of 36 identities with an Averted gaze condition and a Direct gaze condition, each with 7 levels of Anger and 7 levels of Fear equalized in perceived emotional intensities and a neutral condition, resulting in a total of 1080 items (see Fig. 1a for examples of stimuli).

Experimental procedure

Using the Psychophysics-3 Toolbox (Brainard, 1997;Pelli, 1997), stimuli were projected on a black screen. Each trial was initiated with a white oval delimiting the faces that was kept during all the trial. The white oval appeared for approximately 500 ms, followed by a white fixation point presented at the level of the eyes for approximately 1000 ms (to keep the fixation to the upcoming faces natural and avoid eye movements from the center of the oval to eye regions), than the stimuli appeared for 250 ms. Participants' task was to decide whether the faces expressed Anger or Fear by pressing one of the two buttons localized on two external devices held in their right and left hands, with their right or left index correspondingly (Fig. 1b). An Anger/Fear mapping was used (e.g Anger: Left hand, Fear: Right hand) kept constant for each subject, counterbalanced over all subjects. All stimuli were presented once, resulting in a total of 1080 trials. The experiment was divided in 9 experimental blocks, each consisting of 120 trials, balanced in the number of emotions, directions of gaze, gender and levels of morphs. After each block, the percentage of correct responses was shown to the participants to keep them motivated.

Behavioral data analyses

Repeated-measures ANOVA was performed on the percentage of correct responses and average reaction times, with gaze direction (direct/averted), emotion (anger/fear), and intensity (7 levels of morphs) as within-subjects factors.

Model selection

We performed model-guided analyses of the behavioral data to characterize the observed increase in recognition accuracy for THREAT+ combinations of gaze and emotion. We used Bayesian model selection based on the model evidence (estimated by a 10-fold cross-validation estimation of model log-likelihood, which penalizes implicitly for model complexity without relying on particular approximations such as the Bayesian Information Criterion or the Akaike Information Criterion). We applied both fixed-effects and random-effects statistics previously described in the literature. The fixed-effects comparison assumes all participants to have used the same underlying model to generate their behavior, such that the overall model evidence for a given model is proportional to the product of model evidence for the model for all participants. Based on this model evidence, we compared different models by computing their Bayes factor as the ratio of model evidence of the compared model (Jeffreys, 1961;Kass and Raftery, 1995). The random-effects comparison is more conservative in allowing different participants to use different models to generate their behavior, and aims at inferring the distribution over models that participants draw from (Penny et al., 2010). For this comparison, we computed support for the winning model by the exceedance probability (p exc ), which is the probability that participants were more likely to choose this model to generate behavior over any alternative model.

We started with the simplest model (model 0) that could account for subject's decisions using a psychometric model such as:

P(Anger)= Ф(w*x + b)
where P(Anger) corresponds to the probability of judging the face as angry, Ф to the cumulative normal function, w to the perceptual sensitivity to the display emotion, x to the evidence in favor of Anger or Fear in each trial (emotion strength, from -7 for an intense expression of fear to +7 for an intense expression of anger), and b to an additive, stimulus-independent bias toward one of the two responses/emotions.

We compared this 'null' model which did not allow for contextual influences of gaze direction on the decision process, with two additional models which instantiate two different mechanisms which could account for the observed increase in recognition accuracy for THREAT+ combinations of gaze and emotion. A first possibility (model 1) would be that gaze direction biases emotion recognition in favor of the interpretation signaling higher threat (anger for a direct gaze, fear for an averted gaze). Alternatively (model 2), gaze direction might selectively increase sensitivity to emotions signal-ing higher threat in this context (modeled by a different sensitivity to emotions in THREAT+ vs. THREAT -conditions).

EEG acquisition and pre-processing

An EEG cap of 63 sintered Ag/AgCl ring electrodes (Easycap) was used to record EEG activity. EEG activity was recorded at a sampling rate of 1000 Hz using a BRAINAMP amplifier (Brain Products, BRAINAMP MR PLUS) and low pass filtered online at 250 Hz. The reference channel was placed on their nose and a forehead ground was used. Impedances were kept under a threshold of 10 kΩ.

The raw EEG data was recalculated to average reference, down-sampled to 500 Hz, low-pass filtered at 32 Hz, and epoched from 1 s before to 4 s after the face stimulus onset using EEGLAB [START_REF] Delorme | EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[END_REF]. First, EEG epoched data was visually inspected to remove muscle artifacts and to identify noisy electrodes that were interpolated to the average of adjacent electrodes. Second, independent component analysis (ICA) that excluded interpolated electrodes was performed on the epoched data and ICA components capturing eye blink artifacts were manually rejected. A last, visual inspection was done on the resulting single epochs to exclude any remaining trials with artifacts. After trial rejections, an average of 999 ±10 trials per subject remained.

Time frequency analysis was performed using the Fieldtrip toolbox for MATLAB [START_REF] Oostenveld | FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data[END_REF]. We were particularly interested in motor mubands (8-32 Hz) and thus estimated the spectral power of mu-beta band EEG oscillations using 'multitapering' time frequency transform (Slepian tapers, frequency range 8-32 Hz, five cycles, three tapers per window). The purpose of this multitapering approach is to obtain more precise power estimates by smoothing across frequencies. Note that this time-frequency transform uses a constant number of cycles per window across frequencies, hence a time window whose duration decreases inversely with increasing frequency.

EEG analysis

Time frequency: motor lateralization measures

As the suppression of mu-beta activity in the hemisphere contralateral to the hand used for response is a marker of motor preparation to response (Donner et al., 2009;de Lange et al., 2013), spectral power from 8 to 32 Hz were calculated at each electrode and time point for all subjects. Then for each subject, to obtain the lateralization measures, the spectral power from 8 to 32 Hz for the trials where the subjects responded with their right hand was subtracted from that of the trials where the subjects responded with their left hand. After averaging on all subjects, electrodes where the motor lateralization was maximal from 200 ms before to response time were selected: 'P3,'CP3','C3' for the left hemisphere and 'P4,'CP4','C4' for the right hemisphere. Motor lateralization specific to 'anger' or 'fear' responses was obtained by taking into account the Anger/Fear mapping used and subtracting 'Anger' hand spectral activity to 'Fear' hand spectral activity (the average on 'P3,'CP3','C3' minus the average on 'P4,'CP4','C4' if participants responded 'Anger' with the left hand and vis versa if they responded 'Anger' with the right hand).

Regression analysis: Encoding of the emotional information

In our emotion categorization task, evidence strength corresponds to the intensity of the displayed emotion. On the basis of recent studies (Wyart et al., 2012b(Wyart et al., , 2015)), we therefore performed single-trial regressions of EEG signals against this variable. A general linear regression model (GLM) was used where emotion strength (from 0 for a neutral/emotionless expression to 7 for an intense fear/anger expression) was introduced as a trial-per-trial predictor of broadband EEG signals at each time point after stimulus onset (from 200 ms before to 1 s after stimulus onset), at each electrode. The corresponding parameter estimates of the regression, reported in arbitrary units, were measured per participant, and then averaged across participants to produced group-level averages. The time course of the parameter estimates describes the neural 'encoding' of the relevant (emotion) information provided by the presented facial expression. Electrodes and time points where the parameter estimates of the regression were maximal were selected to further compare between the conditions of interest: Anger Direct and Fear Averted vs Fear Direct and Anger Averted.

Similar general linear regressions were also performed on lateralized mu-beta activity. Once more, the intensity of the emotional expression was entered as a regressor to predict the trial-per-trial motor lateralization activity (calculated as described above) for each time point after stimulus onset. The only important difference is that owing to the 'signed' nature of the motor lateralization index (positive for a contralateralized activity), we expressed the intensity of the emotional expression as signed by the displayed emotion, from -7 for an intense expression of fear to +7 for an intense expression of anger.

Neural-choice correlation analyses

We determined whether residual fluctuations in single-trial EEG signals unexplained by variations in emotion strength (measured by the previous neural regressions against emotion strength) modulated the recognition of the subsequent emotion. This approach is reminiscent of 'choice probability' measures applied in electrophysiology to measure correlations between neural activity and choice behavior [START_REF] Britten | A relationship between behavioral choice and the visual responses of neurons in macaque MT[END_REF][START_REF] Shadlen | A computational analysis of the relationship between neuronal and behavioral responses to visual motion[END_REF][START_REF] Parker | Sense and the single neuron: probing the physiology of perception[END_REF] -by estimating how much fluctuations in recorded neural signals are 'read out' by the subsequent decision (Wyart et al., 2012a(Wyart et al., , 2015)). The advantage of measuring neural-choice correlations within the framework of our computational model is that we could not only establish whether, but also how neural fluctuations influenced the subsequent behavior -either additively as a stimulus-independent bias, or multiplicatively as a change in perceptual sensitivity.

In practice, we estimated the parameters b mod and w mod of these neural modulation terms at each time point following face presentation via an EEG-informed regression of choice for which the neural residuals ɛ from the regression against emotion strength were entered either alone (additive influence, parameter b mod , model 1)

or as their interaction with the strength of the displayed emotion (multiplicative influence, parameter w mod , model 2) as an additional predictor of the subsequent categorical choice, as follows:

(1) p(anger) = Φ(w • x + b + wmod • ɛ• x)

(2) p(anger) = Φ(w • x +b + bmod)

We applied Bayesian model selection to compare between these two possible modulations of the decision process by neural fluctuations using both fixed-effects and random-effects statistical procedures described above.

Source reconstruction analysis

Source analysis was performed using Brainstorm [START_REF] Tadel | Brainstorm: a user-friendly application for MEG/EEG analysis[END_REF] Threat and trustworthiness rating experiment 20 subjects participated to the experiment (10 females, mean age=22.7±0.6). The 36 identities used in the experiment were presented in the neutral condition only. Each identity was presented twice, once with a direct, and once with an averted gaze.

Faces appeared on the screen for 2 seconds after which they disappeared and 2 continuous scales were drawn on the screen. Participants rated the identities on these scales in terms of threat and trustworthiness from "not at all" to "very much" (a text appeared at the top of the scales reminding the instructions: How much is this face threatening/trustworthy?). The order of the scales was randomized across subjects. The scales stayed on the screen until the two responses were given, however subjects were instructed to answer intuitively without spending too much time to decide.

Part 3: Gaze direction influence on emotion categorization: a few unanswered questions (follow-up behavioral studies)

The experiment described in the previous chapter demonstrated that the perception of threatening emotions is directly influenced by the emitter's gaze direction. This was reflected in both subjects' behavior with increased performance and sensitivity to emotions relevant to the observer, and at the neural level with increased 'encoding' of these emotions in sensory and motor regions. A number of follow-up questions emerged from these findings. Do different visual characteristics of the face such as distinct spatial frequencies drive the observed effect on emotion sensitivity?

Is the contextual interaction between gaze direction and emotion specific to anger and fear expressions, as hypothesized by 'appraisal' theories of emotion? Is the observed sensitivity enhancement reciprocal, in other words, does emotion similarly improve the categorization of gaze direction (direct vs. averted)? Does this effect (obtained in young healthy individuals) extend to the general population (all ages, different backgrounds?). In part 3, I will describe four behavioral studies which addressed these different questions.

3.1-General approach in behavioral analyses

The same approach was used to analyze the data of the different behavioral studies and will therefore be generally described. First we started with statistical analyses on accuracy that simply consisted in running repeated-measures ANOVA with gaze direction (direct/averted), emotion (anger/fear), and intensity (levels of morphs) as within-subjects factors. Second, we performed model-guided analyses of the behavioral data to characterize effects observed on accuracy. Depending on the observed results on accuracy and on our hypotheses, we compared between different models that will be described in each section separately. We used Bayesian model selection based on the model evidence, estimated by a 10-fold cross-validation estimation of model log-likelihood, which penalizes implicitly for model complexity without making particular (and necessarily imprecise) approximations such as the Bayesian Information Criterion or the Akaike Information Criterion. We applied both fixed-effects and random-effects statistics. The fixed-effects comparison assumes that all participants used the same underlying model to generate their behavior, such that the overall model evidence for a given model is proportional to the product of model evidence for the model for all participants. Based on this model evidence, we compared different models by computing their Bayes factor as the ratio of model evidence of the compared model (Jeffreys, 1961;Kass and Raftery, 1995). The random-effects comparison is more conservative in allowing different participants to use different models to generate their behavior, and aims at inferring the distribution over models that participants draw from (Penny et al., 2010). For this comparison, we computed support for the winning model by the exceedance probability (p exc ), which is the probability that participants were more likely to choose this model to generate behavior over any alternative model.

3.2-Question 1: Characterization of the effect: contribution of high and low spatial frequencies during Fear/Angry categorization (Experiment 3)

The results of Experiment 2 provided evidence for the influence of gaze direction on the sensory encoding of emotional expressions. In this experiment, we questioned whether different spatial frequencies of the emotional image contribute in dissociable manners to these mechanisms. Indeed, high and low spatial frequencies of facial expressions seem to be processed differently in the human brain. The modulation of spatial frequency conveyed by the faces reveals that different frequency bands contribute distinctively in the recognition of emotions (Smith and Schyns, 2009). Although low spatial frequencies in fearful stimuli increase the activity of emotion selective brain regions [START_REF] Vuilleumier | Distinct spatial frequency sensitivities for processing faces and emotional expressions[END_REF] and brain waves [START_REF] Vlamings | Is the early modulation of brain activity by fearful facial expressions primarily mediated by coarse low spatial frequency information?[END_REF], reverse-correlation techniques during emotion recognition (Smith and Schyns, 2009) have revealed that signals of threat such as fear or anger are conveyed by high spatial frequency bands. Therefore it remains unclear whether signals of threat are transmitted through high or low spatial frequency bands and whether these two fre-quency bands influence differently the categorization of threatening emotions. By separating high and low spatial frequencies, we sought to determine whether one of these frequency bands or both of them contribute more strongly to the observed sensitivity enhancement to threat-signaling emotions (Experiment 2). We hypothesized that increases in emotion sensitivity should be provided by high spatial frequencies (HSF), whereas low spatial frequencies (LSF) could rather change the decision criterion because of the coarseness of facial features provided by LSF.

Methods

Participants 2 groups of 20 participants were included in the experiment (Group 1: 10 females; mean age 21.1 ± 0.2 years, Group 2: 10 females, mean age 22 ± 0.6 years ). All participants were right-handed, with a normal vision and had no neurological or psychiatric history. They provided written informed consent according to institutional guidelines of the local research ethics committee (Declaration of Helsinki) and were paid for their participation. Group 1 performed the task on HSF stimuli while Group 2 performed the task on LSF stimuli.

Stimuli

Stimuli consisted of the 1080 images of facial expressions used in Experiment 2 (after calibration for perceived emotional intensity), filtered into high and low spatial frequencies (Figure 23). Butterworth filters were applied to intact broad spatial frequency images (BSF) using ImageJ [START_REF] Schneider | NIH Image to ImageJ: 25 years of image analysis[END_REF], with a high pass > 24 cycles/images for high spatial frequencies (HSF) (=32), and a low pass <6 cycles/images for low spatial frequencies (LSF) (=4) [START_REF] Vuilleumier | Distinct spatial frequency sensitivities for processing faces and emotional expressions[END_REF].

Experimental procedure

The experimental procedure is similar to the one described in Experiment 2 on a trial to trial basis. Using the Psychophysics-3 Toolbox for MATLAB (Brainard, 1997;Pelli, 1997), stimuli were projected on a black screen. Each trial was initiated with a white oval delimiting the faces that was kept during all the trial. The white oval appeared for approximately 500 ms, followed by a white fixation point presented at the level of the eyes for approximately 1000 ms (to keep the fixation to the upcoming faces natural and avoid eye movements from the center of the oval to eye regions), than the stimuli appeared for 250 ms. Participants' task was to decide whether the faces expressed Anger or Fear by pressing one of the two "ctrl" keys of the keyboard using their right left indexes. An Anger/Fear mapping was used (e.g Anger: Left hand, Fear: Right hand) kept constant for each subject, counterbalanced over all subjects.

In an inter-subject design, 20 subjects only performed the task on HSF stimuli, and 20 subjects only performed the task on LSF stimuli. All stimuli were presented once, resulting in a total of 1080 trials. The experiment was divided in 9 experimental blocks, each consisting of 120 trials, balanced in the number of emotions, directions of gaze, gender and levels of morphs. After each block, the percentage of correct responses was shown to the participants to keep them motivated.

Results

Performance

Repeated-measures ANOVA was performed on the percentage of correct responses, with gaze direction (direct/averted), emotion (anger/fear), and intensity (7 levels of morphs) as within-subjects factors, and group type (HSF group/LSF group) as between-subjects factor.

First, accuracy increased with emotion strength (main effect of emotion strength HSF group F 6,114 =143.9, p<0.001, LSF group F 6,114 =141.6, p<0.001). Second, as expected, an emotion by gaze interaction (HSF group F 1,19 =35.7, p<0.001, LSF group F 1,19 =9.8, p<0.01) was observed on accuracy replicating the results of Experiment 2, with increased performances to THREAT+ conditions (anger with a direct gaze, fear with an averted gaze, see Figure 24a,b). Importantly, there was no interaction with group type on accuracy (all p>0.5). However, a main effect of group was observed with increased accuracy in the HSF group, as expected from coarser facial features for LSF (t-test between groups t 38 =-3.4, p<0.01).

Model Selection

We started with the simplest model (model #0) that could account for subject's decisions using a psychometric model such as:

P(Anger)= Ф(w*x + b)
where P(Anger) corresponds to the probability of judging the face as angry, Ф to the cumulative normal function, w to the perceptual sensitivity to the display emotion, x to the evidence in favor of Anger or Fear in each trial (emotion strength, from -7 for an intense expression of fear to +7 for an intense expression of anger), and b to an additive, stimulus-independent bias toward one of the two responses/emotions. Similarly to Experiment 2, we compared this 'null' model with two additional models which account for the observed increase in recognition accuracy for THREAT+ combinations of gaze and emotion. A first possibility (model 1) would be that gaze direction biases emotion recognition in favor of the interpretation signaling higher threat (anger for a direct gaze, fear for an averted gaze). A second possibility (model 2) is that gaze direction might selectively increase sensitivity to emotions signaling higher threat (modeled by a different sensitivity to emotions in THREAT+ vs. THREAT -conditions).

Finally, we also considered a model (model 3) where both changes on the decision bias and the sensitivity were permitted to compare between the 2 groups of subjects.

In the HSF group, Bayesian model selection revealed that a sensitivity enhancement for THREAT+ combinations explained better the behavioral data than a criterion shift (Bayes Factor ≈ 10 1.5 , p exc > 0.68). In opposition, in the LSF group, a criterion shift toward THREAT+ combinations explained better the behavioral data than a change in sensitivity (Bayes Factor ≈ 10 3.6 , p exc > 0.84). To compare between the two groups, first we looked at whether effects on sensitivity and bias persisted if the model considering both changes is fitted to the data (model 3). In the HSF group, only sensitivity estimates extracted from model 3 were significantly increased for THREAT+ combinations of gaze and emotion (t 19 = 2.9, p < 0.01) (Figure 24c,e). On the contrary, in the LSF group, only decision bias estimates were significantly different (t 19 = 2.8, p = 0.01), with decision bias pulled toward THREAT+ combinations of gaze and emotion (Figure 24d,g). Finally, to have a measure of comparison between the two groups, we use fixed-effects analyses to contrast models that consider 1) model 1 for both HSF and LSF groups, 2) model 2 for both HSF and LSF groups, 3) model 1 for LSF group and model 2 for HSF group, and 4) model 1 for HSF group and model 2 for LSF. We used the log-likelihood of model 0 as a baseline to compare between these four possibilities. This analysis showed that the third possibility was the most likely as it maximized the cross-validated log-likelihood (Figure 24f), which further suggests that a change in sensitivity was conveyed by gaze direction in the HSF group whereas a change in decision bias was conveyed by gaze direction in the LSF group.

Conclusion

The results of Experiment 3 show that the increased sensitivity to threat-signaling emotions is driven by high spatial frequencies. This result is consistent with a change in emotion sensitivity that provides a measure of the sensory precision, i.e. improved perception of visual features provided by the emotional expression. It is also compatible with the increased sensitivity observed in face selective sensory areas in Experiment 2. Threat-signaling emotions thus seem to be conveyed through high spatial frequency channels, contradicting the view that threat is primarily transmitted through low spatial frequencies [START_REF] Vuilleumier | Distinct spatial frequency sensitivities for processing faces and emotional expressions[END_REF], but in accordance with the fact that anger and fear emotions are passed on through high spatial frequencies (Smith and Schyns, 2009).

When emotion categorization had to be performed on low spatial frequencies of faces, gaze rather changed the decision bias toward threat-signaling emotions.

The coarse information provided in LSF emotional faces could not change the sensitivity to threatening signals, but instead influenced the decision bias shifting decisions toward anger when gaze was direct, and choices toward fear when gaze was averted. The effect of gaze on the categorization of threatening emotions therefore seems to be robust although the mechanisms by which its influence is exerted on recognition performance varies as a function of the visual information provided.

3.3-Question 2: Specificity of the effect: Categorization of Happy/Angry expressions (Experiment 4)

In Experiment 2, emotions of negative valence were manipulated, and anger and fear were chosen precisely because gaze impacts their processing in an interactive fashion. Neural and behavioral correlates of this interaction were observed, and we further wanted to control whether this interaction is specific to the manipulated emotions. To do so, we replaced fearful expressions that, when compared to angry, share the valence (negative) but have an opposite effect of gaze, with happy expressions that are of opposite valence (positive vs negative) but share the same impact of gaze. This assumption is based on the shared signal hypothesis that suggests that happy expressions, just like angry expressions, signal approach and are therefore better recognized with a direct gaze. In opposition, fear and sadness are more 'avoidance oriented emotions' and are therefore better categorized with an averted gaze (Adams andKleck, 2003, 2005). In Experiment 4, happy and angry faces were presented to subjects, while they performed a perceptual categorization task using the same design as in Experiment 2 (Happy or Angry?).

Methods

Participants

Nineteen healthy subjects (10 females; mean age, 23.6 ± 0.8 years) participated in the behavioral experiment. All participants were right-handed, with a normal vision and had no neurological or psychiatric history. They provided written informed consent according to institutional guidelines of the local research ethics committee (Declaration of Helsinki) and were paid for their participation.

Stimuli

Stimuli consisted of the same 36 identities (18 females) adapted from the Radboud Faces Database (Langner et al., 2010) used in Experiment 2. They varied in emotion (neutral, angry or happy expressions) and gaze direction (direct toward the participant or averted 45° to the left or right). Using Adobe Photoshop CS5.1 (Adobe Systems, San Jose CA), faces were modified to remove any visible hair, resized and repositioned so that eyes, nose and mouth appeared within the same circumference.

All images were converted to greyscale and cropped into a 280 x 406 pixel oval centered within a 628 x 429 pixel black rectangle.

To vary the intensity of emotional expressions, faces were morphed from neutral to angry expressions and from neutral to happy expression using FantaMorph (Abrosoft, http://www.fantamorph.com/). At first, 7 levels of morphs from neutral to angry expressions and from neutral to happy expressions (separately for direct and averted gaze stimuli) were created using a simple linear morphing transformation.

This resulted in 30 conditions for each identity: 7 levels of morphs * 2 emotions * 2 gaze directions = 28 and 2 neutral stimuli with direct and averted gaze.

We then calibrated (linearized) the morphing between angry and happy expressions by performing an intensity rating pre-test of the emotional expressions and adjusting the morphs based on the results, as we did in Experiment 2.

To determine whether both emotions were perceived as equally intense at all the levels of the morphs, a pre-test was performed on 20 subjects (11 females, mean age, 22.7 ± 0.7 years). A main effect of emotion (F 1,19 =6.8, p<0.05) on perceived intensity ratings was observed, happy stimuli being judged as more intense than angry stimuli (Figure 25). To adjust for this difference between emotions, we linearized the curve of perceived intensities of Anger as a function of the levels of the morphs. We then extracted the percentage values of morphs of angry and happy faces needed such as the perceived intensities fall into this linearized curve. New morphs were correspondingly regenerated. To summarize, the stimuli comprise of 36 identities with an averted gaze condition and a direct gaze condition, each with 7 levels of anger and 7 levels of happiness equalized in perceived emotional intensities and a neutral condition, resulting in a total of 1080 items (Figure 26).

Experimental procedure

Using the Psychophysics-3 Toolbox (Brainard, 1997;Pelli, 1997), stimuli were projected on a black screen. Each trial was initiated with a white oval delimiting the faces that was kept during all the trial. The white oval appeared for approximately 500 ms, followed by a white fixation point presented at the level of the eyes for approximately 1000 ms (to keep the fixation to the upcoming faces natural and avoid eye movements from the center of the oval to eye regions), than the stimuli appeared for 250 ms. Participants' task was to decide whether the faces expressed Anger or

Happiness by pressing one of the two "ctrl" keys of the keyboard using their right left

indexes. An Anger/Happy mapping was used (e.g Anger: Left hand, Happy: Right hand) kept constant for each subject, counterbalanced over all subjects.

All stimuli were presented once, resulting in a total of 1080 trials. The experiment was divided in 9 experimental blocks, each consisting of 120 trials, balanced in the number of emotions, directions of gaze, gender and levels of morphs. After each block, the percentage of correct responses was shown to the participants to keep them motivated.

Results

Performance

Subjects performed better for angry stimuli (F 1,18 =17.5, p<0.001) as compared to happy stimuli (Figure 27a). Performances increased with the emotional intensity (F 1,18 =158.4, p<0.001), and this modulation by intensity was more important for happy stimuli (Emotion by intensity interaction F 6,108 =22.3, p<0.001). In addition, a triple interaction between Emotion, Gaze and intensity (F 6,108 =3.2, p<0.05) was observed, and explained by gaze effects only in low intensities of happy stimuli. For the 3 first levels of happy stimuli, subjects performed better for direct gaze as compared to averted gaze (F 1,18 =6.2, p<0.05).

Model selection

We compared between numerous models describing participants' behavior:

-The 'null' model, model 0, with no effect of emotion or gaze (general sensitivity to anger and fear and general bias to direct and averted gaze).

-Model 1 allowed different emotion sensitivities between anger and happy, based on the results observed on performance that showed a strong effect of emotion.

-Model 2 allowed different sensitivities to all four conditions: anger direct/ anger averted/ happy direct/ happy averted. We considered this model based on the accuracy results that showed a small effect of gaze direction, only on low intensities of happy stimuli. This model allows differentiating between the effects of gaze on angry and happy stimuli.

Finally, based on our hypothesis about direct gaze being more relevant in the present context, we further considered models that could account for the effect of gaze:

-Model 3 allowed different sensitivities to direct and averted gaze.

-Model 4 assigned different response biases for direct and averted gaze.

Model 1 was by far the best model as its probability exceedance was above 85%

when compared with all the other models (fixed-effect comparison with second best model Bayes Factor ≈ 10 3.6 ). The estimated sensitivities and bias parameters from model 1 were in accordance with the effects on performance, as subjects increased their sensitivity to anger as compared to happy, and were more biased toward angry responses (Figure 27c,d). In opposite valence context, subjects were biased towards negative valence and their sensitivity to negative stimuli was enhanced. Surprisingly, gaze direction did not affect anger categorization as in Experiment 2. However, this can be explained by the context of the experiment that included both happy and angry expressions, and therefore rendered all threatening emotions (here anger) as more relevant than non-threatening ones (happiness) independently of gaze.

Conclusion

The results of this experiment suggest that when threatening emotions are presented against positive emotions, gaze direction does not play the same role as compared to when observers have to judge among negative emotions. The presence of happy faces made angry faces generally more relevant to the observer because they signal threat (whether directly relevant for the observer or not). In this setting, gaze direction facilitated the judgment of the generally less relevant emotion, happiness, when its combination with gaze was more salient and only when presented in very ambiguous displays.

Model comparisons combined with results on accuracy however show that the most marked effects only reveal both an increased sensitivity of anger expression and a response bias toward anger, highlighting the importance of threat recognition independently of gaze direction in this context.

These finding thus draw attention on the different types of settings that change emotion recognition: here the context can be viewed as a context of "judgment" [START_REF] Tanaka-Matsumi | Context effects on the judgment of basic emotions in the face[END_REF], whereby choosing between positive and negative emotions shifted the relevant aspect to the observer to threat (anger) independently of gaze.

Finally and most importantly related to the main question addressed in this study, these findings confirm the specificity of the effect of gaze on the categorization of anger and fear emotions, as the combinations with gaze that benefited from increased sensitivities appear to apply selectively to the categorization between negative (and thus potentially threatening) emotions.

3.4-Question 3: Reciprocity of the effect: Emotion influence on gaze categorization (Experiment 5)

Experiments 1, 2 and 3 reveal a strong influence of gaze on the processing of emotional expressions of threat during emotion categorization. As described in the litera-ture, emotional displays seem to reciprocally influence gaze judgments: angry and happy (approach-oriented) facial expressions are more likely to be judged as looking directly at the observer than fearful and neutral expressions [START_REF] Lobmaier | Emotional expression modulates perceived gaze direction[END_REF][START_REF] Ewbank | Why are you angry with me? Facial expressions of threat influence perception of gaze direction[END_REF]. Averted gaze is processed faster and more accurately with a fearful face, while it is the case for direct gaze with an angry expression [START_REF] Adams | Influence of emotional expression on the processing of gaze direction[END_REF]. These experiments suggest that the processing of co-emitted emotion and gaze cues is interdependent. Further, Experiment 1 showed that the combination of social cues emerged independently of the task, suggesting that co-emitted social cues are combined as long as they are relevant to the observer, independently from task demands and focus of attention. Once more, these past studies did not identify the mechanisms by which emotion reciprocally biases gaze categorization.

Although we expected that emotion should reciprocally influence gaze categorization, the question of whether it does so by changing the decision bias, the emotion sensitivity or both remained unanswered. The next study therefore addressed the influence of emotional displays on faces with morphed gaze directions during a gaze categorization task (gaze direct or averted?).

Methods

Participants

Twenty three healthy subjects (12 females; mean age, 23.5 ± 0.7 years) participated in the behavioral experiment. All participants were right-handed, with a normal vision and had no neurological or psychiatric history. They provided written informed consent according to institutional guidelines of the local research ethics committee (Declaration of Helsinki) and were paid for their participation.

Stimuli

Stimuli consisted of 36 identities (18 females) adapted from the Radboud Faces Database (Langner et al., 2010) that varied in emotion (neutral, angry or happy expressions) and gaze direction (direct toward the participant or averted to the left or right).

Using Adobe Photoshop CS5.1 (Adobe Systems, San Jose CA), faces were modified to remove any visible hair, resized and repositioned so that eyes, nose and mouth appeared within the same circumference. All images were converted to greyscale and cropped into a 280 x 406 pixel oval centered within a 628 x 429 pixel black rectangle.

To vary the extent to which gaze was averted, faces were morphed from direct to averted gaze and using FantaMorph (Abrosoft http://www.fantamorph.com/). 8 levels of morphs from direct to averted gaze were created (for all the three emotional expressions of anger, fear and neutral) using a simple linear morphing transformation.

This resulted in 27 conditions for each identity: 8 levels of averted gaze morphs * 3 emotions = 24 and 3 direct gaze stimuli expressing either neutral, or angry or fearful expressions (Figure 28).

Experimental procedure

Using the Psychophysics-3 Toolbox (Brainard, 1997;Pelli, 1997), stimuli were projected on a black screen. Each trial was initiated with a white oval delimiting the faces that was kept during all the trial. The white oval appeared for approximately 500 ms, followed by a white fixation point presented at the level of the eyes for approxi-mately 1000 ms (to keep the fixation to the upcoming faces natural and avoid eye movements from the center of the oval to eye regions), than the stimuli appeared for 250 ms. Participants' task was to decide whether the faces displayed a direct or an averted gaze by pressing one of the two "ctrl" buttons of the keyboard using their right left indexes. A direct/averted mapping was used (e.g., direct: Left hand, averted: Right hand) kept constant for each subject, counterbalanced over all subjects.

All stimuli were presented once, resulting in a total of 972 trials. The experiment was divided in 9 experimental blocks, each consisting of 108 trials, balanced in the number of emotions, directions of gaze, gender and levels of morphs. After each block, the percentage of correct responses was shown to the participants to keep them motivated.

Results

One subject realized that giving the 'averted' response allowed him to obtain good performance, as there are 8 times more averted gaze stimuli than direct gaze stimuli presented in each block. He therefore stopped performing the task correctly in the last 4 blocks, as he only pressed the 'averted' response at each trial. This subject was further excluded from analysis. No other subject adopted such strategy and data were thus analyzed on a total of 22 subjects.

Performances

Accuracy increased (F 7,147 = 224, p<0.001) with levels of averted gaze direction. Accuracy also varied with the emotion displayed by the stimuli as it was highest for fearful stimuli (significant difference with neutral condition T 21 =6.7 and with angry condition T 21 =13.9, p<0.001), than for neutral stimuli, and finally the lowest for angry stimuli (significant difference between angry and neutral T 21 =-11, p<0.001). Importantly, an interaction between emotion and gaze was also observed on the percentage of correct responses (F 2,21 = 42.8, p<0.001). Performance was increased for fear as compared to anger in averted gaze condition (t 21 =-14.5, p<0.001), but the opposite applied to direct gaze condition (t 21 =4.4, p<0.001). Neutral conditions were at an intermediate level between anger and fear, and significantly differed from both anger (T 21 =-9.8, p<0.001) and fear (T 21 =8.1, p<0.001) in the averted gaze condition, and only from fear in the averted gaze condition (T 21 =-3.3, p<0.01) (Figure 29a). (T 21 =-3.7, p=0.001). There was however no difference between the sensitivity to fear and neutral expressions ((T 21 =-0.4, p>0.6) (Figure 29d).

Conclusion

By morphing gaze direction of anger, fearful and neutral emotional expressions during a gaze categorization task, this study shows that emotion improves the accuracy to observer-relevant threatening displays. While this improvement was partly driven by changes in the decision bias, it was also instantiated by changes in sensitivity to gaze direction. Here we considered the degree to which the gaze is averted as the perceptual evidence to the gaze direction task, and show that this sensitivity is increased in fear as compared to anger conditions. This result indirectly implies that the sensitivity to the degree to which the gaze is direct is increased for anger as compared to fear conditions. These findings thus demonstrate that gaze direction categorization is reciprocally influenced by emotions of anger and fear.

Contrarily to our expectation that neutral faces should lie somewhere in the middle between anger and fear emotions, the sensitivity to averted gaze direction in the neutral condition did not differ from fearful faces, but was increased as compared to angry faces. This is surprising in regards to the proposed advantage in the processing of direct gaze in neutral faces [START_REF] Farroni | Eye contact detection in humans from birth[END_REF][START_REF] Senju | Direct gaze captures visuospatial attention[END_REF][START_REF] Conty | When eye creates the contact! ERP evidence for early dissociation between direct and averted gaze motion processing[END_REF][START_REF] Mareschal | Humans Have an Expectation That Gaze Is Directed Toward Them[END_REF], which would rather predict an increased sensitivity to direct gaze in neutral faces (like anger). However, here gaze categorization was performed in a context where neutral faces were presented among threatening emotional faces, which possibly changed the mechanisms underlying the processing of gaze direction. In a threat context induced by the presence of anger and fear emotions, gaze direction in neutral faces seems to be processed similarly as gaze direction in fearful faces. A possible interpretation of the similarity in behavior to fearful and neutral faces is that both can signal an uncertain event to the observer when averted, potentially threat. However when displaying a direct gaze and in contrast to anger, neutral and fearful emotions do not signal a direct threat to the observer. Consistent with this suggestion, increased activity in monkey amygdala to averted as compared to direct gaze in neutral and emotional expressions is relat-ed to greater arousal and attention when facing stimuli that signal uncertainty [START_REF] Hoffman | Facial-expression and gaze-selective responses in the monkey amygdala[END_REF].

The fact that strong effects were observed on decision bias whereas it was not the case in the emotion categorization task partly stems from the experimental design. Here the extent to which gaze is averted is manipulated resulting in unequal conditions of direct and averted gaze conditions whereas there was an equal number of angry and fearful expressions in Experiment 2. Therefore, it was more likely that effects on the bias emerge due to the asymmetry which undoubtedly biases toward averted responses.

To conclude, these findings build on previous studies [START_REF] Lobmaier | Emotional expression modulates perceived gaze direction[END_REF][START_REF] Adams | Influence of emotional expression on the processing of gaze direction[END_REF][START_REF] Ewbank | Why are you angry with me? Facial expressions of threat influence perception of gaze direction[END_REF] that suggested mutual influences between emotion and gaze by showing that these effects are driven by both changes in sensitivity and decision bias favoring observer-relevant signals in the context of threat.

3.5-Question 4: Generalization of the effect to a representative pool of the population (Experiment 6)

The emotion categorization task used in Experiment 2 (and tested on young healthy individuals) was conducted on a sample of population that is representative of the French population. One purpose of this study was to see whether we replicate the increased behavioral sensitivity to threat-signaling emotions in the general population. More importantly, this study was conducted among a set of other social tasks including economic games and political judgments and included questionnaires evaluating anxiety and autism scores as well as life history and backgrounds. The main goal was thus to link between individual traits and emotion categorization in the context of threat, as well as investigate whether similar profiles of people behave in selective manners in different social tasks. Here I will describe only the replication of Experiment 2 without providing results linking the different manipulations as these analyses are ongoing at the moment. This work is conducted in collaboration with Coralie Chevallier, Nicolas Baumard, Lou Safra, Yann Algan and IPSOS.

Methods

Participants

Using the database from the polling organization IPSOS (http://www.ipsos.fr), data were collected from individuals that were representative of the French population in terms of age repartition (16 to 80 years old), socio-professional categories, and level of education. They all provided consent to participate in the study and were remunerated for their participation. 1005 individuals completed the entire set of experiments (512 females, mean age 47.4 ± 0.5 years)

Procedure

The emotion categorization task (Anger or Fear) was part of a set of experiments realized in these individuals. They actually performed two sessions of 22 minutes each. The first session included an anxiety questionnaire (STAI), the emotion categorization task, a task that consisted in choosing which person the participant would vote for (while varying the traits of face stimuli from trustworthy to dominant), and a questionnaire about life history of participants. The second session included 3 economic games (dictator game, trust game, public good games) and 3 questionnaires (social trust, broad autism phenotype BAPQ and social relations). Participants only performed one session of the emotion categorization task that lasted around 6-7 minutes (120 trials). Similarly as in Experiment 2, stimuli were morphed expressions from neutral to angry and from neutral to fearful expressions while gaze direction was either direct or averted. The face appeared for 250 ms and participants had to choose whether the face expressed anger or fear by pressing one of the two keys 's'

or 'l' of the keyboard within 2 seconds. The position of the answers 'anger' or 'fear'

were counterbalanced across subjects.

Results

Analyses were performed on all subjects that performed the task with percentages of correct responses larger than 60 %. After exclusion, the final pool of subjects consisted of 602 individuals (325 females, mean age 44 ± 0.5 years). The high rejection rate (~40%) is probably due to the nature of the task performed in this online procedure. While tasks in online procedures (including all the other tasks of the present study) usually do not require 'correct' responses (such as personality questionnaires, surveys, and preference choices), here it was crucial that participants correctly perform the task. Unfortunately, most probably because participants performing the task outside the lab environment (such as at home) might be easily distracted, a high rejection rate is not surprising and due to this quite 'difficult' task with trials that rapidly succeed one another requiring full concentration.

Performance

The interaction between emotion and gaze was replicated as previously (F 1,601 = 12.8, p<0.001), with increased percentage of correct responses to direct gaze as compared to averted in anger condition (t 22 = 3.8, p<0.001) (Figure 30a). The increased accuracy for averted as compared to direct gaze in fear condition did not reach significance (t 601 = 1.5, p=0.1). Surprisingly, there was a very strong main effect of emotion, with generally increased accuracy in fear condition as compared to anger (F 1,601 = 34.8, p<0.001). As the only difference with previous studies (Experiment 2 and 3) was the few number of trials used per subject, this result suggests that participants might have an initial bias toward fear increasing their accuracy in this condition. This suggestion will be further elaborated below, in the model selection analyses.

Model selection

Because of the very little amount of trials per subject, fixed-effect comparisons were used for model selection in this experiment. Similarly to Experiments 2 and 3 we compared the 'null' model which did not allow effects of gaze direction on the deci-sion process, with three additional models that could explain the increase in recognition accuracy for THREAT+ combinations of gaze and emotion (anger with a direct gaze, fear with an averted gaze). Model 1 would be that gaze direction biases emotion recognition in favor of the interpretation signaling higher threat (anger for a direct gaze, fear for an averted gaze). Model 2 would be that gaze direction might selectively increase sensitivity to emotions signaling higher threat. Model 3 would be that both changes on the decision bias and the sensitivity explain the increased recognition of THREAT+ combinations.

When subtracting the log-likelihood of the null model to all three models, the best model was model 2 that assigned different sensitivities to THREAT+ and THREAT-(Figure 30b). However, the Bayes Factor of this difference was close to 10 0.38 , so model 2 was only approximately 2.4 times better than the other models. Nevertheless, comparison between the extracted parameters of model 2 showed that emotion sensitivity was increased for THREAT+ conditions (t 601 = 2.1, p<0.05) (Figure 30c). On the contrary, no difference between the decision bias extracted from model 1 for direct and averted gaze was evident (p>0.8). Besides, the decision bias extracted from model 2 was highly pulled toward fear responses (t 601 =-8.5, p<0.001) (Figure 30d).

This extremely large bias can justify the increased accuracy of fearful expressions that seem to have covered the effect of gaze on the percentage of correct responses in the fear condition. Just like stated above, this bias stems from the fact that participants only performed 120 trials (in opposition to 1080 trials in Experiments 2 and 3).

Further corroborating this interpretation, when analyses were performed in Experiments 2 and 3 on only the first block (120 trials), similar effects emerged on the response bias (on the total of 64 participants bias toward fear (t 63 =-2.1, p=0.05)). Furthermore, when the data of 46 additional participants (25 females, mean age 22.1±0.4) that performed the same task (in a different study not reported here) is also included, the bias toward fear becomes highly significant on the first 120 trials (t 109 =-3, p=0.003), along with increased performance to fearful as compared to angry stimuli (t 109 =-2.1, p=0.03).

Conclusion

Running the emotion categorization experiment on a large number of participants showed that the effects of gaze on recognition of threatening emotions is robust as it survived outside of the lab environment with a shortened version of the task (1/9). It importantly extends the results to the general population that varies in age (16 to 80 years old), socio-professional categories, and level of education. Certainly, model comparisons only revealed a weak difference between the model allowing an effect of gaze direction on the sensitivity and the model considering an effect on the decision bias. Nevertheless, the interaction between emotion and gaze on the performance, as well as the significant effect of gaze direction on the sensitivity but not on the decision bias, point in the same direction to enable the suggestion that gaze direction enhances the behavioral sensitivity to threat-signaling emotions.

Notably, the shortened version of the task had a drawback: an unexpected bias toward fearful responses emerged suggesting that participants have an initial bias toward fear when categorizing between angry and fearful expressions. A possibility that could explain this initial bias toward fear might be that fear has an evolutionary advantage (Ohman and Mineka, 2001;[START_REF] Ohman | The role of the amygdala in human fear: automatic detection of threat[END_REF] over anger as it might signal danger of a higher level of threat as compared anger. However, this assumption is speculative, and further research is needed to understand the reason behind this initial bias. In this study, correlations between personal traits such as anxiety and the harshness of life history with the degree to which participants show this bias toward fear will be conducted as they might provide insights into the reasons that explain this effect.

More generally, further analyses of this experiment will be performed, to link the results of the emotion categorization task to the outcomes of personality questionnaires and the other social tasks conducted in this survey. As an illustration of a hypothesis that we have on these analyses, one might expect that increased sensitivity to threat-signaling emotions might be even stronger in individuals who grew up in harsh environments.

Conclusion part 3

The four behavioral studies reported in this part allowed to answer a few questions raised by the results of Experiment 2, i.e. the increased sensitivity to threat-signaling emotions modulated by gaze direction during an anger-fear categorization task. First, this effect is driven by high spatial frequencies of the facial expression (Experiment 3). Second, the effect of gaze direction on emotion categorization is specific to the categorization of negative emotions (anger and fear) and does not apply to the categorization of negative (anger) against positive (happiness) emotions (Experiment 4).

Third, the influence of gaze direction on emotion categorization is reciprocal in the sense that emotion similarly increases the sensitivity to gaze direction in conditions signaling higher threat to the observer (Experiment 5). Fourth and finally, gaze direction modulation of the perception of threatening emotions is proven to be robust as it was replicated on a large sample of the general population (Experiment 6). The findings of Part 3 support the increased perceptual and neural sensitivity to threatsignaling emotions and show that this sensitivity enhancement is specific, replicable, robust and independent of task demands.

Together, Experiments 1 to 6 reveal that in threat environment, gaze direction enacts as a contextual factor that increases the sensitivity to specific combinations of gaze and emotion behaviorally and neurally within 200 ms after stimulus onset in sensory and motor regions. This suggests that when threat signals are involved, evolutionary pressure shaped the brain to better perceive social displays indicating high levels of threat.

The next study manipulated perceptual experience of participants with emotion/identity associations while they did a similar fear/anger categorization task on morphed emotional expressions. Would this contextual manipulation, now not related to threat, but rather to day-to-day personal experience, similarly influence emotion categorization by changing the sensitivity to emotional information? Or alternatively, would it only change the decision bias as from an evolutionary point of view, the stakes are less high as compared to recognizing threat signals? One can speculate that while signals related to survival require a better perceptual recognition for fast appropriate reactions, associations related to daily interactions rather need a flexible adaptation as a function of social contexts thereby only biasing perceptual choices but not changing their core encoding.

Part 4: Expectation about the emotions of individual identities as a contextual factor (Experiment 7)

This part will be presented as a draft of a manuscript that is in preparation:

A general mechanism for emotion categorization biases shaped by associative identity-specific expectations.

Introduction

Our daily life is punctuated by the many perceptual decisions that we make on various sources of sensory information. These decisions are rarely made without biases which stem from past experience and depend on the context in which they occur.

Behavioral and neural evidence has shown that prior information biases decisionmaking by facilitating choices congruent with one's expectations [START_REF] Summerfield | Expectation in perceptual decision making: neural and computational mechanisms[END_REF].

In social contexts, faces represent the most relevant stimulus category, and the proper identification of facial features (as a function of identity or expression) is thus crucial for rich social interactions. This facial identification process is known to be influenced by several contextual factors such as biases in social judgments which depend not only on perceptual determinants (e.g., the dominance or trustworthiness of a face), but also on past experience and incidental associations (Todorov et al., 2015). As an illustration of perceptual-related influences, masculine and angry faces are perceived as more dominant (Todorov et al., 2015). Social judgements can also change independently from perceptual features as a function of initial encounters with others, experimentally induced with descriptions specific to each presented identity (Todorov et al., 2007;[START_REF] Schiller | A neural mechanism of first impressions[END_REF]. The impact of associating specific behaviors to identities on the neural encoding of faces can be both dependent and independent of explicit memory, i.e. whether the participant recalls the link between one identity and its associated behavior (Todorov et al., 2007). Moreover, these attributions vary as a function of each person even if the same information is given about an identity, as different opinions can be formed on the same provided behaviors [START_REF] Schiller | A neural mechanism of first impressions[END_REF].

These studies suggest that face encoding is influenced by personal experience, whether observers are aware or unaware of their biases, and vary considerably from an individual to another. However, these previous experiments only explicitly manipulated expectations by providing verbal descriptions about identities on neutral faces. Furthermore, they were directly interested in the neural signatures of these expectations, i.e. how identities associated with a certain attitude are encoded in the brain, but did not investigate whether they modify participants' behavior. Nevertheless, evidence from perceptual tasks about faces shows that previously encountered identities spontaneously modify judgments about facial identities (Apps and Tsakiris, 2013;Liberman et al., 2014). They do so by changing the processing of previously encountered identities in face selective structures, the fusiform gyrus and the superior temporal sulcus (Apps and Tsakiris, 2013), possibly fostering visual stability through decreased neural computations to these identities (Liberman et al., 2014).

While these studies highlight the fact that face perception is influenced by preceding context, they do not manipulate changeable features of facial identities such as emotional expressions.

Here we were interested in whether implicit associations between an emotion and an identity prompt expectations that influence subsequent emotion categorization and the cognitive mechanisms by which they do so. We implicitly manipulated the emotional characteristics of identities by presenting half of them expressing one emotion or another (anger or fear) at the beginning of experimental blocks and tested whether it influenced subsequent emotion categorization. At the end of the experiment, to assess how much participants were aware of the manipulation and whether their own incidental associations biased their decisions throughout the task, we asked them to judge the emotion of all the neutral identities they encountered.

Importantly, a parametric control over emotion strength, provided by the use of 'morphed' facial expressions, afforded fitting decision theoretical models to arbitrate between competing accounts of how expectations about facial emotions influence their categorization. Classical decision theories inspired by signal detection theory indeed distinguish between two types of mechanisms by which prior information could impact emotion categorization (Green and Swets, 1966;Macmillan and Creelman, 2004). Expectation about the emotion of an identity could bias its categorization toward the expected association, thereby additively combining identity and emotion information in accordance with 'Bayesian' accounts of decision-making [START_REF] Griffiths | Optimal predictions in everyday cognition[END_REF][START_REF] Oaksford | Bayesian Rationality: The Probabilistic Approach to Human Reasoning[END_REF]. Alternatively, expectation about the emotion of an identity could increase the perceptual sensitivity to the facial features diagnostic of the emotion congruent with one's expectations, thereby enhancing the quality of processing of the sensory attributes relevant to the emotion categorization task (El Zein et al., under review).

Finally, we also recorded pupillary responses that have recently been related to individual biases both during social categorization of faces from similar or dissimilar race [START_REF] Wu | Through the eyes of the own-race bias: eye-tracking and pupillometry during face recognition[END_REF], and nonsocial perceptual discrimination of visual contrast embedded in noise tasks (Gee et al., 2014). Along these lines, we expected to find modulations of pupillary responses by individual biases, notably through increased responses to decisions against participants ' inclinations (Gee et al., 2014).

Results

Each block started with a short 'induction' period where identities were split randomly into two halves, presented in a pseudo-random order, each half expressing consistently either anger or fear, in order to induce emotion/identity associations (figure 1a).

This manipulation assigned two groups of identities randomized across participants:

identities that belonged to the Anger Group, and identities that belonged to the Fear Group. In the next, 'test' period, all identities expressed an equal number of angry and fearful expressions, and all analyses were performed on these trials only. In both periods, participants had to categorize the facial expression that appeared on the screen as expressing either anger or fear (figure 1b), with no explicit warning about the transition between the two induction and test periods. As gaze direction of anger and fear expressions is known to facilitate the categorization of combinations signaling higher threat to the observer (El Zein et al., under review), we further manipulated gaze direction in an implicit, blocked design to assess whether it interacts with the effects of emotion/identity associations on emotion categorization.

Induced manipulation of emotion/identity associations biases upcoming decisions

The implicit manipulation of identity group successfully created expectations that influenced emotion categorization, reflected in an enhanced accuracy for angry stimuli belonging to the Anger group and fearful stimuli belonging to the Fear group (emotion by induced identity group interaction F 1,30 = 7.71, P <0.01) as compared to the other conditions (figure 2a). A main effect of emotion strength (F 1,30 = 166.6, P < 0.001) confirmed that accuracy decreased with emotion ambiguity. Moreover, we replicated our previous study in regards to the effect of gaze, as accuracy was increased for emotions signaling higher threat -anger with a direct gaze and fear with an averted gaze -as compared to the 2 other conditions (emotion by gaze interaction F 1,30 = 8.5, P <0.01). No other main effect or interaction was significant (all F<2; all P>0.1) suggesting that gaze did not interact with induced emotion-identity associations.

To better characterize the effect of identity group on emotion categorization, we compared between different decision-making models (inspired by signal detection theory) that instantiate the potential mechanisms by which induced identity group can influence emotion recognition. We reasoned that induced associations between identity and emotions could either bias the responses toward congruent emotions (model 1), or increase the sensitivity of congruent emotions (model 2), or both (model 3). Using Bayesian model selection combined with a cross-validation procedure to estimate the log-evidence of the different models, we compared these models with the 'null' model (model 0) that allowed no effect of induced identity group on emotion recognition, and found that only model 1 fitted better the data than model 0 (model 1:

exceedance probability p exc = 0.90; model 2: p exc = 0.41; model 3: p exc = 0.52). A direct comparison between the tested models revealed that model 1 was the most likely model (fixed-effects Bayes factor against second best model ≈ 10 4 , random-effects all p exc > 0.83). Associations between emotion and identity thus influenced the decision bias, by shifting the response toward angry responses when stimuli belonged to an angry group and inversely for fear (model 1, difference between predicted bias for group Anger and bias for group Fear T 30 =2.4, P<0.05) (figure 2c).

The post-test performed by participants at the end of the experiment which consisted in deciding whether the identities expressed more anger or fear during the experiment (figure 1c) allowed assessing whether the observed bias stems from subjects' knowledge of the manipulation. No difference between the mean rating of responses for group Anger and group Fear was observed (T 30 =0.56, P=0.57), and the receiver operating curve (ROC) calculated from rating responses was not significantly different from chance (= 0.504±0.003, t-test against chance level 0.5: T 30 <0.3, p>0.8), suggesting that participants were at chance level in identifying the associations experimentally induced.

Bias toward incidental, explicitly reported emotion/identity associations

We next wondered whether the identities rated explicitly as belonging to fear or anger groups in the subsequent post-test could have influenced individual decisions throughout the experiment. In other words, do people spontaneously associate identities with certain emotions? We therefore considered a new split between identity groups, not the one induced by the experimenter (referred to as the 'induced' identity group), but the one formed through random associations of subjects and expressed in the post-test: the 'incidental' identity group. For each subject, the incidental anger and fear groups consisted of the identities they rated as expressing anger or fear (ratings transformed to binary decisions). Remarkably, a very strong emotion by incidental identity group interaction emerged (F 1,30 = 58.2, P < 0.001), with increased accuracy throughout the experiment for angry stimuli that were considered as angry in the post-test and fearful stimuli that were considered as fearful in the post-test (figure 2b). Model comparisons similar to the one described above showed that models 1, 2 and 3 fitted better the data than model 0 (model 1: exceedance probability p exc = 1; model 2: p exc = 0.99; model 3: p exc = 0.99). A direct comparison between the tested models revealed that model 1 was the most likely model (fixed-effects Bayes factor against second best model ≈ 10 11 , random-effects all p exc > 0.93). Response bias was pulled toward anger for the angry group and pulled toward fear for the fear group (difference between biases T 30 =9.2, P<0.001) (figure 2e).

We therefore decided to integrate both bias effects in one model that would allow an induced and an incidental effect on the response bias (figure 2d). Importantly, even though the incidental bias was very strong (T 30 =8.2,P<0.001), the induced bias effect remained significant (T 30 =2.1, P<0.05).

Incidental biases depend on both stimulus-related and observer-related biases

The previously described findings raise the following question: is the incidental effect driven exogenously by the characteristics of the stimulus (Todorov et al., 2015) or endogenously by the participants? The between-participant consistency in the posttest rating of the different identities offers an unbiased method to address this question: if all subjects unanimously categorized each identity, then it would imply that the incidental effect is not linked to an internal bias, but to the facial characteristics of the identities (in terms of dominance, trustworthiness, etc.). To address that question, we checked whether identities were assigned to fear and anger randomly or unanimously by performing t-tests against zero on the ratings for each identity. Less than half of the identities (n = 14) were significantly most often assigned to the same emotion (ratings significant against zero across subjects P<0.05, 7 toward fear and 7 toward anger, 8 males, 6 females) whereas 18 identities were not significantly assigned to anger and fear. Importantly, the incidental effect on response bias was as strong for consistently and inconsistently assigned identities as there was no difference between the bias for group Anger and group Fear for identities unanimously assigned (T 30 =5.4,P<0.001) and those randomly assigned (T 30 =4,P<0.001) (difference between the 2 effects T 30 =1.3, P>0.18).

People thus appear to create incidental associations when encountering new identities based on a combination of the perceptual features of the identity and their individual biases which likely arise from their own experience. These last results however raised a new question: what explains the fact that 14 specific identities were rated more consistently than the 18 other identities judged randomly/inconsistently?

One hypothesis is that specific traits of theses identities were more pronounced in favor to a specific judgment of fear or anger (Todorov et al., 2015). To test this assumption, 20 new subjects performed trait ratings in terms of threat, trustworthiness, and dominance on the neutral identities (see methods). First, as expected (Todorov et al., 2015), threat and dominance ratings were highly correlated (r=0.85, p<0.001), and both threat (r=-0.79, p<0.001) and dominance ratings (r=-0.5, p<0.01) inversely correlated with trust. Second, the consistency across subjects in emotion ratings expressed as t-values of the performed t-tests for each identity were correlated with the mean subjective trait ratings of each identity: threat ratings (r=-0.57,P<0.001), trust ratings (r=0.42,P=0.01), and dominance ratings (r=-0.5,P<0.01) significantly correlated with the consistency across subjects in emotion ratings, suggesting that identities that were highly rated in terms of social traits were those who led to unanimous categorizations of fear and anger (figure 2f, example for threat rating). Threat (r=-0.8, P<0.001), dominance (r=-0.8, P<0.001) and trustworthiness (r=0.6, P<0.01) ratings correspondingly correlated with the mean post-test rating scores for the consistent group only (same correlations for the inconsistent group all r<0.2, all p>0.3 and difference between the coefficient correlations using fisher r-to-z transformation, all p<0.05). As responses from the anger side of the scales were coded as negative and those from the fear side as positive, these correlations show that the more dominant and threatening the identities are perceived, the more they are judged as angry (figure 2 g,h), while the more the identities are rated as trustworthy the more they are rated as fearful.

Altogether, these findings show that spontaneous associations of identities with an emotion observed on decision bias stem from both stimulus-related social traits provided by the facial features and observer-related biases probably relying on individual preferences generated by past experience.

Pupil dilation tracks effort and individual biases

Based on previous findings (Gee et al., 2014) showing that pupil-linked neuromodulatory systems (notably the locus coeruleus norepinephrine system) respond more strongly when participants respond against their bias, we checked whether we would find signatures of identity biases in pupillary responses. Regressions of the pupil dilation signal in response to presented identities against the main effect of choices toward vs. against biases created by emotion-identity associations, were performed.

They revealed that participants dilate their pupil more strongly when they respond against their bias starting 1.5 s after stimulus onset until 2.4 s (T 30 =2.6, p=0.01)when the comparison concerned identities that congruently belonged to the incidental identity group and the induced identity group (figure 3a,b) (difference between congruent identities and incongruent identities T 30 =-3.1, P<0.01). Although the effect was only significant when combining induced and incidental emotion/identity associations, and thus consolidated associations, these results show that pupillary responses increase when responses are made against biases, probably requiring a stronger effort in the decision process.

Intrinsic biases modulate pupil and EEG responses to Anger and Fear emotions

As results on pupil dilation showed a modulation dependent on biases of participants related to identity/emotion associations to some extent, we next wondered whether intrinsic individual biases -participants' idiosyncratic tendency to perceive facial expressions as angry or fearful, produce similar modulations of pupillary responses independently of identity. Peri-decision pupillary responses indeed tracked intrinsic individual biases toward anger or fear, as pupil diameter was increased when participants responded anger as compared to fear when their bias was toward fear (T 11 =2.4, P<0.05) from 1.6 to 2 sec after stimulus onset, and in opposition, the pupil dilated more strongly to fear response as compared to anger response when participants' bias was toward anger (T 18 =-2.6, P<0.05) from 1.5 to 2.2 sec after stimulus onset (figure 3d). The difference between the two sets of subjects became significant starting 1.2 s until 2.4 s after stimulus onset (T 29 =-3.6, P=0.001) (figure 3c). Moreover, at this timing, individual biases to fear and anger correlated with the main effect of anger/fear responses on pupillary responses (r=-0.58, P<0.001) suggesting that this physiological effect is highly inter-individual dependent (figure 3f).

To further interpret these results, and in particular to determine whether these bias-dependent modulations of physiological responses were present during the decision process, before the response, we checked whether these same individual biases toward anger or fear responses modulated pre-decision electrical brain activity (EEG) that was recorded in a previous experiment including 24 healthy individuals who did exactly the same experimental task -categorization of anger or fear (figure 1b) -without induced emotion-identity associations (El Zein et al., under review).

Participants' behavior was explained with a simple model that assigned two parameters: the sensitivity to the emotional expressions and the individual biases. As we were interested in how individual biases modify brain activity during categorization, we isolated participants that showed a bias toward anger (12 participants) and those who showed a bias toward fear (12 participants) (figure 4a). We then searched for neural signature of such biases, and specifically looked at 'P3' event-related responses because, like pupillary responses, they have been suggested to be linked to the locus coerulus-norepinephrine sytem (Nieuwenhuis et al., 2005). We had previously identified two moments where positive parietal responses peaked in encoding emotion information (El Zein et al, in revision): at 500 ms after stimulus onset, and 100 ms before response time. We therefore isolated activity in parietal electrodes at these time points and examined whether they vary with individual biases. Similarly to pupillary responses, the main effect of emotion on EEG responses (anger/fear) correlated with individual biases across the 24 participants both at 100 ms before response (r=-0.5, p<0.01) (figure 4c), and at 500 ms after stimulus onset (r=-0.46, p<0.05), showing that the more individuals are biased toward anger, the more centro-parietal EEG activity is increased preceding fear vs anger responses (and the opposite for individuals biased toward fear). Splitting the curves representing the time-course of the EEG response main effect into participants biased toward anger and those biased toward fear shows a dissociation between the two groups from 200 ms before response until response time (T 29 =3.1, P<0.01) (figure 4c). This is consistent with the fact that responding against one's bias is more costly, thereby increasing neural processing.

Together, pupillometry and EEG results converge to indicate that individual biases modulate brain responses by increasing responses to effortful decisions against biases. As both parietal responses and pupillary responses have been related to the LC-NE neuromodulatory system, these findings suggest a possible involvement of this system in modulating brain responses as a function of participants' biases -induced, incidental and intrinsic.

Discussion

Social attributions are based on a variety of biases that come from individual experiences and contexts. Previous research had shown that social attributions not only depend on perceptual features within faces of others (Zebrowitz andMontepare, 2005, 2008;Dotsch and Todorov, 2012), but also on individual experience with others that can be manipulated experimentally (Todorov et al., 2007;[START_REF] Schiller | A neural mechanism of first impressions[END_REF]. Here we extend these findings to the social categorization of facial expressions by showing that both induced and incidental associations of identities with specific emotions biased participants' decisions toward bias-congruent interpretations.

These biases as well as identity-independent tendencies to judge faces as expressing a certain emotion were reflected in pupillary responses that were increased to effortful decisions against individual biases. Further, EEG recordings in another identical emotion categorization task supported an increased neural engagement to responses against identity-independent intrinsic biases.

The implicit manipulation of identity association with a certain emotion in only a few trials at the beginning of experimental blocks successfully biased participants' decisions by shifting their choices toward congruent emotions thereby increasing recognition accuracy in these conditions. Noticeably, these associations between identities and emotions are possible among a large number of different identities (32) in a very short period of time, thus suggesting the presence of a rather large 'reservoir' in the brain for the learning of such associations. The existence of such a 'reservoir', implying that the brain has a strong memory dedicated to these associations, is most probably due to their important social significance in daily interactions. As facial expressions of emotions encountered on a day-to-day basis are most often ambiguous, prior expectations, here related to the emotion of individual identities, offer a way to disambiguate their perceptual judgement. Moreover, participants failed to explicitly notice the manipulation, consistently with the fact that social attributions can be constructed through experience implicitly and rapidly (Todorov et al., 2015).

Interestingly, incidental associations between identities and emotions assessed in post-test ratings strongly biased decisions throughout the experiment. Although this result can appear surprising at first sight, it actually might reflect personal experience of each participant, and substantiates the variability of idiosyncratic judgments among individuals [START_REF] Schiller | A neural mechanism of first impressions[END_REF]. It is very plausible that both the induced and incidental associations stem from the same source: personal experience with identities, i.e. in which expression they were previously encountered. In day-to-day life, the mood and social behaviors of people in each individual's surrounding can vary remarkably. If a particular friend is often in a bad mood, it becomes very likely that he/she will be judged as angry or upset on any given day. While in the present study we experimentally manipulated the encounter between identities and observers, incidental associations possibly emanate from resemblance between the identities presented and people from observers' surroundings. Indeed, individuals easily transfer characteristics of identities to others that are similarly looking [START_REF] Kraus | Facial-feature resemblance elicits the transference effect[END_REF][START_REF] Verosky | Generalization of affective learning about faces to perceptually similar faces[END_REF].

Although this interpretation applies to identities that were idiosyncratically judged across participants, the categorization of those identities who were unanimously assigned to anger or fear emotions may rather derive from their perceptual facial characteristics. Identities that were judged to be more dominant and threatening while displaying a neutral expression were also those who were consistently assigned to anger emotion, in accordance with the findings that angry faces are perceived as more dominant (Montepare and Dobish., 2003;Hess et al., 2007). Despite the fact that dominance and threat are probably only characteristics among others that drive the judgment of a face as angry, these associations provide a new example of how social attributions can be based on perceptual facial elements (Zebrowitz and Montepare, 2008).

Our findings demonstrate that the identity of facial expressions was processed even if not required for the emotion categorization task, supporting an automatic holistic processing of faces. Although an integrated processing of a face as a whole seems more intuitive in the context of social interactions, traditional views suggested independent processing of emotion and identity [START_REF] Bruce | Understanding face recognition[END_REF][START_REF] Haxby | The distributed human neural system for face perception[END_REF]. These views are however questioned [START_REF] Calder | Understanding the recognition of facial identity and facial expression[END_REF] as growing evidence support an inter-dependence of these two facial features that seem to reciprocally influence each other (e.g [START_REF] Kaufmann | Expression influences the recognition of familiar faces[END_REF][START_REF] Fox | What is adapted in face adaptation? The neural representations of expression in the human visual system[END_REF][START_REF] Chen | Matching faces with emotional expressions[END_REF]Van den Stock and de Gelder, 2014), suggesting that either of the two is processed even if not relevant to the ongoing task. Here we corroborate the holistic processing of faces by showing that identity was implicitly processed when the only task-relevant information to participants was the emotion expressed by the face.

Pupillary responses were influenced by biases linked to identity/emotion associations. They were increased when participants categorized an identity as expressing the emotion that did not correspond to the biased association. However, this increased pupil dilation to responses against bias applied only to consolidated associations, when the effort to overcome the bias was maximal: when the identities were associated with an emotion both through induced experience and incidental associations. This result builds on the findings of increased pupil dilation when individuals responded against their preferred response during a perceptual detection task (Gee et al., 2014), by showing that responses against biases associated to the stimulus, not only to individual response preferences, similarly influence pupil dilation. Increased pupil response to identities that required responses against expectations can indicate increased arousal and effort that have been since long and repeatedly associated with greater pupillary responses [START_REF] Hess | Pupil Size in Relation to Mental Activity during Simple Problem-Solving[END_REF][START_REF] Hess | Attitude and pupil size[END_REF][START_REF] Kahneman | Perceptual Deficit during a Mental Task[END_REF]Porter et al., 2007).

Individual response biases toward an emotion or the other were also reflected in pupillary responses replicating results of the perceptual detection task mentioned above (Gee et al., 2014). Indeed, similarly to how increases in pupil dilation to 'yes'

responses increased with the degree to which individuals had a conservative bias, pupil increase to anger against fear increased with participants' bias toward fear.

This modulation extends the findings of the previous study that used a perceptual detection task (yes or no) to two alternative perceptual tasks. The observed effect seems to reflect a general mechanism to overcome individual biases, and should a priori apply to any other two-forced choice task, rather than being specific to emotion categorization. Furthermore, brain activity in parietal regions (P3-like components)

showed a similar pattern during an identical emotion categorization task. Activity increased at 500 ms after stimulus onset and 100 ms before response when choices were against individual biases toward anger or fear. As parietal 'P3' EEG potential is associated with attention (Johnson, 1988) and surprise (Mars et al., 2008), this result might be interpreted as increased attention and/or surprise to stimuli triggering effortful responses against biases.

In addition, both 'P3' EEG potential and pupil responses linked to cognitive processing have been suggested to stem from the neuromodulatory coeruleusnorepinephrine system (Nieuwenhuis et al., 2005). Our findings therefore suggest a possible involvement of neuromodulatory systems in conflict processing during categorization. If neuromodulators boost the gain to relevant information (Aston-Jones and Cohen, 2005), it is possible to infer that when individuals have a bias toward a specific choice, a stronger boost will be needed for them to overcome their bias and choose the option against their inclination (Gee et al., 2014).

Two types of biases were observed in the present experiment. The bias toward anger or fear choices is entirely linked to the response and can be illustrated by the general tendency of a negative person to dislike people, therefore being biased to judge new encounters as unfriendly. The bias of associating an identity with a certain emotion is linked to the stimulus and can be depicted by a negative judgment of a specific person that the judger dislikes (or that reminds him/her of a person he/she dislikes). Both effects coming from response-linked individual biases and those based on stimulus-linked associations between identities and emotions triggered similar behavioral and pupillary responses even though the latter is more social specific while the former can apply to any type of bias. This possibly suggests that both effects rely on a similar general mechanism portraying how individuals overcome their bias during decision-making. In the framework of the drift-diffusion model, which suggests that decisions are made through the accumulation of evidence until a threshold is reached (Ratcliff, 1978), this effect would manifest in lower individual thresholds when decisions correspond to the preferred option or when they are congruent with one's expectations.

In contrast to the contextual effects on individual thresholds/biases reported above, when a threat-related contextual factor was manipulated in a similar categorization task, the sensitivity to emotions was increased to threat-signaling emotions suggesting a change in the quality of the evidence accumulation process rather than in the decision threshold (El Zein et al., in revision). From an evolutionary perspective, recognizing a signal of threat requires to better perceive this signal for survival purposes, however recognizing the mood of others is biased by personal experiences but does not necessitate an increased precision. During social interactions, judgments about others certainly require some flexibility as they can vary remarkably as a function of context (e.g., work environments or different countries). The fact that expectations about identities only change decision biases in a response-selective, but not in a less flexible stimulus-selective fashion, might represent a strategy for individuals to adapt in changing environments.

To conclude, we demonstrate that expectations about emotions expressed by identities change their recognition/perception, building on previous findings of contextual influences on social perceptual decisions. Their expression in neural processing and behavior can be described by a general mechanism in which neural engagement increases to effortful decisions against individual biases. Further research should extend the results to other emotions, especially those that we encounter on a daily basis such as happiness and disgust. Understanding how the human brain deals with social decisions influenced by past experience can provide insights into how biased choices are formed in social contexts, such as political choices.

pressed only fear (identities belonging to Fear group). The assignment of identities to Anger and Fear groups varied from a participant to another, with the constraint that all identities were shown as expressing anger or fear across all subjects. To maximize the chance of inducing the associations, only the highest level of emotion strength (7) was presented in these trials, and each stimulus was repeated 3 times.

Importantly, the task during these blocs was unchanged, and subjects had no explicit information about the content of the trials, they were only informed that the task might seem a little easier at the beginning of the blocs. In the rest of the bloc (112 trials), the highest level that was presented in the association blocs was excluded, and levels 1 to 6 from the morph continuum of both emotions were presented in an unbiased fashion (equal number of anger and fear for each identity) as well as the neutral stimuli. After each block, the percentage of correct responses calculated only on the unbiased trials was shown to the participants to keep them motivated. The experiment was divided in 8 experimental blocks, in half of the blocks, stimuli displayed a direct gaze and in the other half they displayed an averted gaze. Participants either started with 4 direct gaze blocs than proceeded to 4 averted gaze blocs, or the opposite and this was counterbalanced across subject. 16 identities were manipulated in the first 4 blocs, and 16 different identities were manipulated in the 4 last blocs.

At the end of the experiment, subjects performed a post-test, during which they saw the 32 neutral identities (with the corresponding gaze direction -same as they saw during the experiment), and they had to rate on a scale whether they think that each identity expressed more anger or fear during the experiment (Figure 1c). The position of anger and fear at the right and left of the scale was counterbalanced across subjects.

Behavioral data analyses

Repeated-measures ANOVA was performed on the percentage of correct responses of the unbiased trials, with gaze direction (direct/averted), identity group (Anger group/Fear group), emotion (anger/fear), and intensity (6 levels of morphs) as withinsubjects factors.

Model selection

We performed model-guided analyses of the behavioural data to characterize the observed increase in recognition accuracy for congruent emotion/identity associations. We used Bayesian model selection based on the model evidence (estimated by a 10-fold cross-validation estimation of model log-likelihood, which penalizes implicitly for model complexity without relying on particular approximations such as the Bayesian Information Criterion or the Akaike Information Criterion). We applied both fixed-effects and random-effects statistics previously described in the literature. The fixed-effects comparison assumes all participants to have used the same underlying model to generate their behavior, such that the overall model evidence for a given model is proportional to the product of model evidence for the model for all participants. Based on this model evidence, we compared different models by computing their Bayes factor as the ratio of model evidence of the compared model (Jeffreys, 1961;Kass and Raftery, 1995). The random-effects comparison is more conservative in allowing different participants to use different models to generate their behavior, and aims at inferring the distribution over models that participants draw from (Penny et al., 2010). For this comparison, we computed support for the winning model by the exceedance probability (p exc ), which is the probability that participants were more likely to choose this model to generate behavior over any alternative model.

We started with the simplest model (model 0) that could account for subject's decisions using a psychometric model such as:

P(Anger)= Ф(w*x + b)
where P(Anger) corresponds to the probability of judging the face as angry, Ф to the cumulative normal function, w to the perceptual sensitivity to the display emotion, x to the evidence in favor of Anger or Fear in each trial (emotion strength, from -6 for an intense expression of fear to +6 for an intense expression of anger), and b to an additive, stimulus-independent bias toward one of the two responses/emotions. We compared this 'null' model which did not allow for contextual influences of identity group on the decision process, with two additional models which propose two different mechanisms which could account for the observed increase in recognition accuracy for congruent combinations of identity and emotion. A first possibility (model 1)

would be that identities associated with an emotion group bias the recognition in favor of the congruent interpretation (anger for group anger, fear for group fear). Alternatively (model 2), expectations about group identity might selectively increase sensitivity to congruent emotions. Finally (model 3), effects could possibly involve both a change in the sensitivity to and response bias toward congruent emotion/identity associations. These models were used for both effects related to the experimentally induced identity group and the individual intrinsic group. After isolating winning models in each case, we used a fourth model that integrated both effects that change response bias.

Pupillometry preprocessing and analyses

Pupil responses were recorded monocularly using an Eyelink 1000 eye tracking system, at a sample rate of 500 Hz. Subjects' head was positioned on a chinrest to avoid movements, and calibration was performed at the beginning of the experiment and every 2 experimental blocks (4 times in total).

Data was resampled offline to 50 Hz. Blinks were rejected by using cubic spline interpolation based on the 200 ms before the onset of the blink and 200 ms after the offset of the blink. High frequency artifacts were also removed using cubic spline interpolation. Afterwards, slow pupil fluctuations were removed using a 10 second sliding window. After removing the trial by trial baseline (-500 ms to the face onset) and standardizing (z-scoring) the activity across trials, the pupil signal was entered in a general linear model. We regressed the pupil data against main effects of responses with and against biases. Parameter estimates of this regression were extracted and averaged across participants.

Identity rating experiment

20 subjects participated to the experiment (10 females, mean age=22.7±0.6). The 32 identities used in the experiment were presented in the neutral condition only. Each identity was presented twice, once with a direct, and once with an averted gaze.

Faces appeared on the screen for 2 seconds after which they disappeared and 3 continuous scales were drawn on the screen. We asked participants to rate the iden-tities in terms of threat, trustworthiness, and dominance on these 3 scales from 'not at all' to 'very much' (a text appeared at the top of the scales reminding the instructions: How much is this face threatening/trustworthy/dominant? accordingly with the scale). The order of the scales was randomized across subjects. The scales stayed on the screen until the three responses were given, however subjects were instructed to answer intuitively without spending too much time to decide.

EEG experiment

24 participants had participated in a previous experiment and performed an emotion categorization task on morphed emotional expressions of Anger and Fear. (For details of the experimental procedures, EEG pre-processing and analyses, see El Zein et al. under review). We concentrated on P3 like component as it has been proposed to be tightly linked to neuromodulatory responses of the locus coerulus system (Nieuwenhuis et al., 2005) also related to pupillometry responses (Aston-Jones and Cohen, 2005). We aimed to check whether we would find the same signatures of responses against and toward individual biases toward anger or fear. To do so, we regressed EEG signals in the isolated parietal electrodes stimulus and response locked against main effect of responses against and toward bias.

Figures

General discussion

The

The evolutionary advantage of threat detection

The work conducted here builds on the extensive research that has been conducted on threat detection by characterizing the way social threats are represented in the human brain. We show that social cues are combined as a function of how much they signal threat to the observer. Here, this signal was provided by angry facial expressions gazing directly at the observer who becomes the target of the threat, and fearful expressions gazing away from the observer signaling a potential danger in the environment. The representation of these emotions signaling threat is independent of which social cue is task-relevant, and thus bypasses the attentional demands of the task at hand. It occurs in sensory, motor and emotion-related regions of the human brain within 170 ms after stimulus onset. This fast, selective, attention-independent encoding of social threats suggests that evolutionary pressure adapted to specifically optimize biologically relevant stimuli [START_REF] Machens | Testing the efficiency of sensory coding with optimal stimulus ensembles[END_REF]. Emotions are proposed to be expressed in facial features for adaptive reasons [START_REF] Darwin | The expression of the emotions in man and animals[END_REF], in the purpose of driving behaviors for survival (LeDoux, 2012[START_REF] Panksepp | Affective neuroscience: The foundations of human and animal emotions[END_REF], 2005). While our findings substantiate these suggestions, they also show that the functional significance of emotions is dependent on other social cues such as gaze direction. This dependence highlights the importance of considering emotions in context [START_REF] Hess | Darwin and emotion expression[END_REF] rather than as discrete entities that originate from 'hardwired' processes [START_REF] Ekman | Universals and Cultural Differences in Facial Expressions of Emotions[END_REF]Friesen 1978).

The use of model-based behavioral and neural analysis in Experiment 2 demonstrated for the first time to our knowledge that threat increases the sensitivity to emotional expressions, allowing a finer and more precise perception of sensory feature combinations signaling high threat to the perceiver. Previous studies had proposed that gaze direction tunes the processing of emotional expressions by changing behavior and neural engagement as a function of threat (Adams andKleck, 2003, 2005;Sato et al., 2004[START_REF] Sato | Amygdala integrates emotional expression and gaze direction in response to dynamic facial expressions[END_REF]Sander et al., 2007;[START_REF] Akechi | The effect of gaze direction on the processing of facial expressions in children with autism spectrum disorder: an ERP study[END_REF]Adams et al., 2012). However, this is the first evidence showing that it does so by increasing the quality of sensory processing of threat-signaling emotions in a bottom-up, attention-independent fashion. Indeed, the P300 evoked potential, that usually varies with attentional resources (Johnson, 1988), was not increased to threat-signaling emotions. Moreover, 'mediation' analyses relating brain signals to behavior and stimulus information showed that choice predictive signals were not increased to these threat-signaling emotions, further suggesting that the increase of sensitivity to these emotions was not sustained by greater top-down attention to threat-signaling emotions (Nienborg andCumming, 2009, 2010;Wyart et al., 2015).

Additional behavioral studies showed that the increased sensitivity to threatsignaling emotions is sustained by high spatial frequencies, stressing the role of the contribution of refined visual features to the increase in the precision of encoding.

They also showed that this effect is not specific to emotion processing, but rather extends to the processing of morphed gaze directions. Emotion and gaze indeed reciprocally influence each other in the eye of threat, in accordance with previous studies [START_REF] Lobmaier | Emotional expression modulates perceived gaze direction[END_REF][START_REF] Adams | Influence of emotional expression on the processing of gaze direction[END_REF][START_REF] Ewbank | Why are you angry with me? Facial expressions of threat influence perception of gaze direction[END_REF].

Here we further show that this influence changes the sensitivity to threat signals,

whether the evidence to gaze direction or to emotion is manipulated. Finally, gaze direction enacted as a contextual factor modulating emotion processing specifically when anger and fear emotions were used. When categorization involved happy and angry expressions, the sensitivity to anger was increased as compared to happiness independently of gaze. This result suggests that in the context of opposite valence categorization, negative emotions (here anger) become generally more relevant to the observer, again highlighting the enhancement of the sensitivity to threat-signaling emotions. It is consistent with studies showing that negative emotions, particularly threatening ones (anger), capture attention in visual search paradigms that include positive and negative emotions [START_REF] Eastwood | Differential attentional guidance by unattended faces expressing positive and negative emotion[END_REF]Ohman et al., 2001;[START_REF] Lo | A quick eye to anger: An investigation of a differential effect of facial features in detecting angry and happy expressions[END_REF].

To conclude, the behavior of participants is highly dependent on threat with increased recognition of threat-related signals sustained by greater sensitivity to these signals. Importantly, the rapid sensitization of neural responses to threat signals sheds light on the speed of threat-dependent modulations of neural processing.

Finally, the results taken together stress the role of the 'perception' stage that is already modulated by the functional significance of the element to perceive [START_REF] Mcarthur | Toward an ecological theory of social perception[END_REF][START_REF] Compton | The interface between emotion and attention: a review of evidence from psychology and neuroscience[END_REF][START_REF] Zebrowitz | The ecological approach to person perception: Evolutionary roots and contemporary offshoots[END_REF], especially in the context of threat:

However, according to the distributed human neural system for face perception, the FG processes invariant aspects of the face while the STS encodes variant features such as gaze and emotion [START_REF] Haxby | The distributed human neural system for face perception[END_REF]. Contrasting this clear dissociation, here we show that both regions can be involved in the processing of variant aspects in the face and their combination. Nevertheless, it remains unclear whether both STS and FG are acting in concert to process contextual facial expressions [START_REF] Baseler | Neural responses to expression and gaze in the posterior superior temporal sulcus interact with facial identity[END_REF], whether the FG receives feedback from the STS [START_REF] Nguyen | Fusing concurrent EEG-fMRI with dynamic causal modeling: Application to effective connectivity during face perception[END_REF], or whether both these regions are gated by activity in other relevant structures to the processing of significant facial expressions to the observer, such as the AMG.

Selective involvement of motor-related regions in the representation of threatrelevant social displays

Experiments 1 and 2 provide evidence of the contribution of motor-related regions to the representation of threatening emotions. In Experiment 1, post-central and parietal regions from the dorsal pathway are involved in the integration of emotion, gaze and gesture as a threatening signal within 200 ms after stimulus onset. In Experiment 2, at the same timing, motor-related signals selectively co-vary with the emotion strength of facial expressions signaling threat to the observer. Moreover, this early neural encoding of threat-signaling emotions in motor preparation signals occurs earlier than the formation of the upcoming choice, and thus depicts the representation of threat signals in motor cortex related to the sensory attributes of facial expressions rather than the choice. These results build on the few previous studies that did report motor activation in relation to emotion processing. Indeed, premotor or motor structures are involved in the perception of threat transmitted through the combination of emotion and gaze [START_REF] Hadjikhani | Pointing with the eyes: the role of gaze in communicating danger[END_REF]Conty et al., 2012), the perception of threatening emotions of anger and fear conveyed by body expressions [START_REF] De Gelder | Fear fosters flight: a mechanism for fear contagion when perceiving emotion expressed by a whole body[END_REF][START_REF] Grèzes | Perceiving fear in dynamic body expressions[END_REF][START_REF] Pichon | Two different faces of threat. Comparing the neural systems for recognizing fear and anger in dynamic body expressions[END_REF][START_REF] Pichon | Threat prompts defensive brain responses independently of attentional control[END_REF], the perception of facial expressions of anger and fear (Balconi andBortolotti, 2012, 2013), as well as the perception of negative natural emotional scenes (Borgomaneri et al., 2014). Altogether, these results converge by showing that the early motor involve-ment during social processing is specific to threat perception, possibly for action preparation related to survival in threatening contexts (Ohman and Mineka, 2001;Frijda, 2009).

In threat settings, contextual within-emitter cues influence emotion processing in effector-specific regions, highlighting the role of motor engagement in the processing of contextually threatening emotions in parallel with face-selective sensory regions, rather than following these sensory regions only for response selection. This early motor activation, which does not predict choice at this latency, thus argues against 'sequential' views of decision-making that predict a cascade of sequential activations from the sensory feature-selective regions to associative regions and finally to effector-selective (motor) regions.

Modulation of the relative contribution of the dorsal pathway by task and indi-

vidual-related relevance to the observer.

The involvement of motor-related structures in the processing of the threat displays reported in Experiments 1 and 2 was modulated by task demands and individual characteristics, respectively.

Task-related modulation

In Experiment 1, the neural sources of the integration of emotion, gaze and gesture varied as a function of whether participants attended emotion or direction cues.

While the involvement of STS and FG was consistent across tasks, emotion-related areas were more importantly involved during emotion categorization (is the person expressing anger or neutral emotions?), while postcentral and parietal regions from the dorsal pathway were rather engaged during direction of attention categorization (is the person looking away or at you?). Similarly, the premotor cortex was engaged in the combination of gaze, emotion and gesture as a function of threat when the task requested to attend directional cues (Conty et al., 2012). Although gaze direction is primarily informative as to whether the person is looking at or away from the observer, pointing gesture also contributes as a cue to determine the direction of attention of a person. Both gaze processing [START_REF] Haxby | The distributed human neural system for face perception[END_REF] and gesture processing [START_REF] Grèzes | Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta-analysis[END_REF][START_REF] Rizzolatti | Neurophysiological mechanisms underlying the understanding and imitation of action[END_REF] involve regions from the dorsal pathway including parietal and/or motor structures. Therefore, the contribution of these regions involved in directional cues processing was enhanced when attention was directed toward them.

Nevertheless, one should keep in mind that these regions were detected when analyses were ran on the study independently of task demands, thus not precluding their involvement even when attention is directed toward emotion processing.

Rather, it is only the weight of their involvement that is modulated by attentional demands. Furthermore, Experiment 2 clearly shows that motor-related regions are involved in the encoding of contextual emotion processing even when attention is directed toward emotion cues. However, the stimuli as well as the analyses were very different not allowing a direct comparison between the 2 experiments. Finally, the difference in analyses that did not allow previous studies to directly measure the sensitivity to emotions might explain the lack of reports of motor involvement in the interaction between emotion and gaze cues in threat contexts (N'Diaye et al., 2009;[START_REF] Sato | Amygdala integrates emotional expression and gaze direction in response to dynamic facial expressions[END_REF]Adams et al., 2012).

Anxiety-related modulation

Experiment 2 builds on the results of Experiment 1 by showing that the involvement ms after stimulus onset. The normal anxiety response is suggested to represent an adaptive response that aims to prepare individuals to deal with threats (Bateson et al., 2011). In this vein, the increased involvement of the motor cortex with anxiety is in accordance with the adaptive role of anxiety in reacting rapidly to threats. Previous studies suggested that high-anxious individuals do not properly integrate contextually relevant information but rather show a bias toward threatening stimuli [START_REF] Bar-Haim | Threatrelated attentional bias in anxious and nonanxious individuals: a meta-analytic study[END_REF] reflected in over-activation of the AMG [START_REF] Bishop | State anxiety modulation of the amygdala response to unattended threat-related stimuli[END_REF]Bishop, 2007).

Here, the results imply that elevated anxiety within healthy individuals does not impair the processing of contextual threat, but rather shifts the representation of threat to regions consistent with the role of anxiety, the motor cortex, for the preparation to react to social threats. To further test this claim, it would be interesting to run an experiment where participants have to take action-related rather than perceptual decisions, such as approaching an individual in naturalistic environments. If the early involvement of the motor cortex in the representation of social threats truly has the role of action preparation when facing social threats, then action-related decisions (e.g., avoid threat) should accordingly be facilitated in high-anxious individuals.

Selective representation of threat signals: is the amygdala the starting point?

Given the importance of the AMG in threat processing [START_REF] Ohman | The role of the amygdala in human fear: automatic detection of threat[END_REF], its contribution to the perception of ambiguous anger and fear expressions (Graham et al., 2007) and its demonstrated involvement in gaze and emotion interaction as a function of threat [START_REF] Adams | Effects of gaze on amygdala sensitivity to anger and fear faces[END_REF](Adams et al., , 2012;;Sato et al., 2004[START_REF] Sato | Amygdala integrates emotional expression and gaze direction in response to dynamic facial expressions[END_REF]N'Diaye et al., 2009), one might expect that it contributes to the observed increased sensitization to threat signals. Although in Experiment 1 the source reconstruction analyses suggested its possible involvement among ventral emotion-related structures, EEG scalp recordings lack from a precise localisation of signals in deep structures such as the AMG. The methodology we use, EEG, is thus not convenient to address whether the AMG contributes or even drives the processing of threat signals in this context. Evidence from other studies shows that the AMG is involved in threat processing even earlier than the latencies we observed, within 100 ms after stimulus onset [START_REF] Luo | Neural dynamics for facial threat processing as revealed by gamma band synchronization using MEG[END_REF][START_REF] Hung | Unattended emotional faces elicit early lateralized amygdala-frontal and fusiform activations[END_REF][START_REF] Liu | Emotion separation is completed early and it depends on visual field presentation[END_REF]. We can thus speculate that the AMG might be engaged in the earlier integration of social cues, and responsible of the enhanced sensitization to biologically relevant signals in sen-sory and motor regions thanks to its direct projections to the ventral visual pathway including the FG and STS [START_REF] Hadj-Bouziane | Amygdala lesions disrupt modulation of functional MRI activity evoked by facial expression in the monkey inferior temporal cortex[END_REF], and to premotor structures in the dorsal pathway [START_REF] Avendaño | A fine structural study of the cells that proliferate in the partially denervated dentate gyrus of the rat[END_REF]Grèzes et al., 2014).

If this is true, then it would suggest that the AMG should dissociate threatsignaling emotions (anger with a direct gaze and fear with an averted gaze) at early latencies independently of anxiety. Indeed, both low and high anxious integrated emotion and gaze cues as a function of threat however in different structures, so if this integration originates in the AMG, than both high and low anxious should dissociate threat-signaling emotion in the AMG. However, results in the literature concerning the role of the AMG in threat processing in relation to anxiety are contradictory.

On one hand, evidence points toward an impaired integration of contextual cues when processing threatening information associated with AMG hyperactivity in high anxious individuals [START_REF] Bishop | State anxiety modulation of the amygdala response to unattended threat-related stimuli[END_REF]Bishop, 2007;Etkin and Wager, 2007). On the other hand, one study showed that greater AMG activation to direct as compared to averted gaze anger increased with individual anxiety (Ewbank et al., 2010), suggesting that AMG integrates contextually threatening information in high anxious individuals.

Two elements can reconcile the findings. First, the influence of context might differ from a study to the other because of the nature of the task used. For example, the hyperactivity of AMG to threat signals is observed during a task that requires judging either houses or faces and reports that fearful faces are processed even when irrelevant to the task in high anxious individuals [START_REF] Bishop | State anxiety modulation of the amygdala response to unattended threat-related stimuli[END_REF]. This result does not preclude that the AMG can integrate observer-relevant threat signals within fearful stimuli, as the AMG might have shown increased activity to fear with an averted gaze as compared to the presented direct gaze in that same experiment.

Second, brain activity reported in fMRI studies does not provide the precise timing of the involvement of the AMG, so the discrepancy of results might simply be due to the fact that fMRI signals are not reflecting what is going on in the AMG at 100 ms.

However, a recent MEG study that implemented source reconstruction algorithms capable of reaching activity in the AMG [START_REF] Dumas | MEG Evidence for Dynamic Amygdala Modulations by Gaze and Facial Emotions[END_REF] during the processing of fearful and neutral faces with direct and averted gaze, contradicts this suggestion at first sight. Indeed, this study did not reveal early interactions between gaze and emotion cues but rather after 200 ms following stimulus onset. Nevertheless, there was no possible measure of the sensitivity to emotional expressions in this study where the faces were passively viewed, thus not excluding that the AMG might increase the sensitivity to threat-related emotions at early latencies.

To conclude, the role of the AMG as a generator of the increased sensitivity to biologically relevant stimuli [START_REF] Sander | The human amygdala: an evolved system for relevance detection[END_REF] has still to be proven, through single neuron recordings in monkeys or implemented patients, or through MEG and EEG recordings thanks to the development of source reconstruction algorithms that are able to reach deep subcortical structures [START_REF] Dumas | MEG Evidence for Dynamic Amygdala Modulations by Gaze and Facial Emotions[END_REF].

Anger and Fear emotions

The experiments conducted in this Ph.D. focused on two specific emotions: anger and fear. These emotions share an important characteristic: they can both signal threat to an observer depending on the context.

Do anger and fear emotions share similarities?

When emotional faces including happiness, disgust, anger and fear are presented for short durations using backward masking, the categorization between anger and fear expressions specifically was disrupted [START_REF] Sweeny | Detecting and categorizing fleeting emotions in faces[END_REF]. This could suggest that the fact that they both represent negative emotions related to threat makes them difficult to tear apart if presented very shortly. However, early stages of the perception of emotional expressions dissociate anger/disgust emotions from fear/surprise emotions, thereby not grouping anger and fear together [START_REF] Jack | Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time[END_REF]. The authors interpret these groupements as originating from the fact that anger and disgust both signal stationary danger, in contrast to fear and surprise that rather signal a fast approaching danger.

The role of gaze direction in the dissociation of anger and fear emotions

Facial information is integrated starting from the eyes region downward in the face as reflected in the N170 face-selective component [START_REF] Schyns | Dynamics of visual information integration in the brain for categorizing facial expressions[END_REF], suggesting that gaze information is necessarily processed during emotion processing. Moreover, diagnostic information in the face for the recognition of fear and anger emotions comprises the eyes [START_REF] Schyns | Information processing algorithms in the brain[END_REF], and the recognition of morphed emotional expressions of anger and fear primarily relies on the eye region [START_REF] Wegrzyn | Categorical Perception of Fear and Anger Expressions in Whole, Masked and Composite Faces[END_REF]. These findings stress the importance of taking into account information in the eye, notably gaze direction, when investigating the processing of anger and fear emotion.

When gaze direction, an inseparable feature from facial expression, is taken into account, anger and fear emotions are functionally dissociated. Indeed, while anger associated with a direct gaze signals a direct danger/threat to the observer, only fear with an averted gaze signals a potential danger in the near environment (Sander et al., 2007). Danger might be considered as 'stationary' [START_REF] Jack | Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time[END_REF] in the case of direct anger as the source of threat is known to the observer (it is the emitter of the angry expression himself) while danger might be considered as 'fast approaching' [START_REF] Jack | Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time[END_REF] in the case of averted fear as it is in the periphery and unknown to the observer at the moment when he/she processes the emotion. Nevertheless, both direct anger and averted fear signal threat to the observer, directly coming from the emitter for direct anger, and coming from the environment for averted fear. The results from Experiment 2 clearly demonstrated that these two displays benefit from increased sensory processing, and exclusively involve motor related regions at their early processing. They highlight the existence of an early stage of processing, where the behavioral and biological relevance of social displays is inherent to the emotion recognition process. They also pinpoint the importance of considering contextual cues in the emotion recognition process, as they can notably change the functional meaning of emotional signals.

Anger and fear: what is the source of the signaled threat?

As just stated above, the work presented in this dissertation supports the groupement of averted fear and direct anger in the same category because of their shared communication of threat/danger to the observer. However, an important difference remains concerning these two displays of emotions. While direct anger is itself the source of the threat to the observer, averted fear only indirectly signals some potential danger in the periphery that is unknown at the moment when the observer processes the fearful expression. One might thus expect that not only averted fear should be processed efficiently, but also information in the periphery as it emanates potentially threatening information.

Accordingly, studies have shown that fearful stimuli, even with a direct gaze, increase attention to the surrounding stimuli (Phelps et al., 2006;[START_REF] Taylor | Fearful, but not angry, expressions diffuse attention to peripheral targets in an attentional blink paradigm[END_REF], and that the gaze of fearful faces and not happy faces is followed more consistently when participants are looking for a threatening target [START_REF] Kuhn | Increased gaze following for fearful faces. It depends on what you're looking for![END_REF]. Gaze following of fearful expressions is also modulated by anxiety traits of observers as high anxious as opposed to low anxious individuals follow the gaze direction of a fearful face more than a neutral face during a perceptual detection task [START_REF] Mathews | The face of fear: Effects of eye gaze and emotion on visual attention[END_REF]. When compared with anger, only fearful expressions (here again, even with a direct gaze) enhance the memory of neutral words presented in the periphery, while on the contrary angry facial expressions are themselves better memorized than fearful expressions [START_REF] Davis | A tale of two negatives: Differential memory modulation by threat-related facial expressions[END_REF]. The fact that even direct fear, and not only averted fear, orients attention to the periphery shows that the function of fearful expressions in signaling information in the environment extends to the perception of fear emotion in general, although in real life situations, it is only if averted that fear signals a potential danger. However, all these effects should at least be enhanced to averted fear (looking at the target to be memorized or judged).

Importantly, concerning the dissociation between anger and fear, the findings stated above draw attention on a clear dissimilarity between these emotions. On one hand, anger represents on its own an important signal that has to be efficiently processed for a proper reaction when facing threat. On the contrary, fear has to be processed efficiently along with information in the periphery possibly containing threatening information that has to be acted upon. Attention orienting to the periphery [START_REF] Taylor | Fearful, but not angry, expressions diffuse attention to peripheral targets in an attentional blink paradigm[END_REF] and greater sensitivity to elements in the periphery (Phelps et al., 2006) following fearful faces have been reported. However, while the former study did not measure sensitivity to the elements in the periphery, the latter did not contrast anger and fearful stimuli. Moreover, both of these studies did not contrast averted and direct gaze direction. It would therefore be interesting to test anger and fear emotions with direct and averted gaze in context with other elements in the periphery. This would allow building on the results of Experiment 2 that demonstrate that both direct anger and averted fear are processed with increased sensitivity, by possibly showing that it is only in the presence of fear, notably averted fear, that sensitivity to the other elements in the periphery will also be increased.

Contextual effects related to congruence vs. significance to the observer Different neural signatures of contextual effects related to congruence vs. significance to the observer?

All experiments 1 to 6 manipulated among other elements contextual within-emitter cues, emotion and gaze direction, which interacted as a function of their significance to the observer. In these social displays however, there was no notion of 'congruence', i.e. an angry expressions looking away from an observer is not 'incongruent' but simply not 'significant' to the observer, who is not the source of the threat signaled by anger.

On the contrary, manipulations related to a change in the environment, involve a 'congruency' factor. For example, changing the emotional scene in which an emotional expression is presented (Righart and de Gelder, 2008b) creates 'congruent' (facial expression and scene congruent such as a happy face in a pleasant environment) and 'incongruent' (facial expression and scene incongruent such as a disgusted face in pleasant environment) conditions. Similarly, manipulating withinemitter body information in a way that it matches or not facial expressions [START_REF] Meeren | Rapid perceptual integration of facial expression and emotional body language[END_REF]Aviezer et al., 2011) creates congruent (e.g., a withdrawal posture with a fearful face) and incongruent conditions (a withdrawal posture with an angry face).

These studies converge by showing that the recognition of a facial expression is facilitated by a congruent body posture or emotional scene. At the neural level, the results are however inconsistent: while the early visual P100 component was shown to be increased to incongruent stimuli induced by body-emotion associations but not the face-selective N170 [START_REF] Meeren | Rapid perceptual integration of facial expression and emotional body language[END_REF], the opposite pattern was observed to scene-emotion associations (Righart andde Gelder, 2006, 2008b). In the latter study, only the N170, and not the P100, was modulated by congruency, by showing increased activity to congruent, not incongruent scene-emotion displays.

However, these studies focused on specific ERP components, the P100 and the N170. They do not report results on the P300, on which increased activity to incongruent displays, that should be similar to 'surprise' signals (Mars et al., 2008) This difference possibly stems from the functional role of these contextual factors during emotion perception. Facial expressions encountered in everyday life are most often ambiguous. Thus, an additive prior influence rather than sensitivity enhancements to help disambiguate the perceived emotion on a daily basis, might stem from the associated biological cost for the organism. Indeed, sensitivity enhancements, that require enhancing the signal-to-noise ratio of neural processing, are probably more costly than additive biases. Accordingly, as combinations of gaze direction and negative emotions result in stimuli signaling threat to the observer, evolutionary pressure might have shaped the brain to prioritize these signals by increasing the perceptual sensitivity to these stimuli, despite the associated neural cost. In contrast, learning the general mood of a relative or a friend might be important for social interactions, but is not crucial in terms of survival. Furthermore, it can be advantageous to keep a flexible behavior when it comes to judging individuals based on experience, as associations based on experience are prone to many variations such as a specific country (different social codes) or work vs. holiday environments.

Therefore, changing the decision bias, and not the more rigid perceptual encoding of stimuli when constructing interpretations about a person, could represent a strategy to adapt in changing environments.

These interpretations apply to the specific manipulation we used in Experiment 7. Certainly, it is different from previously reported experiments that varied congruency simultaneously to the presentation of the emotional expression [START_REF] De Gelder | The perception of emotions by ear and by eye[END_REF][START_REF] Meeren | Rapid perceptual integration of facial expression and emotional body language[END_REF][START_REF] Righart | Context influences early perceptual analysis of faces--an electrophysiological study[END_REF], 2008a, 2008b;[START_REF] Van Den Stock | Body expressions influence recognition of emotions in the face and voice[END_REF], as here congruency effects are only based on previously experienced associations between emotions and identities.

However, one can speculate that model-based analyses of previously reported effects (for example by morphing facial expressions embedded in natural scenes) could reveal an influence of contextual elements related to congruency on the decision bias rather than the emotion sensitivity. This would contrast top-down effects triggered by expectation/congruency cues with bottom-up effects triggered by the combination of cues significant to the observer. Alternatively, this distinction could be less sharp than just stated, as contextual influences might function on a case-bycase basis depending on the precise manipulated factor. Taking again as an example faces in natural contexts, it is plausible that the sensitivity to negative emotions increases in negative contexts, because of the importance of detecting danger in unsafe environments. As an illustration, it is more crucial to perceive a threatening person when walking in a dangerous neighborhood at night, as compared to when walking in a safe neighborhood during the day. This would be consistent with the conclusion drawn from Experiments 1 to 6 suggesting that sensitivity to social signals should be increased whenever observer-relevant threat is involved.

No interaction between the effects of gaze direction and emotional experience with identities on emotion categorization

In Experiment 7, gaze direction was also modulated based on our results in the 6 other experiments. Indeed, we thought it could interact with the expectation manipulation about the emotion of individual identities. More precisely, we expected that at least anger displaying a direct gaze would bias decisions more strongly than averted anger and fear in general. This assumption was based on the fact that angry facial expressions are better memorized than fearful expressions because they directly represent a signal of threat to an observer [START_REF] Davis | A tale of two negatives: Differential memory modulation by threat-related facial expressions[END_REF]. However, no interaction was observed between gaze direction and effects of expectations, showing that they exert independent effects on emotion categorization. Importantly, expectations effects about the emotions of identities should be generalized to all emotions rather than being specific to anger and fear emotions (contrarily to gaze direction effects). A useful follow-up study of Experiment 7 should thus consist of a similar design using day-to-day encountered emotions such as happiness, anger and disgust emotions.

Decision-making in social contexts

Two elements seem to be pivotal to reach a comprehensive understanding of decision-making in humans. First, as it is indisputable that day to day decisions are influenced by contextual elements, taking into account these elements in the study of decision-making is essential. Second, considering decision-making in social contexts is a key step to the general understanding of human decision-making as most human choices are made in social environments. The work conducted in this Ph.D. attempted to characterize the mechanisms of decision-making in social contexts, by considering different contextual factors, and applying decision-theoretical models to understand human choice. In non-social perceptual decision-making studies, prior information that consists of either expectations or attentional cues is typically provided explicitly before the decision-relevant sensory information and is processed consciously by the participant. On the contrary, social stimuli being multi-dimensional and comprising of different complex features, these features can enact as contextual cues that co-occur with (rather than being provided before) the to-be-judged stimulus. Moreover, these contextual cues can implicitly (without being informative to the task, and thus a priori not consciously processed by the participant) influence decisions.

The manipulation of attention and expectations as contextual factors in nonsocial decision-making show that both types of manipulations influence decisions in sensory [START_REF] Esterman | Perceptual expectation evokes category-selective cortical activity[END_REF]Kok et al., 2012), motor (de [START_REF] Lange | Prestimulus oscillatory activity over motor cortex reflects perceptual expectations[END_REF] and more domain-general (Rahnev et al., 2011a;Wyart et al., 2015) regions in the human brain. Here we similarly show that gaze direction, a simultaneous contextual cue to facial expression, increases the sensitivity to threat-signaling emotions in sensory, motor and associative domain-general brain regions. These findings are convergent with the idea that decisions form through a multi-level distributed mechanism [START_REF] Cisek | Making decisions through a distributed consensus[END_REF], encompassing sensory, associative and motor regions. Furthermore, sensory and motor regions were recruited in tandem during perceptual decisions about emotional expressions in line with evidence suggesting that action-related regions contribute early on to the representation of the decision signal [START_REF] Hernández | Temporal evolution of a decision-making process in medial premotor cortex[END_REF][START_REF] Gold | The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands[END_REF][START_REF] Romo | Neuronal correlates of a perceptual decision in ventral premotor cortex[END_REF][START_REF] Heinen | An oculomotor decision process revealed by functional magnetic resonance imaging[END_REF].

Despite the common networks involved in social and non-social decisionmaking, contextual gaze cue strikingly influenced the processing of emotional expressions within 200 ms after stimulus and contextual cue onset in sensory and motor pathways. Certainly, it is not possible to directly compare the effects of contextual manipulations in non-social decisions with social decisions in the classical paradigms used in the field. Indeed, as stated above, expectation cues for example can only be provided before the stimulus while gaze direction is presented together (simultaneously) with the target, i.e., the emotional expression. When expectation cues are provided before the stimulus, they influence motor preparation signals before the stimulus onset, however few hundreds of milliseconds after the cue onset (de [START_REF] Lange | Prestimulus oscillatory activity over motor cortex reflects perceptual expectations[END_REF]. The coincidence of the contextual cue (gaze direction) with the target In contrast with the contextual manipulation related to threat discussed above, the contextual factor tested in Experiment 7 was related to personal experience with identities, and was thus not presented simultaneously with the target stimulus. Although implicit in contrast to expectation cues in the nonsocial domain that are usually explicitly processed and inform the decision (e.g Rahnev et al., 2011a;de Lange et al., 2013), this manipulation is not entirely specific to the social domain (such as gaze direction in a facial expression). Indeed, it is possible to create associations between non-social elements such as shapes and colors for example, and maybe obtain similar effects of biased categorization. Nevertheless, this manipulation demonstrated that associations between identities and emotions are possible in a very short period of time, among 32 different identities. These rapid associations might be due to their social significance in daily interactions. However, a control study demonstrating that the same effects can be observed with 32 different shapes and 2 colors for example, would contradict the social nature of the results observed in this task. Furthermore, different types of biases were evidenced in this study: experimentally induced biases (those driven by our manipulation of adding blocks where individual identities were associated with specific emotions), incidental associations (idiosyncratic associations between identities and emotions), and intrinsic biases toward anger or fear responses independently identity. All three biases seem to operate through a general mechanism where neural engagement is increased to effortful decisions against biases. This conclusion is drawn based on the observed increased pupillary response to choices against biases, that replicates the findings of a study in which the decision was made during a perceptual detection task, thereby stressing the rather general aspect of this mechanism. Moreover, 'P3' related EEG responses showed a similar pattern, again extending the greater signal to decisions against biases to a brain wave associated with domain-general mechanisms (O'Connell et al., 2012), in line with the idea that expected events trigger diminished brain activity [START_REF] Summerfield | Neural repetition suppression reflects fulfilled perceptual expectations[END_REF].

While the perceptual sensitivity to contextually threatening emotions was markedly increased within 200 ms in the brain and modulated by individual anxiety distinguishing threat signals from other stimuli, prior expectation about emotion/identity association acted as a top-down factor only influencing decisions at later stages, in accordance with Bayesian accounts of decision-making [START_REF] Griffiths | Optimal predictions in everyday cognition[END_REF][START_REF] Oaksford | Bayesian Rationality: The Probabilistic Approach to Human Reasoning[END_REF]. Importantly, this dissociation sheds light on the different mechanisms by which different contextual factors influence decisions within social perceptual decisions.

Conclusion

The Third, when expectations about the emotions of individual identities were manipulated, they distinctively influenced emotion categorization by biasing decisions toward associated options and not by increasing the sensitivity to these options.

Along with incidental associations between identities and emotions and intrinsic bi-ases toward anger or fear responses, these induced associations reflected a general mechanism for emotion categorization biases whereby increased neural activations are required to drive unexpected decisions against existing biases. The change in the decision criterion (i.e., a bias) as a function of expectations about the emotion of identities, rather than a change of the sensitivity, could stem from the fact that such associations are related to daily interactions highly dependent on context and thus requiring a flexible adaptation. In everyday life, where emotional expressions are most often ambiguous, it can be useful to decrease the neural cost in disambiguating the perceived emotion through an additive influence on the decision bias.

Understanding how the brain forms perceptual decisions in social environments is crucial, but certainly only represents one piece of the puzzle to reach an integrative view of social functioning and the neural basis of social cognition. The work conducted here is an important step toward the understanding of at least that piece of the puzzle. By taking advantage of a model-based framework, we revealed the underlying computations of brain functioning when deciding upon socially meaningful stimuli, facial expressions (see summary Figure 31). The framework used here opens the door to the prospect of identifying what precisely is altered in psychiatric conditions associated with modified social behaviors, such as autistic individuals that are characterized by significant deficits related to social cognition. Along these lines, using the same stimuli and framework described here, ongoing work in the team investigates whether autistic individuals are capable of integrating social cues similarly as controls during social perceptual decisions. Furthermore, also using the same framework, other work in progress in the team explores how these social perceptual decisions are related to social inclusion of individuals, their social preferences, and their life history.

  Abbreviations .........................................................................................................

Figure 1 .

 1 Figure 1. From Ekman 1999. Pictures of the 7 basic emotions. From left to right, Anger, Fear, Disgust, Surprise, Happiness and Sadness.

Figure 2 .

 2 Figure 2. From Schyns et al 2009. Diagnostic information of 6 basic emotions and neutral face.

Figure 3 .

 3 Figure 3. From Haxby et al. 2000. Model of the distributed human neural system for face perception.

Figure 4 .

 4 Figure 4. From Bishop et al. 2004. Influence of state anxiety on the perception of threatening emotions. a) AMG activation increase to fearful vs neutral with anxiety scores b) Attentional modulation decrease with anxiety scores in the perception of threatening emotions. AF=Attended Fear, AN= Attended Neutral, UF=Unattended Fear, UN= Unattended Neutral.

Figure 5 .

 5 Figure 5. From Righart & DeGelder 2008. Example of stimuli: left, disgusted face in a congruent disgusting environment. Right, disgusted face in an incongruent pleasant environment.

Figure 6 From

 6 Figure 6 From Mobbs et al. 2006. Influence of context and neural correlates of the interaction. a) ratings of happy, neutral and fearful faces in positive, neutral and negative context. b) Amygdala activity reflecting the interaction of context with facial expression (Negative context Fear -Neutral) -(Neutral context Fear -Neutral), and betas for fearful faces in positive, negative and neutral contexts.

Figure 7 .

 7 Figure 7. From Vinciarelli, Pantic & Bourlard, 2009. Social cues such as posture, voice and gaze are combined to form a social signal.

Figure 8 .

 8 Figure 8. From Todorov et al. 2015. Classification images of trustworthy and dominant individuals.

Figure 9 .

 9 Figure 9. From Sander et al. 2007. Ratings of Anger and Fear are influenced by gaze direction.

Figure 10 .

 10 Figure 10. From Meeren et al 2005. Body emotion congruency. Left, example of stimuli. Right, increased P100 for incongruent stimuli.

Figure 11 .

 11 Figure 11. Signal detection theory. a) Individual decision as a function of the presence/absence of the signal leading to 4 possible outcomes. b) Representation of the signal and noise distribution.

Figure 12 .

 12 Figure 12. From Gold & Shadlen 2007. Race model (b) in comparison with the drift diffusion model (a).

Figure 13 .

 13 Figure 13. From Freedman et al 2003. Firing of sensory and decision related cells while monkeys categorize cats and dogs stimuli. Here, the ITC cell tracks evidence to dog stimuli. PFC accumulates evidence to dog stimuli until decision.

  tween face sensory signals and house sensory signals. Sensory responses to faces and houses were computed in face selective (FFA) and house selective areas (parahippocampal place area PPA) in ventral temporal cortex. Only the dorso-lateral prefrontal cortex (DLPFC) fulfilled both conditions as the difference between face selective and house selective regions co-varied with the time course in DLPFC and showed greater response to clearer images. A schematic example of the diffusion model taken from this study is shown in Figure 14, with examples of the faces on the left (more or less degraded information), µ corresponds to slope of the evidence accumulation (the sensitivity to visual information), and a and b are the thresholds to

Figure 14 .

 14 Figure 14. From Heekeren et al. 2004. Accumulation of evidence for a clear (blue) and a degraded face (red) toward face response.

Figure 15 .

 15 Figure 15. From Philiastides and Sajda, 2007. Model of decision processes in the brain throughout time.

Figure 16 .

 16 Figure 16. From Kelly & O'Conell 2014 (modified from Kelly & O'Conell 2013). Domain general decision variable tracking leftward and rightward motion. Activity increases with decision evidence.

Figure 17 .

 17 Figure 17. From Donner et al 2009. Effector selective buildup of choice in PM and Motor cortices. a) Time frequency plot showing contralateral minus ipsilateral hemispheres to the hand used for button press. b) Neural sources of the lateralization measure show in a. c) Time course of the lateralization measure in PM and motor regions.

Figure 19 .

 19 Figure 19. From Gee et al. 2014. Pupil responses increase with yes responses against the bias of conservative individuals. a) Correlation between pupil response and individual biases for yes (left panel) and no (right panel) responses. b) Hits, misses, false alarms and correct rejections for the liberal group (left panel) and the conservative group (right panel).

Figure 20 .

 20 Figure 20. From Delange et al. 2013. Expectations bias motor preparation in pre-stimulus interval.

  principal aim of the work conducted in this Ph.D. was to delve further into the depth of contextual influences during the perception of social stimuli. The first conducted study (Experiment 1) did not use model-based analyses, but took advantage of a time-resolved neural recording technique, electroencephalography (EEG), to identify how and when selective attention influences the perception of multi-dimensional social displays. This study explored whether the neural markers of social displays of threat are affected by which social cue the observer is attending (both at the timing and source level). The second and principal study of this work (Experiment 2) builds on the previous by characterizing how different combinations of social cues are decoded in the brain as a function of how much they portray a threat to the observer. More specifically, this study combined a quantitative modelguided approach with neural recordings (EEG) to address the neural and computational mechanisms of the influence of gaze on emotion perception in the context of threat. Experiments 3, 4, 5 and 6 represent behavioral studies that were designed to answer various questions raised by Experiment 2 by exploiting model-guided analyses based on signal detection theory. In brief, we wanted to further characterize how emotion and gaze were combined by dissociating stimuli into low and high spatial frequencies, test the specificity of the effect to a threat context, check whether emotion reciprocally influences gaze perception, and finally extend our findings to the general population. The last experiment (Experiment 7) aimed at manipulating a different contextual factor during emotion perception: past experience with identities.

  Experiment 1: published in Neuroimage, 2015 Selective attention effects on early integration of social signals: same timing, modulated neural sources. Experiment 1: published in Neuroimage, 2015

  ment consisted of stimuli expressing both anger and fear with direct and averted gaze, and the emotional expressions were morphed allowing the use of model-based behavioral and EEG analysis that provide a clearer window into the mechanisms underlying gaze and emotion interactions. Part 2: Gaze direction as a contextual factor during emotion categorization (Experiment 2) In this experiment, we aimed at characterizing the effects underlying the contextual influence of gaze direction on emotion categorization under social threat. Here we took advantage of model-based analyses to distinguish between different accounts of contextual gaze influences, mediated either by a multiplicative change in the sensitivity to the displayed emotion or by an additive change in the decision criterion (bias) toward one or the other emotion. This novel analysis framework allows building

Figure 21

 21 Figure 21. a) Conventional analyses establish pair-wise relations between stimulus, brain and behavior. b) Model-based single trial analyses relate all three sources of variability.

Figure 22

 22 Figure 22 Pre-test of experiment 2: Intensity judgments for angry and fearful stimuli before (a) and after calibration (b). In a, the black line represents the linearized curve based on which morphs of anger and fear were adjusted.

  cies, only threat-signaling emotions were encoded by face-selective neural signals, reflecting a faster processing of emotions signaling an immediate threat to the observer as a function of their associated gaze. More strikingly, gaze direction also modulated the encoding of emotional expressions in effector-selective regions, in parallel with the effects observed in ventral face-selective areas: only threat-signaling emotions were encoded in response preparation signals overlying human motor cortex at 200 ms following face presentation. Recent work sheds light on the adaptive function of this early representation of threat signals in motor cortex. Disrupting this motor representation using TMS impairs the facial recognition of negative (i.e., potentially threatening) emotions, not positive

Figure 1 .

 1 Figure 1. Stimuli and experimental procedure. (a) Examples of morphed expressions for one identity: morphs from neutral to intense fearful/angry expressions providing evidence for one or the other emotion. Stimuli displayed either an averted or a direct gaze. THREAT+ conditions (in orange) correspond to combinations of gaze and emotion that signal higher threat for the observer as compared to THREAT-conditions (in green). (b) Following fixation, a facial expression appeared for 250 ms, after which the participant had to indicate whether the face expressed anger or fear within 2 seconds. No feedback was provided after response.

Figure 2 .

 2 Figure 2. Enhanced recognition accuracy and perceptual sensitivity to threatsignaling emotions. (a) Proportion of correct responses for (from left to right) averted/anger, direct/anger, averted/fear and direct/fear. THREAT+ combinations of gaze and emotion (in orange) were associated with increased recognition accuracy. (b) Psychometric function representing the proportion of 'anger' responses as a function of the evidence for anger (proportion morph, 0 for neutral, negative towards fear, and positive towards anger) for THREAT+ (orange) and THREAT-(green) combinations of gaze and emotion. Dots and attached error bars indicate the human data (mean ± s.e.m.). Lines and shaded error bars indicate the predictions of the best-fitting model. (c) Parameter estimate for the slope of the psychometric curve (corresponding to emotion sensitivity) for THREAT+ and THREATcombinations. **: p < 0.01, ***: p < 0.001.

Figure 3 .

 3 Figure 3. Enhanced neural encoding of threat-signaling emotions. (a) Middle panel: scalp topography of neural encoding at 280 ms, corresponding to its first peak of the encoding of emotion strength averaged across conditions, and expressed as mean parameter estimates in arbitrary units (a.u.). Dots indicate electrodes of interest where neural encoding was maximal. Left and right panels: encoding time course for THREAT+ and THREAT-conditions at electrodes of interest. Shaded error bars indicate s.e.m. Thick orange and green lines indicate significance against zero at a cluster-corrected p-value of 0.05. Shaded grey areas indicate significant differences between THREAT+ and THREAT-conditions at p < 0.05. (b) Same conventions as (a) at the second neural encoding peak at 500 ms. (c) Same conventions as (a) at the third neural encoding peak at response time. (e) Estimated cortical

Figure 4 .

 4 Figure 4. Absence of threat-dependent enhancement of neural-choice correlations. (a) Middle panel: scalp topography of neural-choice correlations, expressed as the modulation of perceptual sensitivity by EEG encoding residuals at 280 ms, same time point shown in Fig. 3a. Electrodes of interest indicated with dots are the same as in Fig.3a. Left and right panels, time course of the modulation of perceptual sensitivity by EEG encoding residuals expressed in arbitrary units (a.u.). Same conventions as in Fig. 3a. (b) Same conventions as (a) at 500 ms. (c) Same conventions as (a) at response time. The variation of the modulation strength over time is consistent with the variation of the encoding parameter estimate. No difference between THREAT+ and THREAT-is observed.

Figure 5 .

 5 Figure 5. Encoding of threat-signaling emotions in motor response lateralization measures. (a) Top panel, scalp topography before response of the time frequency power in the 8-32 Hz band in the last 100 ms before response, for the trials where subjects responded with their left hand minus the trials where they responded with their right hand. Dots correspond to the selected electrodes, where the effect was maximal. Bottom panel: corresponding neural sources. (b) Time course of response lateralization (time frequency power activity from the contralateral electrodes minus ipsilateral electrodes to the hand used to respond 'fear') towards anger and fear when the choice was anger (red) or fear (blue). Shaded error bars indicate s.e.m. The shaded gray area indicates a significant difference in motor lateralization between Anger and Fear responses. (c) Encoding of emotion strength in response lateralization index for THREAT+ (orange) and THREAT-(green) conditions. Differences between conditions are observed at 200 ms after stimulus onset (stimulus-locked, upper panel) and at response time (response-locked, lower panel). Conventions are the same as in Fig.3 (d) Time course of neural-choice correlations, expressed as the modulation of additive bias by motor lateralization encoding residuals in arbitrary units (a.u.) stimulus-locked (upper panel) and response locked(lower panel). Conventions are the same as in Fig.3.

Figure 6 .

 6 Figure 6. Encoding of emotion strength as a function of reaction times (RT) in motor and parietal structures. (a) Neural encoding of emotion strength for THREAT+ conditions in motor lateralization for fast and slow reaction times (RT): when RTs were fast, the encoding of emotion strength became significant at 150 ms and rose gradually until response; by contrast, when RTs were slow, the encoding of emotion strength became significant later at 540 ms. Shaded error bars indicate s.e.m. Thick dark and light grey lines indicate significance against zero at a cluster-corrected p-value of 0.05. Shaded grey bars indicate significant differences between fast and slow responses. Encoding latency is significantly different between fast and slow RTs ***p<0.001 (b) Emotion strength encoding in parietal electrodes. Convention are the same than (a). Fast responses are associated with a stronger neural encoding of emotion strength, but without any change in encoding latency.

Figure 7 .

 7 Figure 7. Modulation of threat encoding by individual anxiety. (a) Left panel: correlation between state anxiety and the difference of the encoding parameter estimates between THREAT+ and THREAT-conditions in temporal electrodes between 200 and 400 ms. Right panel: encoding parameter estimates in temporal electrodes split into high and low anxious individuals for both THREAT+ and THREAT-conditions between 200 and 400 ms. T+: THREAT+, T-: THREAT-. (b) Left panel: correlation between state anxiety and the encoding parameter estimates in motor lateralization signals for THREAT+ condition at 200 ms. Right panel: encoding parameter estimates in motor lateralization signals split into high and low anxious individuals for both THREAT+ and THREAT-conditions at 200 ms. ***: p<0.001, *: p<0.05.

Figure 23

 23 Figure 23 Example of stimuli used in Experiment 3. Left, averted gaze, Right, direct gaze. BSF=Broad Spatial Frequency, HSF=High Spatial Frequency, LSF=Low Spatial Frequency

Figure 24 .

 24 Figure 24. Results of Experiment 3. a) Accuracy for the four conditions of interest in HSF group, from left to right, anger averted, anger direct, fear averted, fear direct. b) same as a, for LSF group. c) psychometric fits of model 3 that allowed an effect on both sensitivity and bias in HSF group. Differences between the fits for direct and averted gaze are plotted in grey and show that the effect of gaze is maximal for ambiguous stimuli consistently with an effect on the sensitivity. d) same as c for LSF group. Here the different between fits for direct and averted gaze shows a difference on the neutral stimuli, consistently with an effect on the decision bias. e) left, emotion sensitivity for THREAT + (orange) and THREAT -(green) conditions; right, decision bias for direct (dark grey) and averted gaze (light grey). f) fixed-effects model comparison between 1)HSF and LSF model #2, 2) HSF and LSF model #1, 3) HSF model #1 and LSF model #2, 4) HSF model #2 and LSF model #1. When subtracting the log likelihood for model #0, the best model assigns a change in sensitivity for HSF group and a change in bias for LSF group. g) same as e for LSF group.

Figure 25

 25 Figure 25 Pre-test results of Experiment 4. Judged intensity on happy and angry faces. The green curve represents happy stimuli and the red curve represents angry stimuli.

Figure 26 .

 26 Figure 26. Examples of stimuli used in Experiment 4. Only 3 levels of morphs are shown for the example.

Figure 27 .

 27 Figure 27. Results of Experiment 4. a) Accuracy for the four conditions of interest, from left to right, averted anger, direct anger, averted happiness, direct happiness. b) Psychometric fit when considering a model where all parameters are free (4 sensitivities for the four conditions and 2 biases for direct and averted gaze): no difference is observed between conditions. c) Estimated sensitivity parameters of the winning model (model 1): increased sensitivity to anger. d) Estimated bias parameter of model 1: bias toward anger. *p<0.05, p<0.001

Figure 28 .

 28 Figure 28. Examples of stimuli for Experiment 4. Gaze is morphed from direct to averted for the 3 types of emotions.

Figure 29 .

 29 Figure 29. Results of Experiment 5. a) Accuracy for all 6 conditions, from left to right, averted gaze anger (red), fear (blue) and neutral (grey), direct gaze anger fear and neutral. b) Psychometric function representing the proportion of 'averted' responses as a function of the evidence for averted gaze, for anger (red), fear (blue) and neutral (black). Dots and attached error bars indicate the human data (mean ± s.e.m.). Lines and shaded error bars indicate the predictions of the model. c) Parameter estimate of the decision bias in anger, fear and neutral conditions. d) Parameter estimate of the slope of the psychometric curve (corresponding to gaze sensitivity) for anger, fear and neutral. ns: non-significant, *:p < 0.05, **: p < 0.01, ***: p < 0.001.

Figure 30 .

 30 Figure 30. Results from Experiment 6. a) Accuracy in the 4 conditions, from left to right, anger averted, anger direct, fear averted and fear direct. b) Model comparison between model 1 (effect on decision bias), model 2 (effect on sensitivity) and model 3 (effect on both sensitivity and bias). c) Emotion sensitivity parameters for THREAT + and THREAT -conditions extracted from model 2. d) Decision bias parameter extracted from model 2.

Figure 1 .

 1 Figure 1. Stimuli and experimental procedure. (a) Left panel, the first 48 trials of each block consisted of identities expressing either anger or fear at the most intense level of emotion, in the purpose of creating expectations about the emotions of individual identities. Right panel, an equal number of morphed anger and fear expressions were presented in the other 112 trials of the block, examples are shown for one identity: morphs from neutral to intense fearful/angry expressions providing evidence for one or the other emotion. (b) Following fixation, a facial expression appeared for 250 ms, after which the participant had to indicate whether the face expressed anger or fear within 2 seconds. No feedback was provided after response. (c) At the end of the experiment, participants performed a post-test in which they rated on a scale whether they think each neutral identity (that appeared for 2 sec) expressed more anger or fear during the experiment.

Figure 2 Figure 2 .

 22 Figure 2

Figure 3 Figure 3 .

 33 Figure 3

Figure 4 Figure 4 .

 44 Figure 4

  work conducted in this Ph.D., including 7 experiments in healthy humans, aimed at characterizing contextual influences during social perceptual decisions. Experiments 1 through 6 modulated within-emitter contextual factors including gaze direction, posture, emotion and spatial frequencies of the face. The goal of the observer was manipulated in Experiment 1 and Experiment 5 by changing the focus of attention of observers with tasks requiring to process either emotion or gaze cues. Experiments 2 and 6 manipulated elements characteristic of the observer including anxiety, age and social backgrounds. Finally, Experiment 7 manipulated observer's experience in the environment, by changing individual encounters with identities that expressed more or less one emotion over the other.These contextual manipulations led to the first conclusion that multiple mechanisms underlie the processing of social displays, in this case emotional expressions (Figure31). Social cues including emotion, gaze and gesture are represented simultaneously and interactively in the brain in sensory, motor and emotion-related regions. Within-emitter contextual factors thus interact and boost the bottom-up processing of combined signals relevant to the observer (e.g., signalling threat).Importantly, this interaction was found to be independent of top-down attention. Selective attention rather modulates the relative contribution of brain regions to this interaction relevant to the task at hand. Individual anxiety similarly influences the neural sources of emotion and gaze interaction by selectively increasing the involvement of action preparation regions during threat perception. Finally, prior experience enacts as a distinct top-down factor biasing responses toward expected interpretations and increasing neural activity to effortful decisions towards non-default interpretations. The use of a model-based framework to analyse the behavioural and neural data recorded during this Ph.D. provided a compelling tool to characterize and clarify the mechanisms underlying contextual influences on social perceptual decisions. It allowed dissociating the effects of co-emitted social cues in threat contexts with topdown influences of prior experience. Behavioral modelling also provided a way to answer follow-up questions concerning the enhanced processing of threat signals, by showing its specificity, reciprocity and generalizability. Importantly, model-based neural analysis further clarified the precise mechanisms by which gaze direction tunes emotion processing and allowed to dissociate the functional roles of sensory and motor response enhancements to threat in relation to anxiety.

Figure 31 .

 31 Figure 31. Summary of the main findings. Gaze direction, an inherent cue to facial expression, influenced emotion recognition by increasing the perceptual encoding of observer-relevant signals in threat contexts. Task-related attention did not modify the timing of the representation of observer-relevant signals, but changed the relative contribution of feature-relevant regions during stimulus processing. Individual anxiety similarly changed the brain regions where observer-relevant threat signals were encoded. Finally, expectations about emotion/identity associations biased the decision criterion toward congruent associations. Both sensory face-selective regions (STS: Superior temporal sulcus, FG: Fusiform gyrus) and motor related regions (PM: Premotor, M: motor) were involved in the processing of contextually threatening emotions. Whether the amygdala (AMG) drives these effects remains an open question.

  of ventral and dorsal pathways within 200 ms during the processing of threat signals stems from an increased sensitivity to these signals. Interestingly, a double dissociation between the effects in temporal ventral structures and dorsal motor structures as a function of anxiety was observed. While low-anxious individuals selectively encoded observer-relevant threat signals in temporal regions, high-anxious individuals did so in the motor cortex. Thus the involvement of motor related regions was modulated by individual anxiety of participants. The more participants were anxious, the more they engaged their motor cortex in the representation of threat related signals at 200

  , would have been expected. If this was the case, it would clearly help dissociating such congruency effects from effects related to the significance of the stimulus to the observer. The effect related to significance to the observer induced by the manipulation of the gaze direction of emotional expression indeed did not show modulations of the P300 as a function of gaze (see Part 2, Experiment 2). In any case, additional neuroimaging experiments manipulating both congruency and observer-relevant information should be conducted to affirmatively state that these types of contextual elements, both facilitating behavioral performance, are different at the level of the brain. Nevertheless, at the behavioral level, it is possible to dissociate increased performance related to a change in sensitivity -i.e., better discriminability of emotions, from a change related to a shift in individual decision bias -i.e., participants idiosyncratic choices. If contextual effects related to congruency differ from those related to the significance of the social signal to the observer (a change in the decision bias vs. a change in the sensitivity to the emotion), it would provide a clue into the mechanisms of these two types of effects.Identity-specific expectations bias responses rather than change the sensitivity to congruent emotion-identity associationsExperiment 7 addressed in part the issue raised above: do contextual elements related to congruence similarly influence emotion categorization than those related to the relevance to the observer? In this experiment, we altered participants' emotional experience with individual identities (by adding initial blocks where half of the identi-ties express one emotion or the other) to test whether and how this would affect subsequent emotion categorization. This manipulation created congruent conditions, i.e. identities expressing the emotion they were associated with in the biased blocks, and incongruent conditions, i.e. identities expressing the opposite emotion to the one they were associated with. Emotional experience with individual identities biased participants' behaviors toward congruent responses, without affecting the sensitivity to congruent displays of identity and emotion. Past experience with identities thus distinctively influenced emotion categorization in comparison with effects of gaze direction.

(

  emotional expression) allowed demonstrating the context-specific involvement of these motor preparation signals at the same timing of the sensory encoding of stimulus information. Furthermore, the social aspect of the stimuli we used allowed to show a functional dissociation of sensory and motor regions as a function of individual characteristics, here anxiety. While high-anxious individuals encode negative emotions indifferently in sensory face-selective regions, their motor cortex activates at early latencies only in response to threat-signaling emotions. The early and concurrent involvement of motor and sensory regions draws important constraints on traditional theories of decision-making by showing parallel and functionally dissociable, rather than successive and co-dependent, responses to social threats in sensory and motor pathways.

  work conducted in this Ph.D. reveals distinctive cognitive mechanisms by which contextual factors guided perceptual decisions on emotional expressions. First, our findings highlight how decisions on multi-dimensional social stimuli are influenced through increased fast bottom-up processing of displays relevant to the observer. Gaze direction indeed tunes the processing of emotional expressions of anger and fear by increasing the sensitivity to signals of threat, direct anger and averted fear, within 200 ms in sensory and motor pathways. The enhanced sensitivity to direct anger and averted fear is mainly transmitted through high spatial frequencies, stressing the role of refined facial features in the better perception of threat signals. Moreover, the influence of gaze direction on emotion categorization is specific to anger and fear emotions, as choosing between happiness and anger shifted the relevant aspect to the observer to threat (anger) by generally increasing the sensitivity to anger independently of gaze. The enhanced sensitivity to threat signals, and the timing of their representation, is unchanged during emotion categorization and gaze categorization, demonstrating its independence from task related attentional demands. Second, individual characteristics of participants influence the early (200 ms) processing of contextually threatening emotions of anger and fear: while highanxious individuals selectively show enhanced sensitivity to threat-signaling emotions in motor cortex, the enhancement observed in face-selective temporal cortex is only driven by low-anxious individuals. This result dissociates the functions of sensory and motor systems in relation to anxiety, by pinpointing the adaptive role of anxiety in reacting rapidly to social threats, thereby recruiting the motor cortex in the early encoding of threat signals. Complementing the results described above, it sheds light on the fact that the behavioral and biological relevance of the stimuli are inherent to the emotion recognition process at an early stage of processing.

Participants Stimuli Task Face duration Behavioral effect Neural basis

	Adams&Kleck 2003 N'Diaye et al. 2009 Cristinzio et al. 2010 Sato et al. 2010 Healthy adults Healthy adults Healthy adults patients with amygdala damage and healthy controls Lobmaier et al. 2008 Healthy adults	Study 1: A/F, protoypical and ambiguous; study 2: H/S, prototypical. Both studies: Gaze D/Av A/F/H mild and intense, Gaze D/Av A/F/H, gaze D/Av H/A Dynamic and static, Gaze D/Av A/F/H, gaze D/Av	1) Categorization A/F; 2) categorization H/S Rating of emotional intensity 7 scales intensity than the 6 emotions Gender categorisation task during fMRI; post test: valence and arousal rating Gaze direction judgement: Face looking at you or not?	until response Gaze shift: 0.1 s/ dynamic emotion 0.6s 2 s 1.5 s 1 s	1) Emotion*gaze interaction on RT: faster for Emotion*gaze interaction: AD judged more intense than AAv , opposite for fear Emo* gaze interaction for controls only: Emotion scales: more A for D, more F for Av -Intensity scale A more intense with D, F with Av protoypical AD and FAv-More F labels than A for Av on ambiguous expressions; 2) interaction on RT: faster for HD and SAv Greater arousal rating for direct gaze under dynamic and not static, no effect on gender More answers "looking at you" for H and A	AD + FAv > Aav + FD: AMG, FG, mPFC only for mild intensities Right AMG damage impairs gaze emotion interactions on behavior _ AD + HD > Aav + Hav: left AMG only in dynamic stimuli _
	Adams et al. 2005 Healthy adults Adams et al. Healthy adults Ewbank et al. 2009 Healthy adults Sander et al. 2007 Healthy adults Klucharev & Sams 2004 Healthy adults Adams&Franklin 2009 Healthy adults Perett et al 2011 Healthy adults	Photographs: Study 1: Neutral faces; Study 2: A/F, prototypical and ambiguous; Study 3: A/F/S/H; All studies: Gaze D/Av Fear with D/Av gaze A/F, gaze D/Av Dynamic shematic faces controlled for AU: H/F/A, Gaze D/Av H/A, gaze D/Av A/F, gaze D/Av H/ F/ A, gaze D/Av, eyes and no eyes condition	1) Rating of neutral faces on scales of A/F/S/H ; 2) choose emotion label between the 4, then intensity rating; 3) same as study 1-2 faces presented one with D and one with Av gaze. Passive viewing during fMRI Gaze judgement: face looking left right or directly at them Rating on 7 scales: general intensity + 6 basic emotions Detect repetition of gaze during EEG Gaze judgment: direct or averted gaze Gaze judgement: Attending to you or not ?	until 0.3 or 1 sec 0.2 s response 1.1 s 0.3 s until response 0.15 s	1) Emotion*gaze interaction: H+A higher rating for D, F+S for Av; 2) more labels A and higher intensity rating of A for D /same for for FAv; 3) _ Tendency to judge A expressions as looking at the observer interaction, ratings higher for H+A in D gaze, and for F+S in Av. Interaction emo*gaze on accuracy and RT, quicker FAv/ AD-correlation with the main effect of gaze (faster for Av than D) _ Emotion scales: more A for D, more F for Av -Intensity scale A more intense with D, F with Av More answers attending to you for H expressions, even in the no eyes condition	_ Fav>FD left AMG when 0.3 s ; FD>Fav _ left AMG when 1 sec AD+Hav > Aav + HD: interaction at 270-_ 450 ms in parietal Adults: _ electrodes _
	Hess&Adams 2007 Rigato et al. Calder et al. 2012	Healthy adults Emotions H/S/F/A, Gaze D/Av Healthy adults and infants Neutral/ F/H, gaze D/Av Healthy adults and 8 years old A/F, gaze D/Av	1) Rating on 6 basic emotion scales; 2) rating of emotional reactions: cheerful irritated anxious miserable surprised and repulsed Passive viewing of cartoons, with sounds to keep attention-during EEG Gaze judgement: face looking left right or directly at them	until response 1 s 0.4 s	1) D gaze drives better recognition, opposite pattern for F, 2) anxiousness and feeling of repulsion higher for AD vs AAv, for F only increased for Sav feeling repulsed higher, anxious feeling _ Tendency to judge A expressions as looking at both adults and children observers	HD+FAv>HAv+FD frontocentral Nc HD>HA>A _ interaction on occ channels around 250 ms; infants: _
	Adams et al. 2003 Healthy adults Emotions A/F/H, Gaze D/Av Sato et al 2004 Healthy adults A/N, Head and gaze Av or D Akechi et al. Typically devoloping and autistic children (9-17 years) A/F, gaze D/Av Conty et al. 2012 Healthy adults A, gaze and head D/Av, Pointing gesture or not	Gender categorization Gender categorization during fMRI; post test: intensity of experienced emotion and Categorization anger/ fear during EEG Gaze judgement: is the person adressing you or another	2 s 0.7 s 1.2 s 1.3 s	_ more negative feeelings, no effect on gender Interaction face direction*emotion for experienced emotion only, anger direct elicits emo gaze interaction for typical only with better performance anger D and fear A Only behavioral pretest : more self involvement for anger direct pointing	Aav + FD > AD + FAv : Left AMG correlation with AD>Aav : left amygdala + Interaction AD + FAv > Aav + emotion*gaze*gestur e at 200 ms in PM, AD pointing gesure > FD: N170 for typical all other conditions
	recognized emotion Passive viewing Table 1: Main findings of studies that investigated emotion by gaze interactions. N/A:Non Applicable. A:Anger, F:Fear, S:Sad task experienced emotion Hoehl & Striano 2008 7 months infants A/F, gaze D/Av 1 s _ AD>Aav: Nc -F>A : N290 P400: A>F
	Fav>FD: STS, IPS , H:Happy, D:Direct, Av:Averted. AMG=Amygdala, STS: Superior temporal sulcus, IPS: Inferior temporal sulcus , FG: Fusiform gy-Hadjikani et al. 2008 Healthy adults F/Neutral, Gaze D/Av Passive viewing 0.3 s _ FG, OFA , AMG, rus, OFA: occipital face area , HYP: hypothalamus, Pal: Pallidum, PM: Premotor, SPL: Superior parietal lobule. occ:occipital, Nc: HYP, Pal, PM, SPL EEG component fronto-central negativity.

Acknowledgments

Acknowledgements: This work was supported by grants from the French National Research Agency ANR-11-EMCO-00902, ANR-11-0001-02 PSL*, ANR-10-LABX-0087 and by INSERM. We wish to thank Laurent Hugueville for his useful technical help.

To my parents

Abbreviations

sustains decision-making and information processing (Nieuwenhuis et al., 2005).

More specifically, the phasic activity of LC is linked to the P300, both reflecting the performance and the engagement of subjects performing a task. This framework therefore suggests that a possible measure of LC-NE activity is the P300 recorded at the scalp of the brain. Another physiological measure that directly reflects LC-NE activity is pupil dilation as it highly reflects the rate of LC neurons activity (Figure 18). Based on this relation between pupil dilation and LC-NE activity, several studies in humans measure pupil dilation using eye-tracking during behavioral tasks. Pupil dilation is associated with visual detection [START_REF] Privitera | Pupil dilation during visual target detection[END_REF], encodes decision information (e.g [START_REF] Einhäuser | Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry[END_REF][START_REF] Cheadle | Adaptive gain control during human perceptual choice[END_REF] and is increased to uncertain/surprising events [START_REF] Preuschoff | Pupil dilation signals surprise: evidence for noradrenaline's role in decision making[END_REF] and effort (Porter et al., 2007).

In a recent study with a signal detection task (visual target with noise present or absent), pupil dilation was shown to reflect choices throughout the decision process and to track participants' individual biases (Gee et al., 2014). Pupil dilation was stronger before yes responses (signal present) than before no responses, and this increase correlated with individual criterions (calculated based on SDT). The more participants were conservative (bias toward no responses), the more their pupils dilated when they responded "yes" against their bias (Figure 19). This finding was interpreted as possibly resulting from a surprise signal (yes responses being more infrequent in conservative individuals and therefore more surprising). Further, the authors interpreted this finding within the race model in decision-making, where two Experiment 2: paper under review

Anxiety dissociates the adaptive functions of sensory and motor response enhancements to social threats

Results on performance show the same pattern as in Experiment 2, with increased accuracy for combinations of gaze and emotion signaling higher threat (THREAT +, direct gaze with anger, averted gaze with fear), whereas accuracy for neutral seems to be at an intermediate level between both. This replicates reciprocity effects observed in the literature on morphed stimuli of gaze direction [START_REF] Lobmaier | Emotional expression modulates perceived gaze direction[END_REF][START_REF] Adams | Influence of emotional expression on the processing of gaze direction[END_REF][START_REF] Ewbank | Why are you angry with me? Facial expressions of threat influence perception of gaze direction[END_REF]. Next we wanted to check whether, just like gaze direction increased the sensitivity to combinations of emotion and gaze signaling higher threat in Experiments 2 and 3, emotion increases the sensitivity to gaze direction when displaying these combinations.

Model selection

We started with the simplest model (model 0) that could account for subject's decisions using a psychometric model such as:

where P(Averted) corresponds to the probability of judging the gaze as averted, Ф to the cumulative normal function, w to the perceptual sensitivity to the display gaze, x to the evidence in favor of averted gaze in each trial (from 0 for a direct gaze to +8 for the most averted gaze), and b to an additive, stimulus-independent bias toward one of the two responses/gaze directions. As effects of bias were already visible on the model-free responses of participants (Figure 29a), we chose to consider a model that consisted of 6 free parameters: 3 decision biases and 3 gaze direction sensitivity for the 3 emotions (Anger, Fear, Neutral). This allowed checking whether, in addition to the clear differences on the decision biases between the three emotions, emotion also increased gaze direction sensitivity to combinations signaling higher threat.

The parameters extracted first confirmed that there was a difference in decision biases for the 3 emotions, with differences between anger and fear (T 21 =-8.2, p<0.001), anger and neutral (T 21 =-2.5, p=0.01), and fear and neutral (T 21 =-3.2, p<0.01) (Figure 29c). Importantly, differences were observed on sensitivity parameters between anger and the two other emotions: neutral (T 21 =-4.2, p<0.001) and fear A general mechanism for emotion categorization biases shaped by associative identity-specific expectations Marwa El Zein 1,2,* , Valentin Wyart 1, ‡ , and Julie Grèzes

Abstract

Expectations in perceptual decision-making influence neural activity and behavior by facilitating the processing and recognition of expected stimuli. During social interactions, most of these expectations are based on past experience. In particular, expectations about others' characters and moods build upon previous encounters, but the mechanisms underlying their acquisition and expression in human behavior remain unclear. Here we show that experimentally induced associations between emotion and identity create expectations which influence emotion categorization: identities which were initially associated with a particular emotion were later more likely to be categorized as expressing that emotion. Interestingly, while this effect was apparent in categorization judgments but not reported explicitly by participants in a subsequent recall test, incidental expectations emerged simultaneously based on idiosyncratic (false) associations. Computational modeling demonstrated that both induced and incidental expectations shift participants' decision criterion about facial expressions of emotion. These effects were reflected in peri-decision pupillary responses which increased when participants responded against their biases about consolidated emotion-identity associations. Pupil dilation showed similar increases when responses were made against participants' intrinsic biases -i.e., their tendency to respond anger or fear independently of identity. Pre-decision neural activity was modulated accordingly, in the form of increased centro-parietal EEG signals when subsequent responses were made against intrinsic biases. Together, these findings indicate that past experience -in the form of induced and incidental emotion-identity associations as well as intrinsic biases, shape the perception of others' emotions.

Pupil-linked neuromodulatory systems express these multiple expectations through increased responses to effortful decisions against existing associations and biases.

Methods

Subjects

Thirty eight healthy subjects (19 females; mean age, 22.3 ± 0.5 years) participated in the experiment. All participants were right-handed, with a normal vision and had no neurological or psychiatric history. They provided written informed consent according to institutional guidelines of the local research ethics committee (Declaration of Helsinki) and were paid for their participation. Seven subjects (4 females) were excluded from analysis, one because of low performances on the task, and the six others because of loss of pupillometry responses due to excessive blinks or movements (more than 40% of the experiment).

Stimuli

Stimuli consisted of 32 identities (16 females) adapted from the RadBoud Faces Database (Langner et al., 2010) that varied in emotion (neutral, angry or fearful expressions) and gaze direction (direct toward the participant or averted 45° to the left or right). The intensity of emotional expressions varied with 7 levels of Anger and 7 levels of Fear equalized in perceived emotional intensities and a neutral condition (see figure 1.a for examples of stimuli). A complete description of the stimuli is provided in El Zein et al, under review.

Experimental procedure

The task was also similar to the previous study (El Zein et al. under review) and consisted in categorizing the faces as fearful or angry. Using the Psychophysics-3 Toolbox (Brainard, 1997;Pelli, 1997), faces appeared for 250 ms on a black screen, after which subjects had to give their response by pressing one of the two 'control' buttons localized on the keyboard with their right or left index (a maximum of 2 seconds to respond before the next trial).

To manipulate the expectation of participants about identity/emotion associations, the 48 first trials of each bloc consisted of biased trials: half of the identities expressed only anger (identities belonging to Anger group), and the other half ex-'...invariant properties whose detection serves important adaptive actions, such as the transformational invariant fighting or the affordance danger, may be so strongly manifested in the stimulus information [START_REF] Mcarthur | Toward an ecological theory of social perception[END_REF] Sensory and motor regions in emotion processing: functional roles?

Our findings demonstrate the involvement of both sensory and motor-related regions in the processing of emotional expressions modulated by contextual gaze direction at early latencies, within 200 ms after stimulus onset. The quality of representation of emotions signaling threat to the observer was indeed enhanced in these regions.

What are the functional roles of these two sets of regions in relation to the processing of contextually threatening emotions?

Common spatiotemporal characteristics of sensory activations during the processing of faces and observer-relevant signals from faces.

The fusiform gyrus (FG) and superior temporal sulcus (STS) were responsible of combining gaze and emotion cues at 170 ms in both Experiments 1 and 2. These brain regions [START_REF] Kanwisher | The fusiform face area: a module in human extrastriate cortex specialized for face perception[END_REF][START_REF] Puce | Temporal cortex activation in humans viewing eye and mouth movements[END_REF][START_REF] George | Contrast polarity and face recognition in the human fusiform gyrus[END_REF], as well as the evoked potential N170 [START_REF] George | Electrophysiological correlates of facial decision: insights from upright and upside-down Mooney-face perception[END_REF], have been repeatedly associated with face processing. Moreover, studies investigating the source of this N170 reported either the FG [START_REF] Itier | Inversion and contrast polarity reversal affect both encoding and recognition processes of unfamiliar faces: a repetition study using ERPs[END_REF][START_REF] Rossion | Early lateralization and orientation tuning for face, word, and object processing in the visual cortex[END_REF][START_REF] Watanabe | The spatiotemporal dynamics of the face inversion effect: A magneto-and electro-encephalographic study[END_REF] or the STS [START_REF] Batty | Early processing of the six basic facial emotional expressions[END_REF][START_REF] Henson | Electrophysiological and haemodynamic correlates of face perception, recognition and priming[END_REF][START_REF] Itier | Source analysis of the N170 to faces and objects[END_REF][START_REF] Itier | Face, eye and object early processing: what is the face specificity?[END_REF][START_REF] Watanabe | Human MT/V5 activity on viewing eye gaze changes in others: A magnetoencephalographic study[END_REF] as responsible of N170 generation.

Together, these findings thus suggest that social cues are combined into a single percept as a function of their relevance to the observer in threat contexts at the same spatiotemporal characteristics of general face processing, i.e., 170 ms in temporal regions involving STS and FG. This suggests that contextual within-emitter cues influence emotion processing at the basic level of facial sensory representations, modifying the very first step of the formation of social perceptual decisions, i.e., the perceptual 'encoding' of the facial expression.