My title - Relais d`information sur les sciences de la cognition
Transcription
My title - Relais d`information sur les sciences de la cognition
THESE DE DOCTORAT DE L’UNIVERSITE PIERRE ET MARIE CURIE Spécialité Sciences cognitives Ecole doctorale Cerveau Cognition Comportement Présentée par Guillaume Dezecache Pour obtenir le grade de DOCTEUR de l’UNIVERSITÉ PIERRE ET MARIE CURIE Studies on emotional propagation in humans : the cases of fear and joy Soutenue publiquement le 17 décembre 2013 devant le jury composé de : Pr. Natalie SEBANZ, Rapporteure Dr. Daniel HAUN, Rapporteur Pr. Robin DUNBAR, Examinateur Dr. Mathias PESSIGLIONE, Examinateur Pr. Dan SPERBER, Examinateur Dr. Didier BAZALGETTE, Examinateur Dr. Pierre JACOB, Directeur de thèse Dr. Julie GREZES, Directrice de thèse 1 Contents Abstract 8 Résumé 9 Foreword 11 Chapter One: The crowd in 19th and 20th century early social psychology 15 The seven key-characteristics of crowd behavior . . . . . . . . . . . . . . . . . 16 How are mental and emotional homogeneity achieved within crowds? . . . . . 17 The concept of contagion in early crowd psychology, and its epistemological consequences for our current understanding of the process of emotional transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Chapter Two: How can emotions of fear and joy propagate in crowds? 23 How can emotions become collective? . . . . . . . . . . . . . . . . . . . . . . . 23 What we know from the study of dyadic interactions . . . . . . . . . . 23 Can emotional transmission go transitive? . . . . . . . . . . . . . . . . 26 Evidence for unintentional emotional contagion beyond dyads (Dezecache et al., 2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 What psychological mechanisms are at work? . . . . . . . . . . . . . . . . . . 34 “Social comparison” models . . . . . . . . . . . . . . . . . . . . . . . . 34 “Conditioning” models . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 The “primitive emotional contagion” model . . . . . . . . . . . . . . . 35 2 Emotional contagion as emotional communication . . . . . . . . . . . . 36 How do we react to others’ emotional displays? . . . . . . . . . . . . . 37 How do shared-representations and emotional processes cooperate in response to social threat signals? (Grèzes & Dezecache, in press) . . . . . . . . . 37 Is emotional transmission equivalent to contagion? . . . . . . . . . . . . . . . 48 Emotional transmission as a process of influencing others . . . . . . . 49 Emotional transmission 6= contagion . . . . . . . . . . . . . . . . . . . 50 An evolutionary perspective on emotional communication (Dezecache, Mercier & Scott-Phillips, 2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Summary of this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Chapter Three: Why do emotions of fear and joy propagate in crowds? 65 Why should we expect emotions to spread in crowds? . . . . . . . . . . . . . . 65 Information acquisition and sharing at the basis of emotional crowds . 65 Humans spontaneously compensate others’ informational needs in threatening contexts (Dezecache et al., in preparation) . . . . . . . . . . . . . . . . 67 Summary of this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Chapter Four: General discussion 83 Summary of the main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Emotions of fear and joy can be transmitted beyond dyads . . . . . . . . . . . 83 Emotional transmission as a process of influencing others . . . . . . . . . . . . 84 Emotional transmission is sensitive to others’ informational needs . . . . . . . 86 Beyond audience effects: how others’ mental states can influence transmission of emotional information behavior . . . . . . . . . . . . . . . . . . . . . . . . . 87 Emotional transmission is not contagion . . . . . . . . . . . . . . . . . . . . . 89 Epilogue: Emotional transmission beyond triads 91 Where traditional views might have gone wrong . . . . . . . . . . . . . . . . . 92 Revising the key-characteristics of crowd behavior . . . . . . . . . . . 92 Are crowd members suggestible? . . . . . . . . . . . . . . . . . . . . . 95 3 The “myth” of crowd panics . . . . . . . . . . . . . . . . . . . . . . . . 97 Why don’t crowds panic? Tentative explanations . . . . . . . . . . . . . . . . 99 Emotional crowd behavior is regulated by emerging social norms . . . 99 Modern crowd psychologists face serious methodological issues . . . . . 101 Different levels of analysis at the source of the dilemma . . . . . . . . 103 Natural reactions to threat: affiliative tendencies vs. self-preservative responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 General references 106 Appendix 120 4 Acknowledgments Quand j’étais petit, je voulais devenir footballeur, et je voudrais remercier la foule de gens qui m’ont, consciemment ou inconsciemment, empêché de tenter même de poursuivre ce grand rêve. Ils m’ont inéluctablement conduit à la poursuite d’une thèse. Au sein de cette longue liste, il y aurait d’abord mon père et ma mère: m’obliger à faire mes devoirs tous les soirs, ne posséder pendant très longtemps qu’une télé en noir-et-blanc qui empêchait de distinguer les joueurs de l’équipe adverse, et m’emmener à la messe pendant l’heure de Téléfoot ont précipité mes souhaits footballistiques à la grande casse de l’existence. Je ne saurais oublier mes fidèles compagnons calaisiens aux-pieds-carrés qui ont su me faire oublier le drame de ce rêve bafoué : Maxime "Le Duc" Carpentier, François Timmerman, Jean-Baptiste Sodooren, Rémy Mazet, Amaury Decourcelle, Guillaume Tourbeaux, Aurélien Fournier (qui, soit dit en passant, a les pieds un peu plus adroits), Raphaël Foulon... Il y en aurait beaucoup d’autres mais, Dieu soit loué, ils n’auront jamais la curiosité d’ouvrir cette thèse et de regretter de n’y point voir leur nom. Je tiens également à remercier Alexandre Billon : un jour de pluie comme Villeneuve-d’Ascq en connaît beaucoup, il me conseilla la lecture de la Contagion des Idées de Dan Sperber. Cet ouvrage m’a éloigné à tout jamais des terrains de football et des œuvres complètes de Lévinas. De Villeneuve-d’Ascq, il me faudrait aussi remercier Claudio Majolino : s’il ne tenait pas spécialement à ce que je quitte les rangs de la phénoménologie, son peu de sympathie pour les sciences cognitives m’a permis de publier mon premier petit article scientifique. La découverte des travaux de Dan Sperber m’a complètement bouleversé. J’en ai immédiatement jeté mon exemplaire de l’Archéologie du Savoir (si je n’y comprenais pas grand chose, j’y tenais beaucoup). Merci à toi Dan d’avoir été, pendant toute la durée de mon master et de ma thèse, un superviseur attentif et bienveillant. L’équipe du Nash (Olivier Morin, Nicolas Baumard, Coralie Chevallier, Hugo Mercier, Olivier Mascaro, Jean-Baptiste André, Anikó Sebestény, Hugo Viciana, Nicolas Claidière) ont énormément apporté à ce travail de thèse : puisqu’ils ont tous au moins cinq ans d’avance mentale sur moi, ils m’ont aidé à éviter certaines erreurs de parcours. 5 Sur le chemin de la rue d’Ulm, j’ai eu la grande chance de tomber sur Laurence Conty. Sa croisade contre les interminables blablas et pour les approches expérimentales m’a contaminé. Je lui en suis cent fois gré. Je dois également la remercier de m’avoir fait rencontrer Julie Grèzes. Julie est sans doute la personne à qui ce travail doit le plus: je lui suis infiniment reconnaissant de m’avoir pris dans son équipe malgré mes aptitudes mathématiques d’enfant de CP, d’avoir supporté mes protocoles farfelus, d’avoir écouté mes pleurnichements extrascientifiques, et de m’avoir emmené avec elle s’enterrer à Cerisy avec des psychanalystes. J’ai énormément appris à ses côtés, et si tout était à refaire, je pleurnicherais moins mais je conduirais tout le reste de l’exacte même manière. Un grand merci à Pierre Jacob. Alors qu’il aurait pu m’imposer des idées bien plus sages que celles que j’ai adoptées, il m’a laissé une totale liberté intellectuelle, a écouté mes positions et changements de position avec enthousiasme et indulgence. Il me faudrait également remercier Etienne Kœchlin, pour avoir accueilli le philosophemarxiste-rasta que j’étais au sein de son laboratoire, et Laura Chadufau pour sa patience et son énergie. L’orientation évolutionnaire de ce travail doit énormément à Robin Dunbar. Si je suis revenu d’Albion avec une étude sur "les gens qui rigolent dans les bars" qui m’a valu mille (gentilles) moqueries, j’en suis aussi retourné avec la certitude de vouloir participer à la grande investigation de ce qu’être humain veut dire. Merci également à Bronwyn Tarr, Rob Stubbs, Taiji Ogawa, Réka Bakonyi, Juan Aragon, Aurélie Barré, Andy Smith et Raffaella Smith pour ces incroyables moments à Oxford. Grâce à Marina Davila-Ross, j’ai pu m’exiler un petit mois sous le soleil Zambien : j’y matais du chimpanzé le jour, et je me frottais aux talentueux (mais un poil tricheurs) footballeurs du Chimfunshi Wildlife Centre le soir. J’y ai laissé dix kilos, mais y ai attrapé des mollets musclés et des rêves de primatologue : quand je serais grand, je veux faire tout comme Marina. Enfin, s’ils ne m’ont pas donné le salaire de Zlatan Ibrahimovic, je voudrais remercier Didier Bazalgette et la DGA-MRIS, ainsi que le Conseil Régional d’Île-de-France pour leur soutien 6 financier. Promis, votre argent n’a pas été gaspillé. Après avoir salué les grands, au tour des petits pairs : Aux Cogmastériens : Camillia Bouchon, qui ne manquera pas d’être en retard à ma soutenance de thèse ; Romain Ligneul, qui peut envisager une carrière de moniteur de ski s’il fait un flop en recherche ; Victor Bénichoux, le tacleur fou, à qui je dois la perte de 10 ongles ; Bahar Gholipour, Marie-Sarah Adenis, Jean-Rémy Martin, Clio Coste, Anne Kösem, Louise Goupil, Raphaëlle Abitbol, Zina Skandrani, François Le Corre, Léonor Philip, Margaux Perez, Aurélie Coubart, Valeria Mongelli, Klara Kovarski, Hadrien Orvoën. . . Et j’en oublie sans doute. Aux étudiants du LNC : la liste est trop longue, et ils se reconnaîtront. A la Social Team : Emma "Podouce" Vilarem, Marwa "Habibi" El-Zein, Michèle "MichMich" Chadwick, Terry Eskenazi, Lise Hobeika, Lucile Gamond, Matias Baltazar : vous allez me manquer. Enfin, je voudrais dédier ce travail à mon grand-père : s’il abhorrait les dreadlocks, je suis certain qu’il aurait adoré me voir aboutir un travail, et le présenter en costume-cravate dans un endroit prestigieux. J’espère que l’excellence de tous ces gens aura été contagieuse ! 7 Abstract Crowd psychologists of the 19th and 20th centuries have left us with the idea that emotions are so contagious that they can cause large groups of individuals to rapidly and spontaneously converge on an emotional level. Good illustrations of this claim include situations of crowd panic where largemovements of escape are thought to emerge through local interactions, and without any centralized coordination. Our studies sought to investigate the propagation of two allegedly contagious emotions, i.e., fear and joy. This thesis presents two theoretical and two empirical studies that have investigated, at two different levels of analysis, the phenomenon of emotional propagation of fear and joy: firstly, at a proximal level of analysis (the how-question), I discuss the potential mechanisms underlying the transmission of these emotions in crowds, and the extent to which emotional transmission can be considered analogous to a contagion process. Secondly, at an evolutionary/ultimate level of analysis (the why-question), I ask why crowd members seem to be so inclined to share their emotional experience of fear and joy with others. I present a study showing that the transmission of fear might be facilitated by a tendency to modulate one’s involuntary fearful facial reactions according to the informational demands of conspecifics, suggesting that the biological function of spontaneous fearful reactions might be communication of survival-value information to others. Finally, I discuss the implications of these studies for the broader understanding of emotional crowd behavior. Emotional transmission; emotional contagion; emotional communication; fear; joy; crowd psychology 8 Résumé Les psychologues de la foule des 19e et 20e siècles nous ont légué l’idée que les émotions sont si contagieuses qu’elles peuvent conduire un grand nombre d’individus à rapidement et spontanément adopter une même émotion. L’on pense par exemple aux situations de panique de foule, où, en l’absence de coordination centrale, des mouvements de fuite collective sont susceptibles d’émerger. Les travaux présentés dans cette thèse se proposent d’étudier la propagation de deux émotions considérées comme particulièrement contagieuses, la peur et la joie. Leur propagation est étudiée à deux niveaux d’analyse : d’abord, au niveau proximal (la question du "comment"), je discute les mécanismes potentiels permettant à l’émotion de se propager en foule; aussi, je soulève la question du bien-fondé de considérer la transmission émotionnelle comme un processus de contagion. Dans un second temps, au niveau d’analyse évolutionnaire ou ultime (la question du "pourquoi"), je pose la question de savoir pourquoi les individus de la foule ont ainsi l’air de partager leur états émotionnels de peur et de joie avec leurs voisins. A ce propos, je présente une étude montrant que la transmission de la peur peut être facilitée par la propension du système cognitif humain à moduler l’intensité des réactions faciales liées à la peur, en fonction de l’état informationnel de leurs congénères. Ces résultats suggèrent que les réactions faciales spontanées de peur ont pour fonction biologique la communication, à autrui, d’information cruciale pour la survie. Pour finir, je discute les implications de ces travaux pour notre compréhension plus générale des liens entre émotions et comportement de foule. Transmission émotionnelle; contagion émotionnelle; communication émotionnelle; peur; joie; psychologie de la foule 9 "La majorité était venue là par pure curiosité, mais la fièvre de quelques-uns a rapidement gagné le cœur de tous." Tarde, 1903 Figure 1: Inside the Iroquois Theatre (Chicago, USA) while the fire raged (October 1871). Source: Everett, 1904 10 Foreword The overall aim of this thesis is to investigate the proximate mechanisms and the biological function of the production and reception of emotional signals of fear and joy in humans, and more precisely, to attempt a response to a twofold question: how and why do we involuntarily transmit our emotions of fear and joy to others? Although it would have been relevant to consider the transmission process for the whole range of emotions humans can feel and express, I have focused on the emotions of fear and joy. Expressions of fear, as they signal an imminent threat in the environment, are most likely to spread in large groups and to structure collective behaviors. Numerous examples of mass hysteria can indeed be found in historical records (Bernstein, 1990; Cook, 1974; Evans & Bartholomew, 2009; Headley, 1873; Hecker & Babington, 1977; Kerckhoff & Back, 1968; Stahl & Lebedun, 1974; Wessely, 1987). While collective euphoria might be less common, joy can also lead to emotion-based collective behavior (Ehrenreich, 2007; Evans & Bartholomew, 2009), and is known to spread like an "infectious disease" in social networks (Fowler & Christakis, 2008; Hill, Rand, Nowak, & Christakis, 2010). In this respect, emotional crowd situations (as they are conceptualized in the early social psychology and sociology literature [Tarde, 1903, 1890; Le Bon, 1896; Sighele, 1901; Pratt, 1920]) provide a fruitful framework for the study of emotional propagation. To use the phrase coined by Gustave Le Bon (1896), emotions are highly contagious and spread like germs in groups of individuals. Crowd situations, by the over-proximity they impose on crowd members (Moscovici, 1993, 2005), are highly conducive to a wide diffusion of emotions and to the rapid adoption, in large groups of individuals, of “similar affective states and patterns of 11 behavior through local interactions and without any prior centralized coordination” (Raafat, Chater, & Frith, 2009). It is important to note that, for the sharing of emotional information between individuals A and B to constitute a genuine case of emotional transmission, B’s emotional state must be caused by the perception of A’s emotional signals, rather than by B’s perception of the source of A’s emotion. This condition enables a distinction to be made between cases of mere collective reactions to a single event (e.g., a general panic provoked by the news of a sudden financial crisis, where people rush to bank branches to collect their savings; in this case, the collective emotion is only accidental as it does not Figure 2: in the USA. Source: Wikimedia result from a set of local transmissions of information between agents – see Figure 2A) and Commons; Figure 2B shows a stampede resulting from a blaze (The “Valley Parade fire genuine cases of transmission-based emotional disaster”) which occurred in the stadium of collective behavior (e.g., a stampede caused by Bradford (United Kingdom) in 1985. Source: a blaze; in this latter case, the collective emotion The Sun. Schemas on the right show, for each is the outcome of a set of local transmissions of representation, the likely causal route leading fear and anxiety between individuals – see Fig- to the collective panic. ure 2B). It is fairly difficult to argue that such “pure” cases of transmission-based emotional collective behaviors actually exist, but it is crucial to distinguish between these two prototypical cases as each one is based on a distinct causal route and therefore calls for a very different cognitive explanation. Surprisingly, such a distinction is virtually absent from the investigations of crowd phenomena by early social psychologists. Last, there are two important points to take into consideration concerning the studies de- 12 veloped in this thesis: Firstly, we focused on three types of signaling media or effectors: the face, the body, and the vocal system (for the production of emotional vocalizations), each medium being likely to employ a specific signature for each emotion (for facial signals, cf. Ekman, 2007; for bodily or postural signals, cf. de Gelder, 2006; for vocal signals, cf. Sauter et al., 2010). This claim, however, is disputed, as far as facial signals are concerned (Barrett, 2011; Fridlund, 1994). We are fully aware that emotions can also be expressed through numerous other media including chemosensory signals (Mujica-Parodi et al., 2009), verbal (Rimé, Corsini, & Herbette, 2002) and prosodic (Frick, 1985) signals. The massive use of internet-based communication nowadays would also have called for the examination of the possibility of emotional transmission through emoticons (Derks, Fischer, & Bos, 2008; Marcoccia, 2000). We have decided, however, to concentrate on signals that could actually play a significant role in real crowd contexts. Secondly, while the transmission of affective information encompasses the transfer of various kinds of contents, such as moods (which are long-lasting and diffuse phenomena with no obvious physiological signature) and emotions (which are briefer phenomena with a somewhat specific physiological signature) (Ekman, 1994), we have focused exclusively on the transmission of emotions: their duration and physiological signature make them easier to study in laboratory settings. Moreover, crowd phenomena are seldom long-lasting, thus involving the transmission of emotions rather than that of moods. To sum up, this work is dedicated to the examination of transmission-based emotional collective behavior, and attempts to tackle two main issues: through which cognitive mechanisms do emotions propagate within crowds? Why do people in crowds tend to involuntarily transmit their emotions to their conspecifics? Before examining these questions in more detail, we will discuss traditional views on crowd behaviors, and the theoretical history of the concepts of contagion in the science of crowd behavior. We will also examine the constant use (and the epistemological consequences) of the metaphor of the disease by early social psychologists and sociologists when describing the spread of emotions in large groups. Finally, and after having presented the main 13 characteristics of the phenomenon of emotional transmission and the broad class of plausible psychological mechanisms that might serve it, we will discuss the possibility that such mechanisms may actually apply to the transmission of emotions in crowd situations. 14 Chapter One: The crowd in 19th and 20th century early social psychology Crowd behaviors and the spread of emotions in groups were central questions for the sociological tradition in the 19th and early 20th centuries. The numerous analyses by Gustave Le Bon (1896), Gabriel Tarde (1903; 1890) and Scipio Sighele (1901) (among others), along with the many crowd panic scenes in movies (e.g., “The Steps of Odessa” scene in Sergei Einsenstein’s famous movie, The Battleship Potemkin [1925]) have continuously fed the collective imagination and shaped a popular representation of the “crowd mind”. Of main interest here, early crowd psychologists were probably among the first to conceptualize emotions in groups systematically as disease or germs, and to introduce the term contagion to explain how emotions might be transmitted between individuals in large groups. As will be shown below, this tradition of crowd psychologists and their writings has not only had massive epistemological consequences on the way we understand emotion-based collective behavior (i.e., how it has affected its popular representation), but has also had an important impact on the way we conceptualize the process of transmission of emotional information, i.e., often understood as passive, ineluctable and somewhat dangerous. In what follows, we will briefly analyze the epistemological impact of this tradition on our popular representations of crowd behavior, which has indeed been extensively and convincingly discussed elsewhere (e.g., Couch, 1968; Drury, 2002; Reicher & Potter, 1985; Reicher, 1996, 2001; Schweingruber & Wohlstein, 2005). We will also briefly explore the cognitive aspects of the influence of a large number of people on individual cognitive functioning, and the 15 impact these factors have on the spread of emotions within crowds. Finally, we will discuss the introduction of the concept of contagion and its impact on our current understanding of the emotional transmission process. The seven key-characteristics of crowd behavior Crowd behavior, as a research topic, has immediate appeal to most audience. It is true that its relative simplicity, as well as the quasi-absence of deep conceptualization makes the subject easily communicable. However, the reason for this “widespread interest” in the investigation of crowd behavior is to be found elsewhere. Despite the fact that very few of us have ever been stuck in a panicking crowd, it would seem that we all have some idea about how crowds and their members behave in emergency situations. Popular understanding of emotional crowd behavior, which could be quickly summarized by using striking formulae such as Serge Moscovici’s “the crowd is a social animal that has broken its leash” (Moscovici, 1980) or Gabriel Tarde’s “the crowd is an anonymous and monstrous beast” (Tarde, 1890), seems to operate systematically around seven key-characteristics (Schweingruber & Wohlstein, 2005), which are often redundant: (i). Irrationality: because they are participating in collective action, crowd members become incapable of rational thought, even though each individual is perfectly rational in isolation. (ii). Emotionality: because crowd members are not capable of rational thought, their behavior is driven solely by their affects. This is because of a genuine incapacity to inhibit their impulses. (iii). Suggestibility: crowd members are the “slaves of [their] impulses” (Le Bon, 1896): they are highly susceptible to the ideas, acts and emotions of other group members. (iv). Destructiveness: crowd members are especially prone to anti-social behaviors, or as termed by Scipio Sighele (1901), “a crowd is a substratum in which the germ of evil spreads very easily, while the germ of good nearly always dies for lack of the necessary conditions for survival”. 16 (v). Spontaneity: this point follows on from (i), (ii) and (iii). Acts perpetrated by crowds are mostly spontaneous, i.e., not planned in advance. (vi). Anonymity: People in crowds feel anonymous because they are surrounded by many other individuals. Anonymity tends to increase antisocial behaviors, as the presence of a great number of individuals decreases the risk of being held responsible for the acts perpetrated. Scipio Sighele deals with this problem of collective responsibility in his book La Foule Criminelle (1901). (vii). Unanimity: everyone in the group behaves and feels in exactly the same way. These characteristics paint a rather extravagant picture of the crowd which has greatly contributed to its popular success. However, and although it is hard to acknowledge a genuine scientific stance in Le Bon’s and others’ writings, those writings were in fact intended to be a scientific contribution to understanding how individuals in a group situation can, so spontaneously and rapidly, become extremely homogeneous in their mental and emotional states. How are mental and emotional homogeneity achieved within crowds? According to Le Bon’s perspective, mental and emotional homogeneity are fundamental characteristics of crowds. In his work The Crowd: a Study of the Popular Mind (1895), Gustave Le Bon set out to explain how such homogeneity might be achieved in groups. To this end, he identified three main causes that might contribute to the emergence of crowd behavior ("crowd" being equated with "mental and emotional unity"). It should be noted that it is not clear whether these causes were thought of as different stages of the crowd formation process, or whether they were conceptualized as independent forces which could be combined. Firstly, the mere presence of a great number of people would have the immediate effect of degrading the conscious self and the personality of individuals, as well as their sense 17 of responsibility. At the same time, each one would gain a feeling of "invincible power" (as stated by Le Bon himself). This first cause is often termed submergence (Reicher, 2001) and has subsequently been explored, through the concept of deindividuation, by many social psychologists (Cannavale, Scarr, & Pepitone, 1970; Diener, Fraser, Beaman, & Kelem, 1976). It has been shown, in particular, that anonymity in a group tends to increase antisocial behavior (due to the disappearance of any sense of responsibility) and to favor the use of poor rationalizations when participants are asked to account for their previous antisocial acts (i.e., relying on collective excuses, such as “but everyone did the same”, instead of basing them upon personal reasons). It therefore appears that the presence of many individuals can reduce each one’s sense of responsibility and control over his own actions (compared to how that individual would behave in isolation). As far as the mechanisms which support this process of deindividuation are concerned, these could be a decline in self-evaluation processes (one simply becomes incapable of judging one’s own behavior in terms of personal standards), and/or less concern about social evaluation (one no longer judges one’s own behavior in terms of social norms or standards). When personal and social values no longer operate, any sense of guilt disappears. This would definitively pave the way for the production of antisocial behaviors (Zimbardo, 1969). For others (e.g., Diener, 1977), the mechanisms at the basis of deindividuation might be purely cognitive: the presence of a great number of individuals would saturate one’s capacity to process information, thus making it impossible to monitor one’s own actions or guide behavior according to personal standards. Inevitably, each individual becomes unable to protect himself from the stimulation of other crowd members, and is therefore strongly inclined to mimic their actions. The second general cause leading to the emergence of crowd behavior is known as mental contagion. This can be seen as a consequence of the process of submergence: since crowd members have lost their capacity of self-evaluation, they become incapable of resisting passing ideas and emotions. Gustave Le Bon (1895) considers this process to be comparable to that of hypnosis: “The most careful observations seem to prove that an individual immerged for some length of time in a crowd in action soon finds 18 himself – either as a consequence of the magnetic influence given out by the crowd, or from some other cause of which we are ignorant – in a special state, which much resembles the state of fascination in which the hypnotized individual finds himself in the hands of the hypnotizer. The activity of the brain being paralyzed in the case of the hypnotized subject, the latter becomes the slave of all the unconscious activities of his spinal cord, which the hypnotizer directs at will. The conscious personality has entirely vanished; will and discernment are lost. All feelings and thoughts are bent in the direction determined by the hypnotizer.” The third cause, that of suggestion, appears to be closely linked to that of mental contagion, and serves as defining the sort of ideas and emotions that emerge from the crowd. As noted in the key-characteristics (see above), these behaviors are necessarily antisocial and destructive. While the loss of personality does not necessarily imply that crowd members will commit antisocial acts, the fact that all members share an uncivilized, brutal and primitive common-ground (the so-called racial unconscious) dramatically restricts the range of possible behaviors that can emerge: “It is more especially in Latin crowds that authoritativeness and intolerance are found developed in the highest measure. In fact, their development is such in crowds of Latin origin that they have entirely destroyed that sentiment of the independence of the individual so powerful in the Anglo-Saxon. Latin crowds are only concerned with the collective independence of the sect to which they belong, and the characteristic feature of their conception of independence is the need they experience of bringing those who are in disagreement with themselves into immediate and violent subjection to their beliefs.” Combined together, these three causes would highly favor emotional crowd behavior: a 19 newcomer, as soon as entering the crowd, will feel outnumbered and will lose any self-control as a consequence (submergence). Being incapable of self-evaluating his own behavior, he will become contaminated by passing ideas and emotions (mental contagion). Those ideas will necessarily be anti-social as mental contagion operates on a shared and primitive commonground, which is composed of ideas that are brutal in nature (suggestion). As a consequence, emotions that are raw and primal (such as anger and fear) would be highly favored by this process. For social neuroscientists, the description of emotions as highly contagious has an immediate appeal: they themselves make extensive use of the concept of "contagion", a word that carries many preconceptions and which strictly limits our conception of emotional transmission. The concept of contagion in early crowd psychology, and its epistemological consequences for our current understanding of the process of emotional transmission Although the concept of contagion can be traced back to medieval philosophers (Robert, 2012), its use to describe the way ideas, behaviors and emotions can be transferred from individual to individual in large groups, has become systematic with the early discourses on crowd behavior (Rubio, 2010). In this respect, the word "contagion" is employed by Le Bon (Le Bon, 1896) to describe the mechanism which accounts for the rapid mental and emotional homogeneity that can be attained in any sort of group: “Ideas, sentiments, emotions, and beliefs possess in crowds a contagious power as intense as that of microbes. This phenomenon is very natural, since it is observed even in animals when they are together in number. Should a horse in a stable take to biting his manger the other horses in the stable will imitate him. A panic that has seized on a few sheep will soon extend to the whole flock.” (Le Bon, 1896). 20 In this passage, emotions are compared to microbes, with respect to their power of propagation. Interestingly enough, Gustave Le Bon writes that the phenomenon of emotional contagion might not only be found in humans, but would also be shared with other social mammals: groups of humans will not be analyzed differently than herds of horses or flocks of sheeps. In all those species, emotions spread like diseases. Such conceptualization has had three main consequences for our understanding of crowd behavior: Firstly, it has made the discourse look scientific in the eyes of the audience (Rubio, 2010). The term "contagion" was indeed conventionally employed by scholars of medical studies. This need for scientific legitimacy becomes evident with Gustave Le Bon’s argument that the phenomenon of contagion must be classified with other phenomena such as hypnosis or madness, thus claiming further scientific credentials by associating his research with the work of Jean Martin Charcot and Hippolyte Bernheim, among other sources. The second important consequence of the introduction of the concept of contagion is that it efficiently serves Le Bon’s ideological purpose: the mere use of the lexical field of disease ("contagion", "microbe", "madness", "disorder", "agoraphobia", etc.) immediately makes group behavior look pathological. What happens in crowds suddenly becomes abnormal and deserves no rational explanation. Moreover, it suggests that, like parasites, emotions could be dangerous and one should keep clear of gatherings. Thirdly, and most importantly in the context of this thesis, the systematic use of the concept of contagion has also dramatically shaped the way in which we understand the process of emotional transmission, whether these emotions are transmitted in groups or in strictly dyadic contexts. Emotional transmission is necessarily thought to be primitive, fast, passive, unintentional, irrepressible, and somewhat dangerous as it cannot be inhibited. As a consequence, the spread of emotions can be scientifically analyzed, as is the spread of disease. This is clear when considering the works of Nicholas A. Christakis and colleagues (e.g., Fowler & Christakis, 2008; Hill et al., 2010) who treat the spread of moods by explicitly using a disease epidemiology model. Within this type of model, moods are transmitted in large networks, from node to node, through social contact, over a long period of time, and 21 in a spontaneous and automatic fashion. Not surprisingly, most of these characteristics can also be found in Elaine Hatfield and colleagues’ account of the phenomenon of emotional transmission, which also assumes, purely and simply, that emotional transmission can be conceived as a contagion process (Hatfield et al., 1994). I should make it clear that the above analysis only aims at an objective description of the presence and epistemological consequences of introducing the concept of "contagion" to describe the process of affects being spread in groups. The use of this concept is, in itself, interesting to analyze, as authors could have used a more neutral term, such as "transmission". This would not have presupposed that agents are passive in the process of exchanging their emotions, nor that this process is irrepressible. What we need to examine is whether the process of spontaneous emotional transmission can appropriately be termed "contagion". In other words, whether emotional transmission is an automatic (or unconditional), irrepressible and dangerous process. The mechanisms at the heart of emotional propagation in crowds are the subject of chapter 2. 22 Chapter Two: How can emotions of fear and joy propagate in crowds? How can emotions become collective? As shown above, emotions have been conceptualized as very contagious elements since Le Bon’s work on crowd behavior. The numerous studies reporting spontaneous transmission of emotions between two individuals seem to confirm that emotions are highly “contagious” and legitimate the use of such a metaphor. What we know from the study of dyadic interactions Human beings’ propensity to communicate their emotions via facial, bodily and vocal signals, and their converse propensity to catch others’ emotions are recognized as striking (Hatfield, Cacioppo, & Rapson, 1994; Schoenewolf, 1990). Newborn babies are alleged to be highly sensitive to the distress of their fellows, crying in response to others’ cries (Simner, 1971; Dondi, Simion, & Caltran, 1999); they can pass this on, in turn, to their parents, who are known to be highly responsive to the distress of their offspring (Frodi et al., 1978; Wiesenfeld, Malatesta, & Deloach, 1981). On a more positive note, joy can be similarly contagious: in the words of composers Larry Shay, Mark Fisher and Joe Godwin, and the song made popular by Louis Armstrong (1929), “when you are smiling, keep on smiling / The whole world smiles with you.” 23 In fact, emotional contagion is so ubiquitous in human affairs that, according to social psychologist and psychotherapist Elaine Hatfield and her colleagues (Hatfield, Cacioppo, & Rapson, 1993; Hsee, Hatfield, & Chemtob, 1992), one might even gain the most valuable information about a target’s affective states by focusing on one’s own feelings during an ongoing social interaction, rather than by pursuing explicit and conscious reasoning about the target’s own account of his emotional state. In this respect, a personal anecdote related by Elaine Hatfield and Richard L. Rapson, at the beginning of their popular book Emotional contagion (Hatfield et al., 1994), is particularly illuminating. They reveal that, during psychotherapeutic sessions, therapists might fail to grasp their clients’ affective states, while at the same time being contaminated by them: “For over a decade, Richard L. Rapson and I (Elaine Hatfield) have worked together as therapists [. . . ]. One day [. . . ], Dick [Richard L. Rapson] complained irritably at the end of a session: ‘I really felt out on a limb today. I kept hoping you’d come in and say something, but you just left me hanging there. What was going on?’ I was startled. He had been brilliant during the hour, and I had not been able to think of a thing to add; in fact, I had felt out of my depth and ill at ease the whole time. As we replayed the session, we realized that both of us had felt on the spot, anxious, and incompetent. The cause of our anxiety soon became clear. We had been so focused on our own responsibilities and feelings that we had missed how anxious our client had been. [. . . ] Later, she admitted that she had been afraid the whole hour that we would ask her about her drug use and discover that she had returned to her abusive, drug-dealing husband.” Readers who are familiar with psychotherapy tradition may even be surprised by the somewhat “naive” reaction of Elaine Hatfield and Richard L. Rapson when they realize the contagious power of others’ affective states: early theorists such as Sigmund Freud and Carl Jung had long warned their fellows to keep their distance from their patients, affectively 24 speaking. While the former advised therapists to put themselves in the shoes of a surgeon when dealing with a patient (Freud, 1912), the latter was reminding them that therapists who think they can protect themselves from their client’s emotions are seriously in error (Jung, 1968): “It is a great mistake if the doctor thinks he can lift himself out of [the emotional contents of the patient]. He cannot do more than become conscious of the fact that he is affected.” Supporting the view that emotional contagion constitutes an ineluctable process, an experiment carried out in Elaine Hatfield’s research team (Uchino, Hsee, Hatfield, Carlson, & Chemtob, 1991) showed that prior expectations about a target’s emotional states do not alter the subjects’ subsequent susceptibility to those emotions, as revealed by their emotional self-reports, as well as by their facial emotional expressions during exposure to the targets’ displays. These studies would suggest that considering emotions as contagious elements could be appropriate: at no stage in the contagious process do agents intend either to emit or to react to emotional signals. In this respect, emotions operate just like diseases. One thing that has remained unclear, however, is whether emotions could spread beyond dyads. Work by Alison L. Hill and colleagues (Hill et al., 2010) (cited above) seems to suggest that moods can indeed spread widely in social networks. The issue is very different, though, when dealing with emotions in the context of crowd behavior: unlike the diffusion of moods in social networks, the spread of emotions in crowds is not a diffuse and long-lasting phenomenon but is instantaneous and rapid. Moreover, while the diffusion of moods relies on repeated and reciprocal interactions through numerous transmission media (including text messages and phone calls), the spread of emotions in crowds can be based solely on the non-reciprocal transmission of information through facial, vocal, bodily, and possibly verbal signals (all of which imply the physical presence of participants). In sum, for emotional crowd behavior to emerge, information must circulate between each individual crowd member, until 25 emotional homogeneity is reached. Can emotional transmission go transitive? This necessary condition for emotions to spread in crowds raises an important technical issue: if it has largely been shown that one individual (“Individual A”) can transmit her emotions to another (“B”), there is no evidence that Individual B can, in turn, transmit emotional information to a third individual (“C”) who has no perceptual access to A’s emotional displays. In other words, for emotional information to spread in crowds, emotions need to pass the minimal condition of being transitively contagious, i.e., that they can be transmitted from A to C, through B. This question has been the subject of empirical investigation, the results of which are reported in our paper published in the international peer-reviewed journal PLoS One, in June 2013. My contribution to this work was as follows: conception and design of the experiment, collection and analysis of the data, and writing of the paper. 26 Evidence for Unintentional Emotional Contagion Beyond Dyads Guillaume Dezecache1,2*, Laurence Conty3, Michele Chadwick1, Leonor Philip1, Robert Soussignan4, Dan Sperber2,5., Julie Grèzes1*. 1 Laboratoire de Neurosciences Cognitives (LNC), INSERM U960, and Institut d’Etudes de la Cognition (IEC), Ecole Normale Supérieure (ENS), Paris, France, 2 Institut Jean Nicod (IJN), UMR 8129 CNRS and Institut d’Etudes de la Cognition (IEC), Ecole Normale Supérieure, and Ecole des Hautes Etudes en Sciences Sociales (ENS-EHESS), Paris, France, 3 Laboratoire de Psychopathologie et Neuropsychologie (LPN, EA2027), Université Paris 8, Saint-Denis, France, 4 Centre des Sciences du Goût et de l’Alimentation (CSGA), UMR 6265 CNRS, 1324 INRA, Université de Bourgogne, Dijon, France, 5 Department of Cognitive Science, Central European University (CEU), Budapest, Hungary Abstract Little is known about the spread of emotions beyond dyads. Yet, it is of importance for explaining the emergence of crowd behaviors. Here, we experimentally addressed whether emotional homogeneity within a crowd might result from a cascade of local emotional transmissions where the perception of another’s emotional expression produces, in the observer’s face and body, sufficient information to allow for the transmission of the emotion to a third party. We reproduced a minimal element of a crowd situation and recorded the facial electromyographic activity and the skin conductance response of an individual C observing the face of an individual B watching an individual A displaying either joy or fear full body expressions. Critically, individual B did not know that she was being watched. We show that emotions of joy and fear displayed by A were spontaneously transmitted to C through B, even when the emotional information available in B’s faces could not be explicitly recognized. These findings demonstrate that one is tuned to react to others’ emotional signals and to unintentionally produce subtle but sufficient emotional cues to induce emotional states in others. This phenomenon could be the mark of a spontaneous cooperative behavior whose function is to communicate survival-value information to conspecifics. Citation: Dezecache G, Conty L, Chadwick M, Philip L, Soussignan R, et al. (2013) Evidence for Unintentional Emotional Contagion Beyond Dyads. PLoS ONE 8(6): e67371. doi:10.1371/journal.pone.0067371 Editor: Manos Tsakiris, Royal Holloway, University of London, United Kingdom Received January 4, 2013; Accepted May 17, 2013; Published June 28, 2013 Copyright: ß 2013 Dezecache et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The research was supported by a DGA-MRIS scholarship and the Agence National of Research (ANR) "Emotion(s), Cognition, Comportement" 2011 program (ANR 11 EMCO 00902), as well as an ANR-11-0001-02 PSL*, and an ANR-10-LABX-0087. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] (GD); [email protected] (JG) . These authors contributed equally to this work. psychologists, probably due in part to the difficulty of producing group-like phenomena in laboratory settings [7]. Emotional contagion (here, the "tendency to automatically mimic and synchronize facial expressions, vocalizations, postures, and movements with those of another person and consequently to converge emotionally" [8]) is commonly studied in dyadic interactions (see [8] for an extensive review). If however emotional homogeneity within a crowd is to be achieved through transmission from individual to individual, it is not sufficient that humans should be tuned to catch others’ emotions in dyadic interactions. It is also critical that humans should be tuned to reproduce the emotional cues they observe to a degree sufficient to spontaneously spread emotional information to other crowd members. This is needed for emotions to be transitively contagious: the perception of individual A’s emotional expressions by individual B should ultimately affect the emotional experience of an individual C who is observing B but not A. Such a minimal situation of transitive emotional transmission may be, we surmise, at the basis of emotional contagion on a much larger scale. What is also critical here is that emotional contagion should take place automatically rather than as a result of people’s decision to influence others or to accept such influence. Introduction Emotional crowds - where groups of individuals come to adopt similar affective states and patterns of behavior through local interactions and without any prior centralized coordination [1] were a major topic in the nascent field of social psychology in the Nineteenth and early Twentieth centuries. Social scientists such as Gabriel Tarde [2], Scipio Sighele [3] or Gustave Le Bon [4] theorized about the emergence of such collective behaviors and the psychological impact crowds have over their members. Crowds were characterized as milieux where affects spread very rapidly and in an uncontrollable manner (e.g., [4]). As a consequence, a group of individuals who are not acquainted with one another may spontaneously come to adopt the same behavior (e.g., a collective flight in crowd panic), giving the impression of ’mental unity’ within the group [4]. A necessary condition for the emergence of such collective behavior is the propagation of emotions across individuals. How can emotional information circulate from one individual to another in a way that rapidly achieves emotional unity of the crowd? Despite a few notable exceptions [5,6], emotional transmission processes occurring in groups has been neglected by later social PLOS ONE | www.plosone.org 1 June 2013 | Volume 8 | Issue 6 | e67371 Emotional Contagion Beyond Dyads In the present study, we investigated the transmission of emotional information in transitive triadic chains where the behavior of an individual A was observed by a participant B who was herself observed by a participant C. Joy and fear were chosen as target emotions because of their relevance to coordinated behavior and, arguably, their survival and fitness value [9,10] makes it particularly likely that they should easily spread in groups. As both motor and affective processes are implicated in emotional contagion [11,12], we recorded in a first experiment, the electromyographic (EMG) activity of zygomaticus major (ZM) and corrugator supercilii (CS), two muscles that are respectively involved in the production of smiling and frowning [13] and may be differentially induced by unseen facial and bodily gestures of joy and fear [14]. The activity of ZM and CS, as well as the skin conductance response (SCR) (a measure of physiological arousal [15]), were recorded in participants C while they were watching a participant B’s face. B herself was either watching a video of full bodily expressions including vocalizations of joy or fear displayed by another individual A, or, in a control condition, a video without social or emotional content. While our protocol was designed to investigate transitive emotional transmission in terms of facial patterns and physiological arousal in C, it was not designed to investigate the mechanisms behind facial reactions: whether these facial reactions qualify as rapid facial reactions [16] and whether they are mediated by motor-mimetic [17,18] or affective/ emotional appraisal [16,19,20] processes cannot be addressed here. As shown in figure 1, participants B and C were sitting in adjacent booths during the experiment. While numerous studies did report an impact of the presence of an audience on the intensity of facial expressions of emotions [21,22], importantly here, participants B were not informed that the participant in the adjacent booth was watching them. Finally, to help determine the degree to which the emotional expressions of B were explicitly perceptible, and hence the nature of emotional transmission from B to C, we presented in a followup experiment the video recordings of individuals B’s faces to naive judges who were asked to label the emotional expressions of B. We predicted that an emotion displayed by individual A would be transmitted to individual C via individual B (figure 1) even though B was not aware that she was being watched, and even when her emotional reactions could not be explicitly identified by individual C. Testing the transitivity of emotional contagion processes in this way may not only provide insight concerning the spread of affects in groups and crowds; it may also shed light on what may be the nature and function of emotional signaling mechanisms from an evolutionary point of view. Materials and Methods (a) Ethics Statement We obtained ethics approval from the local research ethics committees (CPP Ile de France III and Institut Mutualiste Montsouris) for the two experiments. All provided written informed consent according to institutional guidelines of the local research ethics committee. (b) Experiment 1 (i) Participants. Thirty male participants (mean age 24.6 y 60.73 SE, range 18–36 y) were recruited to represent C in the emotional transmission chain. We chose female participants to represent individuals B in the transmission chain because numerous studies suggest that women are facially more expressive than men (e.g., [23]). Sixty female participants (mean age 24 y 60.48 SE, range 18–36 y) were thus recruited to represent B. All of the participants had normal or corrected-to-normal vision, were naive to the aim of the experiment and presented no neurological or psychiatric history. All provided written informed consent according to institutional guidelines of the local research ethics committee and were paid for their participation. All the participants were debriefed and thanked after their participation. (ii) Stimuli. The stimuli presented to B (and standing for A) consisted of 45 videos (mean duration 6060620 ms, range 6000– 6400 ms) of size 6206576 pixels projected on a 19-inch black LCD screen. The videos of emotional conditions depicted 15 actors (8 females, 7 males) playing joy (n = 15) and fear (n = 15), using facial, bodily as well as vocal cues. These videos were extracted from sessions with professional actors from the Ecole Jacques-Lecoq, in Paris, France. The stimuli of the non-social condition (n = 15) displayed fixed shots of landscapes that were shot in the French countryside. All stimuli were validated in a forced-choice task where 15 participants (6 females, 8 males, mean age 22.5 y 61.46 SE) were Figure 1. The experimental apparatus. Participant B (on the right of the picture) is isolated from participant C (in the middle) by means of a large black folding screen. On the left of the picture is the recording device, concealed to C. Stimuli were presented to B using a screen located in front of her; a webcam was placed on top of the screen so as to display B’s face on C’s screen. doi:10.1371/journal.pone.0067371.g001 PLOS ONE | www.plosone.org 2 June 2013 | Volume 8 | Issue 6 | e67371 Emotional Contagion Beyond Dyads during the session. B was then asked to select the emotion displayed on the video in a forced-choice task, choosing the appropriate emotion from between three options (joy, fear, none) and to rate the intensity of the emotion on a 9-point scale. After having responded to these two questions, B waited for a period of time (between 15 and 20 sec) before a new video sequence began. Note that B was wearing headphones, and that this was done to improve the auditory input provided to B as well as to prevent any auditory cues about the content of the videos to be transmitted to C. Also, before joining participant C in the experimental room, Bs were told that there would already be somebody in the experimental room participating in an experiment led by another research team, and that it was important to enter the room as quietly as possible. At the same time, B was also told that the words ‘‘Start’’ and ‘‘End’’ would not disturb this other participant who was wearing headphones. Critically, during the experimental session, participant B never saw participant C who was hidden by a folding screen. (v) Specific procedure for participant C. While participant B was instructed, participant C was installed in the experimental room (placing of EMG and SCR electrodes, see (vi) Data acquisition) and was told that he will watch two other participants (one after the other) watching different movies with non-social or emotional content. His task was to report on a sheet of paper, after each trial, what he thought the other participant had just seen between the words ‘‘Start’’ and ‘‘End’’. C was also told to remain silent throughout the experiment. (vi) Data acquisition. Using the acquisition system ADInstruments (ML870/Powerlab 8/30), we continuously recorded the EMG activity of C using Sensormedics 4 mm shielded Ag/AgCl miniature electrodes (Biopac Systems, Inc) (sample rate: 2 kHz; range: 20 mV; spatial resolution: 16 bits). Before attaching the electrodes, the target sites on the left of C’s face were cleaned with alcohol and gently rubbed to reduce inter-electrode impedance. Two pairs of electrodes filled with electrolyte gel were placed on the target sites: left ZM and left CS muscles [24]. The ground electrode was placed on the upper right forehead. Last, the signal instructed to determine the emotional content of the video, selecting from among 7 possible choices (anger, disgust, joy, fear, surprise, sadness or none). The stimuli were correctly categorized: joy stimuli were labeled as depicting joy (93% of the responses selected the ‘joy’ label, contra 4% for the ‘sadness’ label, and less than 1% for the five other labels); fear stimuli were labeled as depicting fear (97% of the responses selected the ‘fear’ label, contra less than 1% of the responses for the six other labels); finally, non-social stimuli were labeled as not depicting any emotion (94% of the responses selected the ‘none’ label, contra 4% for the ‘joy’ label, and less than 1% for the five other labels). (iii) Overall procedure. After their arrival, the first two participants (one female participant, representing B; and one male participant representing C) were told that they will take part in two distinct experiments and were escorted to two separated rooms. The second female participant B was called in one hour later so as to replace the former female participant. (iv) Specific procedure for participant B. While participant C was escorted to and set up in the experimental room, participant B underwent training in the experimental procedure in a waiting room, so as to lead B to believe that she was going to participate to a completely different experiment. The procedure (see figure 2) consisted in the presentation of the videos in a random order on a black LCD screen of size 19-inch. Each video was preceded by a 250 ms beep followed by the presentation of the word ‘‘Start’’ for 1000 ms. At the end of each video, the word ‘‘End’’ appeared on the screen for 1000 ms. B was instructed to pronounce these words sufficiently loudly to permit her speech to be recorded by the webcam’s microphone and was told that this would help the experimenter distinguish between the different trials in a further analysis. This was actually done to inform C that a video was beginning or ending. Furthermore, B was told that she would be filmed via a webcam placed on the top of the screen and that this was solely done to check whether she actually paid attention to the movies. In fact, her reflection was retransmitted onto C’s screen (figure 1) but none of our B participants reported being aware that she was being watched by another participant Figure 2. The experimental protocol timeline for participants B and C. Specific instructions are inserted between asterisks. The subject of the photograph has given written informed consent, as outlined in the PLOS consent form, to publication of her photograph. doi:10.1371/journal.pone.0067371.g002 PLOS ONE | www.plosone.org 3 June 2013 | Volume 8 | Issue 6 | e67371 Emotional Contagion Beyond Dyads the screen) were rejected. The resulting stimuli (n = 609) consisted of videos of size 7206421 pixels of length 6 sec projected on a 19inch black LCD screen and represented 22.5% of the videos recorded during the Experiment 1. (iii) Procedure. The judges were confronted with all the videos, presented in a random order. They were told that they were going to watch videos of women perceiving emotional or non-social movies. Before each trial, a grey screen with the indication ‘‘Get ready…’’ was presented for 400 ms, followed by the video. Participants were asked to press the appropriate key on a keyboard when they recognized joy, fear, or non-social signs in the women facial expressions. They were then required to wait 500 ms for the next video to appear on the screen. (iv) Data analysis. A Cohen-Kappa coefficient test was used to measure the inter-rater agreement. To explore the performance of the judges against chance-level, we performed a series of threechoice binomial tests. was amplified, band-pass filtered online between 10–500 Hz, and then integrated. Integral values were then offline subsampled at 10 Hz resulting in the extraction of 100 ms time bins. Concerning the recording of SCR, 2 bipolar finger electrodes (MLT116F) were attached with a VelcroTM attachment straps to the first phalanx of the index and middle-fingers of the nondominant hand. The SCR was recorded at a sampling frequency of 2 kHz with a high-pass filter at 0.5 Hz, and then offline subsampled at 2 Hz resulting in the extraction of 500 ms time bins. (vii) Data analysis. Due to the nature of our protocol (stimuli of long duration, expectation of signals of low amplitude), we deliberately chose not to prevent participant C’s free facial movements though they were instructed that they should not move their arms nor their head during the presentation of the stimulus. Consequently, we had to exclude those participants whose data were too noisy: data from ZM (n = 4 participants), CS (n = 8 participants) and SCR (n = 4 participants) were thus rejected prior to the analysis. Moreover, EMG trials containing artifacts were manually rejected, following a visual inspection. Participants with a high rate of trial rejection were excluded from the statistical analysis for the relevant signal, (n = 3 for ZM, n = 5 for CS), leaving a total of n = 23 for ZM, n = 17 for CS for the statistical analysis. For SCR recordings, responses beginning before the first second following the video presentation were rejected and participants with a high rate of trial rejection or with absence of SCRs were excluded of the statistical analysis (n = 7) leaving a total of 19 participants for the statistical analysis. Then, for EMG data, the pre-stimulus baseline was computed over 500 ms before the video onset. EMG activity per trial was obtained by extracting the maximal change from the baseline level occurring between 500 to 6000 ms after the video onset. As we could not predict when, in relation to B’s processing of the stimuli, C’s facial activity would occur, we considered the maximal activity in this large temporal time window. For SCR data, the pre-stimulus baseline was computed over 1500 ms before the video onset. SCR activity per trial was obtained by extracting the maximal change from baseline level occurring between 1000 to 6500 ms after the video onset. Data for each trial was then natural-log transformed for both EMG and SCR activity. Finally, data were submitted, separately for each physiological measure, to repeated measures ANOVA using Emotion (joy vs. non-social vs. fear) as within-subject factors. Taking into account the sphericity assumption, we adjusted the degrees of freedom using the Greenhouse-Geisser correction where appropriate (e value). Finally, Bonferroni corrections were employed to account for multiple testing. Post-hoc comparisons were also performed for the analysis of simple main effects. Results First, we tested whether facial cues of joy were transitively transmitted from A to C, via B. Figure 3A displays the mean ZM response in participants C depending on the emotional content displayed in A and presented to participants B. Typically involved in the production of smiles and preferentially activated during the perception of joy expressions [25], ZM activity was expected to increase in C when B was watching videos depicting joy. Our analysis of ZM activity showed a significant main effect of Emotion (F2, 22 = 7.96, p = 0.001, e = 0.70, corrected p = 0.004, b = 0.715, g2 = 0.266). ZM activity was significantly enhanced in C when B was watching joy expressions compared to non-social stimuli (t22 = 2.90, p,0.01, d = 0.45) and when compared to fearful expressions (t22 = 3.05, p,0.01, d = 0.62). Moreover, ZM activity was not different between fear and non-social conditions (t22 = 1.39, p.0.1). These results show that muscular activity in C was specific of the emotional content observed by B, revealing a transitive motor transmission of joy expressions. Second, we tested whether facial cues of fear were transitively transmitted. We therefore compared the activity of the CS across the conditions. The CS pulls the brows together and is often used as a measure of negative emotional reactions to negative stimuli [26], notably fear-related stimuli (e.g., snakes in [27] or facial and bodily expressions of fear [14]). Our analysis showed a significant main effect of Emotion (F2, 16 = 5.46, p,0.01, b = 0.752, g2 = 0.334). CS activity was significantly enhanced in C when B was watching fearful expressions compared to non-social stimuli (t16 = 2.91, p = 0.01, d = 0.68) and to joy expressions (t16 = 2.83, p,0.05, d = 0.53). Last, CS activity was not different between joy and non-social conditions (t16 = 0.56, p.0.1). Figure 3B shows the mean CS activity across the conditions. Again, we found that muscular activity in C matched the emotional expressions watched by B. Third, we tested whether the transmission process also involved an arousal component or whether it was only limited to facial reactivity by comparing the SCR activity across the conditions. The statistical analysis revealed a significant main effect of Emotion (F2, 18 = 7.32, p,0.01, b = 0.924, g2 = 0.289). A significant increase of SCR was found in C when B was watching joy expressions, compared to when she was watching non-social stimuli (t18 = 3.75, p = 0.001, d = 0.24). A similar pattern was observed for fear vs. non-social (t18 = 23.21, p = 0.005, d = 0.27). Lastly, no difference was found between joy and fear (t18 = 20.19, p.0.1). The results suggest an increase of physiological arousal in C when B was watching emotional content, irrespective of the (c) Experiment 2 (i) Participants. Three judges (2 female, 1 male, mean age 23.3 y 62.66 SE, range 18–26) were recruited. All of the participants had normal or corrected-to-normal vision, were naive to the aim of the experiment and presented no neurological or psychiatric history. All provided written informed consent according to institutional guidelines of the local research ethics committee and were paid for their participation. All the participants were debriefed and thanked after their participation. (ii) Stimuli. The recordings of the first 16 B participants were each cut into 45 videos corresponding to the 45 trials performed during the experiment. Videos containing artifacts (e.g., B participants moving beyond of the scope of the webcam, concealing her face with her hand/fingers, or looking away from PLOS ONE | www.plosone.org 4 June 2013 | Volume 8 | Issue 6 | e67371 Emotional Contagion Beyond Dyads Figure 3. Electromyographic (zygomaticus major [ZM] and corrugator supercilii [CS]) and skin conductance (SCR) responses in participant C relative to the emotional content perceived by participant B. (A) EMG response of ZM in C relative to the emotional content perceived by B. (B) EMG response of CS in C relative to the emotional content perceived by B. (C) SCR responses in C relative to the emotional content perceived by B. Black lines indicate significant effects at *P,0.05; **P,0.01; ***P,0.001. Error bars indicate SEM. doi:10.1371/journal.pone.0067371.g003 exact nature of the perceived emotion. Figure 3C displays the mean SCR across the conditions. Finally, to investigate the nature and reliability of information which was transmitted from B to C, three judges who were blind to our hypotheses were requested to explicitly recognize signs of joy, fear or neutrality (when watching non-social cues) on B’s faces, using a forced-choice task, in a follow-up experiment. We performed a Cohen-Kappa coefficient test that provides a measure PLOS ONE | www.plosone.org of inter-rater agreement for qualitative items [28]. This test revealed a strong agreement between the judges (mean k value = 0.78; k value for joy items = 0.77; k value for non-social items = 0.89; k value for fear items = 0.67). The judges were at chance-level in recognizing joy signs in B’s faces (Three-choice binomial, p..1) and above chance-level in recognizing fear signs and neutrality displayed by B (Three-choice binomial, p = 0.01 and p,0.001 respectively). These results suggest that the 5 June 2013 | Volume 8 | Issue 6 | e67371 Emotional Contagion Beyond Dyads Though it is tempting to generalize this finding to all type of emotions, the fact that cues related to the experience of fear could be recognized in B’s face may also indicate that the extent to which emotional expressions are spontaneously expressed by their observers might well depend on their reference or content. In this respect, expressions related to immediate and urgent threats (such as expressions of fear) might more easily induce explicit cues in the face of their observers. Be that as it may, they must be such unintentional cues that they explain the well-documented fact that emotional contagion can occur without conscious access [8]. An impressive study by Tamietto et al. [14], in particular, reported emotional transmission in cortically-blind patients. Note that one limitation of this study is the absence of physiological measures in B. Further experiments could test each step in the spread of emotions in transitive situations and provide information about a potential decrease in physiological responses from A to C, or conversely, a gradual increase in emotional information, that is to be expected in crowd contexts [4]. Finally, our findings point out an important theoretical issue, the distinction between cues and signals. Cues can be defined as stimuli that elicit a specific cognitive or behavioral response that goes beyond the mere perception of the cue itself. Signals can be defined as cues that have the function of eliciting such a response [36,37]. Are the subtle emotional cues produced by B and picked up by C a mere side effect of B’s emotional arousal caused by the recognition of A’s emotion, or do these cues have the function of eliciting a similar emotional response in the third party? In other terms, are they not merely cues but signals? In our study, participant B did not know that she was being observed and did not therefore intend to communicate anything by means of her facial expression (of which she may well have been unaware). The fact that, at least in the case of joy, these expressions were not recognized by judges strongly suggests that participant C’s use of these cues was not intentional either. The cues we are talking about are neither intentionally emitted not intentionally attended to. The fact that B nevertheless produced unintentional cues strong enough for them to influence participant C can be interpreted as evidence that these emotional cues are biological adaptations, the function of which is to transmit an emotion in a non-intentional way. If so, how is this function adaptive? A possibility worth exploring is that facial activity in B is an evolved cooperative behavior that consists in the unconscious and spontaneous signaling of survival-value information that may induce appropriate emotional and preparatory behavior in our conspecifics. Such a mechanism would be adaptive, on the one hand, in threatening situations where flight and mobbing behaviors are optimal strategies; and, on the other hand, in favorable situations where signaling to conspecifics the presence of non-scarce rewarding features of the environment may foster social bonds. More work would be required to ascertain whether unintended and not consciously attended cues of specific emotion are in fact evolved signals that contribute to the fitness of those who produce them and to that of those who are influenced by them. Our study, we hope, offers some new insights and raises new questions about the spread of emotions across individuals in group settings. This should help revive a once prolific intellectual tradition – the social psychology of crowds – which has contributed so much to the study of human collective behavior in the past. transmission of an emotion from B to C may be independent of an explicit recognition of the emotional signs displayed on B’s faces, at least in the situation where B herself perceived joy expressions. Yet, there was a difference between the two experiments: while judges were exposed to several participants B, C was only exposed to two participants. As a consequence, we cannot exclude the possibility that there were differences in susceptibility to emotional cues in B between participants C and judges. Discussion Our findings indicate that emotional expressions of joy and fear can be spontaneously transmitted beyond dyads. Overt expressions of an emotion in an individual A caused in an observer B the involuntary production of subtle cues that induced an emotional reaction in a third individual C (who had perceptual access to B but not to A). The facial reactions triggered in C were characteristic of the type of emotions displayed by A, as revealed by the EMG responses of our participants. Activity of the ZM muscle region was heightened in C when B perceived the display of joy in A (in the form of facial, bodily and vocal signals) compared to when B was perceiving displays of fear in A or non-social stimuli. Activity of the CS muscle region, on the other hand, was heightened in C when B was observing expressions of fear compared to when B was watching expressions of joy in A or non-social stimuli. Although the use of CS as an index of fearful facial reactions is a limitation to demonstrate a transitive motor transmission of fearful expressions, according to the FACS nomenclature [13], facial expressions of fear usually involve the widening of the eyes (AU5), a raising of the eyebrows (AU1+2: activity of the frontalis) co-occurring with frowning (AU4: activity of CS), as well as the stretching of the mouth sideways (AU20). Thus, if the frontalis activity is indeed used in the EMG literature to measure facial reactions associated with the experience of fear (e.g., [16,19,29]), CS is also relevant (e.g., [14,27]) as it is known to reflect a more general negative facial response and is recruited in fearful facial expressions. Numerous studies report the production of subtle and specific facial reactions in front of facial, bodily, as well as vocal expressions of emotions [16,19,20,25,26,30–34]. Here we extend these results to a minimal element of a crowd situation by showing, for the first time, that the perception of the facial reactions of an individual (B), herself perceiving an emotional display (A), triggers the release of a specific facial pattern in a third party (C). Importantly, C’s reactions were not limited to a set of facial motor responses but involved emotional arousal, as evidenced by the increase in SCR during the emotional conditions (joy and fear) compared to the non-social condition. Importantly, the lower SCRs we observed during the non-social condition provide evidence against interpreting SCR increases for fear and joy expressions as the physiological consequences of attentional process only [35]. This increase of SCR response to emotional conditions as compared to non-social condition does not merely reflect an overall increase of arousal for vision of a body versus vision of a non-body stimulus as SCR was recorded in C who only sees a social agent B. Moreover, given that SCR activity is found to be coupled with specific muscular activity during emotional conditions, it is unlikely that observed SCR would not reflect the processing of emotional content. Of interest here, judges in the follow-up experiment were at chance level when asked to recognize joy cues in B’s faces. This indicates that transitive emotional transmission could occur, even in the absence of explicit recognition of emotional information in the pivot individual’s face, on the basis of mere unintentional cues. PLOS ONE | www.plosone.org Acknowledgments The authors would like to thank Juliane Farthouat, Emma Vilarem, MarieSarah Adenis and Auréliane Pajani for technical assistance and help in data 6 June 2013 | Volume 8 | Issue 6 | e67371 Emotional Contagion Beyond Dyads collection and Karl Boyer for help in designing the figures. We are grateful to Dr. Sylvie Berthoz (INSERM U669 & IMM) for administrative support. Author Contributions Conceived and designed the experiments: GD LC DS JG. Performed the experiments: GD MC LP. Analyzed the data: GD LC. Contributed reagents/materials/analysis tools: RS. Wrote the paper: GD JG DS. References 20. Grèzes J, Philip L, Chadwick M, Dezecache G, Soussignan R, et al. (2013) SelfRelevance Appraisal Influences Facial Reactions to Emotional Body Expressions. PLoS ONE 8: e55885. doi:10.1371/journal.pone.0055885. 21. Fridlund AJ (1991) Sociality of solitary smiling: Potentiation by an implicit audience. 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Educational and Psychological Measurement 20: 37–46. 29. Lundqvist LO (1995) Facial EMG reactions to facial expressions: A case of facial emotional contagion? Scandinavian Journal of Psychology 36: 130–141. doi:10.1111/j.1467-9450.1995.tb00974.x. 30. Dimberg U, Thunberg M (1998) Rapid facial reactions to emotional facial expressions. Scandinavian Journal of Psychology 39: 39–45. doi:10.1111/14679450.00054. 31. Dimberg U, Thunberg M, Elmehed K (2000) Unconscious Facial Reactions to Emotional Facial Expressions. Psychological Science 11: 86–89. doi:10.1111/ 1467-9280.00221. 32. Hess U, Blairy S (2001) Facial mimicry and emotional contagion to dynamic emotional facial expressions and their influence on decoding accuracy. International Journal of Psychophysiology 40: 129–141. doi:10.1016/S01678760(00)00161-6. 33. Hietanen JK, Surakka V, Linnankoski I (1998) Facial electromyographic responses to vocal affect expressions. Psychophysiology 35: 530–536. doi:10.1017/S0048577298970445. 34. 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Konvalinka I, Xygalatas D, Bulbulia J, Schjødt U, Jegindø EM, et al. (2011) Synchronized arousal between performers and related spectators in a firewalking ritual. Proceedings of the National Academy of Sciences 108: 8514. 7. Niedenthal PM, Brauer M (2012) Social Functionality of Human Emotion. Annual Review of Psychology 63: 259–285. doi:10.1146/annurev.psych.121208.131605. 8. Hatfield E, Cacioppo JT, Rapson RL (1994) Emotional contagion. Cambridge Univ Pr. 9. Buss DM (2000) The evolution of happiness. American Psychologist 55: 15. 10. Öhman A, Mineka S (2001) Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review 108: 483–522. doi:10.1037/0033-295X.108.3.483. 11. Hess U, Philippot P, Blairy S (1998) Facial reactions to emotional facial expressions: affect or cognition? Cognition & Emotion 12: 509–531. 12. Moody EJ, McIntosh DN (2006) Bases and Consequences of Rapid, Automatic Matching Behavior. Imitation and the social mind: Autism and typical development: 71. 13. Ekman P, Friesen WV (1978) Facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto, CA. 14. Tamietto M, Castelli L, Vighetti S, Perozzo P, Geminiani G, et al. (2009) Unseen facial and bodily expressions trigger fast emotional reactions. Proceedings of the National Academy of Sciences 106: 17661–17666. 15. Sequeira H, Hot P, Silvert L, Delplanque S (2009) Electrical autonomic correlates of emotion. International Journal of Psychophysiology 71: 50–56. doi:10.1016/j.ijpsycho.2008.07.009. 16. Moody EJ, McIntosh DN, Mann LJ, Weisser KR (2007) More than mere mimicry? The influence of emotion on rapid facial reactions to faces. Emotion 7: 447–457. doi:10.1037/1528-3542.7.2.447. 17. Chartrand TL, Bargh JA (1999) The chameleon effect: The perception– behavior link and social interaction. Journal of Personality and Social Psychology 76: 893. 18. Bavelas JB, Black A, Lemery CR, Mullett J (1986) ‘‘I show how you feel’’: Motor mimicry as a communicative act. Journal of Personality and Social Psychology 50: 322–329. doi:10.1037/0022-3514.50.2.322. 19. Soussignan R, Chadwick M, Philip L, Conty L, Dezecache G, et al. (2013) Selfrelevance appraisal of gaze direction and dynamic facial expressions: Effects on facial electromyographic and autonomic reactions. Emotion 13: 330–337. doi:10.1037/a0029892. PLOS ONE | www.plosone.org 7 June 2013 | Volume 8 | Issue 6 | e67371 What psychological mechanisms are at work? “Social comparison” models A first broad class of models can be said to rely on a process of “social comparison” whereby observers adopt targets’ affective states by conscious reasoning and by imagining themselves in the same situation. Such a model can be traced back to Adam Smith’s observations (1759): “Though our brother is upon the rack, as long as we ourselves are at ease, our senses will never inform us of what he suffers. They never did and never can carry us beyond our own persons, and it is by the imagination only that we form any conception of what are his sensations... His agonies, when they are thus brought home to ourselves, when we have thus adopted and made them our own, begin at last to affect us, and we then tremble and shudder at the thought of what he feels. [. . . ] By the imagination we place ourselves in his situation, we conceive ourselves enduring all the same torments, we enter as it were into his body, and become in some measure the same person with him, and thence form some idea of his sensations, and even feel something which, though weaker in degree is not altogether unlike them." More modern accounts of this model do exist (e.g., Bandura, 1969), but they share the similar problematic assumption that some sort of deliberation is at the core of the process of emotional transmission. Such a costly cognitive process can hardly account for what happens in crowd panics, where emotions seem to be transmitted very rapidly. “Conditioning” models Other models, which rely on associative processes, have been put forward to account for the primitive character of emotional contagion. Their proponents argue that emotional responses 34 can be conditioned or unconditioned and that this accounts for most cases of emotional contagion (Aronfreed, 1970). For instance, one can generalize from situations where smiles are expressed in response to pro-social behavior and a sense of well-being, or learn that fearful expressions are associated with stressful situations (conditioning). Also, one could well have unconditioned responses of fear and joy in reaction, respectively, to fearful and joyful expressions. These models are appealing by virtue of their simplicity, but that is precisely where the shoe pinches: they are so simple that they are completely imprecise, mechanistically speaking. This probably explains why the “primitive emotional contagion” model, which is causally explicit, has been so popular in recent years. The “primitive emotional contagion” model Through their work on the processing of social cues, John Bargh and his colleagues have consistently shown that we have a natural tendency to mimic, spontaneously and unconsciously, the postures of individuals we are interacting with (Chartrand & Bargh, 1999). This “mimicry” is not restricted to postures, as the perception of facial expressions of emotion is also known to induce, in observers, slight activity in the same facial muscle as the target’s within the first second after exposure to the stimulus (Dimberg, 1982; Dimberg, Thunberg, & Elmehed, 2000; Dimberg & Thunberg, 1998). The effects of such behavioral mimicry are, for Hatfield and colleagues (Hatfield et al., 1994), at the basis of “primitive emotional contagion” which is a three-step process: (i) first, observers tend to mimic and synchronize their overall behavior (facial expressions, bodily postures, vocal behavior) with the target with whom they interact; (ii) doing so, observers adopt a muscular configuration which, through muscular feedback, alters the emotional experience in a way congruent with their adopted muscular configuration; (iii) consequently, observers and the target individual converge emotionally. Each of these three steps has been widely documented in the literature: (i) people indeed tend to precisely and rapidly mimic and to synchronize their facial expressions (Dimberg, 1982; Dimberg et al., 2000; Moody, McIntosh, Mann, & Weisser, 2007; Soussignan et al., 2013), 35 bodily postures (Bernieri & Rosenthal, 1991; Bernieri, 1988) and vocal behavior (Cappella & Planalp, 1981; Cappella, 1981, 1997). (ii) There is also some evidence that facial (Bush, Barr, McHugo, & Lanzetta, 1989; Laird, 1984; Lanzetta & Orr, 1980), postural (Stepper & Strack, 1993) and vocal (Hatfield & Hsee, 1995) feedback can alter, in an emotion-specific way, the subjective emotional experience. In sum, these three steps, when causally combined, could indeed allow for the transmission of emotion between two interacting agents. It must, however, be pointed out that this model suffers from three main limitations: firstly, there is no evidence that the first two stages are in fact causally linked in the process of emotional transmission. Secondly, it relies entirely upon the mechanism of motor mimicry (where several works indicate that rapid facial reactions to emotional faces rely on affective processing: Dimberg, Hansson, & Thunberg, 1998; Grèzes et al., 2013; Moody et al., 2007; Soussignan et al., 2013). Thirdly, it presupposes that an observer’s emotional reaction should completely match that of the individual being observed where, in fact, certain social contexts may not favor the sharing of emotional experiences between two agents (should I really pick up my competitor’s joy, or my enemy’s fear?). This third problem, although it is acknowledged by Hatfield and colleagues, is totally incompatible with the view that motor mimicry is at the basis of emotional transmission. This model, generally speaking, is based on equating the transmission of emotional information with contagion, i.e., a process where observers are passive and where they mandatorily “catch” the various emotions expressed by the target. In this respect, cognitive models that explain how humans take into account the various characteristics of the models’ emotional expression to flexibly respond to others’ emotional signals (sometimes in a congruent way, by adopting an emotional experience similar to that of the observer) can be a good remedy. Emotional contagion as emotional communication One of the assumptions shared by all three types of models mentioned above is the common belief that “emotional contagion” is a special process which relies on dedicated psychological mechanisms. This is particularly obvious when considering the model of “primitive emotional contagion”: by assuming that individuals A and B share their emotions, which are therefore 36 taken to be replica of each other, advocates of this model typically restrict their investigation to psychological processes that allow for a mirroring between A and B. But, even if some sort of “interpersonal similarity” (as described by De Vignemont & Jacob, 2012) between A’s and B’s emotional states is necessary for a given social interaction to be classified as “emotional contagion”, this does not mean that B’s reactions to A’s emotional displays rely on mechanisms that automatically ensure the congruency between the emotional states of A and B. In fact, emotional contagion could be a subset of emotional communication processes: confronted with emotional signals, observers may accidentally react in a congruent manner (thus leading to emotional contagion). But they may equally react in a complementary or even incongruent fashion, which case, emotional contagion would merely be a case of reaction to others’ emotional signals, where the emotions of A and B happen to be similar. Explaining the proximal mechanisms of emotional contagion thus brings us back to the wider issue of which psychological mechanisms allow humans to react to others’ emotional displays. How do we react to others’ emotional displays? The question of the proximal mechanisms that support our reactions to others’ emotional signals deserves an entire thesis. My own research agenda has been very different and I will only suggest a few tentative answers to this question. Together with Dr. Julie Grèzes, we have proposed a cognitive and neural framework to explain how the human brain interprets and reacts to others’ expressions of threat. We also briefly mention the possibility that this model could be extended to apply to other emotional displays (such as joy, which is of main interest here). The model has been described in a paper recently published in the journal Neuropsychologia (October 2013). As stated above, this model comprehends emotional contagion as a specific case of emotional communication. In other words, emotional contagion is considered to be a subset of a larger set which comprises the whole range of emotional communication. 37 Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Contents lists available at ScienceDirect Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia How do shared-representations and emotional processes cooperate in response to social threat signals? Julie Grèzes a,b,n,1, Guillaume Dezecache a,c a b c Cognitive Neurosciences Lab., INSERM U960 & IEC—Ecole Normale Supérieure, Paris 75005, France Centre de NeuroImagerie de Recherche (CENIR), Paris, France Institut Jean Nicod, UMR 8129 & IEC—Ecole Normale Supérieure, Paris, France art ic l e i nf o Keywords: Emotional communication Threat signals Opportunities for action Shared motor representation Amygdala Premotor cortex a b s t r a c t Research in social cognition has mainly focused on the detection and comprehension of others’ mental and emotional states. Doing so, past studies have adopted a “contemplative” view of the role of the observer engaged in a social interaction. However, the adaptive problem posed by the social environment is first and foremost that of coordination, which demands more of social cognition beyond mere detection and comprehension of others’ hidden states. Offering a theoretical framework that takes into account the dynamical aspect of social interaction – notably by accounting for constant interplay between emotional appraisal and motor processes in socially engaged human brain – thus constitutes an important challenge for the field of social cognition. Here, we propose that our social environment can be seen as presenting opportunities for actions regarding others. Within such a framework, non-verbal social signals such as emotional displays are considered to have evolved to influence the observer in consistent ways. Consequently, social signals can modulate motor responses in observers. In line with this theoretical framework we provide evidence that emotional and motor processes are actually tightly linked during the perception of threat signals. This is ultimately reflected in the human brain by constant interplay between limbic and motor areas. & 2013 Elsevier Ltd. All rights reserved. 1. Introduction “Actions are critical steps in the interaction between the self and external milieu” (Jeannerod, 2006). We are continuously confronted with a great number of opportunities for actions in our environment, and we are constantly collecting information in order to select the most relevant set of motor commands from among numerous potential action plans so as to respond to environmental challenges (Cisek, 2007; Cisek & Kalaska, 2010). This ability to form multiple motor plans in parallel and to flexibly switch between them brings survival advantage by dramatically reducing the time one takes to respond to environmental challenges (Cisek & Kalaska, 2010; Cui & Andersen, 2011). These action possibilities emerge from the relationship between species and their milieu, as well as from the interaction between individuals and their more immediate environment. They thus depend both on long-term attunement (at the evolutionary time-scale, through cognitive adaptations and natural selection), and on short-term attunement (at the n Corresponding author at: Laboratoire de Neuroscience Cognitive, INSERM U960 and IEC Ecole Normale Supérieure, 29 Rue d’Ulm, 75005 Paris, France. Tel.: þ 33 1 44 32 26 76; fax: þ33 1 44 32 26 86. E-mail address: [email protected] (J. Grèzes). 1 http://www.grezes.ens.fr/. proximal level, through developmental patterns as well as through local accommodation) (Kaufmann & Clément, 2007). Note that contextual assumptions (through observer/actor's preferences and skills, as well as objects’ characteristics) also play an important role in the interactions between individuals and their milieu and ultimately shape action opportunities. Although the concept of action opportunities has mostly been used to account for interactions between animals and non-social physical objects in the world, it may equally apply to our interactions with the social world. We would therefore perceive our physical and social environments as maps of relevant action opportunities in a space which can also include potential actions of another present in one's own space (Sebanz, Knoblich, & Prinz, 2003; Sartori, Becchio, Bulgheroni, & Castiello, 2009; Bach, Bayliss, & Tipper, 2011; Ferri, Campione, Dalla Volta, Gianelli, & Gentilucci, 2011). Again, contextual assumptions do play a role within such a framework: observers’ skills and the characteristics of the social objects (the individual[s] with whom one interacts) shape opportunities for action regarding others; they are function of one's own needs (Rietveld, De Haans, & Denys, 2012) and attitudes towards others (Van Bavel & Cunningham, 2012). Opportunities for action may also well emerge from emotional signals (Grèzes, 2011; Dezecache, Mercier, & Scott-Phillips, 2013). A fearful display, for instance, invites observers to act upon it, whereby observers select among numerous potential actions 0028-3932/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019 Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i J. Grèzes, G. Dezecache / Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 2 (fleeing from the threatening element, fighting against it or rescuing potential endangered congeners, among other numerous potential actions) according to their preferences and appraisal of the situation. The concept of opportunities for action thus constitutes a fruitful framework within which we can better understand social and emotional perception and its modulation in different individuals. One critical consequence of the view that our social world features opportunities for action in response to others’ emotions is the necessity to propose an adequate cognitive and neural model of social interaction: behaviour and brain activity should reflect the processing of multiple representations of potential actions in response to others’ behaviour, and their selection through the use of external, as well as internal sensory information. It also requires that socio-emotional understanding be tightly linked with social interactive skills (McGann & De Jaegher, 2009). The building of such a view, we shall argue, supposes the integration of two separate systems in the brain, i.e., the motor and the emotional systems that have been mainly studied independently in the literature. We believe the synthesis of these two lines of research will help generate a novel framework to better understand the cognitive and neural mechanisms which ensure the initiation of adaptive responses during social and emotional interactions. Since much of our work has been dedicated to the perception of threat signals, we will exclusively focus on the perception of fear and anger in others’ face and body. The possibility that limbic and motor processes similarly interact during the perception of other emotional signals (such as joy and disgust) will be briefly discussed. 2. Shared motor representations: A key mechanism for the understanding of others’ actions and emotions. 2.1. Perception of actions The neural basis underpinning our ability to represent and understand the actions of others has been the object of considerable research in both monkeys and humans. It is now acknowledged that perceived actions are mapped onto the motor system of the observer, activating corresponding motor representations (henceforth “shared motor representations”). The motor system of the observer simulates the observed action by issuing corresponding motor commands that account for predictions of immediate outcomes of the perceived action (e.g. Jeannerod, 2001; Wilson & Knoblich, 2005). Shared motor representations were shown to be more selectively tuned to process actions that (1) conform to biomechanical and joint constraints of normal human movement (Reid, Belsky, & Johnson, 2013; Saygin, 2007; Dayan, Casile, Levit-Binnun, Giese, & Flash, 2007; Elsner, Falck-Ytter, & Gredebäck, 2012), and (2) are simple, and familiar within the observer's motor repertoire (Calvo-Merino, Grèzes, Glaser, Passingham, & Haggard, 2006; Kanakogi & Itakura, 2011) or for which the observer has acquired visual experience (Cross, Kraemer, Hamilton, Kelley, & Grafton, 2009; Jola, bedian-Amiri, Kuppuswamy, Pollick, & Grosbras, 2012). Shared motor representations sustained by premotor, motor, somatosensory and parietal cortices (Grèzes & Decety, 2001; Morin & Grèzes, 2008; Caspers, Zilles, Laird, & Eickhoff, 2010; Van Overwalle, 2008; Shaw, Grosbras, Leonard, Pike, & Paus, 2012; Molenberghs, Hayward, Mattingley, & Cunnington, 2012) allow us to identify “what” the action is and “how” it is or will be performed (Thioux, Gazzola, & Keysers, 2008; Hesse, Sparing, & Fink, 2008). 2.2. Limits If shared motor representations play a key role in deciphering and predicting other's actions, they are, per se, not sufficient to allow for interpersonal coordination. What is involved in the perception of opportunities for actions during social interaction is, cognitively speaking, very different from what shared motor representations are known to do, that is, to allow for the simulation of an observed motor pattern (Rizzolatti, Fogassi, & Gallese, 2001). We assume those action opportunities to be emergent properties of the observer-environment interactions, such that interaction with the social world triggers a wide range of opportunities for actions in the engaged observer. It was shown that in an interactive context, the perception of another individual's gestures can override pre-planned actions towards physical objects: the opening of an empty hand or the mouth induces, in observers, changes in the trajectory of their grasping gesture toward an object (Sartori et al., 2009; Ferri et al., 2011). Importantly, these gestures here were perceived as a request to be given the object or to be fed, and not to reproduce the perceived action. These experiments strongly support the hypothesis that our brain processes both physical and social information as currently available potential actions, and that the context strongly impacts the selection between these action opportunities. This perspective about social interaction calls for re-examination of previous findings on the neural bases suggested to sustain the shared representations. Activity in parietal cortex and connected premotor and motor regions (dorsal visuomotor stream) may also reflect the implementation of multiple representations of potential actions that one can perform (Cisek, 2007; Cisek & Kalaska, 2010). Within the dorsal visuomotor stream of the macaque brain, 20% of motor neurons showed object-related visual properties (canonical neurons) related to specification of the potential action triggered by the perceived object (Rizzolatti & Fadiga, 1998; Murata, Gallese, Luppino, Kaseda, & Sakata, 2000; Raos, Umiltá, Murata, Fogassi, & Gallese, 2006). In parallel, 17% of motor neurons of dorsal visuomotor stream showed action-related visual properties (mirror neurons—(Gallese, Fadiga, Fogassi, & Rizzolatti, 1996)) associated with the understanding of other individuals’ behaviour (Rizzolatti & Sinigaglia, 2010). Among these 17%, only 5.5% code for a strictly congruent action in the motor and the visual domain, whereas 8.6% code for two or more actions in the visual domain, and 1.3% for non-congruent actions. Similar proportions were revealed in the human supplementary motor cortex (Mukamel, Ekstrom, Kaplan, Iacoboni, & Fried, 2010): 14% of the recorded neurons in area responded to congruent observed actions, but 10% responded to non-congruent observed actions. Before viewing all action-related visual activities in dorsal visuomotor stream as shared motor representations processes, one may first suggest that motor neurons that responded to noncongruent observed actions should not be considered mirror neurons, but should be categorized as “social” canonical neurons, that is, neurons that are active when foreseeing a possible social interaction (vs. interaction with an object as for canonical neurons) and preparing oneself accordingly (Dezecache, Conty, & Grèzes, 2012). Also, one may suggest that, in parallel to shared motor representations activity, there is neural activity that is involved in the processing of the oberver's potential opportunities for action in response to other individuals' behaviour. 2.3. Perception of emotions The concept of shared motor representation is also influential in the emotional domain. The perception of others’ emotional expressions is taken to trigger an automatic and non-affective motor matching (termed ‘mimicry’) of the perceived expressions (Hatfield, Cacioppo, & Rapson, 1993; Hatfield, Cacioppo, & Rapson, 1994; Chartrand & Bargh, 1999; Williams, Whiten, Suddendorf, & Perrett, 2001; Carr, Iacoboni, Dubeau, Mazziotta, & Lenzi, 2003; Niedenthal, Barsalou, Winkielman, Krauth-Gruber, & Ric, 2005; Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i J. Grèzes, G. Dezecache / Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Lee, Josephs, Dolan, & Critchley, 2006; Dapretto et al., 2006), which could precede and even cause emotion through facial and bodily feedback, ultimately generating emotional contagion in the observer (Hatfield et al., 1993; McIntosh, 2006; Niedenthal et al., 2005). Some researchers therefore consider this non-emotional motor convergence to be independent of internal emotional reactions and/or to sustain a communicative role by allowing the observer to inform the emitter that they have understood the expressed state (Bavelas, Black, Lemery, & Mullett, 1986; Hess & Blairy, 2001). The motor (and eventual emotional) convergence between the emitter and the observer could also serve to enhance social and empathic bonds bolstering social communication, prosocial behaviour and affiliation (e.g., Chartrand & Bargh, 1999; Yabar & Hess, 2006). In parallel, others have suggested that a direct and implicit form of understanding others is achieved through embodied simulation (e.g. Gallese, 2001; Niedenthal, 2007). It is proposed that a vicarious activation of somatosensory representations (“as if loop” which bypasses the facial musculature) when observing the emotional expressions of others is what facilitates their recognition (Adolphs, Tranel, & Denburg, 2000; Pourtois et al., 2004; Pitcher, Garrido, Walsh, & Duchaine, 2008; Banissy et al., 2011; Maister, Tsiakkas, & Tsakiris, 2013). 2.4. Limits One important limit can be raised regarding the role of shared representations in the emotion domain. As Jeannerod stated: “emotional contagion can only provide the observer with the information that the person he sees is producing a certain type of behaviour or is experiencing a certain type of emotion; but, because it does not tell the observer what the emotion is about, it cannot be a useful means for reacting to the emotion of that person, and would not yield an appropriate response in a social interaction. Imagine facing somebody who expresses anger and threat: the adaptive response in that case seems to be avoidance rather than imitation, i.e. not to experience anger oneself, but to experience fear and eventually run away” (Jeannerod, 2006 pp. 147). In other words, the shared representations system is, in itself, not sufficient to allow for appropriate reactions in the observer. Note that this critique of the shared motor representation system specifically call into question its lack of details about how sharing others’ motor states can significantly prepare oneself to react in a relevant fashion; this is complementary to other criticisms that targets the very contribution of motor mirroring processes to mindreading (Borg, 2007; Jacob, 2008) but consistent with critiques focusing on the role of shared motor representations in the production of empathetic responses (de Vignemont & Singer, 2006; de Vignemont & Jacob, 2012) in that it suggests that perceiving cues of emotion in others is, in itself, not sufficient to produce an appropriate response. The production of appropriate responses should ultimately rely on appraisal processes that lie beyond the scope of the shared motor representations system. We suggest that the processing of opportunities for action during social interaction involves a specific brain network, a set of specific neuron populations and specific mechanisms that differ (for the most part) from the shared motor representations network. There are brain regions that do not display motor and mirroring properties – such as the amygdala, the brain's key emotional centre – that are fundamental in the evaluation of social signals and in exerting significant influence over the selection of one's own adaptive reaction and that should thus be taken into account. Characterizing the neural specificities of the network serving the processing of opportunities for action during social interaction, beyond the shared motor representation system, constitutes a challenging step in our understanding of the processing of social emotional signals. This will be the topic of the next 3 section. As stated above, we will focus our discussion on threat signals, i.e., displays of fear and anger. 3. Emotions motivate actions in the observers (emotion-toactions processes) Emotional displays, which constitute a medium for biological communication, can be seen as tools which influence the behaviour of the agents with whom we interact (Grèzes, 2011; Dezecache et al., 2013). They promote fast processing and elaboration of adapted social decisions and responses in the observer (Frijda, 1986; Frijda & Gerrod Parrott, 2011). Surprisingly, although emotions are believed to promote adaptive social decisions and responses in others, most research on emotions in humans has focused on the sensory (Adolphs, 2002) or sensorimotor (Gallese, 2001; Niedenthal, 2007) processing of emotional signals and associated attentional capture (Vuilleumier & Pourtois, 2007). More generally, the literature on emotion has taken for granted that the basic task of social cognition is the detection and comprehension of others’ mental and affective states2. Yet, efficient coordination during emotional social interactions, which constitutes a step beyond the mere detection of others’ emotional states, has been mostly overlooked. As a result, cognitive and anatomical links between structures that detect emotions in others and structures that prepare motor responses to cope with these emotional signals have been largely neglected or studied apart. In consequence, little is known about the anatomical substrates which allow the limbic system to influence purposive actions, i.e., to prepare a coordinated set of motor commands necessary to face social demands, through interaction with the cortical motor system. The following subsection summarizes the anatomical and functional evidence in support of functional interactions between the limbic and the motor systems during the perception of threat signals. 3.1. Anatomical evidence In non-human primates, anatomical tracing and electrophysiology studies in monkeys provide compelling evidence that the amygdala (AMG) plays a role in two key functions: processing the emotional significance of features of the environment, and interfacing with motor systems for the expression of adaptive behavioural responses (see Damasio et al., 2000; LeDoux, 2000; Barbas, 2000). The closely linked network composed of visual areas (fusiform gyrus (FG) and superior temporal sulcus (STS)), the AMG and the lateral inferior frontal gyrus in humans (BA 45/47) forms the anatomical substrate of the first function, i.e. the evaluation of the emotional significance of sensory events (Ghashghaei & Barbas, 2002). As for the second function, there is abundant animal brain literature suggesting that a hierarchically-organized subcortical circuit constituted by the central nucleus of the AMG, the hypothalamus, the bed nucleus of the stria terminalis and the periaqueductal gray matter mediates species-specific basic survival behaviours (Holstege, 1991; the Royal Road, Panksepp, 1998). The basolateral complex of the AMG, in concert with the ventromedial prefrontal cortex and the ventral striatum (nucleus accumbens), underlies the modulation and regulation of these visceral functions and behavioural choices (Price, 2003; Mogenson, Jones, & Yim, 1980; Groenewegen & Trimble, 2007). 2 There has been a general tendency in the field of social neuroscience to concentrate on the “contemplative” part of the social interaction (i.e., putting participants in position of observers who are trying to decipher others’ mental states) and to ignore the preparation of an adaptive but flexible response which is an essential part of social interaction. Whether the stress on shared-representation constitutes a symptom or the cause of this tendency is an interesting question, but it lies out of the scope of the paper. Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i J. Grèzes, G. Dezecache / Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 4 In addition, there is consistent evidence from monkey, cat and rat studies, that the magnocellular division of the basal nucleus of the AMG complex sends direct projections to cortical motor-related areas (cingulate motor areas, SMA and pre-SMA, lateral premotor cortex (PM), primary motor cortex and somatosensory cortex) (Avendano, Price, & Amaral, 1983; Amaral & Price, 1984; Llamas, Avendano, & Reinoso-Suarez, 1977; Llamas, Avendano, & Reinoso-Suarez, 1985; Macchi, Bentivoglio, Rossini, & Tempesta, 1978; Sripanidkulchai, Sripanidkulchai, & Wyss, 1984; Morecraft et al., 2007; Jürgens, 1984; Ghashghaei, Hilgetag, & Barbas, 2007). These latter findings provide a potential mechanism through which AMG can influence more complex and subtle behaviours elicited during social interactions other than the well-known automatic stereotypical emotional behaviours (Llamas et al., 1977; Chareyron, Banta Lavenex, Amaral, & Lavenex, 2011). 3.2. Neuroimaging evidence In humans, it is currently unknown whether there are direct anatomical connections between the AMG and the cortical motor system. Yet, using functional magnetic resonance imaging (fMRI), we and others have revealed, during the perception of emotional displays, co-activation of the AMG and motor-related areas, notably the PM (Isenberg et al., 1999; Whalen et al., 2001; Decety & Chaminade, 2003; Carr et al., 2003; de Gelder, Snyder, Greve, Gerard, & Hadjikhani, 2004; Sato, Kochiyama, Yoshikawa, Naito, & Matsumura, 2004; Grosbras & Paus, 2006; Warren et al., 2006; Grèzes, Pichon, & de Gelder, 2007; Pichon, de Gelder, & Grèzes, 2008; Hadjikhani, Hoge, Snyder, & de Gelder, 2008; Pichon, de Gelder, & Grèzes, 2009; Pouga, Berthoz, de Gelder, & Grèzes, 2010; Van den Stock et al., 2011; Pichon, de Gelder, & Grèzes, 2012; Grèzes, Adenis, Pouga, & Armony, 2012). Moreover, functional connectivity was revealed between the amygdala and motor-related areas (Qin, Young, Supekar, Uddin, & Menon, 2012; Ahs et al., 2009; Roy et al., 2009; Grèzes, Wicker, Berthoz, & de Gelder, 2009; Voon et al., 2010), and direct evidence that emotional stimuli prime the motor system and facilitate action readiness was provided by transcranial magnetic stimulation (TMS) studies (Oliveri et al., 2003; Baumgartner, Matthias, & Lutz, 2007; Hajcak et al., 2007; Schutter, Hofman, & van Honk, 2008; Schutter & Honk, 2009; Coombes et al., 2009; Coelho, Lipp, Marinovic, Wallis, & Riek, 2010; van Loon, van den Wildenberg, & van Stegeren, 2010). Of particular interest, the mean coordinates reported in PM in above-mentioned fMRI studies using facial and body expressions of fear and anger fall within the ventral/dorsal PM border (Tomassini et al., 2007a) (see Fig. 1). Knowing that lateral PM is implicated in motor preparation and environmentally-driven actions (Hoshi & Tanji, 2004; Passingham, 1993) and that in monkeys electrical stimulation of PMv/PMd border triggers characteristic defensive movements (Cooke & Graziano, 2004; Graziano & Cooke, 2006), we suggest that emotional displays, once evaluated in amygdala, prompt or modulate dispositions to interact, as revealed by activity in cortical premotor cortex (see Fig. 1). 3.3. Behavioural markers The fact that emotional expressions trigger actions in the observer is also consistent with recent behavioural studies. Scholars agree that when exposed to emotional expressions individuals display rapid facial reactions (RFRs) detectable by electromyography (EMG) (Bush, McHugo, & Lanzetta, 1986; Dimberg & Thunberg, 1998; Dimberg, Thunberg, & Elmehed, 2000; Hess & Blairy, 2001; McIntosh, 2006). In a recent study (Grèzes et al., 2013), we raised the question of whether RFRs, instead of reflecting the function of the shared-representations system (see Section 2.3.), would reveal, in the observer, preparation of appropriate actions in response to social signal. To this end, we manipulated two critical perceptual features that contribute to determining the significance of others’ emotional expressions: the direction of attention (toward or away from the observer) and the intensity of the emotional display (Grèzes et al., 2013). Electromyographic activity over the corrugator muscle was recorded while participants observed videos of neutral to angry body expressions. From a shared motor representation perspective (see Section 2.3.), one should expect either (1) no early RFRs in absence of facial expressions as the body alone does not provide the cues necessary for facial motor matching (strict perspective); (2) congruent RFRs to others’ angry faces, irrespective of the direction of attention of the emitter (Chartrand & Bargh, 1999) or (3) less mimicry when directed at the observer as anger conveys non-ambiguous signals of non-affiliative intentions (Bourgeois & Hess, 2008; Hess, Adams, & Kleck, 2007). Yet, self-directed bodies induced greater RFRs activity than other-directed bodies; additionally RFRs activity was only influenced by the intensity of anger expressed by self-directed bodies (see Fig. 2). Our data clearly indicate that facial reactions to body expressions of anger are not automatic and cannot be interpreted as pure non-affective motor mimicry. A strict motor mimicry process is indeed not sufficient to explain why RFRs are displayed to non-facial and non-social emotional pictures (Dimberg & Thunberg, 1998), emotional body expressions (Magnee, Stekelenburg, Kemner, & de Gelder, 2007; Tamietto et al., 2009) and vocal stimuli (Bradley & Lang, 2000; Hietanen, Surakka, & Linnankoski, 1998; de Gelder, Vroomen, Pourtois, & Weiskrantz, 1999), nor why they are occasionally incongruent with the attended signals (Moody, McIntosh, Mann, & Weisser, 2007). By revealing that RFRs were influenced by the self-relevance of the emotional display which varies as a function of the emitter's direction of attention and the intensity of his/her emotional expression, our data rather suggests that RFRs are behavioural markers of an emotion-to-action process allowing for the preparation of adaptive but flexible behavioural responses to emotional signals. 3.4. Interindividual variability in healthy populations and psychiatry The idea that we perceive our physical and social environment as consisting of numerous opportunities for action entails that social understanding is intertwined with social interactive skills (McGann & De Jaegher, 2009). Indeed, as mentioned above, the set of opportunities for actions, as it emerges from the specific relationship between a single agent and features of its environment, critically depends on this agent's abilities and preferences. In this respect, disorders that impair an individual's ability to accurately detect opportunities for action (Loveland, 2001), such as autism spectrum disorders (ASD), should reveal abnormal interplay between limbic and motor systems. ASD are neurodevelopmental disorders characterized by a unique profile of impaired social interaction and communication (e.g. Lord et al., 1989) with a major impact on social life (American Psychiatric Association, 1994). Of importance here, individuals with autism display “a pervasive lack of responsiveness to others” and “marked impairments in the use of multiple nonverbal behaviours to regulate social interactions” (American Psychiatric Association, 1994). The facts that remarkable maturation process of the brain's affective and social systems spans from childhood to adulthood, and that social cognitive skills need extensive tuning during development may explain why ASD and other developmental disorders are often associated with pervasive social skill impairments (Kennedy & Adolphs, 2012). Moreover, social cognitive abilities are subject to important inter-individual variability: there are large individual differences even in healthy individuals (Kennedy & Adolphs, 2012) and the severity of ASD Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i J. Grèzes, G. Dezecache / Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 5 Fig. 1. (A) Group average activations elicited by fear vs. neutral dynamic expressions (top: Grèzes et al., 2007; bottom: Pouga et al., 2010), anger vs. neutral expressions (middle: Pichon et al., 2008). (B) Bar charts representing the number of activations found during the observation of neutral actions (blue) (see meta-analysis by Morin & Grèzes, 2008) and during the observation of facial and body expressions of fear and anger (green) (meta-analysis performed for this review on all papers that to our knowledge found PM activity). The mean coordinates along the z axis for emotion is 50. (C) Border between the ventral and the dorsal premotor cortex in the human brain (Tomassini et al., 2007). (D) On top, the lateral view of the monkey brain with the parcellation of the motor and parietal cortex (Rizzolatti, Fogassi, & Gallese, 2001), on the bottom, drawings represent the final defensive postures evoked by the electrical stimulation of the border between PMv and PMd. characteristics are posited to lie on a continuum extending into the typical population (as measured by Autism Spectrum Quotient, ASQ) (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001). For example, typical adult performance on behavioural tasks that are impaired in ASD individuals is correlated with the extent to which they display autistic traits (Nummenmaa, Engell, von dem Hagen, Henson, & Calder, 2012), strongly suggesting that the boundary between typical and pathological populations is not clear cut and might be better viewed as a continuum. Given this (the importance of inter-individual variability in social cognitive abilities as well as the blurred boundary between typical and atypical populations), it is important to track the development of pathological characteristics while consistently collecting anatomical data. To our knowledge, only one study has looked at age-related changes in AMG connectivity and showed drastic changes in the intrinsic functional connectivity of the basolateral nucleus of AMG with sensorimotor cortex, with weaker integration and segregation of amygdala connectivity in 7-to 9-yold children as compared to 19-to 22-y-old young adults (Qin et al., 2012). Also, Greimel et al. (2012) recently demonstrated that age-related changes in grey matter volume in AMG and PM differed in ASD as compared to typically developing (TD) participants. We revealed, in adults with ASD, atypical processing of emotional expressions subtended by a weaker functional connectivity between AMG and PM (Grèzes et al., 2009). Similarly, Gotts et al. (2012) showed, using a whole-brain functional connectivity approach in fMRI, a decoupling between brain regions in the evaluation of socially relevant signals from motor-related circuits in ASDs. These results emphasize the importance of studying the integrity of between regions (and even between-circuits) connectivity, rather than looking for mere localized abnormalities. They also suggest the possibility that weak limbic-motor pathways might contribute to difficulties in perceiving social signals as opportunities for actions. Ultimately, such abnormal connectivity should impact on the preparation of adaptive but flexible behavioural responses in social context. 4. Reuniting shared motor representations and emotion-toactions processes in a single cognitive and neural framework Overall, the literature suggests that the AMG works in tandem with cortical motor-related areas and critically raise the question of the functional interplay between shared motor representations and emotion-to-actions processes. To clarify the relationship Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i 6 J. Grèzes, G. Dezecache / Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Fig. 2. (A) Time course of the mean EMG corrugator supercilii activity as a function of the Target of Attention (S for Self (green), O for Other (blue)) and the Levels of Emotion (1–4). Activity reflects average activation during each 100-ms time interval. B) Mean activity over the corrugator supercilii region between 300 and 700 ms. The mean (SEM) activity is represented as a function of (Left) the Target of Attention (Self (green), Other (blue)) and the Levels of Emotion (1–4) and, (Right) only for Self-oriented conditions for the 4 Levels of Anger. *p o0.05. between these two processes, we studied the perception of dynamic emotional body expressions. In our studies, we concentrated on two emotions: fear and anger expressions (both signal threat), as the ability to quickly detect environmental danger and to subsequently prepare a set of adequate motor commands is very adaptive. These two emotions are ideal to address the link between the motor and the limbic systems. In our initial fMRI experiments (Grèzes, Pichon, & de Gelder, 2007; Pichon et al., 2008; Grèzes et al., 2009; Pouga et al., 2010), we presented neutral and emotional body expressions displayed in either still or dynamic formats. Such a factorial design allows us to disentangle the involvement of shared motor representations from emotion-to-actions processes during the perception of emotional displays. The comparison between dynamic and static actions triggered differential neural activity in brain areas associated with shared motor representations (STS, parietal, somatosensory and inferior frontal gyrus IFG44). On the other hand, the comparison between emotional and neutral expressions prompted activity in brain areas we suggested to be related to emotion-to-actions processes (fusiform gyrus and STS, AMG and PM). Of interest here, activity in the posterior part of inferior frontal gyrus (IFG44)) was clearly related to the perception of dynamic actions, whether neutral or emotional, whereas activity in the PM was only related to the perception of angry dynamic expressions (see Fig. 2). Also, the distribution of the activations reported during the perception of threatening faces or bodies in several studies including ours is different from the one found during the observation of neutral actions (see Fig. 1B). Altogether, these results suggest that the two systems could run in parallel. To further explore the relation between these two processes, we performed psycho-physiological interactions (PPI—functional connectivity) analyses on data collected by Pouga et al. (2010) to identify (i) changes in the connectivity pattern of shared motor representations when an action becomes emotional; and (ii) changes in the connectivity pattern of emotion-to-actions processes when an fearful stimulus becomes dynamic (Grèzes and Pouga, unpublished data—see Fig. 3). Two areas in the right hemisphere were selected for shared motor representations, IFG44 and posterior part of STS, and two for emotion-to-actions, STS and AMG. The STS is a brain region common to both processes (Pichon et al., 2008). The results revealed that when an action becomes emotional, the STS informs a subcortical circuit that represents a major output channel for the limbic system involved in visceral and basic survival behaviours (Holstege, 1991) which is also under the control of the orbitofrontal cortex (Price, 2003). In parallel, one of the main nodes of shared motor representations network (IFG44) increases its connectivity with somatosensory cortices which could reflect the representation of the sensory and somatic states (i.e. “what it feels like”) of the perceived body expression of emotion (Gallese & Goldman, 1998; Adolphs, 2002) (see Fig. 3A— Supplementary Table 1). Furthermore, our results further revealed that, when fearful postures become dynamic, both the STS and the AMG increased their connectivity with visual areas but more interestingly here, with the pre-SMA and the border between ventral and dorsal premotor cortex (PMd/PMv) (see Fig. 3B— Supplementary Table 2). Together these results suggest that when facing the emotional display of others, two processes are working in parallel: shared motor representations, which comprise components of the perceived action and associated predicted somatosensory consequences that anticipate the unfolding of other's impending behaviour and feelings, and the emotion-to-actions processes that influence the preparation of adaptive responses in the observed to emotional signal. The picture provided by fMRI alone was however limited by the poor temporal resolution of the BOLD response. Therefore, in a follow-up study, we combined electroencephalography (EEG) with fMRI to determine whether shared motor representations and emotion-to-action processes interact, and if they do, when and where this happens in the human brain (Conty, Dezecache, Hugueville, & Grèzes, 2012). Participants viewed dynamic stimuli depicting actors producing complex social signals involving gaze, a pointing gesture, and the expression of anger. We demonstrated that the emotional content of the stimuli was first processed in the Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i J. Grèzes, G. Dezecache / Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 7 Fig. 3. (A) Left) Statistical parametric maps of brain activation in response to the observation of (a) dynamic versus static emotional expressions (red, top: anger (Pichon et al., 2008), bottom: fear (Pouga et al., 2010)) and dynamic versus static neutral expressions (green). Right) Parameter estimates (arbitrary units, mean centered) of the PMv/ PMd border (top: xyz ¼50 2 48) and of the posterior part of inferior frontal gyrus IFG44 (bottom: xyz¼ 52 16 32). AS: Anger Static; AD: Anger Dynamic; NS: Neutral Static; ND: Neutral Dynamic. (B) Using Psychophysiological Interaction (PPI), Grèzes and Pouga (unpublished data) addressed changes in the connectivity pattern of two brain areas sustaining shared motor representations (black circles of the middle picture—the pSTS (xyz ¼ 54 46 8) and inferior frontal cortex IFG44 (xyz ¼ 46 12 26)) when an action becomes emotional. Middle picture: statistical maps showing brain activations in the right hemisphere in response to the perception of dynamic body expressions vs. static ones, irrespective of the emotional content, rendered on a partially inflated lateral view of the Human PALS-B12 atlas. Right picture: statistical maps showing increased functional connection with the STS (purple) or IFG44 (green) (see Supplementary Table 1). (C) Changes in the connectivity pattern of emotion-to-actions brain areas (STS (xyz¼ 50 40 2) and Amygdala (xyz ¼18 6 24)) when a static fearful stimuli becomes dynamic. Middle picture: statistical maps showing brain activations in the right hemisphere in response to the perception of fearful expressions vs. neutral ones, irrespective of their nature (static or dynamic), rendered on a partially inflated lateral view of the Human PALS-B12 atlas. Right picture: statistical maps showing increased functional connection with the STS (purple) or the Amygdala (red) (see Supplementary Table 2). Fig. 4. (Top) Stimuli examples. Here, the actor displays direct gaze (but could also in the experiment display averted gaze), angry or neutral facial expression, and a pointing gesture or not. From the initial position, one (gaze direction only), two (gaze direction and emotional expression or gaze direction and gesture), or three (gaze direction, emotional expression, and gesture) visual cues could change. (Bottom) Joint ERP-fMRI results (from Conty et al., 2012). Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i J. Grèzes, G. Dezecache / Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 8 AMG (170 ms) before being integrated with other visual cues (gaze and gesture) in the PM (200 ms). Of interest, the highest level of activity in the PM was revealed for the condition which conveyed the highest degree of potential interaction; i.e., viewing an angry person with gaze and pointed finger aimed at oneself (see Fig. 4). Only a combination of two complementary mechanisms explains the highest level of activity in the PM we observe for the highest degree of potential social interaction: (1) the estimation of prior expectations about the perceived agent's immediate intent, which most probably relies on shared motor representation (Kilner, Friston, & Frith, 2007); and (2) the evaluation of the emotional content and the selection of the appropriate action for the observer to deal with the immediate situation. This study thus provides evidence that shared motor representation and emotionto-actions processes can interact as early as 200 ms after the appearance of a signal of threat. Whether these two complementary mechanisms are also activated during the perception of emotional signals other than fear and anger is an interesting question. While it is reasonable to think that they are also crucial in the processing of other signals related to the presence of threat in the environment (such as disgust), their contribution to the processing of other social signals (e.g., joy) is an empirical question. If electrophysiological recordings in rodents show consistent limbic-motor interactions during sexually arousing contexts (Korzeniewska, Kasicki, & Zagrodzka, 1997), it is still unknown whether they can be extended to other positive or socially rewarding contexts, as well as whether they are preserved in humans. 5. Summary In this paper, we argue that social signals that include emotional displays (here: signals related to threat) can be considered as prompting a wide range of opportunities for actions in the observer, even if these opportunities do not materialise into overt actions. When facing threat displays of others, one need to decipher the emitted emotional signal and predicts its immediate future while preparing to respond to it in an adaptive way. We argue that the two processes are sustained by shared motor representations and emotion-to-actions mechanisms, respectively. These two mechanisms can be prompted independently by the same stimuli and can either run in parallel (Bavelas et al., 1986) or together (Conty et al., 2012). Acknowledgements The presented work was supported by a Human Frontier Science Program Grant (RGP 0054/2004), an EU Six Framework Program (N1 NEST-2005-Path-IMP-043403), an ACI Neurosciences intégratives et computationnelles 2004 program, an Agence National of Research (ANR) “Emotion(s), Cognition, Comportement” 2011 program (Selfreademo), by the Fondation Roger de Spoelberch and by INSERM. The department is supported by ANR11-0001-02 PSLn and ANR-10-LABX-0087. We wish to warmly thank all our collaborators, and notably Terry Eskenazi and Michèle Chadwick for their useful comments on the manuscript. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.neuropsychologia. 2013.09.019. References Adolphs, R. (2002a). Neural systems for recognizing emotion. Current Opinion in Neurobiology, 12, 169–177. Adolphs, R., Tranel, D., & Denburg, N. (2000). 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A functional MRI study of human amygdala responses to facial expressions of fear versus anger. Emotion, 1, 70–83. Williams, J. H. G., Whiten, A., Suddendorf, T., & Perrett, D. I. (2001). Imitation, mirror neurons and autism. Neuroscience and Biobehavioral Reviews, 25, 287–295. Wilson, M., & Knoblich, G. (2005). The case for motor involvement in perceiving conspecifics. Psychological Bulletin, 131, 460–473. Yabar, Y. C., & Hess, U. (2006). Display of empathy and perception of out-group members. New Zealand Journal of Psychology, 36, 42–50. Please cite this article as: Grèzes, J., & Dezecache, G. How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia (2013), http://dx.doi.org/10.1016/j.neuropsychologia.2013.09.019i Is emotional transmission equivalent to contagion? Following the Le Bonian tradition, recent psychological literature presents the phenomenon of emotional transmission as analogous to a process of contagion: it is thought to be largely automatic and irrepressible, both from the standpoint of the contaminator (who does not have the intention of contaminating others), and from that of the receivers (who end up being involuntarily and unintentionally contaminated). In this respect, emotional transmission is often characterized as automatic behavior (Hatfield et al., 1994), i.e., behavior triggered by external events or features from the environment, in the absence of any control by the subject (Bargh & Williams, 2006). In what follows, I tend to avoid the concept of "automaticity" and prefer to use that of "irrepressibility". Although they may appear semantically interchangeable, these concepts encompass two distinct ideas: a process can be automatic (it is triggered whenever a certain stimulus is present) but not irrepressible (it can be inhibited even at an early stage). In fact, both Le Bon and the current tradition are largely inaccurate when they describe the process of emotional transmission as being a contagious process. Many factors are known to influence the reception of emotions in observers. This is consistent with considering the process of emotional transmission as a process of communication of information. In order to be a stable, any medium of communication should allows both emitters and receivers to benefit from exchanging information (Krebs & Dawkins, 1984): if transmitting information is too costly (e.g., if emotional displays constantly reveal internal states that had better be concealed), emitters will stop emitting; conversely, if receiving information is detrimental to receivers (e.g., to be contamined by the joy of your enemy), receivers will stop considering the signals of emitters. To consider emotional transmission as a contagious and irrepressible process entails that receivers will constantly be contamined by the emotions of emitters. This is implausible as it threatens the very stability of emotional transmission, which ultimately is an instance of emotional communication. To keep emotional transmission stable, proximal mechanisms must exist to make responses flexible over the course of emotional interactions. Before addressing this question further, it is important to specify to what extent emotional transmission can be construed as a process of transmission of information. 48 Emotional transmission as a process of influencing others It is intuitively tempting to base our understanding of the transmission of emotional information on a human language-based metaphor, which consists in thinking that, over the course of an emotional interaction, emitters provides meaning that is coded into a given structure (the signal), and that receivers have to decode the signal to retrieve the meaning (Green & Marler, 1979). Such application of linguistics-inspired features to emotional communication suffers from a major inconsistency, i.e., the absence of clear representational parity in emitters and receivers. Indeed, the function of the production of, say, anger expressions is not the share of a certain piece of information related to the emotional state of the emitter but rather, to exert threat over the recipient. Similar problems have been posed for non-human communication by the primatologist Drew Rendall and his colleagues (Rendall, Owren, & Ryan, 2009). They promote the view that animal signals are best understood as "tools for influencing the affect and behavior of others" (Rendall & Owren, 2010), a definition which is consistent with evolutionary biologists’ definition of communication. They define communication as a process of influencing or affecting other organisms rather than the transmission of a concrete entity ("the mental and emotional states of the sender") which would have to be decoded from the signal itself (Maynard-Smith & Harper, 2004; Scott-Phillips, 2008). The use of such a definition has further lead Drew Rendall and colleagues to the provocative claim that the vervet monkey leopard-related calls (famously described in Seyfarth, Cheney, & Marler, 1980) would mean "run into a tree!", if they ever happened to carry information at all (Scott-Phillips, 2010). Thus, non-human signals can be said to carry a form of imperative content (Sterelny, 2011), rather than a descriptive one. Doing so, they stress the idea that non-human signals do not intend to convey meaning but are rather means to influence others. Such signals would be effective through the exploitation, by their producers, of sensory biases in the receivers. Producers would produce signals that have evolved to match preexisting sensory biases in order to generate desirable effects (behavioral responses) in the receiver. Good examples of these signals are the squeaks, shrieks and screams typically produced by many primate species. Because of their highly aversive aspect, these screams can effectively 49 have the desire effect on the receiver, such as forcing a mother to nurse, or preventing aggressors to carry on with their attacks (Rendall et al., 2009; Rendall & Owren, 2010). Such a definition of communication may also be appropriate when dealing with the transmission of emotional information. Yet, where the question of the meaning of emotional signals is directly addressed in the literature, a linguistically-inspired information frame is generally employed, and entails representational parity between emitters and receivers. The classical view indeed assumes that, during the course of any emotional communication event (i.e., somebody expressing fear), observers are committed to the job of fully interpreting others’ emotional life through the use of a set of decoding processes whose function is to extract a meaning out of a structure that have previously been encoded by the producer (Scherer, 2009). As a consequence, emotion theorists could be better off if they were considering emotional communication as a process of influence (where emitters try to exert pressures on recipients) rather than as a process of exchange of information (that ultimately supposes representational parity between emitters and recipients). Emotional transmission 6= contagion As mentioned above, if emotional transmission is a considered as communication, it follows that the process cannot be completely equated with contagion. In any communication system, the cognitive systems of emitters and receivers are tuned to selectively emit and respond to others’ signals. This has dramatic consequences on our understanding of so-called emotional “contagion”: the reception of emotional information cannot be irrepressible. This, however, does not mean that emitters and receivers have control over their acts during the course of emotional communication: features of the cognitive system may indeed have been selected to selectively produce and react to emotional displays without the subjects experiencing voluntary control (such feelings being situated at the proximal level of analysis). This issue is the subject of an article I have co-authored with Dr. Hugo Mercier and Dr. Thom Scott-Phillips, which is to be found in the international peer-reviewed journal Journal of Pragmatics (July 2013). 50 + Models PRAGMA-3768; No. of Pages 13 Available online at www.sciencedirect.com Journal of Pragmatics xxx (2013) xxx--xxx www.elsevier.com/locate/pragma An evolutionary approach to emotional communication Guillaume Dezecache a,b,*, Hugo Mercier c,**, Thomas C. Scott-Phillips d b a Laboratory of Cognitive Neuroscience (LNC), Inserm U960 & IEC, Ecole Normale Superieure (ENS), 75005 Paris, France Institut Jean Nicod (IJN), UMR 8129 CNRS & IEC, Ecole Normale Superieure & Ecole des Hautes Etudes en Sciences Sociales (ENS-EHESS), 75005 Paris, France c CNRS, L2C2, UMR5304, Institut des Sciences Cognitives (ISC), 69675 Bron Cedex, France d School of Psychology, Philosophy and Language Sciences, University of Edinburgh, Edinburgh EH8 9AD, United Kingdom Received 8 May 2012; received in revised form 18 June 2013; accepted 19 June 2013 Abstract The study of pragmatics is typically concerned with ostensive communication (especially through language), in which we not only provide evidence for our intended speaker meaning, but also make manifest our intention to do so. This is not, however, the only way in which humans communicate. We also communicate in many non-ostensive ways, and these expressions often interplay with and complement ostensive communication. For example, fear, embarrassment, surprise and other emotions are often expressed with linguistic expressions, which they complement through changes in prosodic cues, facial and bodily muscular configuration, pupil dilatation and skin colouration, among others. However, some basic but important questions about non-ostensive communication, in particular those concerned with evolutionary stability, are unaddressed. Our objective is to address, albeit tentatively, this issue, focusing our discussion on one particular class of non-ostensive communication: emotional expressions. We argue that existing solutions to the problem of stability of emotional communication are problematic and we suggest introducing a new class of mechanisms---mechanisms of emotional vigilance---that, we think, more adequately accounts for the stability of emotional communication. © 2013 Elsevier B.V. All rights reserved. Keywords: Evolution; Ostensive communication; Non-ostensive communication; Emotional signals; Vigilance 1. Introduction Communication is ostensive if, when we communicate, we not only provide evidence for our intended speaker meaning, but we also make manifest our intention to do so (Grice, 1989; Sperber and Wilson, 1995). Much human communication is ostensive---but we also communicate in many non-ostensive ways, such as body language and various expressions of emotion. Often, ostensive and non-ostensive behaviours complement one another. For example, fear, embarrassment, surprise, and other emotions are often expressed ostensively---with, say, linguistic expressions. At the same time, they are also expressed in non-ostensive ways with, among others, changes in prosodic cues (Frick, 1985), facial (Ekman, 1993) and bodily muscular configuration (James, 1932), pupil dilatation (Bradley et al., 2008) and skin colouration (Shearn et al., 1990). These ostensive and non-ostensive expressions are typically expected to be consistent with one another: we do not verbally express fear but at the same time produce the facial expressions associated with * Corresponding author at: Laboratory of Cognitive Neuroscience, Ecole Normale Supérieure, 29 rue d’Ulm, 75005 Paris, France. Tel.: +33 144322638; fax: +33 144322974. ** Corresponding author at: L2C2, Institut des Sciences Cognitives, 67 boulevard Pinel, 69675 Bron Cedex, France. E-mail addresses: [email protected] (G. Dezecache), [email protected] (H. Mercier), [email protected] (T.C. Scott-Phillips). 0378-2166/$ -- see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.pragma.2013.06.007 Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 2 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx happiness, or vice versa. If such contradictions occur, observers have to reject one or the other observation as an unreliable guide to the focal individual’s state of mind. Although pragmatics is typically concerned with ostensive communication, non-ostensive behaviour also plays an important role in communication, and it often interacts with ostensive communication in non-trivial ways (e.g., in conversational contexts, see Proust, 2008). However this is not sufficiently reflected in the literature. Many fundamental questions about the role of non-ostensive behaviour in communication are unaddressed. In this paper, we specifically consider the evolution of non-ostensive communication. From an evolutionary perspective, a central question for any communication system is ‘‘what prevents dishonesty?’’. Although there is a substantial literature devoted to the evolution of the abilities that allow humans to produce and understand language (e.g., Pinker and Bloom, 1990; Bickerton, 1992; Jackendoff, 2003), the problem of honesty has not figured greatly in those discussions (but see Dessalles, 2007; ScottPhillips, 2008a; Sperber et al., 2010). The ‘honesty’ of non-ostensive communication is even less studied, at least from an evolutionary perspective. In this paper, our agenda is principally diagnostic: we wish to highlight the evolution of non-ostensive communication as a topic worthy of future research and suggest tentative answers intended to spur future research. Moreover, our discussion of non-ostensive communication will be focused on one particular type of non-ostensive communication: emotional signals. Note that we are specifically concerned with the involuntary use of emotional signals. Emotional expressions can also be used voluntarily, and this opens up an interesting current area of pragmatic research (see e.g., Wharton, 2009). There are three reasons for our focus on emotional expressions.1 First, as discussed above, they are frequently displayed alongside ostensive signals, which they complement and interact with (Proust, 2008). Second, the functioning of emotional expressions has been the focus of much previous research (e.g., Fridlund, 1994; Ekman, 2003a), and so our discussion can be informed by a wealth of previous work. Third, there have been tentative answers to the issue of the honesty of emotional communication (e.g., Mortillaro et al., 2012; Mehu et al., 2012; Owren and Bachorowski, 2001; Hauser, 1997; Ekman, 2003b) but none are wholly satisfactory, for reasons we shall document. The paper is structured as follows: in the next section (section 2) we introduce a broad evolutionary framework, including definitions of key terms, providing the scaffolding for subsequent discussion; in section 3 we ask whether emotional expressions can be seen as communicative at all; in sections 4 and 5 we discuss the risks of deception in emotional communication, and how they can be avoided; in these sections, we also discuss and reject conventional hypotheses that have been proposed to account for the stability of emotional communication, and introduce a new class of mechanisms which, we think, may allow for the stability of emotional communication---mechanisms of emotional vigilance; in section 6, we discuss the question of why non-ostensive communication persists in humans; finally, in section 7, we discuss the question of interplay between the so-called emotional vigilance mechanisms, and those of epistemic vigilance that have previously been introduced by Sperber et al. (2010). 2. Communication and its evolution We define communication in the following way: communication occurs when an action (a signal) produced by an individual organism causes a change (a reaction) in another organism, where both the signal and the reaction have been designed for these purposes (Scott-Phillips, 2008b; Table 1). If the action has been designed for these purposes, but the reaction has not, then the interaction is coercive; and if the reaction has been designed for these purposes but the action has not, then the interaction is a cue. The overall situation is summarised in Table 1. Fig. 1 gives an everyday example of all three types of interaction. The example of signalling/communication in the figure is ostensive, but the definition applies equally well to non-ostensive communication. Indeed, this framework is a generalised version of one developed in evolutionary biology (Maynard-Smith and Harper, 2003; Scott-Phillips, 2008b), which is concerned with the communication systems of a wide variety of different species, almost all of which do not involve ostension. There are other approaches to defining communication (e.g., Hauser, 1997; Reboul, 2007; see ScottPhillips, 2008b for a review). We adopt the definition that we do for two reasons. First, it is the only approach that works across a range of prima facie cases, in the sense that they correspond to our intuitions about what is and is not a signal/ cue/coercive behaviour (Scott-Phillips, 2008b). Second, the clear functional distinction that it makes between cues and signals is particularly important for questions concerning the evolution and stability of communication systems. From an evolutionary perspective, the classic question in the study of communication is stability (Maynard-Smith and Harper, 2003; Searcy and Nowicki, 2007). Signallers should presumably evolve to send signals leading to responses that are in their best interests. Yet, these interests may conflict with the receivers’ best interests. If such is the case, receivers should in turn evolve not to attend to the signal, and this would then lead the system to collapse. The same logic can also 1 Expressions and signals are used as synonyms. Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx 3 Table 1 Definitions of signal/response, cue and coercion (Y = yes; N = no). See main text for discussion. Signal/response Cue Coercion Function of action to cause reaction? Function of reaction to be caused by action? Y N Y Y Y N Adapted from Scott-Phillips (2008a). [(Fig._1)TD$IG] Fig. 1. Everyday examples of the distinction between signal/response, cue and coercion. (from Scott-Phillips and Kirby, 2013): this image depicts a young man (in the centre) pushing his colleague from her chair. This act involves three kinds of interaction with the audience: the first interaction involves the young man and the colleague he is pushing. This interaction is an example of coercion. The second interaction, involving the young man and the colleague he is laughing with, is a case of communication: the act of pushing is a signal whose purpose is to affect the female colleague who is witnessing: her smile is the response. Finally, in the third interaction -- between the young man and his boss [this latter being at the left side of the image] --, the pushing is a cue: it informs the boss about the behaviour of his employee, even though this was not its function. Note that the example of signalling behaviour in this figure is ostensive but the definition also applies to non-ostensive communicative behaviours, as we will argue throughout the paper. apply over individual lifetimes: if an individual’s communication is regularly unreliable (for example, because she is dishonest), then others will learn not to pay too much attention, if any, to what the focal individual has to say. This is exactly the outcome described in Aesop’s fable of the Boy That Cried Wolf. The evolution and stability of communication thus presents a strategic problem: what prevents widespread deception, and the consequent collapse of the system? (Note that we are using ‘deception’ in functional terms. There is deception when one organism exploits another’s organism sensitivity to certain signals to its own benefits. As such, and as long as it has to do with non-ostensive communication,2 deception does not entail volition or consciousness from the sender.) Whatever the answers to these questions are in any particular case, the consequence of these strategic concerns is that stable communication systems should be beneficial for both parties (Scott-Phillips, 2010b). If they were not then one party would stop emitting or attending to the signal. It is important to recognise that this is a problem for all evolved communication systems, and not just those where signal production is voluntary, or intentional. This is because it is a problem at the ultimate, rather than proximate, level of analysis. Ultimate explanations are concerned with why a behaviour exists; proximate explanations with how it works (see Scott-Phillips et al., 2011 for extensive discussion). The dynamic of natural selection leads to organisms whose behaviour is designed to maximise their inclusive fitness (Grafen, 2006), and ultimate explanations of behaviour explain how a particular behaviour contributes to that. For example, if lying is explained in terms of how it will lead to beneficial outcomes for the speaker, then that is an ultimate explanation if the beneficial outcomes eventually lead to positive fitness consequences, on average. If, on the other hand, lying is explained in terms of psychological motivations, then this is a proximate explanation: this sort of explanation is concerned with how the benefits are achieved, i.e., how behaviour operates. Both ultimate and proximate explanations are complementary and required for a proper understanding of behaviour (Mayr, 1963; Tinbergen, 1963; Scott-Phillips et al., 2011). In the case of emotional communication, solutions to the problem of honesty and stability of emotional communication have often confused the proximal and ultimate level of explanation by suggesting that emotional expressions are honest 2 While deception in the context of non-ostensive communication does not presuppose any conscious intention to manipulate the receiver, it is not true of ostensive communication where senders often consciously intend to fool receivers (Maillat and Oswald, 2009). Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 4 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx because they are involuntary, being mandatorily associated with a corresponding emotional experience (e.g., the Duchenne smile3 in Owren and Bachorowski, 2001). The issue is that a proximal mechanism (the involuntariness of emotional displays) is offered to answer an ultimate question (about the honesty of emotional communication). We detail below the more specific issues with this answer. Moreover, neglecting the ultimate/proximal distinction can also lead to other misunderstandings, such as equating signals defined at the ultimate level, as we have done, with signals that result from intentional, voluntary, and strategic decision-making (which are all proximate level phenomena). With this in mind, we now turn to the question of what keeps human communication stable. For ostensive communication, this question receives relatively commonsensical answers. The ultimate reason it is stable is that the benefits to dishonesty are outweighed by the social costs of ostracism that will follow if one is perceived as a liar or an otherwise unreliable communicator (Lachmann et al., 2001; Scott-Phillips, 2010a) (this is indeed what happens to the boy in Aesop’s fable). From a proximate perspective, a suite of cognitive mechanisms allows humans to be vigilant towards communicated information: we filter the information we receive via communication so that we are not unduly misled. These mechanisms have recently been termed epistemic vigilance (Sperber et al., 2010). However with regard to nonostensive communication, and expressions of emotion in particular, the situation is less immediately clear. We will address this issue in section 4. However before we are able to do that, we must address an important preliminary question, about exactly what we mean by emotional displays, and whether they qualify as signals at all. 3. Are emotional expressions genuine signals? Our goal is to apply the logic of the evolution of communication to emotional expressions. A first and necessary step must be to establish that emotional expressions are indeed signals, following the definition offered above. Although it intuitively seems as if the function4 of at least some emotional expressions is communicative---to let others know we need them when distress is expressed, for example---this assumption will not always be justified. Behaviours often inform others only incidentally (i.e., they are cues). Indeed, Darwin (1872) suggested that this is exactly what emotions are. For example, the widening of the eyes created by fear could allow the individual to enlarge her visual field and be better prepared to react to potential threats (Susskind et al., 2008). The question is whether these pre-existing behaviours underwent later selection because of any informative function they might have. How can we show this? A signal entails specific adaptations for the signal in both senders and receivers. The sender must do more than merely coerce the receivers and the receivers must do more than merely respond to a cue. Unfortunately, it is extremely difficult to provide conclusive arguments for either of these claims. Instead, two weaker types of evidence are usually provided in support of the claim that emotional expressions are genuine signals. The first argument is simply that some traits of emotional expressions are difficult to account for without recourse to their role in communication. This is quite commonsensical in the case of sadness and joy for instance. Other cases are more ambiguous. As suggested above the expression of fear could have adaptive effects as action preparation (Susskind et al., 2008; Vermeulen and Mermillod, 2010). Similarly, the facial features of disgust can have direct adaptive consequences, such as narrowing the eyes to prevent exposure to potentially toxic substances (Susskind et al., 2008). Yet it has been argued that these potential benefits are slight and unable to account for the whole expression. For instance, Susskind et al. (2008) have shown that the functional importance of sensory acquisition in fearful expressions is limited to the upper visual field. For the individual producing the display, the benefits in terms of sensory acquisition enhancement might then be relatively small compared to the costs involved in making the display highly discriminable. If this is the case, it would be more likely that fearful expressions would have ultimately been selected for a signalling purpose by virtue of their high discriminability. More generally, the difficulty in accounting for the configuration of emotional displays in terms of efficient action preparation suggests that action preparation might not be their function (Fridlund, 1994). It does not follow from this first argument that emotional expressions are adaptive as signals; they could still be mere accidents. If emotional expressions are signals, they should be designed as such. In particular, they should be sensitive to the social context. It makes little sense to emit a signal if there is no one to receive it. More complex social modulations could also be expected: hiding distress from an enemy, concealing envy from a friend, etc. At a very broad level, Dobson has shown that, among non-human primates, facial mobility (namely, the set of facial movements a species can produce) is predicted by group size (Dobson, 2009). This result suggests that the evolution of facial mobility on the whole serves social functions. Other evidence from non-human animals indicates that the expression of fear is socially modulated (Sherman, 1977; Alcock, 1984; Chapman et al., 1990). In humans, it has been shown that the social context modulates 3 Duchenne smiles are considered as genuine smiles (Ekman et al., 1990), smiles that are associated with the experience of joy, and which differ from faked or ‘polite’ smiles that do not involve the activation of the palpebral part of the orbicularis oculi. 4 Our use of the concept of function reflects that of proper function, following Millikan (1989). Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx 5 the expression of pain and distress (Badali, 2008) and sadness (Zeman and Garber, 1996). Similarly, smiling has been shown to be very heavily socially modulated (Fridlund, 1994). For instance Kraut and Johnston (1979), observed the smiling behaviour of people in various social settings, and concluded that smiling was so strongly associated with social motivation that its link with an internal experience of positive feelings was tenuous. The evidence that emotional expressions are socially modulated is very suggestive that they are indeed signals. However, prima facie, it is also compatible with the hypothesis that they are but a form of coercion. If emotional expressions have evolved to influence the behaviour of other individuals, they should be also expected to be socially modulated. For instance, observers may have started to respond to some purely accidental features of distress as cues that help was required. It would then have been beneficial to exaggerate distress cues in order to influence observers more easily, and the behaviour would have become merely coercive. If it was in the observers’ best interest to specifically attend to these new, exaggerated behaviours, they could evolve specific mechanisms to do so. Thus to complete the demonstration that emotional expressions are signals, it would therefore be necessary to show that observers are not merely responding to cues. We know of no strong empirical evidence supporting this claim. However, in many cases the usefulness of the original cue, whatever that may have been, is limited. It would be extremely surprising if the mechanisms designed to detect and react to the emotional expression were still only targeting that cue. To take an example, the raising of the lips triggered by anger cannot be a reliable cue that we are about to be bitten. Clearly, the detection of the cue has evolved beyond its original function. The arguments exposed here may fall short of a strong demonstration that emotional expressions are genuine signals. Yet we consider them to be sufficiently suggestive to at least shift the burden of proof to those who would claim that emotional expressions have no signalling function. It is now possible to turn to the challenges raised by emotional expressions due to their communicative character. 4. The risks of emotional signals and how to ward them off Like all communication, emotional signals can be dangerous. In particular, receivers run the risk of being deceived by senders. An individual who would always submit to anger displays or help in response to signals of sadness would be easily abused. In section 2, we distinguished between proximate and ultimate answers to this question, and emphasised that both are needed for a proper understanding of a trait. As we will shortly discuss, previous psychological research on emotional displays has suggested and described a range of proximate mechanisms that may be involved. However the ultimate question of how these mechanisms maintain the stability of emotional communication has received less attention. More precisely, as detailed below, previous explanations are problematic as they are based on the dubious assumption that there is an unfakeable relationship between emotional display and emotional experience. Evolutionary theory suggests three broad classes of ways in which communication systems can be kept evolutionarily stable at the ultimate level (Davies et al., 2011; Maynard-Smith and Harper, 2003): (i) individuals may share a common interest, such that there is no incentive to lie; (ii) there may be a causal, unfakeable relationship between signal form and signal meaning (an index); or (iii) there may be costs associated with the signal. These costs may in turn be either handicaps, where the costs are associated with the production of the signal itself, and are paid by honest signallers as a guarantee of honesty (Zahavi, 1975; Grafen, 1990; Godfray, 1991); or they may be deterrents, where the costs are associated with the consequences of the signal, and are hence paid by dishonest signallers (Lachmann et al., 2001). The question is: which of these most likely describes emotional communication? The most common answer has been to rely on explanation (ii), stressing the fact that producing dishonest emotional signals can be very difficult (see Owren and Bachorowski, 2001, 2003; Hauser, 1997 for crying). At a proximal level of explanation, Ekman and his colleagues have tried to demonstrate that some emotional signals---such as the famous Duchenne smile---are practically impossible to voluntarily fake (Ekman et al., 1980). The logic behind this argument is that the honesty of emotional signals is guaranteed by the lack of voluntary control. Someone who would want to fake genuine joy, for instance, would simply be unable to do so. As a result, at the proximal level, receivers would be certain at least that when an emotion is expressed, it is genuine. By contrast, Ekman allows for the possibility that the suppression of (some) emotional signals can be learned in the form of ‘display rules’ (Ekman et al., 1969). Ekman may be right that the main danger faced by receivers is not the voluntary inhibition but the voluntary production of emotional signals. Still, even if we assume that he is right and emotional expressions are, most of the time, involuntarily produced, his answer is unsatisfying for at least three reasons. The first reason is that this explanation lies at the proximal level of analysis and says little or nothing about the ultimate one. The problem of honesty occurs regardless of what the proximate mechanism is; that is precisely why it is the defining problem of animal signalling theory, where the vast majority of signals, if not all of them, are ‘involuntary’ (see MaynardSmith and Harper, 2003). The second reason that makes accounts relying on lack of voluntary control unconvincing is that they are not evolutionary plausible. As pointed out by Frank (1988) and Fridlund (1994), if the voluntary control of emotional signals Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 6 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx had brought fitness benefits, it would have evolved. Indeed, there appears to be no essential physiological constraint that would have prevented natural selection from selecting the ability to voluntarily produce emotional displays. The voluntary use of emotional displays is evolutionary plausible as it involves structures that already exist (neural and motor pathways for one’s voluntary control over most of facial muscles (Rinn, 1984)). In fact, the ‘‘involuntariness’’ argument rests upon one single example, that only concerns facial expression and neglect other muscular events that play a crucial role in emotional attribution (such as bodily movements, see: De Gelder, 2006; Grèzes et al., 2007): the mention of the orbicularis oculi, a muscle that is involved in blinking and in the production of so-called Duchenne smiles, and whose palpebral part is considered to be impossible to activate voluntarily. Yet, this is one of the few examples of muscles that cannot be activated voluntarily and that play a substantial role in emotional communication. Moreover, as Ekman (2003b) acknowledges, the characteristics that may allow observers to distinguish between faked and spontaneous emotional expressions are subtle (i.e., morphology, symmetry, duration, speed of onset, apex overlap, ballistic trajectory and overall cohesion of the display given the context) and one has to carefully pay attention to them in order to detect liars. In fact, it is still to be shown that the non-credibility of a display, investigated in laboratory settings (e.g., Mehu et al., 2012) where participants are urged to pay a lot of attention to the emotional displays, can reliably and rapidly be detected in more ecological contexts. Together, these elements cast serious doubts on the idea that the difficulty to control emotional displays can be part of the explanation for the honesty of emotional communication. The third (and probably the more serious) problem is that voluntarily faked emotional signals are not always the main threat to receivers. Take anger as an example. Let’s assume that the function of anger expressions is to signal a readiness to inflict costs to another individual if that individual fails to submit in some way. If the response to anger displays were automatic submission, a receiver would clearly be at risk of senders voluntarily expressing anger to make them submit for no good reason. But the receiver would also be at risk if senders were actually angry, but not in a position to follow up on their threat. Just as evolution could have led to the development of voluntary control of emotions, evolution could have led to the development of ‘fake’ emotions. For instance, senders could become genuinely angry even when they are not really willing to engage in a potentially costly confrontation. The emotion, including its cognitive, physiological and expressive correlates would be exactly similar to anger, except that if the receiver failed to submit, the sender would not assault her. We may note another implausibility in Ekman’s account, one that relates to the costs potentially incurred by senders instead of receivers. As noted by Fridlund ‘‘any reasonable account of signalling must recognise that signals do not evolve to provide information detrimental to the signaller.’’ (1994, p. 132). For instance, if expressing distress in some circumstances could regularly hurt a sender’s interests---by making enemies aware of one’s weaknesses, say---then the expression of distress could not be automatic, it would have to be modulated, whether it is by voluntary control or not. We mentioned above that Ekman allows for the learning of display rules to inhibit the expression of emotions. Yet this is more likely to be a cultural innovation than the built-in mechanism we should expect. Besides the explanation based on the involuntariness of emotional displays, another type of explanation can be found in the literature. It suggests that certain emotional signals are handicaps, and are, as a consequence, honest. Such an explanation has for instance been offered for tears (Hasson, 2009; Hauser, 1997) that are indeed difficult to produce spontaneously. Because they considerably handicap perception and are not easily fakeable, tears honestly signal one’s distress. Such explanation face the same problem as above: evolution could have favoured the deliberate use of tears whenever it is in the interest of the signaller. The alternative explanation we suggest is that receivers are endowed with a suite of mechanisms designed to modulate their responses to emotional signals. These mechanisms might be termed emotional vigilance, in order to emphasise that the functional role they play is equivalent to the role played by mechanisms for epistemic vigilance (see section 2) in ostensive communication. However, it should be emphasised that we do not mean to suggest that the mechanisms involved in the two processes are similar, nor even that defence against misleading emotional signals necessarily requires high level cognitive abilities. Our only objective with this term is to draw attention to the functional equivalence of the two sets of mechanisms. Mechanisms of emotional vigilance are confronted with a complex task. Figuring out when it is beneficial to respond to any given emotional signal requires integrating numerous variables such as the type of signal, its intensity, its source, as well as many features of the specific context in which it is emitted (e.g., Barrett et al., 2007; Barrett and Kensinger, 2010). A child’s extreme anger display when she is told that she cannot have a second serving of ice cream should not elicit submission; a raised eyebrow by a mafia Don may. A complete analysis of the mechanisms of emotional vigilance would therefore require a lengthy emotion by emotion analysis, which is not within the scope of this article. Indeed, one of the strengths of an explanation based on mechanisms of emotional vigilance is that it does not rely on one or a few very specific examples, such as the Duchenne smile or tears. Instead, it can readily extend its logic to all emotional signals, even if we should expect different heuristics to be at play for different emotions. Two general dimensions of vigilance, likely to be observed for all emotions to varying degrees, are delineated. In the case of epistemic vigilance, it has proven useful to distinguish between issues of competence and issues of benevolence Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx 7 (e.g., Mascaro and Sperber, 2009; see also Sperber et al., 2010). An ostensive message can be misleading either because of the deceitful intent of the sender or because she is merely mistaken. In both cases caution should be exerted about the message. The same distinction can be applied to emotional vigilance, as a first step towards more specific characterizations. In the case of epistemic vigilance competence can be seen as a relatively objective measure: did the sender form false beliefs by mistake? But it is far from clear what it means---if it even means anything---for an emotional state to be true. As a result, issues of competence have to be treated differently for epistemic and emotional vigilance. The closest equivalent of the notion of an incompetent sender would be an individual who expresses emotional signals that bear no adaptive relationship whatsoever to the context. The emotional expressions of an individual whose emotional systems would be highly dysfunctional should not be trusted. Such cases, however, should be relatively rare: severely emotionally impaired individuals face a steep evolutionary challenge. Moreover, if the disorder is consistent, it should be relatively easy to flag these individuals as being unreliable and either not pay attention to their emotional signals or at least not react to them in the typical way. Other competence concerns that would have likely been more frequent stem from asymmetries between the incentives of the sender and the receiver. For instance, someone with a strong allergic reaction to bee stings should not be deemed incompetent for expressing a strong fear in the presence of a bee. This fear signal should not be discounted as it provides important information regarding the behaviour of that individual and the appropriate course of action to be taken. Yet it should not produce in observers what is usually thought of as being the automatic reaction to fear, which is fear. The asymmetry in competence does not need to be permanent or long lasting, as in the case of the allergy. For instance, someone can be confronted with a dominant individual whose anger she knows to be based on a mistaken belief. Even if the receiver would otherwise be inclined to submit, it may be worth in this case trying to correct the dominant’s beliefs first. The second broad issue that mechanisms of emotional vigilance have to deal with is the benevolence of senders. In rare occurrences the interests of senders and receivers are perfectly aligned, but in the vast majority of cases there will be some discrepancy. Some very general metrics can be useful to judge the level of interest alignment. Someone’s interests are more likely to align with her in-group than her out-group, with a friend than a stranger, with a brother than a third cousin, etc. Yet even the interests of very close individuals can diverge. When a child expresses pain, there is usually little conflict of interest with her parents. When she expresses anger for not receiving the latest toys, the interests are much more poorly aligned. Similarly, couples often have an incentive to misrepresent their emotions to each other. The converse is also true: the interests of strangers can converge. If we find ourselves stranded on a boat that requires two people for rowing, our interests can become very much aligned with those of a perfect stranger from a group we may otherwise not deem trustworthy. Even if general metrics---in-group vs. out-group and the like---can be useful, they must be supplemented by an assessment of each situation’s specificities. A crucial difference between competence and benevolence issues is in their evolutionary dynamic. In the case of competence, there is no selection pressure to deceive receivers. The individual who is allergic to bee stings is not better off if others also experience fear. By contrast, a stranger who gets angry with us would benefit if we were automatically submissive. As a result, the latter individual has an incentive to deceive us, for instance with anger displays that exaggerate the actual threat. There can therefore be an arms race between senders and receivers, with senders trying to pass through the receivers’ vigilance and receivers evolving more complex mechanisms of emotional vigilance. Such an arms race would not arise in issues of competence. Moreover, the costs incurred by the wrong response to an emotional signal are likely to be higher when the issue is one of benevolence rather than competence. A deceitful sender might purposefully try to inflict the maximum cost upon a receiver---by making her experience an emotion at the worst possible moment---which is not the case for incompetent signals. It is thus reasonable to assume that issues of benevolence rather than competence were the main driver behind the evolution of emotional vigilance. 5. Evidence of mechanisms of emotional vigilance While it is not possible here to make precise predictions regarding the working of mechanisms of emotional vigilance, we can make some more general suggestions. At the most general level, reactions to emotional signals are very unlikely to be automatic, or reflex-like. Instead, they should be heavily modulated by the social context. The competence and benevolence of the source in each particular context should play a role in the response to emotional signals. If the competence or benevolence of the source is dubious, the reaction should be either dimmed or adapted to the specific circumstances. Unfortunately, there is a dearth of relevant evidence. Importantly, the relative paucity of empirical evidence should not be taken as evidence that there is no or little contextual modulation. Simply, the issue has not received the attention it deserves. Instead, research has focused on showing that some reactions to emotional signals are automatic (as in cases of primitive emotional contagion, see Hatfield et al., 1994). Such research would seem to be in direct contradiction with the present predictions. Yet this contradiction is more apparent than real. Most studies on automaticity in this area bear on very quick and subtle reactions such as slight facial movements (e.g., Dimberg et al., 2002) or variations in skin Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 8 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx conductance (e.g., Esteves et al., 1994). Our predictions do not bear mainly on such reactions, but on potentially more costly behaviour. The danger stemming from an automatic reaction to a fear signal, for instance, is unlikely to come from a micro-contraction of some facial muscles. For these reactions to be evolutionarily relevant, they would have to have a substantial impact on behaviour. Another problem with most studies of automaticity is that the relevant contextual modulations are not introduced. Participants have no reason to question the competence or benevolence of the people depicted in the stimuli, so that claims of automaticity cannot be thoroughly tested. It may be worth saying a quick word about the supposed cases of emotional contagion that involve seemingly costly behaviour such as the ‘‘laughter epidemics’’ (e.g., Ebrahim, 1968). If a whole school can start laughing uncontrollably because emotions spread from student to student, it seems as if the automaticity of emotional responses trumps emotional vigilance. Such a conclusion would be hasty, for three reasons. First, emotion epidemics are exceedingly rare--that is what makes them so startling. Evidently, a laughter epidemic is not started every time someone laughs. Second, this type of epidemic only spreads within a closely knit group. In terms of benevolence, these are among the people one should trust the most. Third, it is possible that expressing these emotions may in fact serve the individuals’ interests at that particular time. Laughter epidemics can get students out of school for a few days. Other emotion epidemics have given factory workers a break (see Evans and Bartholomew, 2009). In some contexts, and for some people, emotional vigilance may therefore have no reason to break the spread of these epidemics. Far from undermining the idea of emotional vigilance, the characteristics of emotion epidemics are in fact better explained by postulating mechanisms of emotional vigilance than an automatic response to emotional signals (Mercier, 2013). Some studies have directly tackled the question of the contextual modulation of reactions to emotional signals. Most of them bear on issues of benevolence while only a few results shed light on the treatment of competence. For instance, Zeifman and Brown (2011) have shown that tears are more efficient at conveying sadness when they are shed by adults than by children or infants. A possible interpretation is that infants and children are much more likely than adults to cry in situations that would not qualify as sadness, such as anger (for children) or hunger (for infants). In a way, they are treated as less competent. To the extent that this result would carry to parents’ reactions to their children vs. adult strangers, it would offer a nice contrasting case with benevolence. In the vast majority of cases, a parent’s interests are more in line with those of her child than that of a stranger. Yet, because children have emotional reactions that differ from those of adults, reactions to their emotional signals can be more heavily modulated. More generally, it is important to see caregivers as active in their interaction with crying infants (see Owings and Zeifman, 2004). Another interesting piece of evidence comes from Hepach et al. (2012) who have shown that children, as early as 3 years old, modulate empathetic response towards others according to the appropriateness of their distress (where the target could have been genuinely harmed, could be over-reacting or could signal distress for no reason at all). This result is especially relevant as it shows that, from very early on, reactions to emotional expressions are not automatic but rather heavily modulated by contextual cues. As argued above, competence is not the main issue that receivers have to deal with. Senders whose interests do not align with receivers generally pose a more critical threat. Determining whose interests align with hers is, for the receiver, an arduous task. Many types of cues are likely to be taken into account in order to yield an appropriate assessment. Some of these cues can cover large, fixed categories. Out-group members are less likely to have interests aligned with those of a receiver than her in-groups. It is thus not surprising that people show different responses to emotions expressed by members of these two categories. For instance, ‘‘positive responses to fear expressions and negative responses to joy expressions were observed in outgroup perceivers, relative to ingroup perceivers.’’ (Weisbuch and Ambady, 2008:1; see also Xu et al., 2009; Gutsell and Inzlicht, 2010; Mondillon et al., 2007; Nugier et al., 2009). Other general markers can be used to modulate one’s emotional responses. Attitude towards the sender modulates the receiver’s response, such that when that attitude is negative, the receiver’s facial mimicry can weaken or even become incongruent with the emotion expressed by the sender (Likowski et al., 2008). Similarity between the sender and the receiver is another important moderator of the response to emotional signals (Heider, 1982; Feshbach and Roe, 1968; Sullins, 1991; Epstude and Mussweiler, 2009). Beyond these traits of the receivers, momentary features of the situation are also taken into account. When an individual who would otherwise be trusted---the experimenter---behaved extremely rudely towards the participants, they seemed to rejoice in his misery rather than empathize with it (Bramel et al., 1968). Similarly, Lanzetta and Englis (1989) told participants that they would be either cooperating or competing in a game. While those set to cooperate showed empathetic responses to displays of pleasure and distress, those set to compete showed either no reaction or displayed ‘‘counterempathy’’ (p. 534). 6. Why do we still have emotional signals? So far we have considered ‘‘pure’’ emotional signals, as they are expressed for instance in facial expressions. Yet most emotional signals are in fact mixed with other types of communication---ostensive communication, mostly. The emotional Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx 9 tone helps disambiguate ‘‘I’m scared’’ (that I won’t find a job) from ‘‘I’m scared!’’ (someone is breaking into my house). Moreover, some of these emotional tones are likely to be universal (Scherer et al., 2001; Bryant and Barrett, 2008; Sauter et al., 2010). The linguistic context also plays an important role in disambiguating emotional signals (Barrett et al., 2007). These elements point to the co-evolution of emotional signals and ostensive communication in humans; for instance, if emotional signals are adapted to transmit information with the tone of voice used in spoken communication, they probably co-evolved with language.5 This essential interplay between ostensive and non-ostensive signals becomes very clear when one considers conversational situations. As suggested by Proust (2008), non-ostensive signals may play an important role in transmitting information about one’s own uncertainty in conversational contexts: senders can communicative hesitation about the information they are conveying (e.g., by rolling one’s eyes); recipients can signal their level of understanding (e.g., frowning when something that has been said is unclear). Yet it might not always be advantageous to provide a recipient with sensitive data such as one’s doubts, or as Proust put it, ‘‘evaluations of [one’s own] incompetence’’; conversely, it might be advantageous for the recipient to communicate false information about one’s own uncertainty. Both strategies may threaten the stability of communication and the use of meta-cognitive gestures. Proust’s solution to this puzzle is to argue that the use of meta-cognitive gestures is highly flexible (by selectively restricting others’ access to one’s own meta-cognitive states), and that meta-cognitive gestures are used in situations where cooperation is a priori guaranteed. We think that such solution itself presupposes the combined use of epistemic and emotional vigilance mechanisms that continuously track, over the course of the conversation, possible divergence of interests between signalers and receivers, and regulate the use of meta-cognitive gestures accordingly. We therefore see Proust’s proposal as also suggesting that such mechanisms are needed to explain the stability of emotional communication. Given the continued importance of emotional signals in human communication, we feel entitled to ask a question that may seem whimsical: why do we still have emotional signals? Ostensive communication clearly has a far greater expressive potential. In conversational contexts, senders may well signal their uncertainty using appropriate words; conversely, recipients may signal their lack of understanding using adequate expressions. Yet, non-ostensive signals continue to play an important role in maintaining conversation. Why is it the case? The answer, we surmise, rests at least in part on the argument we have developed in the previous sections: the mechanisms of vigilance that help stabilise emotional signals also explain their continued relevance. Intuitively, it may seem as if emotional signals still exist simply because they express some things better than, or at least differently from, ostensive communication. For instance, when someone tells you, in the course of a face-to-face discussion ‘‘I’m scared’’ with a relatively neutral tone, you don’t infer that she is currently experiencing a high level of fear--maybe she’s worried about her job prospects. It would be hard to convey the level of fear experienced in, say, a home invasion without using at least the fear tone, and probably the facial expression too: it seems that only emotional signals can adequately communicate some emotional states. This, however, may be an artefact of our habits. Imagine that you are chatting with a friend on an instant messaging service. You know she is alone in her house. Interrupting the conversation, she writes to you that she’s hearing someone breaking in, and then ‘‘I’m scared.’’ We suspect that you would have no trouble inferring her emotional state, just as if she had said it with the right tone and facial expression in a face-toface discussion. The reason that a toneless ‘‘I’m scared’’ in face-to-face discussion is not effective at communicating fear is that the fear tone is expected; in its absence, we interpret the utterance differently. Still, one could argue that a lot of context is necessary to disambiguate ‘‘I’m scared’’ in the absence of emotional signals. Emotional signals could therefore be necessary when the context is unclear and there is no time to make it explicit. Again, we suspect this is not a hard limitation of ostensive communication. It is difficult to imagine why a word or an expression with the primary meaning ‘‘I am experiencing a high level of fear’’ (and effective in conveying high levels of arousal) could not have appeared, had it been necessary. One last edge that emotional signals seem to have over ostensive communication is their speed: a simple facial expression can be sufficient to communicate fear. Maybe even a monosyllabic expression would have to take a few more milliseconds to be processed, giving indeed a small edge to emotional signals. Yet this would hardly be critical for most emotional signals such as joy or even anger. And even when speed is of the essence---in the case of fear maybe---the increased expressivity of ostensive communication would probably compensate for the slowdown.6 5 Note that this scenario is not incompatible with scenarios linking emotional communication to paralinguistic systems of communication (e.g., Deacon, 1997) that presuppose independent evolution between those systems of communication and those of linguistic communication. Yet, given their constant interplay in human communication (e.g., in conversational contexts; see Proust, 2008), it is most likely that linguistic and paralinguistic systems of communication have co-evolved more recently. 6 Note that the arguments against emotional signals being preserved thanks to their communicative properties carries to Fridlund’s hypothesis that they serve to convey social motives rather than reveal internal emotional states (Fridlund, 1994). Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 10 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx While it is still possible that the question of the continued relevance of emotional signals could be found in their communicative power, it is worthwhile to consider other potential explanations. One such explanation is that the mechanisms that help insure the stability of emotional signals are very specific and could not easily be replaced by those used to insure the stability of ostensive communication. In particular, the evaluation of ostensive communication relies to some extent on what can be called ‘‘coherence checking’’ (see Mercier, 2012). Coherence checking consists in pitting communicated information against background beliefs. If inconsistencies are detected, they make it less likely that the communicated information is accepted. The efficacy of coherence checking depends on the relative ease of access to knowledge in the relevant domain. If John tells Sarah something about someone she does not know at all, her coherence checking mechanisms will not have much to work with. Emotional states are likely to create such asymmetries in access to information: observers have less access to an individual’s emotional states than the individual herself. This is also true of the social motives---willingness to aggress, etc.---that emotional signals may communicate (Fridlund, 1994). As a result, coherence checking is not a very practical way to evaluate emotional signals. Given that we cannot easily rely on coherence checking to evaluate them, how do emotional signals manage to remain stable? A possibility is that emotional signals are indices. Their stability would be guaranteed by the unfakeability of the signals: emotional signals would simply be too costly to fake, making them intrinsically honest. While it is difficult to discount this hypothesis, as we pointed out earlier, the specific mechanisms that make emotional signals so hard to fake have remained elusive. The other possibility is that emotional signals remain stable because senders are deterred from sending too many dishonest signals. For deterrence to be possible, receivers must have some way of reacting appropriately to emotional signals. If their reactions were fully automatic, not modulated by source or context, all senders would be equally successful, precluding the possibility of deterrence. Following the arguments and evidence reviewed in sections 4 and 5, we argue that humans are endowed with mechanisms that allow them to appropriately react to emotional signals. These mechanisms make deterrence possible and contribute to the stability of emotional signals. Whether emotional signals remain stable because they are indices or because dishonest signals are deterred, specialised mechanisms tailored to emotional signals are required. As a result, we surmise that they are in part responsible for the continued relevance of emotional signals. 7. The interplay between epistemic vigilance and emotional vigilance mechanisms Sperber et al. (2010) have hypothesised that mechanisms of epistemic vigilance have evolved to protect people from deleterious communicated information. One possibility is that the term applies to all the mechanisms allowing humans to perform this function, whether the signals are ostensive or not, emotional or not, etc. In this case, emotional vigilance would be a special case of epistemic vigilance. Another possibility is that epistemic vigilance mostly refers to ostensive communication, in which case emotional vigilance could be seen as a companion set of mechanisms. In any case, this is a purely semantic point. The more substantial issue is the following: can there be specialised heuristics to ward-off deception in non-ostensive (and, more specifically, emotional) vs. ostensive communication? If yes, then using the term ‘emotional vigilance’ to refer to the mechanisms that instantiate these heuristics seems warranted. While the issue is ultimately an empirical one, a strong a priori argument can be offered that such specialised heuristics exist, as so many parameters differ between nonostensive, emotional communication and ostensive communication, from their phylogenetic history, to the format in which they are encoded, or the cues on which they are based. Moreover, it is even likely that there are heuristics that only apply to the signals associated with one emotion. While we do not deny that there might be heuristics that are valid for emotional and ostensive communication, they are likely to interact with more specific ones. Generally, the massively modularist point of view tacitly adopted by Sperber et al. (2010), supports the existence of distinct mechanisms of emotional vigilance. 8. Conclusion Our goal in this paper has been to apply the logic of the evolution of communication to emotional expressions. If emotional expressions are genuine communicative signals, we need to explain what keeps them stable. In other words: what prevents senders from manipulating receivers, and how do receivers stay safe? While there might exist a consensual explanation for what keeps ostensive communication stable (namely, that dishonesty is not a payoff strategy given the social costs of ostracism), it was not clear what could be the explanation for guaranteeing the stability of non-ostensive communication. This state of affairs, we think, is due to the popularity of Ekman’s view among emotion psychologists: being involuntary, emotional expressions are honest and therefore safe for receivers to accept. As we have pointed out, this is a proximal account and does not explain why we would not have evolved the capacity to fake emotional signals. What we need is therefore an explanation at the ultimate level. At an ultimate level, communication can either be kept stable (i) if senders and receivers share a common interest, (ii) if signals cannot be faked, or if (iii) signals induce costs. In the case of emotional communication, (i) and (ii) are unlikely. In Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 + Models PRAGMA-3768; No. of Pages 13 G. Dezecache et al. / Journal of Pragmatics xxx (2013) xxx--xxx 11 this paper, we have shown that the most likely option lies in the third kind of explanations: being equipped with mechanisms of emotional vigilance, receivers would be able to evaluate the signals they receive and to punish dishonest signallers who would provide them with false information. These mechanisms would act as deterrents: dishonest signaller would run a risk, at least that of losing their ability to influence receivers in the future. This, we believe, would have led to the stability of emotional communication. This position can also have important implications at the proximal level: a broad range of phenomena in social psychology (e.g., emotional contagion, empathy) are often described as being automatic responses to some stimuli. 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With Dan Sperber, he has developed an argumentative theory of reasoning according to which our reasoning abilities have been designed by evolution to allow us to exchange arguments rather than to reason on our own. He is now a CNRS researcher at the L2C2 in Lyon. Thomas C. Scott-Phillips is a research fellow at Durham University. His principal research area is the evolution and cognition of human communication and language. In 2010 he received the British Psychological Society’s award for Outstanding Doctoral Research, and in 2011 the New Investigator Award from the European Human Behaviour and Evolution Association. Please cite this article in press as: Dezecache, G., et al. An evolutionary approach to emotional communication, Journal of Pragmatics (2013), http://dx.doi.org/10.1016/j.pragma.2013.06.007 Summary of this chapter Three main lessons can be drawn from this second chapter dedicated to the question of how emotions propagate in crowds: – Firstly, and although emotional transmission has been investigated mainly in dyadic settings, emotions of fear and joy can be transmitted beyond dyads, and may therefore give rise to emotion-based collective behavior. They can, notably, be transmitted in an unintentional and subtle fashion. – Secondly, emotional transmission can be considered as a special case of emotional communication, where agents happen to experience emotional states which share similar features. This does not mean, however, that the cognitive mechanisms of emotional transmission are special. Emotional transmission between A and B may well be conceptualized in terms of B’s reaction to A’s displays. In this respect, emotional transmission and other processes of emotional communication may share a similar cognitive and neural framework. – Thirdly, emotional transmission cannot be equated with a contagious process. In fact, emotional transmission is known to be modulated by many factors and is not irrepressible. 64 Chapter Three: Why do emotions of fear and joy propagate in crowds? Why should we expect emotions to spread in crowds? As has been shown in the previous chapter, emotions of fear and joy may spread in an unintentional and subtle manner beyond dyads, and could therefore give rise to larger emotion-based collective behavior. I have already proposed tentative alternative answers to the question of the proximate mechanisms at the basis of this process, but the question of “why” emotions of fear and joy are so spontaneously transmitted now needs to be examined. Information acquisition and sharing at the basis of emotional crowds Imagine the following situation: you are walking in a crowded but quiet street. Suddenly, you see dozens of people turning round and starting to scream, while running in your direction. As one of them (we will call him Peter) comes nearer you, you perceive his face, which displays terror: his eyebrows are raised; his eyes and mouth are wide open. Given such information, it is most likely that you will immediately run for your life, long before even realizing that these people are panicking, and long before you have fully assessed the situation. As you try and escape the main street, and without any intention of doing so, you yourself display signs of anxiety, through the configuration of your face and body, and, possibly through your production of vocalizations. These signals inevitably inform your neighbor (“Mary”) of the presence of a threat in her immediate environment. Mary, in turn, shows anxiety, ultimately 65 spreading the information that something has to be avoided. This chain of acts of information transmission may lead to the emergence of collective behavior (such as collective flight), based on the very information (the presence of a threatening element in the environment nearby) that you yourself have contributed to spread. In sum, the resulting collective behavior is the outcome of many local processes of informational transmission. It should be noted that, as developed in the previous chapter, one does not need to understand the process of emotional information transmission as anything other than a process of influence of others’ behavior (Dezecache, Mercier, & Scott-Phillips, 2013). Over and above the question of the proximate mechanisms (the how-question) that make these transmissions possible (subject of the previous chapter), it is also relevant to question their biological function (the why-question): (i) firstly, why are people so inclined to rely on others’ informational resources – especially in crowd contexts? Why receivers feel emotions upon receiving another’s emotional display? (ii) Secondly, why do we have the impression that people spontaneously share information in crowd contexts? Why do emitters feel “compelled” to display their own emotional expression? Question (i) is, in fact, fairly easy to answer. Human sensory processing is limited: when navigating in an uncertain and informationally-rich environment, we are particularly attentive to others’ behavior. Since only a small number of us may have perceptual access to adaptive features of this uncertain and highly fluctuating environment, it is advantageous to keep constant track of others’ overall behavior, as this might provide us with information about that environment, thus reducing its uncertainty (Couzin, 2009; Couzin, 2007). Such monitoring of others’ behavior is particularly obvious when considering the scenario proposed above, which is based on the transmission of fear: Peter’s fearful facial displays (along with the many other cues provided by his overall behavior) are important to track, as they allow for the production of rich inferences about the danger level in the environment. In a similar vein, facial displays related to the experience of joy, although they are not linked to survival issues, are also worth tracking as they could be associated with others’ cooperative intents (Mehu, Little, & Dunbar, 2007; Reed, Zeglen, & Schmidt, 2012). More generally, it seems highly beneficial to share others’ experience of joy as it may benefit the organism 66 (Fredrickson, 1998; 2004). Our experiment reported in chapter 2 and Dezecache et al., 2013 suggests that we are indeed endowed with mechanisms that are tuned to react to others’ emotional signals of fear and joy. Interestingly, we are also endowed with mechanisms tuned to produce emotional signals of fear and joy that can cause emotional reactions in others. But why should we be so inclined to share these emotions of fear and joy with others? This issue relates to the question of the biological function of the mechanism producing emotional reactions when confronted by emotional signals. As seen in chapter 2 and in Dezecache et al., 2013, for emotional homogeneity to be achieved in crowds, it is not sufficient for you to receive the information from Peter; it is also important that the information reaches Mary. If Mary can easily infer the presence of danger from the numerous cues provided by your own action of running away, some of your behavior (such as facial or vocal displays) may have evolved specifically to inform her. This implies that the mechanisms producing these displays are selectively tuned to her informational needs. Spontaneous emotional reactions to emotional events related to the experience of fear and joy might be a function of the relevance of the information to the audience, as well as of the composition of that audience. We experimentally addressed this question for a subset of emotional displays, i.e., facial expressions of emotion of fear and joy by manipulating the relevance of the information for others. The results are presented in the following manuscript, which has not yet been submitted. The experiment was conceived and designed by Dr. Julie Grèzes, Dr. Laurence Conty, Lise Hobeika, and myself; I collected the data with Lise Hobeika, and analyzed them by myself. Finally, I subsequently wrote the paper together with Dr. Julie Grèzes and Dr. Laurence Conty. 67 1 Humans spontaneously compensate for others’ informational needs in 2 threatening contexts 3 Dezecache G.,1,2,* Hobeika L.,1 Conty L.,3, Jacob P.2 & Grèzes J.1* 4 5 1 6 Supérieure (ENS), 75005 Paris, France; 2Institut Jean Nicod (IJN) – UMR 8129 CNRS & IEC – 7 Ecole Normale Supérieure & Ecole des Hautes Etudes en Sciences Sociales (ENS-EHESS), 8 75005 Paris, France; 3Laboratory of Psychopathology and Neuropsychology (LPN, EA2027), 9 Université Paris 8, Saint-Denis 93526 cedex, France 10 * Laboratory of Cognitive Neuroscience (LNC) - INSERM U960 & IEC - Ecole Normale Authors for correspondence ([email protected] & [email protected]) 11 Abstract 12 It is often said that, at a certain stage of their evolutionary history, mechanisms producing 13 facial reactions have been selected for communication of adaptive-value information to 14 conspecifics. Yet, strong empirical evidence for this claim is lacking. Here, we tested whether 15 the apparatus producing emotional facial displays is spontaneously sensitive to others’ 16 informational needs. Participants were confronted with content that varied in adaptive value 17 (fearful, joyful, and neutral content) and were sitting next to conspecifics who had more or 18 less informational access to the same content. Larger electromyographic activity over fear- 19 specific facial muscle during the perception of fear and when conspecifics’ informational 20 access was at its lowest indicated that participants involuntarily and spontaneously 21 compensated others’ informational needs, at no benefit to their performance at the task. 22 Beyond confirming the communicative function of spontaneous emotional facial displays of 23 fear, these results also suggests an interesting parallel with language, both apparatus being 24 tuned to selectively produce signals that are intended to reduce other’s uncertainty. 25 26 27 Introduction 28 When confronted by emotional events, humans typically produce, along with bodily and 29 vocal signals, sets of distinct involuntary facial movements [1] whose configuration is known 30 to be emotion-specific [2,3]. Although the proximate cognitive mechanisms mediating those 31 facial reactions have been considerably investigated (see [4] for a review), the question of 32 their biological function (i.e., why they exist at all) has little been studied. 33 According to the two-stage models of the evolution of facial displays [5,6], facial expressions 34 of emotions have first originated for intrapersonal sensory regulatory functions before being 35 selected, later during evolutionary history, for their communicative function. While 36 footprints of the first selective pressure (the selection of mechanisms designed to optimize 37 sensory acquisition through specific facial muscular configuration) can indeed been found 38 (for instance, expressing fear enhances sensory acquisition [6,7]), evidence for the 39 subsequent selection of mechanisms designed to optimize communication of adaptive-value 40 information to conspecifics are often restricted to “audience effects”, i.e., more frequent or 41 larger displays when conspecifics are present compared to when there is no conspecifics to 42 pick up the information [8–11]. Much stronger evidence of this second selective pressure 43 could however be found in investigating the extent to which the production of facial displays 44 is sensitive to others' perspective during emotional co-perception. Particularly relevant is the 45 question of whether others’ informational needs influence the production of such displays. 46 The aim of the present study was to uncover whether one individual’s (A) involuntary facial 47 reactions in response to an emotional event vary as a function of his/her knowledge about 48 another’s (B) informational access to that emotional scene. To this end, we manipulated A’s 49 belief about B’s informational access while recording A’s facial muscular activity. We 50 predicted that the amplitude of facial expressions over emotion-specific muscles in a 51 participant A would increase as her co-participant B’s knowledge about the emotional 52 scenes declines. 53 Showing that the mechanism underlying facial emotional expression production 54 spontaneously takes into account other’s informational needs would constitute strong 55 evidence for the operation of a past pressure for the selection of mechanisms optimizing 56 communication of adaptive-value information to others. It would also shed light on the 57 evolution of other communicative mechanisms (such as language) that are known to actively 58 track others’ knowledge states so as to produce signals that are intended to reduce others’ 59 uncertainty [12]. 60 61 Results 62 Thirty participants were assigned the A-role; each of them was paired by an unfamiliar same- 63 sex participant who was assigned the B-role. 64 A’s electromyographic activity per trial was obtained by extracting, for each trial, the mean 65 change from the baseline level occurring in a specific 500-ms time window, after z-score 66 transformation for each trial. This 500-ms time window was defined independently for ZM 67 and CS. The start of this time window was obtained by selecting, using t-tests, the 100-ms 68 time bin where EMG activity began to be statistically larger for the emotion the muscle was 69 specific to (fear for CS; joy for ZM) compared to the two other conditions (Fear > Joy and 70 Fear > Neutral for CS; Joy > Fear and Joy > Neutral for ZM). Figure 1 shows activity of both 71 muscles over time; the grey square shows, for each muscle, the selected 500-ms time 72 windows (700 - 1200 ms for CS; 1600 - 2100 ms for ZM). Data were then submitted, 73 separately for each physiological measure, to repeated measures ANOVA using Emotion 74 (fear vs. neutral vs. joy) and Information (20% vs. 60% vs. 100%) as within-subject factors. In 75 a second ANOVA, we compared activity for Social vs. Solitary blocks using Emotion (fear vs. 76 neutral vs. joy) and Sociality (Social blocks vs. Solitary blocks) as within-subject factors. 77 Bonferroni corrections were employed to account for multiple testing. Post-hoc comparisons 78 were also performed for the analysis of simple main effects. Results are summarized on 79 Figure 2. 80 81 82 83 [FIGURES 1 AND 2 ABOUT HERE] 84 CS activity in A during the perception of fear is modulated according to B’s informational 85 access 86 As revealed by a main effect of factor Information (F2,48 = 3.607, p = .035, η2 = 0.131), CS 87 activity was larger for blocks where A represented B’s informational access to the video as 88 being restricted to 20%, compared to when A thought B would have full access to them 89 (100%) (t24 = 3.045, p < .01), whatever the content of the video was. As the goal of the study 90 was to investigate the modulation of emotion-specific muscular activity (fear for CS; joy for 91 ZM), we systematically compared the impact of A’s representation of B’s informational 92 access on CS responses for each emotional content independently, even if we did not find an 93 interaction between factors Emotion and Information. Consistent with our hypothesis, 94 results revealed differences between 20% and 100% (t24 = 2.930, p < .01) and 60% and 100% 95 (t24 = 2.578, p = .016), a pattern which was specific to fear condition (all other ps > .1). Such 96 modulation of CS activity during fear perception according to B’s informational access was 97 independent of A’s affective appraisal of the stimuli: mean level of EMG responses over the 98 CS was indeed not correlated with A’s judgments about perceived intensity of fear videos (r2 99 = .-327, p > .1). These results indicate that CS activity in A during the perception of fear is 100 affected by B’s informational access: A’s representation of B’s low informational access 101 during the perception of fear favors larger activity in the CS. 102 103 ZM activity in A during the perception of joy is independent of B’s informational access 104 As for the ZM activity, statistical analysis revealed neither effect of factor Information nor 105 interaction between factors Emotion and Information (both ps > .1). These results suggest 106 that ZM activity in A during the perception of joy is independent of B’s informational access. 107 ZM responses could neither be explained by A’s affective appraisal of the stimuli, as 108 muscular activity over this muscle was not correlated with A’s judgments about perceived 109 intensity of joy videos (r2 = .010, p > .1)., 110 111 112 CS and ZM activity in A in presence or absence of B (Social vs. Solitary conditions) 113 Finally, ANOVAs using Emotion and Sociality as factors revealed neither effect of Sociality 114 nor interaction between factors Emotion and Sociality for CS (Sociality: F1,24 = 0.110, p > .1; 115 Emotion*Sociality: F1,24 = 1.421, p > .1). 116 Concerning ZM activity, we found no effect of factor Sociality (F1,24 = 0.183, p > .1) but an 117 interaction between factors Emotion and Sociality (F1,24 = 3.382, p < .05). Post-hoc tests 118 revealed that ZM responses in A were larger when facing neutral stimuli in B’s presence than 119 when alone (t24 = -2.111, p < .05); there was also a tendency for EMG responses to be higher 120 in the ZM when facing fear stimuli during Solitary blocks than during social blocks (t24 = 121 1.819, p < .1). 122 If the absence of increased activity in Social conditions seem to contradict conventional 123 findings that facial activity is more ample in the presence than in the absence of others [13], 124 it should be noted that B’s activity during Solitary blocks was left undetermined to A. As a 125 consequence, A could have formed the belief that B was in fact watching a similar content in 126 a different room. This is consistent with results by Fridlund and colleagues [10] showing that 127 implicit audience (an audience that is absent but nonetheless, and elsewhere, engaged in a 128 task related to that of the participant) can potentiate facial activity in participants. 129 130 Discussion 131 Overall, our findings indicate that, when confronted by threat-related stimuli, humans 132 spontaneously and unintentionally take conspecifics’ informational needs into account. They 133 modulate their specific-muscle movements accordingly and at no obvious benefit for 134 themselves. Indeed, spontaneous activity of CS, a muscle implicated in the expression of 135 fearful expression (among other negative emotions) was found to be larger when B had low 136 access to the informational content of the videos (20 or 60% of their content) compared to 137 when B was fully aware of it (100% block), even if B’s informational access was absolutely 138 irrelevant to A’s task during the experiment. This pattern of results should be related to an 139 intrinsic motivation to selectively inform others [14]. Indeed, CS responses during fearful 140 condition were not correlated with perceived risks (as captured by the Intensity scores). 141 Of interest here, B’s perspective had no impact on A’s ZM behavior: indeed, ZM activity was 142 not larger during joy condition when B’s suffered from low informational access. This might 143 be explained by the function of emotional displays involving activity of the ZM, such as 144 smiles that are largely known to originate from greeting signals of non-human primates [15] 145 and therefore, from dyadic contexts. Consequently, A’s potential lack of consideration for B’s 146 informational access during the perception of joy stimuli might correspond to a 147 communication between A and the actors displayed in the videos [16]. 148 The fact that CS activity during the perception of fearful expressions, unlike ZM’s during the 149 perception of joy, was modulated by A’s representation of B’s perceptual access is 150 particularly interesting: unlike fearful-related content which bears immediate survival-value, 151 joy would not have constituted a relevant piece of information to be shared with B. In this 152 respect, relevance of the information to B might not only be a function of A’s representation 153 of her conspecific’s perceptual access, but might also be linked to the value of the 154 information for her immediate survival. This is consistent with previous results showing that, 155 unlike the perception of joy, the perception of fear may lead, in observers, to explicit facial 156 signals [14]. 157 These results are important for the question of the evolution of facial musculature as they 158 strongly suggest that facial movements, at least when produced as responses to emotional 159 events in the environment, may have not only been selected for self sensory-regulation but 160 have also ultimately been selected to communicate survival-value information to 161 conspecifics. Indeed, the mechanisms producing fearful displays spontaneously track others’ 162 informational needs in threatening contexts. This is consistent with the two-stage models of 163 the evolution of emotional expressions which support that facial expressions of emotion 164 would have first originated for sensory regulation, before being more recently co-opted for 165 communicative purposes [5–7]. 166 Finally, our results are particularly relevant to the question of the evolution of 167 communicative abilities. According to several authors [12,17], a critical feature of human 168 language is the selective production of signals that are intended to reduce other’s 169 uncertainty. Recently, it has been shown that wild chimpanzees take into account others’ 170 knowledge when producing alert hoos [18]. Our results are in the same line as they support 171 the view that the production of facial emotional displays, a primitive and evolutionary old 172 means of communication (as it is shared with other social mammals [1,19]), is sensitive to 173 others’ informational needs. 174 175 Methods 176 Ethics. We obtained ethics approval from the local research ethics committees (CPP Ile de 177 France III). 178 Participants. Thirty participants (16 females; mean age 23.3 y ±0.51 SEM, range 20–30 y) 179 were assigned the A-role; thirty others (mean age 23.6 y ±0.46 SEM, range 20–30 y) the B- 180 role. All participants had normal or corrected-to-normal vision, were naive to the aim of the 181 experiment and presented no neurological or psychiatric history. All provided written 182 informed consent according to institutional guidelines of the local research ethics committee 183 and were paid for their participation. All the participants were debriefed and thanked after 184 their participation. 185 Overall procedure. Participants A and B were seated next to each other with a folding screen 186 making each invisible to the other. Both had a computer in front of them and were wearing 187 headphones. Each experimental session was composed of four blocks repeated two times, 188 for a total of 8 blocks: in 6 of the 8 blocks, A and B were reunited in the room (Social blocks); 189 in the two other blocks, A was alone in the room (Solitary blocks). 190 Specific procedure for participants A. Participants A were told that they were going to 191 watch videos either in the presence of another same-sex participant B (6 Social Blocks) or 192 alone (2 Solitary blocks). No reason was provided for the presence of B. Participants A were 193 merely told that, during the Social blocks, B would watch the same videos as they would, 194 although B would have access to more or less content (20%, 60%, 100%). A sample of what B 195 could see was provided to A before the experiment (the image of the video was more or less 196 blurred and the sound distorted, resulting in impaired recognition of the content of the 197 video for 20% and 60% blocks). Interactions between A and B were minimal: they greeted 198 each other before the start of the experiment; also, B was in charge of starting up each block 199 (to the exception of Solitary blocks that were launched by the experimenter), resulting in A 200 and B making sure that they were both ready before B pressed the Start button. 201 Specific procedure for participants B. B’s task consisted in answering to questions related to 202 the content of the videos. In fact, they were never exposed to noisier version of A’s videos; 203 they were nonetheless told not to communicate with A about their content. They were 204 instructed to start the Social blocks after having made sure that A was ready to start. Before 205 the Solitary blocks started, B were escorted outside the room by the experimenter. 206 Videos. There were 45 videos (mean duration 6060±20 ms, range 6000–6400 ms) of size 207 620×576 pixels projected on a 19-inch black LCD screen. Those of emotional conditions 208 depicted 15 actors (8 females, 7 males) playing fear (n = 15) and joy (n = 15), using facial, 209 bodily as well as vocal cues. They were extracted from sessions with professional actors from 210 the Ecole Jacques-Lecoq, in Paris (France). The videos of the neutral condition (n = 15) 211 displayed fixed shots of landscapes. All videos were validated in a forced-choice task (see 212 [14]). 213 Data acquisition. Using the acquisition system ADInstruments (ML870/Powerlab 8/30), we 214 continuously recorded the EMG activity of A using Sensormedics 4 mm shielded Ag/AgCl 215 miniature electrodes (Biopac Systems, Inc) (sample rate: 2 kHz; range: 20 mV; spatial 216 resolution: 16 bits). Before attaching the electrodes, the target sites on the left of A’s face 217 were cleaned with alcohol and gently rubbed to reduce inter-electrode impedance. Two 218 pairs of electrodes filled with electrolyte gel were placed on the target sites: left ZM and left 219 CS muscles [11]. The ground electrode was placed on the upper right forehead. Last, the 220 signal was amplified, band-pass filtered online between 10–500 Hz, and then integrated. 221 Integral values were then offline subsampled at 10 Hz resulting in the extraction of 100 ms 222 time bins. 223 Data analysis. EMG trials containing artifacts were manually rejected, following a visual 224 inspection. Participants with a high rate of trial rejection (> 25%) were excluded from the 225 statistical analysis for the relevant signal, (n = 2 for CS, n = 2 for ZM). Also, due to technical 226 problems, 3 participants were excluded (n = 3 for CS, n = 3 for ZM) prior to the analysis, 227 leaving a total of n = 25 for CS and n = 25 for ZM. 228 References 229 230 1. Darwin C, Ekman P, Prodger P (2002) The expression of the emotions in man and animals. Oxford University Press, USA. 231 232 2. 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Science 142: 1034–1041. doi:10.1126/science.142.3595.1034. 272 273 Acknowledgments 274 The research was supported by a DGA-MRIS scholarship and the Agence National of Research (ANR) 275 "Emotion(s), Cognition, Comportement" 2011 program (ANR 11 EMCO 00902), as well as an ANR-11- 276 0001-02 PSL*, and an ANR-10-LABX-0087. The funders had no role in study design, data collection 277 and analysis, decision to publish, or preparation of the manuscript. 278 279 280 281 282 283 284 285 286 287 288 289 Figure Legends 290 291 292 Fig. 1 Muscular activity for CS (top) and ZM (bottom) for each emotion and over time. The 293 grey square shows the 500-ms time window where activity becomes to be statistically 294 significant for the emotion the muscle is known to be specific to (higher for fear than for joy 295 and neutral for CS; higher for joy than for fear and neutral for ZM). 296 297 Fig. 2 Muscular activity for CS (left) and ZM (right) for each emotion and for each level of A’s 298 representation of B’s informational access (20%, 60% and 100%). Black lines indicate 299 significant effects at *P<0.05; **P<0.01. Error bars indicate SEM. 300 Summary of this chapter The results presented in the previous section of this chapter suggest that, when confronted by emotional signals of fear, humans spontaneously and unintentionally take into account the informational needs of their neighbors, even when there is no benefit to be gained for their own performance in the task. We think that this could be the mark that emotional transmission of information related to fear via facial activity is cooperative behavior, the purpose of which is to selectively inform others. It also appears that, as we failed to show any impact of others’ informational needs when participants were confronted by emotional signals of joy, the relevance of the information to others is a critical parameter that can modulate the intensity of emotional facial activity. Information related to threat would be more relevant for others, as it is linked to immediate adaptive challenges in the environment. The impact of this research for the wider understanding of emotional crowd behavior is obvious: when information is linked to adaptive challenges, the spread of emotions could be facilitated by mechanisms whose function is to intensify the activity of emotional signaling media (here, emotional facial behavior) to increase informational access to other crowd members, and help them prepare adaptive responses by triggering motor reactions in them (De Gelder, Snyder, Greve, Gerard, & Hadjikhani, 2004; Grèzes et al., 2007). Although this study did not explore other parameters, the relevance of information to the audience should not be the only significant one to be taken into account. The composition of the audience (kin/non-kin; familiar/unfamiliar individuals; in-group/out-group members) may also influence the sharing of adaptive information with others in threatening contexts. Even if it has largely been shown that the identity of the emitter is important for the transmission of emotion to the receiver (see Hatfield et al., 1994 for a review), the impact of the receivers’ identity on the propensity to share emotional information with others has not been investigated. Research dealing with the collective response to threat suggests that seeking proximity and maintaining contact with familiar individuals is a primary drive in individuals confronted by threatening elements (see Mawson, 2005 for a review). “Familiar people” can include all those towards whom we are supportive (such as kin [Madsen et al., 2007] and other members of personal social networks [Dunbar & Spoors, 1995]). 81 Both the relevance of the piece of information to be shared and the composition of the audience and its degree of cooperativeness are expected to modulate an individual’s propensity to share the emotional information one is confronted with. Finally, although we have concentrated on facial expressions, the intensity of vocal and bodily displays in reaction to threatening signals might well be sensitive to others’ informational demands. In fact, all types of signaling media (including verbal signals: Luminet, Bouts, Delie, Manstead, & Rimé, 2000) can be expected to be sensitive to others’ informational demands. 82 Chapter Four: General discussion Summary of the main findings Throughout this thesis, we have shown that (i) humans are endowed with cognitive mechanisms that are tuned to react spontaneously and involuntarily (though selectively (Dezecache, Mercier, Scott-Phillips, 2013) to others’ emotional signals of fear and joy, in a way that can be congruent with others’ emotional experience (Grèzes & Dezecache, in press). (ii) In response to these signals, we in turn spontaneously and involuntarily produce signals (they can be subtle) which can induce emotional experience of fear and joy in a third-party who has no access to the emotional source (Dezecache et al., 2013). (iii) Emitters are also endowed with mechanisms that can selectively affect the intensity of facial response as a function of the relevance of the information to the audience (Dezecache et al., in prep). A combination of these elements contributes to an explanation of how and why emotions of fear and joy can spread on a large scale. Emotions of fear and joy can be transmitted beyond dyads Studies investigating the process of emotional transmission (again, the book of Hatfield and colleagues [Hatfield et al., 1994] is a major reference here), have only considered emotional transmission in dyadic contexts, where observers (B) “catch” the emotion of emitters (A). For emotions of fear and joy to become collective, third-parties (C) who have perceptual access to B but not to A must also be “contamined” by A’s emotions of fear and joy, through 83 B. In Dezecache et al., 2013 (Chapter 2), we reproduced a minimal crowd situation where an agent C was observing an agent B, herself observing an agent A. Crucially, C did not have perceptual access to A, and B was not aware of being monitored by C. We were able to show that emotions of fear and joy could be transmitted from A to C, via B, even if B did not produce signals that could be explicitly recognized. In this respect, we found that expressions of joy in B were recognized below chance level by independent judges, while signals of fear could be detected above chance level. This suggests that the spread of fear, as opposed to that of joy, is facilitated and that there is a tendency, in B, to exaggerate facial expressions when they are of immediate relevance for others (fear signals a direct threat in the environment). This is compatible with our findings in chapter 4 and in Dezecache et al. (in preparation). Emotional transmission as a process of influencing others These findings raise an important theoretical question. In our paper Dezecache, Mercier & Scott-Phillips (2013) (chapter 2), we made a distinction between cues, in general, from signals, which are a special type of cue. Cues can be defined as stimuli that elicit a specific cognitive or behavioral response going beyond the mere perception of the cue itself. Signals can be defined as cues that have the function of eliciting such a response (Scott-Phillips, 2008). Are the subtle emotional cues produced by B a mere side effect of B’s emotional arousal caused by the perception of A’s emotion, or do these cues have the function of eliciting a similar emotional response in others? In other words, are they not just cues but signals? A trait can have a function in two ways: by being a biological adaptation that contributes to the fitness of the organisms endowed with it; or by being intentionally used by an agent in order to fulfill this function. In our study however, B did not know that she was being observed and thus did not intend to signal anything by means of her facial expression (which she may well have been unaware anyway). The fact that, at least in the case of 84 joy, these expressions were not recognized by judges strongly suggests that participant C’s use of these cues was not intentional either. The cues we are talking about are neither intentionally emitted nor intentionally attended to; they don’t have an intended function. Are these emotional cues therefore biological adaptations, whose function is to transmit an emotion in a non-intentional way? And if so, how is this function adaptive? One possibility we explored was that facial activity in B is an evolved, cooperative type of behavior that consists in the involuntary and spontaneous signaling of information of adaptive value, which induces appropriate emotional and preparatory behavior in our conspecifics. Such a mechanism would be adaptive, on the one hand, in threatening situations where flight and mobbing behaviors are optimal strategies; and, on the other hand, in favorable situations where signaling to conspecifics the presence of non-scarce rewarding features of the environment may foster social bonds. It is an open question whether unintended and non-consciously attended cues of specific emotion are in fact evolved signals that contribute to the fitness of the people who produce them and to that of those who are influenced by them. If unintentional cues of emotions are mere side effects of the emotional state, then their amplitude should vary only in relation to the intensity of the arousal of which they are a side effect. If, on the other hand, these cues are signals, then their amplitude should vary in relation to the adaptive value of their being picked up by other individuals. Such audience-directed variations could be triggered by: (i). The relevance of the information to the audience (the type of emotion and its relevance to the situation at stake): emotions that are advantageous to share (such as fear) should determine (everything else, and the degree of arousal in particular, being equal) stronger unintentional cues than emotions the sharing of which may be less important (such as joy) or harmful (such as boredom or envy). In addition, the relevance of the information should matter: unintentional cues should be stronger when the audience stands to gain useful information from sharing it than when it does not. A recent study of vocal signals in wild chimpanzees (Crockford, Wittig, Mundry, & Zuberbühler, 2012) offers a suggestive point of comparison in this respect. Their results revealed that the best predictor of call rates in response to the presence of a snake was the state of 85 knowledge of the conspecifics, thereby demonstrating that threat-related vocal signals are selectively produced according to their informational value for others. It could be that implicit emotional cues among humans are selectively produced according to their informational value for others. (ii). The presence of an audience and its composition: unintentional cues should be stronger when there are others to pick them up. Also, unintentional cues of emotions should be stronger when the individual emitting them has already had cooperative interactions with the audience. In the experimental design developed in our research article Dezecache et al. (2013) (chapter 2), participant B did not know that she was being watched, so none of the conditions were satisfied. The fact that she nevertheless produced unintentional cues strong enough for them to influence participant C can be interpreted either as evidence that these cues are mere side effects, or as evidence that, even in the absence of reinforcing factors, these effects are strong enough to serve a communication function. Emotional transmission is sensitive to others’ informational needs In our research paper Dezecache et al. (in preparation) presented in Chapter 4, we specifically addressed the question of whether spontaneous and involuntary subtle facial cues that are produced when confronting emotional stimuli of fear and joy (these have been documented in numerous studies, where they are termed “mimicry”, see Dimberg et al., 1998; 2000; Moody et al., 2007; Soussignan et al., 2013) are mere cues (that can have the function of optimizing the observer’s preparatory behavior [Susskind et al., 2008; Vermeulen, Godefroid, & Mermillod, 2009]), or whether they have evolved to specifically inform others. To test this, we chose to manipulate the relevance of that piece of information to the audience: information could be important to share (fear), less important (joy), or of no relevance to the audience (neutral content). Neighbors could also have greater or lesser perceptual access to the source, thus making the information more or less relevant to share. Crucially, 86 the task performed by our participants was not linked to the sharing of information. They were merely informed that they would be accompanied by another participant who would watch the same videos, with more or less informational access. Our results revealed that spontaneous and involuntary facial reactions to a fear content in participants were modulated by the perceptual access of their neighbors, even at no benefit for their performance at the task. The fact that such sensitivity to others’ informational demands was not found when observers were confronted with joy can be explained, either by the weaker relevance of a joy content to neighbors, or by the fact that participants’ smiling behavior (a facial expression that is typical of the experience of joy and often associated with appeasement and affiliative intentions (Fridlund, 1994; Goldenthal, Johnston, & Kraut, 1981; Kraut & Johnston, 1979; Mehu & Dunbar, 2008)) could have been directed towards the characters expressing joy in the source stimuli. It could be said that observers, when confronted with signals of threat, show a certain sense of responsibility (though unintentional) towards their neighbors as they spontaneously and unconsciously compensate for the latter’s lack of informational access. However, it must be pointed out that, for the sharing of emotional information to remain beneficial for senders (an issue which is further developed in chapter 2 and in my article Dezecache, Mercier & Scott-Phillips, 2013), sharing should be restricted to potential cooperators. This point is a subject worthy of empirical investigation. Beyond audience effects: how others’ mental states can influence transmission of emotional information behavior These results are also of interest for the fierce debate that took place between the “emotional readout” and the “behavioral ecology” views of facial behavior, some twenty years ago. For emotional readout theorists (Ekman, 2007; Izard, 1971, 1977), core emotions (which include joy and fear) consist of affect programs that, when activated by the presence of emotional stimuli, trigger characteristic muscular and physiological patterns, as well as a 87 distinct phenomenological experience. Although social conventions can modulate their intensity through the operation of display rules (Malatesta & Haviland, 1982), facial emotional displays are held to express inner emotional states. Against this perspective, behavioral ecologists (Bavelas, Black, Lemery, & Mullett, 1986; Chovil, 1991, 1997; Fridlund, 1994) have argued that facial emotional displays are “social tools” (Smith, 1980) which influence other people’s behavior, and signal the senders’ intentions towards recipients (Fridlund, 1994). Expressions that are typically associated with the experience of fear by emotional readout theorists, signal a readiness to submit, to flee, or an invitation to others to run for their lives. Expressions that are typically related to joy signal a readiness to appease, to play, or to affiliate. In short, facial displays can well be seen as social motives (Chovil, 1997). One strong argument in support of the behavioral ecologists was that the intensity and occurrence of facial displays are heavily modulated by so-called “audience effects”. Bowlers do not smile because of the solitary emotion experienced when winning a game, but because and when they are interacting with other bowlers. Similarly, hockey fans’ smiling behavior, although related to the outcome of the game, is found to be more strongly dependent on whether they were with friends or facing opponent fans (Kraut & Johnston, 1979). Similar results were obtained in laboratory settings: electromyographic activity over the zygomaticus major was recorded in four conditions varying in their degree of sociality. A monotic increase was found ranging from (a) a condition where participants were alone, (b) a condition where participants were alone but believed that there was a friend nearby, (c) a condition where participants were alone but thought that their friend was viewing the same video in a different room, to (d) a condition with presence of a friend. This increase was independent of participants’ ratings of the amusing videotapes viewed (Fridlund, 1991). As well as showing that audiences’ emotional reactions can be implicitly elicited, such results strongly suggest that smiling behavior is principally related to audience effects, and associates poorly with vicarious experiences of joy. In these studies by behavioral ecologists, sociality is defined as “the extent to which individuals can fully interact with each other through the auditory and visual channels of language” 88 (Chovil, 1991). Our proposal is more ambitious, as it takes sociality to include the state of knowledge of other individuals, and demonstrates that the “relevance of the information to others” (defined as a function of the uncertainty states of others) is also an important component in the expression of facial displays. Emotional transmission is not contagion In this thesis, we have also discussed the theoretical legacy of early crowd psychologists for today’s understanding of the transmission of emotional information in crowds. They conceptualized emotions as germs or disease, and such metaphors continue to haunt our representation of the process of emotional transmission. Emotional transmission is indeed often conceptualized as a passive, mandatory and irrepressible process (e.g., Hatfield et al., 1994). However, the conception of emotional transmission as emotional communication rules out the possibility of the process being mandatory and irrepressible. For any communication to evolve and be stable, both emitters and receivers must find benefits in communicating. If receivers had always been receptive to emotional signals in a way which was beneficial to emitters but detrimental to themselves, emotional communication would have collapsed. Conversely, if emitters had not found any benefit in communicating, emotional communication would not have been stabilized. After ruling out conventional hypotheses to account for the stability of emotional communication, we argued (Dezecache, Mercier & Scott-Phillips, 2013 – chapter 2) that the only mechanisms capable of explaining the stability of emotional communication would suppose that receivers react flexibly to emitters’ emotional signals, by actively (although not necessarily consciously) evaluating the emitters’ level of benevolence and competence. This contradicts the very idea that, as in the transmission of disease, emotional transmission is an inflexible and compulsory process. In fact, many factors can impact and reduce the phenomenon. To link this with the wider issue of the transmission of emotion in collective contexts, 89 the considerations made in Dezecache, Mercier & Thom-Scott-Phillips (2013) might also contribute to explaining the phenomenon of hysterical laughter, as in the case of the “Tanganyika laughter epidemic”, which occured in Tanzania in 1962 (Rankin & Philip, 1963). Several schools were then closed following attacks of laughter among pupils. In total, the spread of laughter lasted for a total of eighteen months, had an impact on fourteen schools, and affected approximately one thousand children. Such cases do not contradict the fact that emotional transmission is a flexible process but rather confirms it: being the victim of a laughter attack has little cost and high benefits. Instead of blocking the spread of emotions, mechanisms of emotional vigilance could have facilitated it. As contemporary crowd psychologists (Drury, 2002; Reicher et al., 2004) have repeatedly suggested, an attempt should be made to avoid conceptualizing the process of emotional information in terms of “contagion”. Since Le Bon’s work, the disease metaphors have been contaminating (no pun intended) our understanding of the process of emotional transmission. For scholars investigating the process of the spread of emotions, use of this metaphor has prejudicial consequences, as it supposes that the process is irrepressible and mandatory. An effective strategy to provoke some sort of “paradigm shift” in emotional transmission psychologists would most probably be multiple: one would be to avoid the concept of “contagion”; and another to explore empirically the many factors that can restrain the process. If I may suggest a third one, this would be to stress the idea that, as emotional transmission (of fear, joy, and probably other emotions), is, after all, a process of communication of information, it is flexible in nature: all depends on the identity of the emitter, on that of the receiver, and crucially, on the relevance of the information to be shared by the emitter, and to be adopted by the receiver. 90 Epilogue: Emotional transmission beyond triads: implications and limitations of our findings for the understanding of emotional crowd behavior The work developed in this thesis is based on the notion that early crowd psychologists were accurate in their accounts of how an emotional crowd behaves, i.e., that emotions could spread in an irrepressible fashion and that people fled irrationally and with no consideration for their crowd neighbors. Throughout this dissertation, however, crucial aspects of these accounts have been undermined. Even if emotions could indeed be spontaneously, involuntarily and subtly transmitted beyond dyads, I have argued that emotional transmission is not an irrepressible and mandatory process. This view is consistent with modern accounts of crowd psychology. Their proponents have been trying to debunk the myths of popular representations of emotional crowd behavior for more than a decade. 91 Where traditional views might have gone wrong Revising the key-characteristics of crowd behavior Let us first of all return to the key characteristics presented in the first chapter, considered to be accurate descriptions of the dynamics of crowd behavior. Crowds were thought to be irrational, emotional, suggestible, destructive, spontaneous, anonymous and unanimous. These characteristics painted an extravagant picture of the crowd and have contributed to the popular success of “crowd behavior” as a topic of investigation. Crucially, they have also influenced public order policing (Hoggett & Stott, 2010) and have had tremendous consequences for the scientific investigation of crowd behavior, by virtually imposing ideological views (Quarantelli, 2001): “Crowds are the elephant man of the social sciences. They are viewed as something strange, something pathological, something monstrous. At the same time they are viewed with awe and with fascination. However, above all, they are considered to be something apart. We may choose to go and view them occasionally as a distraction from the business of everyday life, but they are separate from that business and tell us little or nothing about normal social and psychological realities.” Reicher (2001) Stephen Reicher’s view is consistent with David Schweingruber and Ronald T. Wohlstein’s (2005) review of numerous sections in sociology textbooks dedicated to crowd behavior. Their samples show that scientific discourse on crowds is – just like the popular representations of them – largely contaminated by the majority of the seven key-characteristics mentioned above. More precisely, and according to Stephen Reicher and Jonathan Potter (1985), layunderstanding of crowd behavior and traditional discourse share typical features such as (i) a constant de-contextualization (the interpretation of crowd movements is detached from their ideological motives, e.g., rioters appear brutal when one fails to mention the things they are 92 fighting against; people running for their lives seem to be behaving irrationally when their reason for doing so is not taken into consideration, etc.), (ii) a serious lack of interest in the dynamics and internal processes of crowd formation, and (iii) an inappropriate emphasis on the negative consequences of crowd events. These typical errors must have been committed intentionally as they form part of a genuine ideological agenda: “On an ideological level, Le Bon’s ideas serve several functions. Firstly, it acts as a denial of voice. If crowds articulate grievances and alternative visions of society - if, in Martin Luther King’s resonant phrase, crowds are the voice of the oppressed - then Le Bonian psychology silences that voice by suggesting that there is nothing to hear. Crowd action by definition is pathological, it carries no meaning and has no sense. Secondly, this psychology serves as a denial of responsibility. One does not need to ask about the role of social injustices in leading crowds to gather or the role of state forces in creating conflict. Being outside the picture they are not even available for questioning. Violence, after all, lies in the very nature of the crowd. Thirdly, Le Bon’s model legitimates repression. Crowds, having no reason, cannot be reasoned with. The mob only responds to harsh words and harsh treatment. Like the mass society perspective from which it sprang, but with more elaboration and hence with more ideological precision, the Le Bonian position defends the status quo by dismissing any protests against it as instances of pathology.” Reicher (2001) In fact, as for sociologist Vincent Rubio (2008), the very concept of a crowd is a longstanding ideological construct (its origins and characteristics can be traced back to Plato’s Republic), which aims at legitimizing social control and strengthening political order over recurrent mass movements. Indeed, there appear to be no parameters that would help define or recognize crowds: a crowd is neither a precise set of n individuals, nor a density of 93 population within a delimited space. Rather, the word “crowd” might simply be a pejorative term to designate a group of people we disagree with, and against whom we presume that repression is justified. The fact that certain social policies in today’s world are indeed based on traditional assumptions of crowd behavior (Drury, Novelli, & Stott, 2013; Drury, 2002; Hoggett & Stott, 2010) supports this claim. Does this mean that the key-characteristics proposed by traditional views are all to be left behind? For David Schweingruber and Ronald T. Wholstein (2005), accounts of crowd behavior would be more accurate if freed from them. In emergency situations, people do not lose their minds (as the “irrationality” and “emotionality” characteristics predict), they most frequently react calmly, cooperate actively and attempt to evacuate buildings in an orderly fashion (Bryan, 1980; Clarke, 2002; Drury, Cocking, & Reicher, 2009a; Johnson, 1987; Keating, 1982). Similarly, the characteristic of “destructiveness” has to be rejected. Crowds are seldom violent, and antisocial behavior is often the fact of isolated and small groups within the crowd (Reicher, Stott, Cronin, & Adang, 2004; Stott & Reicher, 1998). Moreover, crowd situations do not appear to favor anonymity, as crowds are often formed of subgroups composed of individuals known to each other (Aveni, 1977). Finally, “unanimity” might only be an illusion, resulting from observers having to deal with many individual behaviors (McPhail, 1991). Two characteristics, however, seem to resist thorough examination: “spontaneity” (emergency situations necessarily emerge suddenly and unexpectedly) and “suggestibility”. This latter characteristic is particularly relevant to the present work, as “suggestibility” has to do with the power of emotions to spread. Although the concept has long been a pillar of popular representations of emotional crowd behavior, the idea that suggestibility is a key-crowd characteristic is seriously undermined by modern accounts of crowd behavior. In fact, it appears that traditional views on crowd behavior has generally neglected social aspects of the situation (the fact that crowd members share a common social identity that can decisively modulate the spread of emotions), and put too much stress on the irrepressibility of emotional transmission in crowds, and its role in structuring crowd behavior. 94 Are crowd members suggestible? As set out in the first chapter, three main factors were thought to contribute to the emergence of emotional crowd behavior: firstly, “deindividuation” hinders fully-fledged self-monitoring processes in crowd members; secondly, “mental contagion” results in mental and emotional unanimity among crowd members; finally, “suggestion” restricts the range of ideas and emotions that can be shared within the group. The role played by these factors in the emergence of crowd behavior has been severely criticized by more recent accounts of such behavior. Firstly, the idea that being part of a crowd entails deindividuation needs to be revised. Anonymity can well lead to generous and pro-social behaviors (Johnson & Downing, 1979). On more theoretical grounds, while advocates of deindividuation consider people as having one single identity and set of norms, a meta-analysis of sixty studies has proved them wrong by showing that people in groups take on situation-specific identities and norms. Grouping does not therefore necessarily entail anti-social behavior; crowd members can, alternatively, adopt a social identity favoring pro-social conduct (Postmes & Spears, 1998). Be that as it may, the belief that the collective threatens individuality is still doing well in academic as well as non-academic circles. The second general cause, that of mental contagion, has also been discussed by later works, and the criticisms made can be summarized as follows: even if it is true that individuals within crowds could adopt their neighbors’ behavior, they are only likely to do so if they share the same ideological motives and endorse a similar social identity. In this respect, an analysis of the St Paul’s riots in Bristol (United Kingdom) in 1980 confirms that rioters reacted selectively to other group members’ actions, in accordance with their conformity to a shared ideology and identity. St Paul’s rioters stoned banks and police officers, but immediately reprimanded isolated crowd members that were aiming at public transport buses and other ideologically irrelevant targets (Reicher, 1984). In fact, far from being anarchic, crowd actions are complexly structured; mental contagion would therefore be restricted or selective. One could argue that, as far as fear and joy-based collective behaviors are concerned, it might not be obvious to see where and when the sharing of a social identity could play a role in structuring crowd members’ personal behavior (e.g., why I would choose any other option 95 but to escape in emergency situations). Again, it has been shown, surprisingly, through field studies and virtual reality experiments, that social identity in fact promotes coordination and cooperation in emergency cases and prevents people from panicking (Drury, Cocking, & Reicher, 2009a, 2009b; Drury et al., 2009). For example, public safety agencies could encourage solidarity between crowd members in evacuation situations, by priming people with a collective identity label to be used in public announcements (such as, "Parisians", "Fans of the Paris Saint-Germain", etc.). All in all, mental and emotional transmission appears to be a more flexible and repressible process in crowds, compared to what was previously thought. Finally, the third cause, that of “suggestion” is dubious. More modern accounts of crowd behavior (such as Reicher, 2001) argue that the range of possible actions is not fixed (as the so-called racial unconscious described by Gustave Le Bon [1896] suggests) but ultimately determined by the situation at stake, as well as by the social identity adopted by crowd members. Being contaminated by your crowd neighbors’ anger is largely dependent on your own assessment of the situation and how far you approve their actions (whether or not they share your political ideas, for instance). All in all, the whole narrative of crowd behavior needs to be revised, to read as follows: a newcomer, upon entering the crowd, may or may not adopt the collective identity that has contributed to its formation; having adopted this identity, he will be contaminated by passing ideas and emotions (a form of mental “contagion”, if we may say so) that are consistent with the endorsed social identity. This is the reason why crowds can act pro-socially as well as anti-socially. Modern accounts of crowd behavior stress the idea that social identity plays an important role in shaping emotion-based collective behavior. When reading contemporary crowd psychologists, it would even appear that social identity plays the prominent role in the emergence of emotional crowd behavior: social identity structures emotions by facilitating or inhibiting their spread. For social neuroscientists, this state of affairs can sound surprising as it conflicts with the somewhat primitive aspect of emotional transmission (Hatfield et al., 1994), a process that is well beyond voluntary and conscious control. How could social identity shape such a 96 process? This issue is difficult to tackle, especially because crowd psychologists and social neuroscientists study at different levels of analysis. However, as I have argued in chapter 2 and in Dezecache, Mercier & Scott-Phillips (2013), emotional transmission is not a mandatory and non-repressible process. Part of the explanation may therefore lie in the presence of inhibitory mechanisms that operate to slow down or stop the process of emotional transmission when it becomes too costly for receivers. In this event, it would help them select complementary responses, instead of reacting congruently with the emitter. Be that as it may, one conclusion reached by modern accounts of emotional crowd behavior seems literally to contradict one of our own. As stated above, people seldom panic in emergency situations, and yet, we found that fear is spontaneously and unintentionally transmitted beyond dyads (chapter 2 and Dezecache et al., 2013). Moreover, people seem to share spontaneously the information that something must be avoided, by unintentionally intensifying their emotional facial activity, when confronted by threatening elements (chapter 3 and Dezecache et al., in prep.). Our experimental results thus suggest that fear should spread rapidly, widely and intensely in groups. This would ultimately lead to panic in crowds, i.e., situations where, because they see or foresee a major physical danger as imminent and know that escape routes are limited (Quarantelli, 1954), people react with excessive fear and self-preservation behaviors (such as immediate flight, a behavior that is selfish in nature) (Mawson, 2005). The “myth” of crowd panics The idea that crowds do not panic in emergency situations is counter-intuitive. At a personal level, I have tried to convince people of this state of affairs on many occasions. Reactions are always immediate and virulent. People are skeptical, although they cannot remember ever having been stuck themselves in a panicking crowd. They prefer to try and to prove you wrong on the basis of the many mass media reports they can recall of ‘stampedes’ occurring all over the world. Needless to say, these reports are ideologically oriented, in the sense that they rely on traditional views of crowd behavior (Tierney, Bevc, & Kuligowski, 2006). In fact, deaths in crowds are very often due to compressive asphyxia, which itself is 97 due to space limitations (Fruin, 1993; Helbing, Johansson, & Al-Abideen, 2007; Zhen, Mao, & Yuan, 2008). Emotional states of “panic” are therefore not necessary elements to explain deaths in crowd disasters. The laws of Physics governing forces are sufficient to explain how a large group of people can exert physical force that could result in others being fatally crushed. Moreover, the judgment that panic occurred is often made by observers without the participants’ point of view being taken into consideration (Fahy, Proulx, & Aiman, 2009). Participants’ reports concerning mass emergency events do indeed paint a very different picture of collective reactions to threat. In this respect, Guylène Proulx and Rita F. Fahy (2004) analyzed 745 first-hand reports from 435 survivors of the attacks on the World Trade Center in September 2001 (New-York, USA). These personal reports were collected during the year following the attacks, from newspaper articles, television programs, personal websites, and through email exchanges with survivors. They were examined through content analysis, where a set of questions allowed for “interviewing” the report (Johnson, 1987). Questions focused on the means of exit, the types of cues that gave the survivors information about the disaster, the starting time and progress of the evacuation, their perception of others, help received from other survivors and help given. These personal reports revealed that, although the perception of risk was high (84% of the participants had moderate or full knowledge of what had happened: they knew something major was happening not necessarily knowing exactly what it was), and half of participants reported obstruction during evacuation (e.g., debris, doors jammed, overcrowdedness, smoke), mutual help between survivors was frequent (found in 46% of the reports) and people perceived their fellows as reacting calmly and in an orderly way (57%). Only one third of the survivors reported that others were upset (crying, shouting, or showing signs of anxiety or nervousness). In fact, as far as panic-related behavior is concerned, panic was individual, not collective. It is interestingly to note that, even if people sometimes describe their own behavior as panicky in emergency contexts, objective examination in fact shows that behavior in such contexts is often rational and prudent (Brennan, 1999). Many case studies confirm this view. These include Norris R. Johnson’s reassessment of survivors reports from the “The Who concert stampede” in Cincinnati, USA in 1979 (John- 98 son, 1987) attended by more than 18,000 people. Although the 11 deaths by crushing were reported by the media as being the outcome of a general panic, Johnson showed that the 46 statements (from police officers, employees and private security guards) did not report competition between crowd members for gaining access to safer locations (as could be expected when collective panic occurs), but instead revealed that people were frequently helpful and tried to prevent others from being crushed (40% of the reports). This suggests that “social norms”, rather than being extinguished in crowds, continue to prevail widely and to structure crowd behavior. Why don’t crowds panic? Tentative explanations Emotional crowd behavior is regulated by emerging social norms How can social norms still prevail in emergency situations? According to social psychologists John Drury and Stephen Reicher (Drury & Reicher, 2010), “people in a crowd develop a shared social identity based on their common experience during an emergency. This promotes solidarity which results in coordinated and beneficial actions”. Evidence comes from a collection of 45-to-90-minute interviews with survivors of mass emergency events, i.e. situations involving a large number of people, including the presence of a clear threat, and with limited exit possibilities (Drury et al., 2009a). Major events included the Sinking of the Jupiter in Greece (1968) – where more than 400 people had to escape a boat which was sinking; four people died in this tragedy –, the evacuation of Canary Wharf in London, United Kingdom (September 11th, 2001) – where people quickly evacuated skyscrapers after news of the attacks on the World Trade Center in New York, as similar attacks were foreseen in the business districts of other major cities throughout the world –, and the Bradford football stadium fire in the United Kingdom (1985) – where fiftysix people died following burns or smoke inhalation. All the eleven major events reported in the study presented ideal conditions for people to panic on a massive scale, i.e., to display selfish self-preserving behaviors. 99 After giving free accounts of their story, participants were questioned about their own behavior as well as that of other participants: what they themselves did, how quickly others evacuated, whether evacuation was smooth, whether people cooperated or helped each other, or whether they behaved selfishly. Crucially, they were also asked about their identification with other crowd members: how they felt towards them and whether they felt any sense of unity with each other. Coding of the interviews revealed, as expected, that helping was more commonly reported than selfish, self-preservation behaviors (forty-eight instances of helping behavior reported vs. nine instances of selfish behaviors). Interestingly, most participants identified strongly with other crowd members (twelve out of twenty-one). A survivor from the Hillsborough stadium disaster in Nottingham, United Kingdom (1989), where ninety-six people died, reported: “All of a sudden everyone was one in this situ- when when a disaster happens when a disaster happens, I don’t know, say in the war somesomewhere got bombed it was sort of that old that old English spirit where you had to club together and help one another, you know, you had to sort of do what you had to do, sort of join up as a team, and a good example of that would be when some of the fans got the hoardings and put the bodies on them and took them over to the ambulance.” It should be noted that explicit reference is made to the “old English spirit” which is supposed to bond crowd members together. As predicted by social identity theorists (Hogg & Williams, 2000; S. Reicher, 2001; Reicher, 1987; Tajfel, 1978), people adopt specific social identity depending on the situation. Among these high-social identification reports, 92% reported a feeling of shared or common fate (vs. 67% for people who weakly identified). They were also more likely (compared to people who identified weakly) to report a feeling of being personally endangered (67% vs. 56%) Interestingly, helping was more frequently reported in high-identification survivors (34 recollection of helping events vs. 14 for low-identification 100 survivors). Altogether, these results suggest that the adoption of a social identity, linked to a greater sense of shared or common fate, favors mutually-supportive behavior between crowd members. Similar results have also been found for survivors of the London bombings in 2005 (Drury et al., 2009b) Causality between a shared sense of danger and pro-social tendencies is, however, very difficult to establish. Being a survivor of a dramatic event, and having felt a sense of togetherness with other crowd members could lead to reinterpreting behavior that was self-preserving as being more altruistically-oriented. In other words, survivors could have a brighter picture of what happened, merely because they felt good towards others, or because they survived the disaster. This brings us back to a set of methodological issues which might well explain why social neuroscientists and social psychologists may disagree when trying to explain why people do not succumb to massive panic in emergency contexts. Modern crowd psychologists face serious methodological issues While social neuroscientists study emotional transmission in an implicit fashion, by measuring behaviors that are involuntary in their activity (such as emotional facial activity, skin conductance, cardiac rhythm etc.), social psychologists rely on participants’ reports. They may base their accounts on interviews in newspapers, using partial responses to questions they know nothing about. When they do directly confront survivors, interviews are conducted many years after the disaster occurred. During this lapse in time, the survivors’ interpretation of events could become greatly distorted. Memory distortion following traumatic events is indeed known to occur (e.g., Schmolck, Buffalo, & Squire, 2000). Because they have survived a major disaster, participants also tend to give a more positive view of what actually happened. This would also explain why, in Drury et al.’s study cited above, people who felt a sense of togetherness with others reported more pro-social behaviors between crowd members. In fact, they might have interpreted behavior in a way which made their sinister fellows appear nicer. 101 John Drury and colleagues used virtual reality to challenge such a claim. They examined whether spontaneous behavior in virtual crowds could be consistent with explicit reports made by crowd disaster survivors (Drury et al., 2009). In three experiments, participants were immersed in a video game and were required to evacuate, as quickly as possible, a tube station on fire. They could be hindered in this process by the rest of the crowd. Fortunately, they were able to push (as many times as they wanted to) in order to make their way to the exit more quickly. They were even encouraged to do so as exit time was limited: a “danger of death” gauge on the top of the screen showed time running out. During the course of their escape, they were confronted by four injured virtual characters that they could help (at a cost for their escape time), or ignore (at no cost). The overall behavior of participants was rated according to the number of “pushing” and “helping decisions” they took during the course of the escape. Results of the three experiments showed that perception of threat could indeed enhance identification with the group as a whole, and that people who identified strongly with others pushed less and helped more than those with low-identification. Regrettably, however, no financial incentive was offered to motivate participants to escape as quickly as possible. On a more general note, using virtual reality experiments to simulate events where people risk death might only partially reveal how people would actually behave in real-life situations. Such experimental protocols might therefore fall short of encouraging the kind of spontaneous and implicit behaviors triggered in actual emergency situations. One methodological possibility to pursue would be to explore emotional crowd behavior in emergency contexts using video recordings of real events. Data exist (such as for the pilgrimage of Makkah in Saudi Arabia: Johansson, Helbing, Al-Abideen, & Al-Bosta, 2008) but, as far as I know, they have not yet been coded to investigate individual behavior or pro-social and selfish self-preservation tendencies in crowd members. Opportunities for studying collective reaction to threat could also be found in the collection of video recordings of haunted house attractions. The Fear Factory at the Niagara Falls, in Canada (see: http://www.nightmaresfearfactory.com/) upload photos of people’s reactions to a threatening element each week. Studying and coding people’s behavior towards others (whether they grip the others, and whether they are gripped back, whether they try to escape or seek social 102 comfort) could provide priceless data of how people react collectively when confronted by with immediate threat. Different levels of analysis at the source of the dilemma Apart from the methodological issues faced by modern crowd psychologists, another reason for the discrepancy between social neuroscientists’ perception of emotional “contagiousness” (or the capacity of emotions to be transmitted in a spontaneous and wide-spread fashion) and the quasi-absence of emotionally-driven collective behavior in actual crowds reported by modern accounts of crowd psychology, might lie at the heart of the difference in levels of analysis explored by scholars of each discipline. When conducting the experiment on emotional transmission beyond dyads (chapter 2 Dezecache et al., 2013), we recorded activity by certain muscles as well as skin conductance response. The increased responses of these indices during emotional content only serve to confirm that emotional information can indeed be transmitted beyond dyads, not that participants will react in a panicky way on the behavioral scale. In fact, regulation of affect can occur, through the operation of inhibitory mechanisms (Kim & Hamann, 2007; Ochsner, Bunge, Gross, & Gabrieli, 2002; Sagaspe, Schwartz, & Vuilleumier, 2011), and emotional information of fear can well be received and have an impact on the recipient’s facial muscular and autonomous nervous system activities, but nonetheless lead to a fully-fledged non-self preserving response at the macroscopic level. Natural reactions to threat: affiliative tendencies vs. self-preservative responses Other possibilities worth exploring are the natural reactions to threat. When re-examined, they could give clues about the reasons why people do not massively take flight in emergency contexts. According to Anthony R. Mawson (2005), and despite the fact that classical views assume that typical individual responses to threat are self-preserving in nature (the well-known 103 “flight or fight” motto, where flight is spontaneously directed towards a safer place, with no special consideration for others’ behavior) (Quarantelli, 2001), it appears that responses to threat (in non-human as well as in human animals) are primarily affiliative (people seek for familiar individuals, Bowlby, 1975) and are not solely guided by the will to flee towards a safer environment. During natural disasters (such as a tornado: Form & Nosow, 1958), people were shown to turn towards loved ones before deciding to flee (Fitzpatrick & Mileti, 1991); in fire emergencies, people tend to form clusters of familiar individuals (Bryan, 1980, 1985), and, again, it is often difficult to get people evacuated as they tend to wait for all their familiar individuals to be reunited before considering evacuation (Sime, 1983). Over and above the ties of familiar individuals, there is ample evidence that people in emergency situations continue to act in their social role (by, for example, helping weaker people) before evacuating themselves (Feinberg & Johnson, 2001). In fact, it appears that crowd members, even if they do take into account the exit possibilities, tend to move towards familiar persons as well as towards the exits, both of which are signals of safety (Sime, 1985). Evacuation movements thus tend to be a complex interaction between movements away from danger and towards places and figures that appear safer. Such data could resolve our dilemma as it would explain why, instead of leading to widespread collective flight, emotional transmission of fear could well be effective by promoting the social bonding and concern for others that are typically observed in emergency situations. Summary This epilogue has revisited the postulates of the traditional investigation of crowd behavior. By examining the conclusions of modern accounts of crowd behavior, we have seen that, far from being irrational and overwhelmed by fear and anxiety, people in emergency situations tend to stay calm and even show pro-sociality towards their fellows. They are even more prone to do so if they identify themselves with the group as a whole. 104 This state of affairs appears to contradict the idea that fear can be widely transmitted, and, more generally, the basic intellectual project of this thesis. This dilemma can, however, be resolved by stressing that (i) modern crowd psychologists and social neuroscientists study behavior at different levels of analysis, and that (ii) the idea that fear spreads among large groups does not mean that people will react with self-preserving behavior (such as fleeing), as natural fearful responses are mostly affiliative (seek for familiar places and people). 105 General references Aronfreed, J. (1970). 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In Nebraska Symposium on Motivation. 119 Appendix 120 GUILLAUME DEZECACHE Born 29 June 1987 | French citizenship | +33 (0)6 86 50 00 21 | [email protected] EDUCATION Université Pierre & Marie Curie (Paris, France) PhD in Cognitive Science 2010 - present University of Oxford (Oxford, UK) Visiting Student in Cognitive & Evolutionary Anthropology 2011 - 2012 Ecole des Hautes Etudes en Sciences Sociales (Paris, France) MA of Cognitive Science with highest honours 2008 - 2010 Université Lille 3 (Lille, France) BA of Philosophy with honours 2005 - 2008 RESEARCH Institute of Cognitive Studies – Ecole Normale Supérieure (Paris, France) PhD position in Cognitive Science (36 months) Topic: Studies on basic emotional signaling systems in human crowds Supervision: Julie Grèzes & Pierre Jacob, in collaboration with Dan Sperber Techniques: Electromyography and physiological recordings, behavioral experiments, theoretical work Chimfunshi Wildlife Orphanage (Chimfunshi, Zambia) Research intern in Primatology (1 month) Topic: Multimodal communication in semi-wild chimpanzees Supervision: Marina Davila-Ross Technique: Video recording 2010 - present 2012 Institute of Cognitive and Evolutionary Anthropology – University of Oxford (Oxford, United Kingdom) Visiting student in Cognitive & Evolutionary Anthropology (5 months) Topic: Laughter and social bonding in humans 2011 - 2012 Supervision: Robin Dunbar Technique: Direct observation GUILLAUME DEZECACHE PAGE 2 Institute of Cognitive Studies – Ecole Normale Supérieure (Paris, France) Research intern in Social Neuroscience (9 months) Topic: Transitive emotional contagion in humans Supervision: Julie Grèzes, Dan Sperber & Laurence Conty Technique: Electromyography and physiological recordings 2009 - 2010 Institute of Cognitive Studies – Ecole Normale Supérieure (Paris, France) Research intern in Social Neuroscience (6 months) Topic: The spatiotemporal integration of visual social cues Supervision: Laurence Conty & Julie Grèzes Technique: EEG under fMRI 2009 Institute of Cognitive Studies – Ecole Normale Supérieure (Paris, France) Research intern in Philosophy of Psychology (5 months) Topic: The cognitive mechanisms of collective action Supervision: Elisabeth Pacherie Technique: Documentary work 2009 Institute of Cognitive Studies – Ecole Normale Supérieure (Paris, France) Research intern in Developmental Psychology (5 months) Topic: The sense of justice in young children (3-to-5 y.o.) Supervision: Nicolas Baumard & Dan Sperber Technique: Paper-and-pencil 2009 AWARDS & GRANTS Fyssen postdoctoral fellowship (Fyssen Foundation) Full Ph.D. scholarship (French Ministry of Defense) Mobi’Doc International mobility fund (Île-de-France Regional Council) Ph.D. offer (London School of Economics) Merit scholarship (French Ministry of National Education) 2014 - 2016 2010 - present 2012 2010 2008 2 GUILLAUME DEZECACHE PAGE 3 PUBLICATIONS Grèzes J. & Dezecache G. (in press). How do shared-representations and emotional processes cooperate in response to threat signals? Neuropsychologia. Dezecache G., Mercier H. & Scott-Phillips T. (2013). An evolutionary perspective to emotional communication. Journal of Pragmatics. Dezecache G., Conty L. & Grèzes J. (2013). Social affordances: is the mirror neuron system involved? Commentary on target article of Schilbach and colleagues. Behavioral and Brain Sciences, 36(4), 417-418. Dezecache G., Conty L., Philip L., Chadwick M., Soussignan R., Sperber D. & Grèzes J. (2013). Evidence for unintentional emotional contagion beyond dyads. PLoS ONE, 8(6):e67371. doi:10.1371/journal.pone.0067371 Dezecache G. & Grèzes J. (2013). La communication émotionnelle ou le jeu des affordances sociales. Santé Mentale. 177, 26-31. Soussignan R., Chadwick M., Philip L., Conty L., Dezecache G. & Grèzes J. (2013). Self-relevance appraisal of gaze direction and dynamic facial expressions: effects on facial electromyographic and autonomic reactions. Emotion, 13(2), 330-337. doi:10.1037/a0029892 Grèzes J., Philip L., Chadwick M., Dezecache G., Soussignan R. & Conty L. (2013). Self-relevance appraisal influences facial reactions to emotional body expressions. PLoS ONE, 8(2):e55885. doi:10.1371/journal.pone.0055885 Dezecache G. & Dunbar R.I.M. (2012). Sharing the joke: the size of natural laughter groups. Evolution & Human Behavior, 33(6), 775-779. doi:10.1016/j.evolhumbehav.2012.07.002 Grèzes J. & Dezecache G. (2012). Communication émotionnelle: mécanismes cognitifs et cérébraux. In P. Allain, G. Aubin & D. Le Gall (Eds). Cognition Sociale et Neuropsychologie. Solal: Marseille. Conty L., Dezecache G., Hugueville L. & Grèzes J. (2012). Early binding of gaze, gesture and emotion: neural time course and correlates. Journal of Neuroscience, 32(13), 4531-4539. doi:10.1523/JNEUROSCI.5636-11.2012 Dezecache G. (2009). Phénoménologie et sciences cognitives : une psychologie du cognitiviste? Methodos, 9. 3 GUILLAUME DEZECACHE PAGE 4 TALKS The proximate mechanisms and biological function of emotional transmission of fear and joy in humans International Symposium on Vision, Action and Concepts (Tourcoing, France) 10/2013 Mise en évidence de la contagion émotionnelle Journée interdisciplinaire PSL (Paris, France) 05/2013 La contagiosité des émotions : études sur les basiques de partage émotionnel chez l’humain Séminaire doctoral d’études cognitives (Lyon, France) 03/2013 Studies on basic emotional signaling systems in humans Psychology seminar of the University of Portsmouth (Portsmouth, UK) 09/2012 Sharing the joke: the size of natural laughter groups Séminaire doctoral de l’Institut Jean Nicod (Paris, France) 06/2012 La contagiosité des émotions: études sur les systèmes basiques de partage de l'information émotionnelle chez l'humain Journée IHPST/IJN (Paris, France) 06/2012 Emotional contagion beyond dyads CERE Emotion Conference (Canterbury, UK) 05/2012 w/ Olivier Morin Ce qui manque aux émotions contagieuses pour être morales, et aux émotions morales pour être contagieuses Journée d'étude Morale et Cognition: Les Emotions (Nanterre, France) 09/2011 Evidence for transitive emotional transmission in humans 9th Annual London Evolutionary Research Network Conference. (London, UK) 11/2011 Evidence for transitive emotional transmission through facial signaling 23rd Annual Conference of the Human Behavior and Evolution Society. (Montpellier, France) 06/2011 w/ Julie Grèzes Bases cérébrales de la communication émotionnelle Colloque L'Empathie (Cerisy-la-Salle, France) 06/2011 w/ Julie Grèzes Emotional signalling as cooperation Workshop Coordination and Cooperation: Game-Theoretical and Cognitive Perspectives (Paris, France) 05/2011 Transitive emotional contagion Workshop LNC (Paris, France) 02/2011 4 GUILLAUME DEZECACHE w/ Hugo Mercier Communication and emotion: an evolutionary approach Communication and Cognition (Neuchâtel, Switzerland) PAGE 5 01/2011 POSTER PRESENTATIONS Dezecache G., Hobeika L., Conty L. & Grèzes J. “Is communication the biological function of spontaneous emotional facial reactions?”. Minds in Common: 2nd Aarhus-Paris Conference on Coordination and Common Ground. Paris (France). June 2013. Dezecache G., Hobeika L., Conty L. & Grèzes J. “Is communication the biological function of spontaneous emotional facial reactions?”. Embodied Inter-subjectivity: the 1st-person and the 2nd-person perspective: an interdisciplinary summer school. Aegina (Greece). June 2013. Dezecache G., Gay F., Conty L. & Grèzes J. “Emotions' contagiosity and cognitive interference: what can we learn from a modified STROOP task?”. IPSEN Conference New Frontiers in Social Neuroscience. Paris (France). April 2013. Dezecache G. & Dunbar R.I.M. “Size and structure of spontaneous laughter groups”. CERE Emotion Conference 2012. Canterbury (UK). May 2012. Dezecache G. & Grèzes J. “'Take care of me!': what do emotional expressions mean?”. Colloque Le Cerveau Social. Saint-Denis (France). May 2011. Dezecache G., Conty L., Chadwick M., Philip L., Sperber D. & Grèzes J. "'That she makes you happy makes me happy': an experiment of transitive emotional contagion (preliminary results)”. Colloque Le Cerveau Social. Saint-Denis (France). May 2011. Dezecache G., Grèzes J. & Jacob P. “An evolutionary perspective on emotional contagion”. Journée scientifique des doctorants de l’ED3C. Paris (France). March 2011. Dezecache G. & Mercier H. “Emotional vigilance: how to cope with the dangers of emotional signals?” 8th Annual Conference London Evolutionary Research Network Conference. London (UK). November 2010. Dezecache G., Conty L., Chadwick M., Sperber D. & Grèzes J. “I fear your fear of her/his fear: an experiment of transitive emotional contagion (preliminary results)” Conference on Shared Emotions, Joint Attention and Joint Action. Aarhus (Denmark). October 2010. Dezecache G. & Mercier H. “Emotional vigilance: how to cope with the dangers of emotional signals?” 1st Interdisciplinary Meeting of the DEC. Paris (France). October 2010. 5 GUILLAUME DEZECACHE PAGE 6 Dezecache G., Conty L., Chadwick M., Sperber D. & Grèzes J. “I fear your fear of her/his fear: an experiment of transitive emotional contagion (preliminary results)”. 1st Interdisciplinary Meeting of the DEC. Paris (France). October 2010. TEACHING UVSQ (Versailles, France) Evolution, Psychology & Culture (72h) Undergraduate students UCBL (Lyon, France) Introduction to Evolutionary Psychology and Human Behavioral Ecology (6h) Medical students Université Paris 8 (Saint-Denis, France) Emotional contagion (2h) Undergraduate students Université Paris 10 (Nanterre, France) Emotional contagion: cognitive mechanisms and neural basis (2h) Postgraduate students 2012 & 2013 2012 & 2013 2012 & 2013 2012 6 The Journal of Neuroscience, March 28, 2012 • 32(13):4531– 4539 • 4531 Behavioral/Systems/Cognitive Early Binding of Gaze, Gesture, and Emotion: Neural Time Course and Correlates Laurence Conty,1,2 Guillaume Dezecache,2 Laurent Hugueville,4 and Julie Grèzes2,3 1 Laboratory of Psychopathology and Neuropsychology, Université Paris 8, 93526 Saint-Denis, France, 2Laboratory of Cognitive Neuroscience, Inserm, Unité 960, Ecole Normale Supérieure, 75005 Paris, France, 3Centre de NeuroImagerie de Recherche, 75651 Paris, France, and 4Université Pierre et Marie CurieParis 6, Centre de Recherche de l’Institut du Cerveau et de la Moelle Epinière, Unité Mixte de Recherche S975, 75013 Paris, France Communicative intentions are transmitted by many perceptual cues, including gaze direction, body gesture, and facial expressions. However, little is known about how these visual social cues are integrated over time in the brain and, notably, whether this binding occurs in the emotional or the motor system. By coupling magnetic resonance and electroencephalography imaging in humans, we were able to show that, 200 ms after stimulus onset, the premotor cortex integrated gaze, gesture, and emotion displayed by a congener. At earlier stages, emotional content was processed independently in the amygdala (170 ms), whereas directional cues (gaze direction with pointing gesture) were combined at ⬃190 ms in the parietal and supplementary motor cortices. These results demonstrate that the early binding of visual social signals displayed by an agent engaged the dorsal pathway and the premotor cortex, possibly to facilitate the preparation of an adaptive response to another person’s immediate intention. Introduction During social interactions, facial expressions, gaze direction, and gestures are crucial visual cues to the appraisal other people’s communicative intentions. The neural bases for the perception of each of these social signals has been provided but mostly separately (Haxby et al., 2000; Rizzolatti et al., 2001; Hoffman et al., 2007). However, these social signals can take on new significance once merged. In particular, processing of these social signals will vary according to their self-relevance, e.g., when coupled with direct gaze, angry faces are perceived to be more threatening (Sander et al., 2007; Hadjikhani et al., 2008; N⬘Diaye et al., 2009; Sato et al., 2010). So far, it remains unclear how these social signals are integrated in the brain. At the neural level, there is some evidence that emotion and gaze direction interact in the amygdala (Adams and Kleck, 2003; Hadjikhani et al., 2008; N⬘Diaye et al., 2009; Sato et al., 2010), a key structure for the processing of emotionally salient stimuli (Adolphs, 2002). The amygdala may thus sustain early binding of visually presented social signals. Electroencephalography (EEG) studies suggest that the interaction between emotion and gaze direction occurs at ⬃200 –300 ms (Klucharev and Sams, 2004; Received Nov. 8, 2011; revised Jan. 16, 2012; accepted Jan. 18, 2012. Author contributions: L.C. and J.G. designed research; L.C. and G.D. performed research; L.H. contributed unpublished reagents/analytic tools; L.C. and J.G. analyzed data; L.C. and J.G. wrote the paper. This work was supported by the European Union Research Funding NEST Program Grant FP6-2005-NEST-Path Imp 043403, Inserm, and Ecole de Neuroscience de Paris and Région Ile-de-France. The authors declare no competing financial interests. Correspondence should be addressed to either of the following : Dr. Laurence Conty, Laboratory of Psychopathology and Neuropsychology, EA 2027, Université Paris 8, 2 rue de la Liberté, 93526 Saint-Denis, France, E-mail: [email protected]; or Dr. Julie Grèzes, Cognitive Neuroscience Laboratory, Inserm, Unité 960, Ecole Normale Supérieure, 29 Rue d’Ulm, 75005 Paris, France, E-mail: [email protected]. DOI:10.1523/JNEUROSCI.5636-11.2012 Copyright © 2012 the authors 0270-6474/12/324531-09$15.00/0 Rigato et al., 2010), but direct implication of the amygdala in such a mechanism has yet to be provided. It has also been established that, when one observes other people’s bodily actions, there is activity in motor-related cortical areas (Grèzes and Decety, 2001; Rizzolatti et al., 2001) and that activity reaches these areas 150 –200 ms after the onset of a perceived action (Nishitani and Hari, 2002; Caetano et al., 2007; Tkach et al., 2007; Catmur et al., 2010). Its activity being modulated by social relevance (Kilner et al., 2006) and by eye contact (Wang et al., 2011), the motor system is thus another good neural candidate for the integration of social cues. Here, we set out to experimentally address whether the emotional system or the motor system sustains early binding of social cues and when such an operation occurs. We manipulated three visual cues that affect the appraisal of the self-relevance of social signals: gaze direction, emotion, and gesture. To induce a parametric variation of self-involvement at the neural level, our experimental design capitalized on the ability to change the number of social cues displayed by the actors toward the self (see Fig. 1a), i.e., one (gaze direction only), two (gaze direction and emotion or gaze direction and gesture), or three (gaze direction, emotion, and gesture) visual cues. We then combined functional magnetic resonance imaging (fMRI) with EEG [recording of event-related potentials (ERPs)] to identify the spatiotemporal characteristics of social cues binding mechanism. First, we analyzed the ERPs to identify the time course of early binding of social cues. We expected a temporal marker of their integration at ⬃200 ms (Klucharev and Sams, 2004; Rigato et al., 2010). Then, we quantified the parametric variation of self-involvement on the neural sources of the ERPs by combining the ERPs with fMRI data. Materials and Methods Participants. Twenty-two healthy volunteers (11 males, 11 females; mean age, 25.0 ⫾ 0.5 years) participated in an initial behavioral pretest to 4532 • J. Neurosci., March 28, 2012 • 32(13):4531– 4539 Conty et al. • Early Binding of Gaze, Gesture, and Emotion Figure 1. Experimental design and stimuli examples. a, Factorial design. The actors displayed direct or averted gaze, angry or neutral facial expression, and a pointing gesture or not. From the initial position, one (gaze direction only), two (gaze direction and emotional expression or gaze direction and gesture), or three (gaze direction, emotional expression, and gesture) visual cues could change. b, Time course of a trial. Before stimuli presentation, a central fixation area appeared for 500 ms at the same level as that at which the actor’s head subsequently appeared. Participants were instructed to focus their attention on the actor’s face, to avoid saccades and eyeblinks during the duration of the trial, and to judge whether or not the actor’s nonverbal behavior was directed at them. validate the parametric variation of self-involvement in our paradigm. Twenty-one healthy volunteers participated in the final experiment (11 males, 10 females; mean age, 23.4 ⫾ 0.5 years). All participants had normal or corrected-to-normal vision, were right-handed, and had no neurological or psychiatric history. Stimuli. Stimuli consisted of photographs of 12 actors (six males). For each actor, three social parameters were manipulated: (1) gaze direction [head, eye gaze, and bust directed toward the participant (direct gaze condition) or rotated by 30° to the left (averted gaze condition)]; (2) emotion (neutral or angry); and (3) gesture (pointing or not pointing). This manipulation resulted for each actor in eight conditions of interest. For each of the actors, we created an additional photograph in which they had a neutral expression, arms by their sides, and an intermediate eye direction of 15°. This position was thereafter referred to as the “initial position.” For all stimuli, right- and left-side deviation was obtained by mirror imaging. Thus, each actor was seen under 16 conditions: 2 gaze directions (direct/averted) ⫻ 2 emotions (anger/neutral) ⫻ 2 gestures (pointing/no pointing) ⫻ 2 directions of gaze deviation (rightward/leftward), resulting in 192 stimuli. For each photograph, the actor’s body was cut and pasted on a uniform gray background and displayed in 256 colors. Each stimulus was shown in such a way that the actor’s face covered the participant’s central vision (⬍6° of visual angle both horizontally and vertically) while the actor’s body covered a visual angle inferior to 15° vertically and 12° horizontally. Procedure. Each trial was initiated for 500 ms by a fixation area consisting of a central red fixation point and four red angles delimiting a square of 6° of central visual angle in the experimental context. This fixation area remained on the screen throughout the trial, until the appearance of a response screen. The participant was instructed to fixate the central point and to keep his/her attention inside the fixation area at the level of the central point during the trial, avoiding eye blinks and saccades (for additional details about instructions, see Conty and Grèzes, 2012). Given the importance of an ecologically valid approach (Zaki and Ochsner, 2009; Schilbach, 2010; Wilms et al., 2010), we kept our design as naturalistic as possible. To do so, an apparent movement was created by the consecutive presentation of two photographs on the screen (Conty et al., 2007). The first photograph showed an actor in the initial position during a random time, ranging from 300 to 600 ms. This was immediately followed by a second stimulus presenting the same actor in one of the eight conditions of interest (Fig. 1). This second stimulus remained on the screen for 1.3 s. Throughout the trial, the actor’s face remained within the fixation area. An explicit task on the parameter of interest, i.e., to judge the direction of attention of the perceived agent (Schilbach et al., 2006), was used. Thus, after each actor presentation, the participant was instructed to indicate whether the actor was addressing them or another. This was signified by a response screen containing the expressions “me” and “other.” The participant had to answer by pressing one of two buttons (left or right) corresponding to the correct answer. The response screen remained until 1.5 s had elapsed and was followed by a black screen of 0.5 s preceding the next trial. Behavioral and EEG/fMRI experiments. In a behavioral pretest, the above procedure was used, with the exception that each actor stimulus was presented in either the left or right side of deviation (the assignment was reserved for half of the participants). Moreover, following the “me– other ” task, participants had to judge the degree of self-involvement they felt on a scale of 0 to 9 (0, “not involved”; 9, “highly involved”). The response screen remained visible until the participant had responded. Conty et al. • Early Binding of Gaze, Gesture, and Emotion In the scanner, the 192 trials were presented in an 18 min block, including 68 null events (34 black screens of 4.1 s and 34 of 4.4 s). The block was then repeated with a different order of trials within the block. Behavioral data analyses. During both the behavioral pretest and the EEG/fMRI experiment, participants perfectly performed the me– other task (behavioral: mean of reaction time ⫽ 622 ⫾ 23 ms; mean of correct responses ⫽ 97 ⫾ 0.8%; EEG/fMRI: mean of reaction time ⫽ 594 ⫾ 18 ms; mean of correct responses ⫽ 99 ⫾ 0.4%). These data were not further analyzed. For the behavioral pretest, repeated-measures ANOVA was performed on percentage of self-involvement, with gaze direction (direct/averted), emotion (anger/neutral), and gesture (pointing/no pointing) as within-subjects factors. EEG data acquisition, processing, and analyses. In the fMRI, EEGs were recorded at a sampling frequency of 5 kHz with an MR-compatible amplifier (Brain Products) placed inside the MR scanner. The signal was amplified and bandpass filtered online at 0.16 –160 Hz. Participants were fitted with an electrode cap equipped with carbon wired silver/silver– chloride electrodes (Easycap). Vertical eye movement was acquired from below the right eye; the electrocardiogram was recorded from the subject’s clavicle. Channels were referenced to FCz, with a forehead ground and impedances kept ⬍5 k⍀. EEGs were downsampled offline to 2500 Hz for gradient subtraction and then to 250 Hz for pulse subtraction (using EEGlab version 7; sccn.ucsd.edu/eeglab). After recalculation to average reference, the raw EEG data were downsampled to 125 Hz and low-pass filtered at 30 Hz. Trials containing artifacts or blinks were manually rejected. To study the ERPs in response to the perception of the actor’s movement, ERPs were computed for each condition separately between 100 ms before and 600 ms after the second photograph and baseline corrected. P100-related activity was measured by extracting the mean activity averaged on four occipito-parietal electrodes around the wave peak between 112 and 136 ms in each hemisphere (PO7/PO3/P7/P5, PO8/PO4/ P8/P6). Early N170-related activity was measured by extracting the mean activity averaged on four electrodes around the peak between 160 and 184 ms in each hemisphere (P5/P7/CP5/TP7, P6/P8/CP6/TP8). Late N170-related activity was measured similarly around the peak of the direct attention condition between 176 and 200 ms. P200-related activity was measured by extracting the mean activity averaged on six frontal electrodes around the peak between 200 and 224 ms (F1/AF3/Fz/AFz/F2/ AF4). Repeated-measures ANOVA was performed on each measure with gaze direction (direct/averted), emotion (anger/neutral), gesture (no pointing/pointing), and, when relevant, hemisphere (right/left) as within-subjects factors (the analyses pooled over rightward and leftward sides of actor’s deviation). fMRI data acquisition and processing. Gradient-echo T2*-weighted transverse echo-planar images (EPIs) with blood oxygen-level dependent (BOLD) contrast were acquired with a 3 T Siemens whole-body scanner. Each volume contained 40 axial slices (repetition time, 2000 ms; echo time, 50 ms; 3.0 mm thickness without gap yielding isotropic voxels of 3.0 mm 3; flip angle, 78°; field of view, 192 mm; resolution, 64 ⫻ 64), acquired in an interleaved manner. We collected a total of 1120 functional volumes for each participant. Image processing was performed using Statistical Parametric Mapping (SPM5; Wellcome Department of Imaging Neuroscience, University College London, London, UK; www.fil.ion.ucl.ac.uk/spm) implemented in MATLAB (MathWorks). For each subject, the 1120 functional images acquired were reoriented to the anterior commissure–posterior commissure line, corrected for differences in slice acquisition time using the middle slice as reference, spatially realigned to the first volume by rigid body transformation, spatially normalized to the standard Montreal Neurological Institute (MNI) EPI template to allow group analysis, resampled to an isotropic voxel size of 2 mm, and spatially smoothed with an isotropic 8 mm full-width at half-maximum Gaussian kernel. To remove low-frequency drifts from the data, we applied a high-pass filter using a standard cutoff frequency of 128 Hz. Joint ERP–fMRI analysis. Statistical analysis was performed using SPM5. At the subject level, all the trials taken into account in the EEG analyses were modeled at the appearance of the second photograph with a duration of 0 s. Trials rejected from EEG analyses were modeled sepa- J. Neurosci., March 28, 2012 • 32(13):4531– 4539 • 4533 rately. The times of the fixation area (192 trials of 500 ms duration) of the first photograph (192 trials of between 300 and 600 ms) and of the response (192 trials of 1.5 s duration) as well as six additional covariates capturing residual movement-related artifacts were also modeled. To identify regions in which the percentage signal change in fMRI correlated with the ERP data, we extracted the mean amplitude of each ERP peak, trial by trial, subject by subject, and introduced them as parametric modulators of the trials of interest into the fMRI model. This resulted in four parametric modulators (P100, early N170, late N170, and P200) that were automatically orthogonalized by the software. Effects of the ERP modulators were estimated at each brain voxel using a least-squares algorithm to produce four condition-specific images of parameter estimates. At the group level, we performed four t tests, corresponding to P100, early N170, late N170, and P200 image parameter estimates obtained at the subject level. A significance threshold of p ⱕ 0.001 (uncorrected for multiple comparisons) for the maximal voxel level and of p ⬍ 0.05 at the cluster level (corresponding to an extent threshold of 150 contiguously active voxels) was applied for late N170 and P200 contrasts. A small volume correction ( p ⬍ 0.05 corrected for familywise error) approach was also applied to bilateral amygdala using an anatomical mask from SPM Anatomy Toolbox (version 17) for P100, early N170, late N170, and P200 contrasts. The Anatomy Toolbox (version 17) was also used to identify the localization of active clusters. Coordinates of activations were reported in millimeters in the MNI space. Results Behavioral pretest As expected, we found that our stimuli were judged more selfinvolving when displaying direct compared with averted gaze (F(1,21) ⫽ 56.7, p ⬍ 0.001), angry compared with neutral facial expression (F(1,21) ⫽ 9.2, p ⬍ 0.01), and pointing compared with no pointing (F(1,21) ⫽ 21.7, p ⬍ 0.001). Interestingly, interactions were also observed between gaze direction and emotion (F(1,21) ⫽ 8.5, p ⬍ 0.01) and between gaze direction and gesture (F(1,21) ⫽ 4.6, p ⬍ 0.05). Post hoc analyses showed that the effect of emotion was greater when the participant was the target of attention (F(1,21) ⫽ 12.4, p ⬍ 0.01; mean effect ⫽ 15.4 ⫾ 5.2%) than when this was not the case (F(1,21) ⫽ 4.3, p ⬍ 0.05; mean effect ⫽ 6.1 ⫾ 2.1%). Pointing actors were also judged more self-involving when the participant was the target (F(1,21) ⫽ 23.2, p ⬍ 0.001; mean effect ⫽ 12.1 ⫾ 1.5%) than when this was not the case (F(1,21) ⫽ 6.2, p ⬍ 0.05; mean effect ⫽ 5.5 ⫾ 1.4%). The triple interaction between gaze direction, emotion, and gesture failed to reach significance (F(1,21) ⫽ 3.6, p ⬍ 0.07). However, post hoc analyses revealed, as expected, that the feeling of self-involvement increased with the number of self-relevant cues (all t(1,21) ⬎ 2.4, all p ⬍ 0.05; see Fig. 3). As a result, we succeeded in creating a parametric paradigm in which the self-relevance increased with the number of self-oriented social signals. Time course of social visual cue processing and integration Our first step in analysis was to address the time course of social signal processing and their integration. The sequence of short electric brain responses was indexed by three classical and successive generic ERP components: the occipital P100, the occipitotemporal N170, and the frontal P200 (Ashley et al., 2004; Vlamings et al., 2009). As also reported in the literature (Puce et al., 2000; Conty et al., 2007), we observed that N170 in response to direct attention peaked later than in the other conditions (184 ms vs a mean of 168 ms). Thus, N170 was divided into an early component and a late component. We observed a main effect of each factor of interest on P100 activity. Direct gaze (F(1,20) ⫽ 4.52, p ⬍ 0.05), anger (F(1,20) ⫽ 9.16, p ⬍ 0.01), and pointing (F(1,20) ⫽ 17.62, p ⬍ 0.001) condi- 4534 • J. Neurosci., March 28, 2012 • 32(13):4531– 4539 Conty et al. • Early Binding of Gaze, Gesture, and Emotion Figure 2. ERP modulation by social signals: main effects and interactions. The scalp potential maps of each ERP are represented at the top of the figure. The white point indicates the localization of the electrode on which the time course of the different experimental conditions is displayed below each scalp. a, The P100 amplitude was significantly modulated by each social signal but independently. No interaction between factors was observed. b, Early N170 (top) was modulated by emotional expression and gesture but independently. No interaction between factors was observed. Late N170 (bottom) revealed the first interaction between gaze direction and gesture: the condition in which the actor looked and pointed at the participant induced greater activity than other conditions. c, The integration between all social signals was achieved during the P200 formation: the condition in which the actor looked and pointed at the participant with an angry expression triggered higher positive activity than all other conditions. tions induced greater positive activity than the averted gaze, neutral emotion, and no-pointing conditions, respectively. However, no interactions between factors were observed (all F ⬍ 1). Analysis on the early N170 revealed first greater activity in the right than the left hemisphere (F(1,20) ⫽ 10.55, p ⬍ 0.01). Moreover, anger (F(1,20) ⫽ 13.27, p ⬍ 0.01) and pointing gesture (F(1,20) ⫽ 29.53, p ⬍ 0.001) induced greater negative activity when compared with the neutral and no-pointing gesture conditions, respectively. However, no interactions between factors were observed (F ⬍ 1). We thus confirmed that not only gaze direction and emotion but also body gesture are independently processed at early stages (Rigato et al., 2010) (Fig. 2). Analyses run on late N170 revealed a main effect of all the factors. The activity was globally greater in the right than in the left hemisphere (F(1,20) ⫽ 6.56, p ⬍ 0.05). Direct gaze (F(1,20) ⫽ 4.52, p ⬍ 0.05), anger (F(1,20) ⫽ 25.94, p ⬍ 0.001), and pointing condition (F(1,20) ⫽ 19.78, p ⬍ 0.001) induced greater negative activity than, respectively, averted gaze, neutral, and no-pointing condition. The first interaction between gaze direction and gesture emerged on this component (F(1,20) ⫽ 12.27, p ⬍ 0.005). The condition in which the actor pointed and looked toward the subject induced greater activity than all other conditions (all t ⬎ 3.6, all p ⬍ 0.01). The late N170 on temporo-parietal sites thus marked the integration of directional social cues (Fig. 2). On the frontal P200, we observed a main effect of angry expressions (F(1,20) ⫽ 5.51, p ⬍ 0.03) and direct attention (F(1,20) ⫽ 5.02, p ⬍ 0.05). Importantly, however, a triple interaction between gaze direction, emotional expressions, and pointing gesture was detected (F(1,20) ⫽ 4.71, p ⬍ 0.05). The most selfrelevant condition, in which the actor expressed anger, looked, and pointed toward participants, induced greater positive activity than all other conditions (all t(1,20) ⬎ 2.15, all p ⬍ 0.05). Moreover, P200 activity tended to increase with the number of selfdirected social cues (Fig. 3). Thus far, our data suggest that the integration between three main social signals is achieved just after 200 ms in frontal sites, yet they do not provide information about the neural source of such integration. Brain network involved in the integration of self-relevant visual social cues To explore the brain sources that positively covary with the amplitude of previously identified ERPs, we performed a joint EEG– fMRI analysis. At the subject level, mean amplitudes of P100, early N170, late N170, and P200 peaks (extracted trial ⫻ trial) were introduced as four parametric modulators in the fMRI Conty et al. • Early Binding of Gaze, Gesture, and Emotion J. Neurosci., March 28, 2012 • 32(13):4531– 4539 • 4535 Figure 3. Parametric variation of self-involvement at the neural level. The percentage of subjective self-involvement (with SE; top graphs) and P200 activity (with SE; bottom graphs) is represented as a function of gaze direction (left graphs) and the number of self-directed cues for the direct gaze condition (right graphs). *p ⬍ 0.05; **p ⬍ 0.01; ⬃p ⬍ 0.08. ns, Nonsignificant. model. This method enables us to search for brain regions in which the percentage signal change in fMRI is correlated with the ERP data without a priori assumptions regarding the location (Ritter and Villringer, 2006). At the group level, we calculated t tests for P100, early N170, late N170, and P200 and looked for brain areas in which the percentage signal change in fMRI correlated with ERP amplitudes. The goal of the present study was to identify the spatiotemporal course of social visual signal binding. Thus, we first concentrated on late N170 and the P200 when integration occurred. The right operculum parietal cortex (PFop) extending to somatosensory cortex SII (labeled from here on in as PF/SII) and right supplementary motor area (SMA), extending to primary motor area 4a, positively covaried with late N170 amplitude implicated in the integration of self-relevant directional signals (attention and gesture pointing toward the self). The source of P200 modulations involved in the integration of all available self-relevant cues (directional signals toward the self with emotional expression) was found in the right premotor cortex (PM) (Fig. 4, Table 1). In humans, the border between the ventral PM and dorsal PM is located with 40 ⬍ z coordinates ⬍ 56 (Tomassini et al., 2007). The present source of P200 ranges from z ⫽ 34 to z ⫽ 58 and is thus located in the dorsal part of the ventral PM. This region is likely equivalent to the macaque area F5c (Rizzolatti et al., 2001). It is strongly connected to the SMA, primary motor area M1, PFop, and SII (Luppino et al., 1993; Rozzi et al., 2006; Gerbella et al., 2011) and hosts visuomotor representations (Rizzolatti et al., 2001). To assess whether the emotional system also participates in early binding of gaze, emotion, and gesture, we tested whether ERP components modulated by the emotional content of stimuli (P100, N170, and P200) (Batty and Taylor, 2003; Blau et al., 2007; van Heijnsbergen et al., 2007) were associated with activity in the amygdala, known to be highly involved in threat (Adolphs, 1999) and self-relevance processing (Sander et al., 2007; N⬘Diaye et al., 2009; Sato et al., 2010). To do so, we used that structure bilaterally as a region of interest. BOLD responses in the left amygdala significantly covaried with changes in the early component of N170 (Fig. 4, Table 1). This finding validates our approach by replicating previous results using intracranial ERPs (Krolak-Salmon et al., 2004; Pourtois et al., 2010) and surface EEG (Pourtois and Vuilleumier, 2006; Eimer and Holmes, 2007), showing that information about the emotional content of a perceived facial expression quickly reaches the amygdala (140 –170 ms), in parallel with the processing of other facial cues within the visual cortex. Here, we show that emotional processing in the amygdala occurs just before the integration of directional social signals (gaze and pointing toward the self) detected on the late component of N170. Discussion By coupling fMRI with EEG, we demonstrate for the first time that the integration of gaze direction, pointing gesture, and emotion is completed just after 200 ms in the right PM, possibly to facilitate the preparation of an adaptive response to another’s immediate intention. We confirm that activity within motorrelated cortical areas arises 150 –200 ms after the onset of a perceived action (Nishitani and Hari, 2002; Caetano et al., 2007; Tkach et al., 2007; Catmur et al., 2010) and that the interaction between gaze direction and emotion takes place at ⬃200 –300 ms (Klucharev and Sams, 2004; Rigato et al., 2010). However, in contrast to recent accounts of human amygdala function in social cue integration (Sander et al., 2007; N⬘Diaye et al., 2009; Cristinzio et al., 2010; Sato et al., 2010), we found that emotional content is processed earlier within the amygdala and independently of other cues. Early binding of social cues in the PM 200 ms after stimulus onset may relate to an embodied response that serves evaluative functions of others’ internal states (Jeannerod, 1994; Gallese, 2006; Keysers and Gazzola, 2007; Sinigaglia and Rizzolatti, 2011). The emotional convergence between the emitter and the observer enhances social and empathic bonds and thus facilitates prosocial behavior and fosters affiliation (Chartrand and Bargh, 1999; Lakin and Chartrand, 2003; Yabar et al., 2006; Schilbach et al., 2008), yet strict motor resonance processing cannot explain the present activation in the PM. Indeed, anger expressions directed at the observer are perceived as clear signals of non-affiliative intentions and are thus less mimicked than averted anger expressions (Hess and Kleck, 2007; Bourgeois and Hess, 2008). Activity in the PM may relate to the estimation of prior expectations about the perceived agent’s immediate intent. Hierarchical models of motor control purport that higher and lower motor Conty et al. • Early Binding of Gaze, Gesture, and Emotion 4536 • J. Neurosci., March 28, 2012 • 32(13):4531– 4539 Figure 4. Joint ERP–fMRI results and summary. a, Left amygdala revealed as a neural source of early N170 modulation. Its activity is projected on a coronal section of the MNI template. b, Sources of late N170 modulation: parieto-somatosensory area (PF/SII) activity projected on a coronal section (left) and right SMA projected on a sagittal section (right) of the MNI template. c, Source of P200 modulation: right PM activity projected on a coronal section of the MNI template. d, Summary of the early binding mechanisms of social cues allowing for a complete representation of other’s disposition toward the self. AMG, Amygdala. Table 1. Brain sources covarying with ERP modulations MNI coordinates Anatomical region Sources of early N170 modulations* Amygdala Sources of late N170 modulations SMA Middle cingulate cortex Supramarginal gyrus (PF/SII) Sources of P200 modulations Right PM L/R x y z Z value L ⫺22 R R R R Cluster size ⫺2 ⫺8 3.69 8 4 60 ⫺28 ⫺10 ⫺22 56 50 26 4.13 3.41 4.29 3152 3152 156 58 6 34 3.61 582 12 p ⱕ 0.001 uncorrected, p ⬍ 0.05 uncorrected at the cluster level. * Small volume correction, p ⬍ 0.05 familywise error corrected. L, Left; R, right. modules are reciprocally connected to each other (Wolpert and Flanagan, 2001; Kilner et al., 2007). Within such perspectives, the generative models used to predict the sensory consequences of one’s own actions are also used to predict another’s behavior. Backward connections inform lower levels about expected sensory consequences, i.e., the visual signal corresponding to the sight of another’s action. Conversely, the inversion of the generative models allows for the inference of what motor commands have caused the action, given the visual inputs. The extraction of prior expectations about another’s intention corresponds to the inverse model (Wolpert et al., 2003; Csibra and Gergely, 2007), which needs to be estimated from available cues. Crucially, this estimation is proposed to be implemented in the bottom-up path from the temporal cortex to the inferior parietal lobule (PF) to the PM during the observation of the beginning of an action (Kilner et al., 2007). Thus, the present activity in the PM may reflect prior expectations about another’s communicative intention, first built from directional cues (gaze and pointing gesture) in the dorsal pathway before integrating the emotional content in the PM. Only then could prior expectations influence, through feedforward mechanisms, the perception of ongoing motor acts via a top-down activation of perceptual areas, generating expectations and predictions of the unfolding action (Wilson and Knoblich, 2005; Kilner et al., 2007). The above-mentioned mechanisms won’t be relevant for novel, unexpected and complex actions for which the goal needs to be estimated from the context without the involvement of low-level motor systems (Csibra, 2007; Csibra and Gergely, 2007). Indeed, these mechanisms rely on the equivalence assumption that the observed actor shares the same motor constraints as the observer, and may thus only apply to actions that are in the observer’s motor repertoire, such as those manipulated in the present study. The question arises as to why P200 and PM activity was greater when the actor expressed anger, looked, and pointed toward participants. One possible explanation for this pattern of activity is that information is filtered as a function of its social salience (Kilner et al., 2006; Schilbach et al., 2011; Wang et al., 2011) before the estimation of prior expectations. An alternative and complementary hypothesis is related to the role of the PM in using sensory information to specify currently available actions Conty et al. • Early Binding of Gaze, Gesture, and Emotion to deal with an immediate situation (Cisek, 2007). Prior expectations about the perceived agent’s immediate intent would thus afford the perceiver specific types of interactions (Gangopadhyay and Schilbach, 2011; Schilbach et al., 2011). Hence, the highest level of activity in the PM reflects the highest degree of potential social interaction, which corresponds here to facing an angry person pointing and looking toward oneself. Indeed, the expression of direct anger signals a probable physical and/or symbolic attack (Schupp et al., 2004), is perceived as threatening (Dimberg and Ohman, 1983; Dimberg, 1986; Strauss et al., 2005), and triggers adaptive action in the observer (Frijda, 1986; Pichon et al., 2008, 2009, 2012; Grèzes et al., 2011; Van den Stock et al., 2011). In accordance with such a view, defensive responses in monkeys are elicited by electrical stimulation at the border between the ventral and dorsal PM (Cooke and Graziano, 2004; Graziano and Cooke, 2006) and are supposed, in humans, to be facilitated within a 250 ms timeframe after the perception of a danger signal (Williams and Gordon, 2007). Here, emotional signals were processed first in the amygdala at ⬃170 ms. Interestingly, a substantial number of studies have shown that lesions of the amygdala not only disrupt the ability to process fear signals (LeDoux, 2000) but can also abolish characteristic defensive behavior in primates (Emery et al., 2001). In this model, the amygdala plays a critical role in initiating adaptive behavioral responses to social signals via its connections with subcortical areas and the PM (Avendano, 1983; Amaral and Price, 1984). Thus, we propose that, after having been processed in the amygdala, emotional information is integrated with self-directed directional cues in the PM, enabling prior expectations to be developed about another’s intentions and the preparation of one’s own action. At ⬃170 ms, emotional processing occurs in the amygdala, independently of self-directed directional cues (gaze direction and pointing gesture). The activation of the amygdala while observers perceived bodily expressions of anger replicates previous studies (Pichon et al., 2009) and supports its proposed role in the automatic detection of threat (Emery and Amaral, 2000; LeDoux, 2000; Amaral et al., 2003; Feinstein et al., 2011). Amygdala damage diminishes the brain’s response to threatening faces at both the ⬃100 –150 and ⬃500 – 600 ms time ranges (Rotshtein et al., 2010), and, in both infants and adults, the interaction between gaze direction and emotion takes place at ⬃200 –300 ms (Klucharev and Sams, 2004; Rigato et al., 2010). Furthermore, previous fMRI studies manipulating self-involvement during face perception revealed that facial expression and gaze direction are integrated in the medial temporal poles (Schilbach et al., 2006; Conty and Grèzes, 2012) or in amygdala (Adams and Kleck, 2003; Hadjikhani et al., 2008; N⬘Diaye et al., 2009; Sato et al., 2010). Here, we show that the binding of emotion with gaze direction and pointing gesture arises at ⬃200 ms in the PM. This suggests that the pattern of integration revealed previously using fMRI could reflect later rather than early processes. Before being integrated with emotional content in the PM, self-directed directional cues (gaze direction and pointing gesture) are firstly merged within 190 ms in the parietal areas (PF/ SII) and in the SMA. Could the absence of interaction at an early stage between directional cues and emotion have been attributable to some feature of the present stimuli and task? First, when present, pointing gesture always indicated the same direction of attention as did gaze. Second, the participant’s task was to judge the actor’s direction of attention (toward the self or another) regardless of the emotional content. This may have led participants to prioritize task-relevant directional cues and thus their integration in the PF/SII and in the SMA for response selection J. Neurosci., March 28, 2012 • 32(13):4531– 4539 • 4537 and preparation (Passingham, 1993; Rushworth et al., 2003), independently of emotion. However, higher activity in the PF/SII and in the SMA for self-directed compared with otherdirected social cues, and right lateralized activations for righthanded participants, do not fully support such an explanation. Rather, right-lateralized activations suggest processing related to representation of another’s action (Decety and Chaminade, 2003). In conclusion, the current data clearly demonstrate that the early binding of visual social cues displayed by a congener is achieved in the motor system rather than in the emotional system. 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Human communities are much larger than those of other primates and hence require more time to be devoted to social maintenance activities. Yet, there is an upper limit on the amount of time that can be dedicated to social demands, and, in nonhuman primates, this sets an upper limit on social group size. It has been suggested that laughter provides the additional bonding capacity in humans by allowing an increase in the size of the “grooming group.” In this study of freely forming laughter groups, we show that laughter allows a threefold increase in the number of bonds that can be “groomed” at the same time. This would enable a very significant increase in the size of community that could be bonded. © 2012 Elsevier Inc. All rights reserved. Keywords: Laughter; Social group size; Social grooming; Endorphins; Social bonding 1. Introduction Although by no means unique to humans (it occurs in great apes: Davila-Ross, Owren, & Zimmermann, 2009; Waller & Dunbar, 2005), laughter is one of the most distinctively human behaviors (Gervais & Wilson, 2005; Provine, 2001). While a number of (not necessarily mutually exclusive) hypotheses have been suggested for its function (signaling social or mating interest: Grammer, 1990; Grammer & Eibl-Eibesfeldt, 1990; Li et al., 2009; Martin & Gray, 1996; Mehu & Dunbar, 2008; emotional contagion: Bachorowski & Owren, 2001, Owren & Bachorowski, 2003; social bonding: Dunbar, 2004; Dunbar et al., 2012), laughter in humans is characteristically highly social and intensely contagious (Provine, 2001). The occurrence of laughter during an interaction also significantly increases the perceived satisfaction with the interaction (Vlahovic, Roberts, & Dunbar, 2012). ⁎ Corresponding authors. Guillaume Dezecache is to be contacted at the Laboratory of Cognitive Neuroscience, Ecole Normale Supérieure, 29 rue d'Ulm, 75005 Paris, France or Robin Dunbar, University of Oxford, South Parks Rd, Oxford OX1 3UD, United Kingdom. E-mail addresses: [email protected] (G. Dezecache), [email protected] (R.I.M. Dunbar). 1090-5138/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.evolhumbehav.2012.07.002 Anthropoid primates are characterized by an unusually intense form of social bonding (Dunbar & Shultz, 2010; Shultz & Dunbar, 2010) that is mediated by an endorphinbased psychopharmacological mechanism effected by social grooming (Curley & Keverne, 2005; Depue, MorroneStrupinsky, et al., 2005; Machin & Dunbar, 2011). Social grooming (the bimanual cleaning and manipulation of a recipient's skin or fur) is limited to dyads since it is physically difficult to groom several individuals at the same time. Given this, its effective broadcast group size (the number of individuals whose state of arousal can be influenced in this way) is one. This, combined with limits on the time available for social grooming (Dunbar, Korstjens, & Lehmann, 2009; Lehmann, Korstjens, & Dunbar, 2007), seems to set an upper limit on the size of social group (or community) that can be bonded through this mechanism (Dunbar, 1993). Laughter is known to release endorphins in much the same way as grooming does (Dunbar et al., 2012), and this has led to the suggestion that the exaggerated forms of laughter characteristic of humans might have evolved out of conventional ape laughter (Davila-Ross et al., 2009, DavilaRoss, Allcock, Thomas, & Bard, 2011) as a device for enlarging the effective size of grooming groups through a form of “grooming-at-a-distance” (Dunbar, 2012). When 2 G. Dezecache, R.I.M. Dunbar / Evolution and Human Behavior xx (2012) xxx–xxx hominins evolved larger social communities than those characteristic of the most social monkeys and apes, some additional mechanism was required to make this possible. Increasing grooming time was not an option because it was already at its upper limit in primates (Dunbar, 1993, Dunbar et al., 2009), but increasing the number of individuals who could be “groomed” simultaneously is a plausible alternative. Laughter as a form of chorusing (sensu Burt & Vehrencamp, 2005; Schel & Zuberbühler, 2012; Tenaza, 1976) seems to fill that role admirably because it allows several individuals to be involved simultaneously. The fact that human laughter shares close structural similarities with ape laughter (Davila-Ross et al., 2009; Provine, 2001) suggests that, if it was the solution to this problem, it may have been an early adaptation, long predating the evolution of speech and language (Dunbar, 2009, 2012). This suggestion raises the question of laughter's efficiency as a bonding mechanism relative to social grooming. Given that grooming has an effective broadcast group size of one, just how large is the broadcast group size for laughter? To determine this, we observed natural social groups in bars and collected data on the number of people who laughed together within these groups. We also sampled the size of the whole social group as well as the size of conversational groups (the number of people engaged in a conversation) to provide benchmark measures that enable comparisons between laughter and conversation (conversation groups are known to have an upper limit of four individuals, irrespective of the size of the social group: Dunbar, Duncan, & Nettle, 1995). 2. Method We censused natural social groups in bars in the United Kingdom (Oxford; 80% of the observations), France (Calais, Lille, and Paris; 14%), and Germany (Berlin; 6%), distinguishing social group size (the total number of individuals present as an interacting group), conversational subgroup size (the number of individuals within the social group taking part in a particular conversation, as evidenced by speaking or obviously attending to the speaker, following Dunbar et al., 1995), and laughter subgroup size (the number of individuals laughing in an obviously coordinated way, following the same definition as for conversational subgroups). Individuals were said to be laughing when they were producing the vocalization which is characteristic of laughter (i.e., a series of rapid exhalation–inhalation cycles: DavilaRoss et al., 2009; Provine, 2001). In total, 501 observations of laughter events were sampled from 450 groups. Groups of at least two people were covertly observed from a close distance (maximum 5 m). A group was selected if it was stable over time and the faces of all members were visible to the observer. As soon as a burst of laughter was produced within the group, the laughter subgroup size was censused, defined as the number of people who produced at least one laughter vocalization before laughter ceased within the group. We also censused the size of the conversational subgroups: individuals were scored as being a member of a given conversational subgroup if they were speaking or paying attention to the speaker (as indicated by direction of eye gaze). Finally, we noted down the size of the social group within which these were embedded (as evidenced by the affiliative interactions among the members over the whole period the group was under observation). While laughter and conversational subgroup sizes could be censused via rapid visual scans, group size censuses required longer and more persistent observation. Groups were censused at 30-min intervals to guarantee the statistical independence of each sample. Nevertheless, groups could be reconsidered for a census before the 30-min interval if they permanently lost or gained a member. 2.1. Statistical analysis Due to the small number of observations at larger social group sizes, data were merged for groups of size 7 to 8, 9 to 10, and 11 to 14. To estimate the optimal size of conversational and laughter subgroups, we performed a series of regression analyses, using the Akaike information criterion (AIC) (Akaike, 1974; Burnham & Anderson, 2002) to select the function that gave the best fit. 3. Results Fig. 1 plots the frequency distribution of social, conversational, and laughter subgroup sizes. Average conversation subgroup size was 2.93±0.05 S.E. (N=501), and average laughter subgroup size was 2.72±0.04 S.E. (N= 501). Conversational subgroups larger than 5 were rare (2.8% of the observations), and none were larger than 10. Similarly, laughter subgroups larger than four were rare (5.6% of the observations), and none were larger than six. Fig. 1. Frequency distribution of social groups, conversation subgroups, and laughter subgroups. G. Dezecache, R.I.M. Dunbar / Evolution and Human Behavior xx (2012) xxx–xxx Overall, approximately 91% of all conversational subgroups contained four or fewer individuals, and 84% of all laughter subgroups contained only two or three individuals. Fig. 2 plots mean conversational and laughter subgroup sizes against social group size. Mean group size is significantly different between the three types of group (social group, conversational subgroups, laughter subgroups) (Kruskal–Wallis: H2=151.441, N=1503; pb.001). Pairwise comparisons reveal that social groups were larger than both conversational (Kruskal–Wallis: H1=236.347, N=1002; pb.001) and laughter subgroups (Kruskal–Wallis: H1=306.204, N=1002; pb.001). There was a significant but only modest correlation between social group size and each of the two other variables (Spearman rank correlations: rs= 0.546, N=501, pb.001 with conversational subgroups; rs= 0.466, N=501, pb.001 with laughter subgroups). Conversational subgroups were significantly larger than laughter subgroups (Kruskal–Wallis: H1=69.856, N= 1002; p=.022), and both variables were strongly correlated (Spearman rank correlations: rs=0.875, N=501, pb.001), suggesting that an increase in conversational subgroup size predicts an increase in laughter subgroup size (and vice versa). The distributions in Fig. 2 suggest that conversational subgroups reach an asymptotic value (cf. Dunbar et al., 1995), whereas the distribution of laughter subgroups has a more explicitly humped shape, suggesting that there may be an optimal group size for laughter to occur. To explore this in more detail, we ran regression analyses on the two distributions, testing a range of alternative functions and using AIC as the criterion of best fit. The best fit with the lowest AIC for conversational subgroups was an S function (r 2=0.797, p=.003, AIC=−13.24), which was significantly better than its best rival (power function: r 2=0.564, p=.032, AIC=−10.56) and considerably better than the null hypothesis of a linear fit (r 2 =0.325, p=.140, AIC=−2.04). Conversational subgroup sizes have an asymptotic value of 4.21, a value close to that found in previous studies (Dunbar Fig. 2. Conversational and laughter subgroups sizes plotted against social group size. 3 et al., 1995). In contrast, the best fit for laughter subgroups was a quadratic function (r 2=0.897, p=.003, AIC=−7.96), which was significantly better than a linear model (r 2=0.054, p=.581, AIC=−2.78). Laughter subgroups are best fit by a humped distribution, with an optimal value of 3.35 for social groups of size about seven. 4. Discussion Our results confirm, with a considerably larger sample, the upper limit of N≈4 on conversation group size reported by Dunbar et al. (1995). In addition, they suggest that there is a similar limit on the number of individuals that can be involved in a laughter event. Ours was, of course, a naturalistic study and thus benefits by all the advantages of ecological validity that this offers. While it might have been possible to run the study in the laboratory with convened groups of predetermined size, it is questionable as to what the advantages of doing so would be since it is difficult to trigger laughter in artificial settings. In retrospect, the seeming intimacy of laughter that emerges from this study makes it especially important that the study was naturalistic. Laughter subgroups are very close to, albeit slightly smaller than, conversational subgroups in size. Laughter subgroups may, however, be more constrained than conversational subgroups in that, unlike the latter, laughter subgroups have an optimal size that depends on the size of the whole social group. Laughter, it seems, is not triggered so easily in very large social groups. The fact that laughter subgroups are smaller than conversational subgroups is surprising because laughter is highly contagious. Unlike conversation, which requires effort and mental concentration to be engaged, laughter can be triggered merely by seeing someone else laugh (Provine, 1992) and is typically much louder, which should make it more easily discerned over greater distances. The limits on conversation subgroup size are thought to arise from acoustical constraints, in particular reflecting ambient noise levels (Webster, 1965), the distance between speaker and hearer (Beranek, 1954), the discriminability of speech sounds (Cohen, 1971; Legget & Northwood, 1960), and visual access to the speaker (Kendon, 1967; Steinzor, 1950). These constraints make the maintenance of large conversational subgroups costly because following a conversation in a large group requires enhanced cognitive effort that one might not be prepared to pay if more fruitful interactions are available. This gives rise to the commonly observed phenomenon that conversation groups readily split into several subgroups once they get too large (Dunbar et al., 1995). The fact that laughter subgroups are of the similar size to conversational subgroups might reflect the fact that, in the contemporary context at least, laughter depends on jokes, and hence speech, and will thus be sensitive to the same factors as speech, including the physical distance between 4 G. Dezecache, R.I.M. Dunbar / Evolution and Human Behavior xx (2012) xxx–xxx the interactants (Chapman, 1975), the relationship between them (Platow et al., 2005), and the similarity in sense of humor (Lynch, 2010). However, this cannot be the whole explanation because we do not need to hear the joke to laugh when everyone else is laughing: under these circumstances; we simply cannot help laughing even if we do not understand the joke (Provine, 2001). Laughter per se does not depend on speech detection and has acoustic (Bachorowski, Smoski, & Owren, 2001; Provine & Yong, 1991; Szameitat, Darwin, Szameitat, Wildgruber, & Alter, 2011) as well as visual (Petridis & Pantic, 2008; Ruch & Ekman, 2001) properties—including the fact that it is invariably much louder—that make it detectable over much greater distances than speech. This makes the fact that laughter subgroups are slightly smaller than conversational subgroups puzzling. It may be relevant that laughter, speech, and nonvocal sounds appear to be processed in quite distinct parts of the auditory cortex (Meyer, Zysset, von Cramon, & Alter, 2005), suggesting that laughter and speech may share only limited properties. This might reflect the fact that they have different functions and dynamics. An alternative explanation for the small size of laughter subgroups (and, in particular, the fact that they are smaller than conversational subgroups) may derive from the fact that laughter is intrinsically more spontaneous and intimate than conversation, and so depends more explicitly on the dynamics of the group and coordination in the mind states of the individuals involved (Weisfeld, 1993). This may make it challenging to have large numbers of people involved. This may relate explicitly to laughter's role in social bonding as a form of chorusing that long predates language (Dunbar, 2012). One could argue that physical constraints (noise, disposition of the tables, number of chairs around the tables) might have constrained the size of the social groups and the associated conversational and laughter subgroups, thereby biasing our observations. However, this seems unlikely since the sizes of our conversational subgroups are identical to those reported by Dunbar et al. (1995) whose observations were collected in contexts where such physical constraints did not hold (large evening receptions, gatherings during fire drills). The fact that hominins evolved social groups that are considerably larger than those of other primates (Aiello & Dunbar, 1993; Dunbar, 2009; Gowlett, Gamble, & Dunbar, in press) has raised the possibility that laughter may have evolved into its present human form specifically to break through the ceiling imposed by more conventional primate bonding processes (Dunbar, 2012). Laughter might fill that role both because it seems to be an effective way of triggering endorphin activation (Dunbar et al., 2012) and because it can be triggered in several individuals simultaneously. Our findings suggest that the “grooming” group for laughter is a little over three individuals. Since all members of the laughter group gain an endorphin surge (unlike the grooming dyad, where endorphins are triggered only in the groomee), this would make laughter three times as efficient as grooming, which would in turn allow a very significant increase in the size of the community that could be bonded (though probably not a trebling of community size since social group size is a not a monotonic function of grooming clique size: see Dunbar, 2012; Kudo & Dunbar, 2001). Language, when it finally evolved, clearly gave a new impetus to laughter because it allowed laughter to be triggered by the telling of jokes, instead of being triggered by nonlinguistic means (e.g., social play, tickling, or socially incongruous situations: Gervais & Wilson, 2005; Vettin & Todt, 2005). Whether this increased the frequency of laughter or simply allowed its timing to be managed more effectively is an interesting question, but not one that can be answered here. 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Emotion Self-Relevance Appraisal of Gaze Direction and Dynamic Facial Expressions: Effects on Facial Electromyographic and Autonomic Reactions Robert Soussignan, Michèle Chadwick, Léonor Philip, Laurence Conty, Guillaume Dezecache, and Julie Grèzes Online First Publication, September 17, 2012. doi: 10.1037/a0029892 CITATION Soussignan, R., Chadwick, M., Philip, L., Conty, L., Dezecache, G., & Grèzes, J. (2012, September 17). Self-Relevance Appraisal of Gaze Direction and Dynamic Facial Expressions: Effects on Facial Electromyographic and Autonomic Reactions. Emotion. Advance online publication. doi: 10.1037/a0029892 Emotion 2012, Vol. 12, No. 6, 000 © 2012 American Psychological Association 1528-3542/12/$12.00 DOI: 10.1037/a0029892 Self-Relevance Appraisal of Gaze Direction and Dynamic Facial Expressions: Effects on Facial Electromyographic and Autonomic Reactions Robert Soussignan Michèle Chadwick and Léonor Philip Université de Bourgogne Ecole Normale Supérieure, Paris Laurence Conty Guillaume Dezecache and Julie Grèzes Université Paris 8, Saint-Denis Ecole Normale Supérieure, Paris What processes or mechanisms mediate interpersonal matching of facial expressions remains a debated issue. As theoretical approaches to underlying processes (i.e., automatic motor mimicry, communicative intent, and emotional appraisal) make different predictions about whether facial responses to others’ facial expressions are influenced by perceived gaze behavior, we examined the impact of gaze direction and dynamic facial expressions on observers’ autonomic and rapid facial reactions (RFRs). We recorded facial electromyography activity over 4 muscle regions (Corrugator Supercilli, Zygomaticus Major, Lateral Frontalis, and Depressor Anguli Oris), skin conductance response and heart rate changes in participants passively exposed to virtual characters displaying approach-oriented (anger and happiness), and avoidance-oriented (fear and sadness) emotion expressions with gaze either directed at or averted from the observer. Consistent with appraisal theories, RFRs were potentiated by mutual eye contact when participants viewed happy and angry expressions, while RFRs occurred only to fear expressions with averted gaze. RFRs to sad expressions were not affected by gaze direction. The interaction between emotional expressions and gaze direction was moderated by participants’ gender. The pattern of autonomic reactivity was consistent with the view that salient social stimuli increase physiological arousal and attentional resources, with gaze direction, nature of emotion, and gender having moderating effects. These results suggest the critical role of self-relevance appraisal of senders’ contextual perceptual cues and individual characteristics to account for interpersonal matching of facial displays. Keywords: emotional expression, gaze, gender, mimicry, self-relevance appraisal 1998). Yet what processes underlie interpersonal behavior matching remains unclear. In particular, whether mimicry accounts for all forms of behavior matching is debated, and especially for RFRs to others’ emotional expressions (e.g., Moody, McIntosh, Mann, & Weisser, 2007). More specifically, when we spontaneously smile or frown upon seeing a person who is smiling or frowning, is it a motor mimicry, an intent to communicate, or an emotional response? One way to resolve this issue is to investigate the impact of gaze direction and facial expressions on RFRs. Indeed, major theoretical approaches make distinct predictions about whether RFRs to others’ facial displays are influenced by perceived gaze behavior, and eye contact has been suggested to be a potent trigger of embodied simulation (Niedenthal, Mermillod, Maringer, & Hess, 2010). According to the mimicry hypothesis, the perception of some behaviors directly and automatically activates our own motor representation of these behaviors (Chartrand & van Baaren, 2009), probably via the so-called mirror neurons system. Although attention may enhance observation– execution matching (Chong, Cunnington, Williams, & Mattingley, 2009), mimicry as an automatic response should also occur when the sender’s gaze is not directed toward an observer. By contrast, according to the communicative Humans often spontaneously match conspecifics’ behaviors, a phenomenon typically termed mimicry (Hess, Philippot, & Blairy, 1999). There is substantial evidence that mimicry is ubiquitous, serves important social functions (e.g., affiliation), and is automatic and nonconscious (Chartrand & van Baaren, 2009), because, for example, the perception of facial expressions may elicit congruent rapid facial reactions (RFRs) (e.g., Dimberg & Thunberg, Robert Soussignan, Centre des Sciences du Goût et de l’Alimentation, Université de Bourgogne, Dijon, France; Michèle Chadwick, Léonor Philip, Guillaume Dezecache, and Julie Grèzes, Laboratoire de Neurosciences Cognitives and Institut d’Etude de la Cognition, Ecole Normale Supérieure, Paris, France; Laurence Conty, Laboratoire de Psychopathologie et Neuropsychologie, Université Paris 8, Saint-Denis, France. This research was supported by a grant from the French National Research Agency (ANR 11 EMCO 00902). We are grateful to Sylvie Berthoz (INSERM U669) for administrative support. Correspondence concerning this article should be addressed to Robert Soussignan, Centre des Sciences du Goût et de l’Alimentation, CNRS UMR 6265, INRA UMR 1324, Université de Bourgogne, Dijon, France. E-mail: [email protected] or [email protected] 1 2 SOUSSIGNAN ET AL. act hypothesis, facial matching is primarily an interpersonal process reflecting some representation and understanding of the sender‘s internal state and should mainly occur if an observer is the target of the sender’s attention (Bavelas, Black, Lemery, & Mullett, 1986). Finally, emotional perspectives, and more specifically appraisal theories of emotion, stress the importance of appraisal dimensions (e.g., pleasantness, self-relevance, coping potential, event compatibility with social/personal norms or values) to account for the differentiation of emotional responses (Scherer, Schorr, & Johnstone, 2001). Within this framework, the meaning of emotional cues perceived by the self is critical and varies as a function of features, such as gaze direction, which may signal that the observer is the target of sender’s attention or that she or he detects the sender is reacting to a salient event in the shared environment (Sander, Grandjean, Kaiser, Wehrle, & Scherer, 2007). For example, angry faces directed toward a receiver, by signaling that he or she is the target of hostility, were perceived as less affiliative (Hess, Adams, & Kleck, 2007) and induced higher corrugator (Schrammel, Pannasch, Graupner, Mojzisch, & Velichkovsky, 2009) and amygdala (N=Diaye, Sander, & Vuilleumier, 2009) activity than did angry faces with averted gaze. On the other hand, fearful faces with averted gaze, by signaling a potential source of danger, were perceived as more intense and negative (Hess et al., 2007; Sander et al., 2007), and elicited higher amygdala activity (N=Diaye et al., 2009) than did fearful faces directed at the observer. To our knowledge, only two studies have tested the effects of both gaze behavior and facial expression on RFRs (Mojzisch et al., 2006; Schrammel et al., 2009). However, besides conflicting results, these studies were limited by (i) including only approachoriented emotions (happiness and anger), (ii) using a judgment task that may affect electromyography (EMG) activity as a result of the cognitive load of the task (Lishner, Cooter, & Zald, 2008) or the encoding of emotional concepts (Halberstadt, Winkielman, Niedenthal, & Dalle, 2009), (iii) manipulating face orientation rather than gaze direction, making unclear whether different amounts of information conveyed in the direct versus averted condition contributed to findings. The aim of this study was to test the differing assumptions concerning the underlying mechanisms of interpersonal matching of facial expressions by examining whether the passive observation of approach-oriented (happiness and anger) and avoidanceoriented (fear and sadness) emotions elicited congruent RFRs and differentiated autonomic responses as a function of gaze direction. Autonomic responses, such as skin conductance response (SCR) and heart rate (HR) deceleration, were recorded because they index sympathetic arousal and attention/orienting responses, respectively (Andreassi, 2000). While motor mimicry and communicative accounts predict that congruent RFRs should be either little influenced by gaze direction or solely affected by eye contact, appraisal models make predictions depending on the self-relevance of facial expressions as a function of gaze direction and gender. For happiness, more congruent RFRs were expected for faces with direct versus averted gaze, as the affiliative value of smiles would be increased if the observer is the object of another’s attention, and more particularly in women because they smile more or are more affiliative than men (Dimberg & Lundquist, 1990; LaFrance, Hecht, & Paluck, 2003). For direct-gaze anger expressions, congruent or incongruent RFRs (i.e., anger or fear) may occur (Moody et al., 2007; Schrammel et al., 2009), and these effects should be moderated by gender, since men display more anger or are more dominant than do women (Brody & Hall, 2008; Hess, Adams, & Kleck, 2005). Concerning autonomic reactivity, the few studies manipulating both happy/anger expressions and gaze behavior failed to find an effect of eye-to-eye contact, as measured by pupil dilation (Mojzisch et al., 2006; Schrammel et al., 2009). However, since emotionally neutral face studies using SCR showed that perceiving a direct gaze elicited higher reactivity than perceiving an averted gaze (e.g., Helminen, Kaasinen, & Hietanen, 2011), we hypothesized higher SCRs and larger HR decelerations in response to facial expressions with direct as opposed to averted gaze. Regarding avoidance-oriented emotions, appraisal models predict that fear faces with averted gaze should be more self-relevant because they signal more clearly the location of a potential danger in the shared environment (Sander et al., 2007). From this perspective, observers should display larger RFRs when exposed to fearful faces with averted in contrast to direct gaze. Finally, for sadness, as their facial expressions signal both the loss of a person/object of importance to the self (Bonanno, Goorin, & Coifman, 2008) and a call for support or help from others (Fischer & Manstead, 2008), averted gaze should more clearly signal “loss and disengagement” (Adams & Kleck, 2005), whereas direct gaze would remain self-relevant for signaling the sender’s need for help or support. Thus, we anticipated congruent RFRs for sad faces regardless of eye direction, with higher RFRs in women than in men, as they have been shown to be more emotionally contagious for this emotion (Doherty, Orimoto, Singelis, Hatfield, & Hebb, 1995). Finally, we predicted earlier RFRs for approach-oriented (500 –1000 ms) than for avoidance-oriented (1000 –2000 ms) emotions following findings from previous studies (Oberman, Winkielman, & Ramachandran, 2009). Method Participants Forty-two adults (21 women) participated to the study. Because of technical problems, data from 11 participants were discarded, leaving 17 women (M ⫽ 23.36 years, SD ⫽ 2.33) and 14 men (M ⫽ 24.78 years, SD ⫽ 3.47) in the final sample. Stimuli We used avatars that are highly controlled realistic stimuli able to induce EMG reactivity and an experience of being with another person (Bailenson, Bloscovich, Beall, & Loomis, 2003; Weyers, Mühlberger, Hefele, & Pauli, 2006). Movies depicting virtual characters were created using Poser 9 software (Smith Micro, Watsonville, CA). The facial expressions were obtained by manipulating polygon groups on a three-dimensional (3D)-–mesh that made up the avatars’ facial structure. The polygon groups were comparable to the action units (AUs) as described in the Facial Action Coding System (FACS) (Ekman & Friesen, 1978). The following codes were used: 6 ⫹ 12 ⫹ 25 ⫹ 26 for happiness, 4 ⫹ 5 ⫹ 24 for anger, 1 ⫹ 4 ⫹ 15 for sadness, and 1 ⫹ 2 ⫹ 4 ⫹ 5 ⫹ 20 for fear. Disgust expressions were also created for pretests using AU9/10. Neutral faces were used as control stimuli. Avatars SELF-RELEVANCE OF GAZE AND EMOTION EXPRESSION (2 men, 2 women) had either direct or averted gaze. Gaze direction was created by angular deviation of the iris structure, in relation to the axis of the head, using a computational displacement of 15° to either side (left/right) to generate counterbalanced conditions. Each movie clip lasted 2 s, with the rise time of high-intensity expression (apex) occurring at 500 ms and then followed by a 1500-ms static expression. Stimuli were presented using a 19-inch LCD monitor with a resolution of 500 ⫻ 490 pixels. The visual angles of stimuli were 22.92° in height and 21.92° in width. Pretests Three groups of adults (N ⫽ 21–32) rated facial features of avatars’ emotion expressions. The pretests revealed that (i) emotional expressions were accurately decoded regardless of gaze direction (from 80.7% to 95.3%), F(5, 115) ⫽ 4.01, p ⫽ .001; (ii) gaze direction was accurately decoded regardless of the type of emotion (from 92.6% to 100%), F(5, 130) ⫽ 2.05, p ⬎ .05; (iii) anger faces with direct gaze were judged more hostile than anger faces with averted gaze (77.34% vs. 47.66%), t(31) ⫽ 5.24, p ⬍ .0001), whereas fearful faces with averted gaze signaled more clearly a danger in the environment than those with direct gaze (75.78% vs. 64.84%), t(31) ⫽ 2.52, p ⫽ .02. Finally, for sadness expressions, about half of participants accurately selected either “loss/disengagement” or “help/support” information, regardless of gaze direction. 3 with electrolyte gel were placed and secured using adhesive collars and sticky tape. Following Fridlund and Cacioppo’s (1986) guidelines, the two electrodes of a pair were placed at a distance of about 1.5 cm over muscle regions associated with emotion expressions (Ekman & Friesen, 1978). Lateral Frontalis muscle activity, which raises outer brow, was used to measure fear expression. Corrugator Supercilii muscle activity, which lowers brows, was used to measure anger expression. Zygomaticus Major muscle activity, which pulls lip corners, was used to measure happy expression. Depressor Anguli Oris muscle activity, which pulls the lips downward, was used to measure sad expression. The ground electrode was placed in the upper part of the forehead. The EMG signals were recorded with a 10-Hz to 500-Hz bandpass filter and a 50-Hz notch filter, rectified and smoothed online using a 500 ms time constant. SCR (in microsiemens) was recorded using bipolar finger electrodes and ADInstruments Model ML116 GSR Amp connected to the PowerLab system. The electrodes were attached with a Velcro strap on the palmar surfaces of the middle segments of phalanges of the second and third fingers of the nondominant hand. Heart activity was recorded from 2 electrocardiogram (ECG) electrodes placed above the right and left wrists. A digital input on the computer detected the R-waves and displayed HR online in beats per minute (bpm) on a separate channel. Data Analysis Procedure On arrival, participants sat in a comfortable chair and were separated by two screens from the experimenter. Following the placement of sensors, they were instructed that involuntary reactions (facial temperature, HR, and SCR) will be recorded in response to avatar’s faces. A cover story was used for facial EMG to minimize demand characteristics and avoid voluntary control of facial muscles (Fridlund & Cacioppo, 1986). Following the completion of a familiarization trial, participants viewed 4 avatars displaying 4 facial expressions (angry, fear, happy, and sad) plus a neutral face, with either a direct or averted gaze. Each trial began with a warning beep (250 ms) followed by a central fixation cross (1000 ms), and then by the avatar movie for 2 s. A blank screen was displayed during 18- to 23-s intertrial intervals. The order of stimuli presentation was randomized across participants using E-Prime software. Psychophysiological Measures They were recorded using AD Instruments PowerLab data acquisition system connected to a PC. The bioelectrical signals were filtered, amplified, and sampled at a rate of 2000 Hz under the control of the LabChart 7 software. The stimulus onset was automatically signaled on the LabChart channels by a Quatech PCMCIA card. As part of the LabChart software, the Video Capture module was used with a Webcam to record visible facial movements of participants to enable a latter visual inspection of movement artifacts. Before attaching the electrodes, the target sites of the skin of the left side of the face were cleaned with alcohol and gently rubbed, and then four pairs of 4-mm shielded Ag/AgCl electrodes filled Because of technical difficulties and consistent electric noise, data of 11 participants were excluded. Movies of the remaining sample (N ⫽ 31) were then inspected to verify the presence of movements unrelated to the activity of muscle regions of interest. No more than 0.5% of trials related to irrelevant movements (e.g., gaping, yawning) were dropped from subsequent analyses. Following visual inspection, EMG amplitudes were calculated during the 300-ms window preceding stimulus onset (baseline) and during 20 time intervals of 100-ms stimulus presentation. The mean EMG amplitudes during subsequent 100-ms time intervals were expressed as a percentage of the mean amplitude of the baseline. Percentage scores were used to standardize the widely differing absolute EMG amplitudes of participants and enable meaningful comparisons between individuals and across sites (Delplanque et al., 2009). SCR was defined as change in the amplitude occurring 1 to 3 s after the stimulus onset (Dawson, Schell, & Filion, 2000). We calculated temporal changes of SCR by subtracting the 500-ms SC baseline preceding stimulus onset (prestimulus) from the maximum amplitude in the six subsequent 500-ms intervals after stimulation onset. SCR data were then log transformed to normalize the distribution of SCR scores (Dawson et al., 2000). HR change was computed off-line by subtracting the 500-ms baseline level prior to each stimulus onset (prestimulus) from the mean of HR over each 500-ms interval of the 4-s window after stimulus onset. We conducted analyses of variance (ANOVAs) with emotion (anger, fear, happiness, neutral, and sadness), gaze (direct, averted), avatar’s sex (male, female), and time (20 intervals for facial EMG, 6 intervals for SCR, and 8 intervals for HR) as within-subjects factors and participant’s gender (men, women) as SOUSSIGNAN ET AL. 4 a between-subjects factor.1 Following the significance of any overall F test, we used Tukey’s honestly significant difference (HSD) tests to compare differences between means. Results and Discussion Facial EMG Happy faces. Significant effects of emotion, F(4, 116) ⫽ 10.41, p ⬍ .00001, p2 ⫽ .26, gaze, F(1, 29) ⫽ 8.32, p ⫽ .007, p2 ⫽ .22, and Emotion ⫻ Time interaction, F(76, 2204) ⫽ 2.29, p ⬍ .0001, p2 ⫽ .07, were found on Zygomaticus activity reflecting larger RFRs to avatars’ happy faces from 700 to 2000 ms (all ps ⬍ .05). As predicted, the interaction between emotion and gaze was significant, F(4, 116) ⫽ 2.78, p ⫽ .03, p2 ⫽ .09, indicating higher Zygomaticus activity to happy faces with direct than averted gaze (p ⫽ .02) (Figure 1a). A marginally significant interaction between gaze and gender was also detected, F(1, 29) ⫽ 3.45, p ⫽ .07, p2 ⫽ .11, with men showing higher reactivity in the direct (4.54%) than in the averted (⫺0.23%) gaze condition (p ⫽ .02), whereas no effect was found in women (direct gaze: 2.20%; averted gaze: 1.30%). Previous studies manipulating gaze behavior provided conflicting results, with one study reporting higher zygomatic activation in response to happy expressions looking at observers (Schrammel et al., 2009), while another found no effect of attention (Mojzisch et al., 2006). Although it is unclear how these contradictory results might be explained, it is interesting that we used—like Schrammel et al. (2009) and unlike Mojzisch et al. (2006)—avatars displaying Duchenne smiles, which are typically considered enjoyment smiles (e.g., Soussignan, 2002). Because enjoyment smiles with eye contact are rewarding cues fostering intimacy and social interaction (Niedenthal et al., 2010), it is possible that their social meaning differs from that of enjoyment smiles with averted gaze. This could have led to more congruent RFRs as part of an interpersonal emotion transfer (Parkinson, 2011) promoting affiliative exchanges. Further studies are needed using RFRs to clarify the issue of the social meanings attributed to different types of smiles as a function of gaze direction (Niedenthal et al., 2010; Soussignan & Schaal, 1996). With regard to participant’s gender, we did not find, as expected, that women displayed larger zygomatic activity than men (e.g., Dimberg & Lundquist, 1990), but only that men exhibited higher zygomatic activity when happy faces looked directly at them as opposed to when happy faces looked away. Thus, women’s smiles in response to others’ happy faces appeared less affected by gaze direction. Although the reason for this finding is unclear, it is possible that motives for affiliation are greater in women than in men (Brody & Hall, 2008), potentiating the level of women’s zygomatic activity regardless of gaze direction. Anger faces. Significant effects of emotion, F(4, 116) ⫽ 5.96, p ⫽ .0002, p2 ⫽ .17, and of Emotion ⫻ Time interaction, F(76, 2204) ⫽ 2.13, p ⬍ .0001, p2 ⫽ .07, were found for Corrugator Supercilii activity reflecting RFRs in participants exposed to anger faces, reaching significance from 700-ms onward (all ps ⬍ .05). The Emotion ⫻ Time ⫻ Gaze ⫻ Participants’ gender interaction was significant, F(76, 2204) ⫽ 1.38, p ⫽ .02, p2 ⫽ .04, as well as the Emotion ⫻ Time ⫻ Participants’ gender ⫻ Avatar’s sex interaction, F(76, 2204) ⫽ 1.46, p ⫽ .007, p2 ⫽ .05. These findings reflected higher RFRs in men when angry faces looked at them than when angry faces looked away (Figure 1b, time windows: 900 –1000 ms, all ps ⬍ .05), and higher RFRs in men exposed to angry expressions of male than female avatars (time windows: 600 –900 ms, all ps ⬍ .05). A previous study (Schrammel et al., 2009) also found a higher Corrugator response to anger faces when virtual characters turned toward observers in comparison with when they looked elsewhere. Taken together, these findings may reflect implicit appraisal of the social meaning of anger expressions that signal hostility, threat, or a potential attack when directed toward the perceiver (Sander et al., 2007), with men possibly more reactive than women, as part of a defensive reaction related to power/status. As anger displays have been linked to the power and dominance of the expresser, and social stereotypes render the perception of these signals more appropriate in men than in women (Hess et al., 2005), it is possible that our findings reflect gender-based stereotypical expectations. Further research measuring both RFRs and social stereotypes in participants exposed to angry expressions varying in gaze direction is required. Fear faces. The following interactions were significant for Lateral Frontalis muscle activity: Emotion ⫻ Time, F(76, 2204) ⫽ 1.46, p ⫽ .006, p2 ⫽ .05, Emotion ⫻ Gaze ⫻ Time, F(76, 2204) ⫽ 1.48, p ⫽ .005, p2 ⫽ .05, and Emotion ⫻ Gaze ⫻ Time ⫻ Participant’s gender, F(76, 2204) ⫽ 1.39, p ⫽ .01, p2 ⫽ .04. Avatars’ fear expressions, in comparison with other emotional expressions, induced an increase in Frontalis activity, beginning during the second 500-ms interval, and reaching significance between 1300 and 1700 ms (all ps ⬍ .05) after stimulus onset. Interestingly, fear faces with averted gaze induced more Frontalis activity than did fear faces with direct gaze (time intervals: 1300 –1600 ms, all ps ⬍ .05), whereas no differences were found for the other emotions. This finding might reflect the critical role of self-relevance appraisal because fear faces combined with averted gaze may more clearly signal a potential threat/danger in the observer’s environment (Hadjikhani, Hoge, Snyder, & de Gelder, 2008). From this perspective, matched RFRs may reflect an interpersonal emotion transfer (Parkinson, 2011). Such an interpretation is strengthened by studies wherein fearful faces with averted gaze induced greater increases in subjective reports and amygdala activity than did fearful faces with direct gaze (Hadjikhani et al., 2008; N=Diaye et al., 2009; Sander et al., 2007). Furthermore, as shown in Figure 1c, women displayed a higher increase in Frontalis activity than did men when exposed to fear faces with averted as opposed to direct gaze (time intervals: 1300 –1600, p ⬍ .05). Fear is believed to occur more in women than in men (Brody & Hall, 2008; Hess et al., 2000) and in studies using self-reports of emotional contagion, women scored higher on the fear subscale than men (Doherty et al., 1995). Thus, more RFRs to fearful faces with averted gaze in women in contrast to men suggest that these facial expressions might be more selfrelevant for women as a possible result of both socialization and gender stereotypes. Sad faces. A significant main effect of emotion was found for Depressor Anguli Oris activity, F(1, 29) ⫽ 4.38, p ⫽ .002, p2 ⫽ 1 We also conducted ANOVAs using the type of muscle as a withinsubjects factor for each type of emotion. The results of the Muscle ⫻ Time interaction revealed expression-appropriate muscles: F(57, 1653) ⫽ 2.26, p ⬍ .0001, for anger; F(57, 1653) ⫽ 1.95, p ⬍ .001, for happiness; F(57, 1653) ⫽ 1.34, p ⫽ .04, for fear; and F(57, 1596) ⫽ 1.36, p ⫽ .04, for sadness. SELF-RELEVANCE OF GAZE AND EMOTION EXPRESSION 5 Figure 1. Mean facial electromyography (EMG) activity as a function of gaze direction, gender, and the nature of emotional facial expressions: (a) Zygomatic Major activity; (b) Corrugator Supercilii activity to anger faces; (c) Lateral Frontalis activity to fear faces; (d) Depressor Anguli Oris activity. Activity reflects average activation during each 100-ms time interval. .13, indicating higher reactivity in participants exposed to sad (1.90%) than to other facial expressions (anger: ⫺0.33%; fear: ⫺0.32%; happy: ⫺0.62%; neutral: ⫺0.83%; all ps ⬍ .05). Moreover, a significant Emotion ⫻ Time ⫻ Participant’s gender interaction was detected (Figure 1d), F(76, 2204) ⫽ 1.28, p ⫽ .05, p2 ⫽ .04, reflecting more activity in women in response to sad than to anger or fear faces (time windows: 1200 –1400 ms; all ps ⬍ .05). We obtained larger RFRs to sad faces for both gaze conditions. This is not surprising since we predicted that sad faces with either direct or averted gaze convey self-relevant signals (disengagement due to a loss, call for social support), as confirmed by our pretest studies. Furthermore, our result showing that women displayed higher Depressor activity than men when exposed to sad faces is consistent with findings indicating they scored higher than men in a sadness subscale of emotional contagion (Doherty et al., 1995), and that they usually expressed more sad expressions than men (Brody & Hall, 2008). Autonomic Data SCR. A main effect of time was found, F(5, 145) ⫽ 3.38, p ⫽ .006, p2 ⫽ .10, indicating that observing human faces elicited a 6 SOUSSIGNAN ET AL. significant increase in SCRs within 1 to 3 s after stimulus onset (p ⬍ .05). A Gaze ⫻ Time ⫻ Avatar’s sex interaction was also found, F(5, 145) ⫽ 4.67, p ⫽ .0005, p2 ⫽ .14, revealing higher SCRs to female avatars with direct than averted gaze within 2.5–3 s after stimulus onset (all ps ⬍ .05). Finally, an Emotion ⫻ Gaze ⫻ Time ⫻ Avatar’s sex significant interaction was detected, F(20, 580) ⫽ 2.01, p ⫽ .006, p2 ⫽ .06 (Figure 2a), revealing higher SCRs to female avatars’ fear expressions with direct gaze than averted gaze (time windows: 2–3s, all ps ⬍ .001). These data are in line with previous studies showing an effect of gaze direction on physiological arousal in response to emotionally neutral faces (Helminen et al., 2011; Nichols & Champness, 1971). However, in our study, mutual eye contact strongly potentiated physiological arousal in participants exposed to fear expressions. As the human amygdala, which directly influences electrodermal activity (Mangina & Beuzeron-Mangina, 1996), is responsive to the larger size of eye whites (i.e., sclera) of fear faces (Whalen et al., 2004), our finding might reflect a higher effect of sclera with direct as opposed to averted gaze on autonomic arousal. Concerning the effect of character’s gender, although we have no clear explanation for this finding, a similar result has been reported by Schrammel et al. (2009), who speculated, with regard to genderspecific norms, that higher arousal in response to female characters might reflect the participant’s expectation of a more affiliative and rewarding interaction from a female than a male partner. HR. A significant effect of time was found, F(7, 203) ⫽ 9.56, p ⬍ .0001, p2 ⫽ .25, as well as a significant interaction between time and avatar’s sex, F(7, 203) ⫽ 2.50, p ⫽ .017, p2 ⫽ .08, indicating that female avatars elicited larger HR deceleration than male avatars. Interestingly, a Gaze ⫻ Time ⫻ Participant’s gender interaction was also detected, F(7, 203) ⫽ 1.81, p ⫽ .08, p2 ⫽ .06, with men and women displaying cardiac deceleration reaching a minimum at about 3 s after stimulus onset, followed by a cardiac acceleration at 4 s in men in the averted in contrast to direct gaze condition (p ⫽ .007), whereas a decrease in heart rate was observed at 4 s after stimulus onset in both gaze conditions in women (Figure 2b). As predicted, the perception of facial stimuli induced heart rate deceleration, consistent with previous studies using both positive and negative facial expressions (e.g., Vrana & Gross, 2004). This suggests that a heart rate decrease likely reflects the allocation of attentional resources to salient stimuli. Interestingly, our findings highlighted that gaze direction influenced heart rate only in men, suggesting that while attentional resources were initially allocated to stimuli in both men and women, men might be more susceptible to social disengagement when the sender’ eyes looked elsewhere. Although further work is required to confirm this result, it is interesting that neurophysiological studies, using event-related potential (ERP), provided evidence that gender influenced the N140 and P240 components of attention to cue stimuli, with women allocating more attention resources to complete the task (Feng et al., 2011). General Discussion To the best of our knowledge, this study is the first to investigate the effects of both the senders’ gaze direction and facial expressions related to approach-oriented (happiness and anger) and avoidanceoriented (fear and sadness) emotions on observers’ RFRs and autonomic responses. It was designed to test assumptions about three possible underlying processes (i.e., automatic motor mimicry, communicative intent, and emotional appraisal) accounting for matched facial reactions to another’s emotional expressions. Taken together, our findings indicate that when participants were not submitted to judgment tasks and passively observed facial expressions, RFRs to both approach and avoidance-oriented emotions cannot be interpreted as involving either direct motor matching or a need to communicate the sender’s emotional expression. While these two perspectives predict that observers’ congruent RFRs should be little influenced by gaze direction (automatic motor mimicry) or solely affected by eye contact (communicative intent), participants in our study displayed congruent RFRs as a function of the social meaning of perceived gaze direction and facial expressions. Thus, our findings are more consistent with predictions from appraisal theories of emotion highlighting the critical role of the detection of self-relevance to account for Figure 2. Time course of (a) SCR magnitude and (b) heart rate changes in participants exposed to avatars as a function of gaze direction, the nature of facial expressions, and gender. SCR ⫽ skin conductance response; bpm ⫽ beats per minute. SELF-RELEVANCE OF GAZE AND EMOTION EXPRESSION differentiated and adaptive responses to salient social events (Scherer et al., 2001). 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Science, 306, 2061. doi:10.1126/science.1103617 Received February 21, 2012 Revision received June 20, 2012 Accepted July 31, 2012 䡲 Self-Relevance Appraisal Influences Facial Reactions to Emotional Body Expressions Julie Grèzes1*, Léonor Philip1, Michèle Chadwick1, Guillaume Dezecache1, Robert Soussignan2, Laurence Conty1,3 1 Laboratoire de Neurosciences Cognitives (LNC) - INSERM U960 & IEC - Ecole Normale Supérieure (ENS), 75005 Paris, France, 2 Centre des Sciences du Goût et de l’Alimentation (CSGA) UMR 6265 CNRS - 1324 INRA, Université de Bourgogne, 21000 Dijon, France, 3 Laboratoire de Psychopathologie and Neuropsychologie (LPN, EA2027), Université Paris 8, Saint-Denis 93526 cedex, France Abstract People display facial reactions when exposed to others’ emotional expressions, but exactly what mechanism mediates these facial reactions remains a debated issue. In this study, we manipulated two critical perceptual features that contribute to determining the significance of others’ emotional expressions: the direction of attention (toward or away from the observer) and the intensity of the emotional display. Electromyographic activity over the corrugator muscle was recorded while participants observed videos of neutral to angry body expressions. Self-directed bodies induced greater corrugator activity than other-directed bodies; additionally corrugator activity was only influenced by the intensity of anger expresssed by selfdirected bodies. These data support the hypothesis that rapid facial reactions are the outcome of self-relevant emotional processing. Citation: Grèzes J, Philip L, Chadwick M, Dezecache G, Soussignan R, et al. (2013) Self-Relevance Appraisal Influences Facial Reactions to Emotional Body Expressions. PLoS ONE 8(2): e55885. doi:10.1371/journal.pone.0055885 Editor: Andrea Serino, University of Bologna, Italy Received March 13, 2012; Accepted January 7, 2013; Published February 6, 2013 Copyright: ß 2013 Grèzes et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the Agence National of Research (ANR) ‘‘Emotion(s), Cognition, Comportement’’ 2011 program (Selfreademo) and by INSERM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] the social context, the perceived emotion [29,30], and the relationship between the expresser and the observer [31]. The present experiment manipulated the self-relevance of stimuli to further verify the contribution of affective processes to RFRs. Recent work converges toward the view that the ability to initiate adapted behaviors in response to others’ emotional signals mainly depends on the capacity to correctly evaluate the functional significance of the emitted signal for the self [32]. Several factors can therefore influence how self-relevant a given emotional signal is, thereby determining how an observer will evaluate and respond to it. Direction of gaze and body posture are among the most socially relevant cues through which we gain information regarding the source of an individual’s emotional reaction and the target of their impending actions. Such cues are particularly significant for anger because of their prime importance in regulating social interactions in both human [33] and non-human [34] primates. Facial expressions of anger have been shown to be more accurately and quickly recognized, and judged to be more intense, when coupled with direct gaze [35–40]. Additionally, Hess et al. [31] revealed an increase in the EMG activity of the orbicularis occuli in response to funny films of increasing intensity in the presence of friends but not of strangers; strongly suggesting that both self-relevance appraisal and the intensity of eliciting stimuli are important determinants of emotional facial reactions. Here we elaborated upon the above-mentioned results by varying two independent critical cues in face to face interactions: body orientation, proven to be important in determining to whom social attention is directed (toward or away from the observer), and Introduction Emotional expressions are critical to the coordination of social interactions by providing information about the emitter’s emotional states and behavioral intentions and by evoking reactions in the observer [1–4]. The research agrees that when exposed to emotional expressions, people display rapid facial reactions (RFRs) detectable by electromyography (EMG) [5–9]. While viewing static or dynamic happy faces elicits increased zygomaticus major activity (pulling the corners of the mouth back and upwards into a smile), angry faces evoke increased corrugator supercilii activity (pulling the brows together) [10–20]. Nevertheless, exactly what mechanism mediates these facial reactions remains a debated issue [8,21–23]. One major theoretical framework proposes that these facial reactions reflect the readout of emotional processing [6,24,25]. Within this framework, the appraisal perspective postulates that a multimodal organization of response patterns (which includes facial expressions and physiological reactions) is established according to appraisal configurations (novelty, coping potential, relevance, etc.) that are emotion-specific [1,26,27]. The emotional readout framework implies that people would be disposed to react with emotion-specific response patterns to biologically relevant stimuli such as expressions of anger [6]; and also that a given facial expression can elicit a different emotion and thus a divergent reaction in the observer, such as, for instance, a posture of submission in response to a threatening expression. This partly explains why facial reactions are less automatic than first thought [28], and why their production varies substantially as a function of PLOS ONE | www.plosone.org 1 February 2013 | Volume 8 | Issue 2 | e55885 Self-Relevance Influences Facial Reactions the intensity of the emotional display (different levels of angry body expressions). We presented dynamic bodily expressions of anger of increasing intensity, directed toward or away from the observer. First, whether previous findings could be generalized to angry body expressions remains to be established, but if affective processes participate in facial reactions, RFRs should be elicited for other forms of emotional communication signals than facial expressions, such as bodily expressions. Second, the observer’s facial EMG responses to emotional expressions as a function of face direction has only been explored in two studies [17,41]. Besides presenting conflicting results, these studies were limited in that subjects were explicitely instructed to determine the presence or absence of eye contact. Thus, by potentially influencing the importance attributed to gaze direction, they might have biased facial EMG activity. Yet, if the relevance of other’s emotional expressions impact the oberver’s affective processing, being the target of an expression of anger is expected to implicitely trigger more activity in the corrugator supercilii, as compared to being a simple observer of that expression. Moreover, the level of muscle activity is expected to fluctuate with the intensity of the displayed expression. Figure 1. 2*4 factorial design. Short movies of neutral (1), mild (2), moderate (3) and intense anger (4) oriented-to-Self and oriented-toOther were presented. doi:10.1371/journal.pone.0055885.g001 Validation of the stimuli Methods Two behavioral experiments were conducted on the selected 96 stimuli. Identification of Anger. This study assessed the ability to identify anger from dynamic body expressions. Participants (n = 20) were requested to decide (forced-choice) for each video whether the expression of the actor was ‘‘neutral’’, ‘‘angry’’ or ‘‘other’’. The order of the stimuli was fully randomized, as well as the order of the response words on the response screen. Categorization rates were percent transformed and submitted to a repeated measures ANOVA with within-subject factors of Target of Attention (Self or Other), Levels of Emotion (1, 2, 3, 4) and Choice (Anger, Neutral, Other). Greenhouse-Geisser epsilons (e) and p values after correction were reported where appropriate. Post-hoc comparisons (two-tailed t-tests) were performed for the analysis of simple main effects when significant interactions were obtained. The ANOVA revealed a main effect of Choice, F(2,38) = 36.57; p,.001, but no main effect of Target (F(1,19) = 1.30; p = .26), nor a main effect of Levels of Emotion, F(3,57) = 2.42; p = .075; e = 0.67; pcorr = .101. Of interest, only the interaction between Levels of Emotion and Choice, F(6,114) = 143.06; p,.001; e = 0.55; pcorr,.001 reached significance. For both Self- and Other-directed expressions, level 1 was correctly categorized as ‘‘Neutral’’ (as compared to ‘‘Anger’’ and ‘‘Other’’, all ps,.001), and levels 3 and 4 as ‘‘Anger’’ (as compared to ‘‘Neutral’’ and ‘‘Other’’, all ps,.001). The response accuracy for these conditions was above 75% and differed from chance level (33%) at p,0.001 (See Fig. 2). This was not the case for the mild levels of anger where accuracy did not significantly differ from chance level (Other2 = 36%, p = .497; Self2 = 39%, p = .195). These mild levels were ambiguous as participants responded ‘‘Neutral’’, ‘‘Angry’’ or ‘‘Other’’ equally for both Self- and Other-directed expressions (all ps..169; See Fig. 2 and Table S1). Subjective Feelings. The second experiment assessed the intensity of the participants (n = 20)’ feelings when confronted with angry body expressions. Participants were requested to evaluate the intensity of Felt Confusion, Surprise, Sadness, Threat and Irritation on 5 graduated scales from 0 to 9. The five scales appeared on the screen following each video, and their order was randomized between subjects. The order of the stimuli was fully randomized. Ratings were submitted to a repeated-measures Ethics The present study obtained ethics approval from the local research ethics committees (CPP Ile de France III and Institut Mutualiste Montsouris) at all institutions where participants were recruited and human experimentation was conducted. Stimuli Eight professional actors (four males) were hired and instructed to begin at neutral and to increase their expression of anger in seven to nine 3 s increments according to the experimenters signal in front of a camera until deemed satisfactory. Performances were filmed with two cameras: one was facing the actor; the second at a 45uangle relative to the first creating the impression that the expression was aimed toward the observer (oriented-to-self condition) or toward another (oriented-to-other condition). Videos were edited using Windows Movie Maker and several 2 sec (25 frames per second) fragments were selected to obtain two extracts for each condition from neutral to extreme anger with two different viewpoints. Clips of actors seen from the side were flipped to obtain equal numbers of left and right videos and faces were blurred using the Adobe After-effect software, to preclude extraction of any emotional cues conveyed by them and restricting information to the body. Selection of the final material was based on the results of a behavioral pilot study. A total of 312 edited video clips including all the original steps from neutral to anger for each actor were presented on a PC screen. Participants (n = 23) were instructed to evaluate the intensity of the actor’s bodily expression on a continuous scale from neutral to high anger. Two-tailed paired ttests were used to compare increments and the results permitted the selection of the most consistently convincing performances of each actor’s range, corresponding to 4 significantly different steps in the degree of expressed anger (p,0.05). We retained 96 videos corresponding to 8 actors, 4 levels of anger (neutral; mild; moderate; intense anger) and 2 points of view (oriented to self and other, both right and left viewpoints). A 264 factorial design was built, with Target of Attention (Self or Other) and Levels of emotion (neutral (1); mild (2), moderate (3) and intense anger (4)) as factors (see Fig. 1). PLOS ONE | www.plosone.org 2 February 2013 | Volume 8 | Issue 2 | e55885 Self-Relevance Influences Facial Reactions when exposed to Self- as compared to Other-directed expressions and increased their rating of intensity of feeling as a function of the increased intensity of the stimuli, these effects were more marked for feelings of Threat (see table S2). Together, these results strongly suggest that the perception of Self-directed angry body expressions mainly prompted a feeling of Threat in the observer, as compared to other feelings (See Fig. 3). Facial EMG experiment Participants. Forty-four participants (21 women) participated in the physiological experiment. All had normal or correctedto-normal vision, were right-handed, naive to the aim of the experiment and presented no neurological or psychiatric history. All provided written informed consent according to institutional guidelines of the local research ethics committee and were paid for their participation. Due to a bad signal-to-noise ratio in physiological signals, four subjects (2 men) were excluded from final analysis leaving 40 participants (mean age = 2460.4 years). Experiment. Participants had to fix a white cross centered on a 19-inch black LCD screen for a random duration of 800 to 1200 ms followed by a silent 2000 ms video showing an actor in one of the eight experimental conditions. Each video was followed by an inter-stimulus interval of 1000 ms. Additionally, 15 oddball stimuli (upside-down video-clips; see below) and 38 null events (black screen of 2 sec) were included pseudo-randomly within the stimulus sequence. The order of the stimuli was fully randomized. Subjects were instructed to press a button each time the upsidedown video-clip appeared to ensure they paid attention to all the stimuli throughout the session. The participants performed at 100% of accuracy (at mean 648622 ms) in this oddball task. Data acquisition and analysis. Using the acquisition system ADInstruments (ML870/Powerlab 8/30), EMG activity was continuously recorded using Sensormedics 4 mm shielded Ag/AgCl miniature electrodes (Biopac Systems, Inc). Fixation cross and stimuli onset were automatically signaled on the channels of the LabChart Pro software by a PCMCIA Parallel Card (Quatech SPP-100). Before attaching the electrodes, the target sites of the left face were cleaned with alcohol and gently rubbed to reduce inter-electrode impedance. Two pairs of electrodes filled with electrolyte gel were placed on the target sites and secured using adhesive collars and sticky tape. Following the guidelines proposed by Fridlund & Cacioppo [42], the two electrodes of a pair were placed at a distance of approximately 1.5 cm over 2 muscle regions associated with different emotional expressions. Activity over the left corrugator supercilii muscle, which lowers brows, was used as a marker of negative emotional expression [6]. Activity over the left zygomaticus major muscle, which pulls lip corners up and indexes pleasure/happiness, was used as a control recording site to verify that participants responded selectively to anger expressions. The ground electrode was placed on the upper right forehead. The signal was amplified, band-pass filtered online between 10–500 Hz, and then integrated. EMG trials containing artifacts were manually rejected. No more than 15% of the trials were dropped per muscles. Integral values were subsampled offline at 10 Hz and log transformed to reduce the impact of extreme values [9,23]. Values were then standardized within participants and within muscle to allow comparisons. Temporal profiles of facial EMG during the first 1000 ms following stimulus onset were investigated by calculating mean amplitudes during 10 time intervals of 100 ms. Pre-stimulus values (computed over 200 ms before the stimuli onset) were then subtracted from post-stimulus activity to measure the activity level caused by viewing each stimulus (i.e., to calculate the change from baseline). EMG activity was thus defined as the change from the Figure 2. Results from the categorization task. Mean percentage for each choice (Anger, Neutral or Other) of the categorization task plotted as a function of the Levels of Emotion (1, 2, 3, 4). doi:10.1371/journal.pone.0055885.g002 ANOVA with within-subject factors of Feelings (Confusion, Surprise, Sadness, Threat and Irritation), Target of Attention (Self or Other) and Levels of Emotion (1, 2, 3, 4). GreenhouseGeisser epsilons (e) and p values after correction were reported where appropriate. Post-hoc comparisons (two-tailed t-tests) were performed for the analysis of simple main effects when significant interactions were obtained. The ANOVA indicated a main effect of Feelings, F(4,76) = 16.09; p,.001, e = 0.82; pcorr,.001, and a main effect of Levels of Emotion, F(3,57) = 48.59; p,.001; e = 0.38; pcorr,.001, but no main effect of Target, F(1,19) = 2.64; p = .12. There was a significant interaction between Feelings * Levels of Emotion, F(12,228) = 19.57; p,.001; e = 0.37; pcorr,.001. The intensity of the Feelings increased with the Levels of Emotion (Level1,Level2,Level3,Level4 - all t(19).36.22; all ps,.001), except for Sadness (Level1 = Level2 = Level3,Level4)(See Table S2 and Fig. 3). Of interest here, there was a significant interaction between Feelings * Target, F(4,76) = 6.25; p,.001; e = 0.68; pcorr = .001. Self- as compared to Other-directed expressions were perceived as more Threatening (t(19) = 2.67; p = .015) and more Irritating (t(19) = 2.54; p = .02). There was no difference for the other Feelings (ps..23). We then conducted a repeated-measures ANOVA with withinsubject factors of Feelings (Threat and Irritation), Target of Attention (Self or Other) and Levels of Emotion (1, 2, 3, 4). This ANOVA revealed a main effect of Feelings, F(1,19) = 14.63, p = .001: participants felt more threatened than irritated when confronted with body expressions of anger (Mean (SEM) Threat = 3.24(.19); Irritation = 2.42(.23)(See Figure 3). It also revealed a main effect of Target, F(1,19) = 7.84; p = .011; a main effect of Levels of Emotion, F(3,57) = 65.51; p,.001; e = 0.42; pcorr,.001, a significant interaction between Feelings * Levels of Emotion, F(3,57) = 14.86; p,.001; e = 0.55; pcorr,.001, a significant interaction between Feelings * Target, F(3,57) = 19.87; p,.001 but no triple interaction between Feelings * Levels of Emotion * Target, F(3,57) = .082; p = .97. Importantly here, while participants rated their feeling of both Threat and Irritation higher PLOS ONE | www.plosone.org 3 February 2013 | Volume 8 | Issue 2 | e55885 Self-Relevance Influences Facial Reactions Figure 3. Intensity of felt emotions. The intensity of Felt Emotions (Threatened, Irritated, Surprised, Confused and Sad) with standarderrors are plotted as a function of the Target of Attention (S for Self, O for Other), and the Levels of Emotion (1, 2, 3, 4). The grey asterisks on the right signal feelings that significantly increased with Levels of Emotion. Blacks asterisks on panels signals feelings that significantly increased for Self as compared to Other-directed body. doi:10.1371/journal.pone.0055885.g003 baseline occurring between 0 and 1000 ms after stimuli onset [10,23]. Finally, mean levels of corrugator and zygomaticus activity were computed separately for each experimental condition. Physiological data were first submitted, separately for each muscle, to repeated measures ANOVA using Target of Attention (Self or Other), Levels of Emotion (1, 2, 3, 4) and Time Windows (10) as within-subject factors. Second, when the Time Windows factor interacted with another factor of interest, we performed post-hoc t-tests to determine the time windows for which the effect occurred and submitted the mean activity of these windows to a new ANOVA using Target of Attention (Self or Other) and Levels of Emotion (1, 2, 3, 4) as within-subject factors. GreenhouseGeisser epsilons (e) and p values after correction were reported when appropriate. Post-hoc comparisons (two-tailed t-tests) were also performed for the analysis of simple main effects when significant interactions were obtained. Results Corrugator activit The ANOVA indicated significant effects of Target of Attention, F(1,39) = 11.05; p = .002, Levels of Emotion, F(3,117) = 2.71; p = .048, and Time Windows F(9,351) = 45.55; p,.001; e = 0.20; pcorr,.001 (See Table S3, and Fig. 4). The interaction between Target of Attention and Levels of Emotion, F(3,117) = 5.39; p = .002; e = 0.77; pcorr = .004, was significant after correction, whereas the other interactions didn’t reach significance after correction: Time Windows6Target of Attention, F(9,351) = 2.63; p = 0.006; e = 0.26; pcorr = .068, and Time Windows6Levels of Emotion, F(27,1053) = 1.66; p = .019; e = 0.23; pcorr = .127. Yet, the triple interaction between Time windows, Target of Attention and Levels of Emotion reached significance after correction, F(27,1053) = 1.67; p,.001; e = 0.23; pcorr = .035. We then submitted the data for each time window to a second ANOVA with within-subject factors of Target of Attention (Self or Other) and Levels of Emotion (1, 2, 3, 4). This analysis revealed that the interaction between Target of Attention and Levels of Emotion was significant between 300 and 700 ms Time windows, all F(3,117).4.4; all pcorr,.01. We thus computed the mean activity between 300 and 700 ms and submitted these data to a second ANOVA with within-subject factors of Target of Attention (Self or Other) and Levels of Emotion (1, 2, 3, 4)(See Table S4, and Fig. 5). This second ANOVA revealed a main effect of Target of Attention. Selfdirected body induced greater corrugator activity than Otherdirected bodies, F(1,39) = 13.02; p,.001. An interaction between Target of Attention and Levels of Emotion was also observed, F(3,117) = 6.31; p,.001; e = 0.75; pcorr = .002, revealing that the effect of Target of Attention increased with the Levels of Emotion: the effect of Target of Attention was not significant at level 1 (i.e. Neutral stimuli-t(39) = 2.605; p = .548); failed to reach significance at level 2, t(39) = 1.855; p = .071; appeared significant at level 3, t(39) = 2.338; p = .025, and reached high significance at PLOS ONE | www.plosone.org 4 February 2013 | Volume 8 | Issue 2 | e55885 Self-Relevance Influences Facial Reactions signals were directed toward them as compared to averted gaze, and the higher the intensity of displayed anger, the stronger their facial reactions. We propose early RFRs to body expressions of anger might be related to an emotional appraisal process [38]. Our data reveal the same influence of the direction of attention in the RFRs to body expressions, as has been shown for faces [35,38]. Using virtual avatars and manipulating face orientation, Schrammel et al. [17,17] demonstrated significantly higher corrugator activity for angry faces with direct gaze as compared to angry faces with averted gaze. More recently, we further provided facial EMG evidence of the critical role of attention on interpersonal facial matching by manipulating gaze direction rather than face orientation [43]. Here, even in the absence of gaze information, self-directed body expressions of anger triggered higher corrugator reactivity as compared to other-directed bodies. Our data converge with the appraisal perspective which proposes that the evaluation of emotional stimuli depends on the degree of self-relevance of the event. Within such a framework, it is proposed that anger should be rated as more intense when coupled with direct gaze as it signals a direct threat for the observer [38,40,44]. Indeed, this was confirmed by our behavioural pre-tests revealing that the perception of self-directed angry body expressions specifically increased the subjective feelings of being threatened. Crucially, we have demonstrated for the first time that the intensity of bodily expressions of anger displayed by a congener enhanced RFRs only when directed toward the self. The absence of such an increase for averted bodies dismisses the possibility that these findings are strictly related to the amount of movement involved in body expressions. Together with the findings of Hess et al. [31] of increased EMG reactivity to funny films of increasing intensity in the presence of friends only, our results imply that it is the interaction between these factors that influences how selfrelevant an emitted signal is and determines the levels of RFRs (here: direction of the emitters’ attention and the intensity of their expression), rather than each factor individually. Moreover, our results strongly suggest that the higher the potential for interaction with another (positive in Hess et al., negative here), the higher the facial reactions in the observer. Recently, using EEG under fMRI, we revealed that the degree of potential social interaction with another relies on the binding of self-relevant social cues 200 ms after stimulus onset in motorrelated areas [45]. The present early RFRs, beginning at 300 ms after stimulus onset, may thus reflect the emotional motor response to being threatened. Activity in the corrugator supercilii muscle is largely accepted as a reflection of negative emotional reactions to negative-valenced stimuli, such as spiders and snakes [6], unpleasant scenes [46] or to negative facial expressions [23,30,47], and has also been demonstrated in response to static body expressions of fear [48,49]. The present activity in the corrugator supercilii muscle triggered in response to body expressions of anger may thus relate to the observer’s negative emotional reaction. As anger displays are appraised as power and dominance signals, which have been shown to trigger divergent rather than convergent responses [50], one can speculate that these RFRs convey a divergent fear response [23,30]. RFRs over the corrugator muscle occur in response to body expressions in the absence of facial information, and regardless of body orientation and of emotional content. Although it is acknowledged that RFRs may result from multiple processes [18,23], the presence of early RFRs in absence of facial expressions cannot be explained by strict motor mimicry as the body expressions here did not provide the cues necessary for facial motor matching. A strict motor mimicry process is indeed not sufficient to explain why RFRs are displayed to non-facial and Figure 4. Time course of the mean EMG activity. A) Over the corrugator supercilii region as a function of the Target of Attention (S for Self (green), O for Other (blue)) and the Levels of Emotion (1,2,3,4). Activity reflects average activation during each 100-ms time interval. B) Over the zygomaticus region as a function of the Target of Attention (S for Self, O for Other) and the Levels of Emotion (1,2,3,4). doi:10.1371/journal.pone.0055885.g004 level 4 of emotion, t(39) = 5.826; p,.001. Interestingly, for Selfdirected bodies, level 1 was significantly different from level 2 (t(39) = 22.687; p = .011); level 2 and level 3 were not significantly different, t(39) = 2.134; p = .897; but level 3 appeared significantly different from level 4, t(39) = 22.342; p = .024. By contrast, the different levels of emotion did not significantly differ in the Otherdirected condition, all ps..434. Finally, post-hoc analyses revealed that activity between 300–700 ms significantly differs from 0 in all experimental conditions (all t(39).4.7;all p,.001) suggesting that all conditions triggered RFRs (see Fig. 5). Zygomatic activity Using zygomatic activity as a control recording site, the ANOVA with Target of Attention (Self or Other), Levels of Emotion (1,2,3,4) and Time Windows of analyses (10), as within-subject factors, did not reveal any main effect nor significant interaction, all F,1.45 (Table S5, Fig. 4). Discussion Previous EMG experiments have consistently demonstrated that people tend to produce facial reactions when looking at other’s facial expressions of emotion. Here, we found that participants displayed early facial reactions to body expressions of anger, as revealed by an increase of corrugator activity occurring 300 to 700 ms after stimulus onset. RFRs were stronger when anger PLOS ONE | www.plosone.org 5 February 2013 | Volume 8 | Issue 2 | e55885 Self-Relevance Influences Facial Reactions Figure 5. Mean activity over the corrugator supercilii region between 300 and 700 ms. The mean (SEM) activity is represented as a function of A) the Target of Attention (Self (green), Other (blue)) and the Levels of Emotion (1,2,3,4) and, B) only for Self-oriented conditions for the 4 Levels of Anger. *p,0.05. doi:10.1371/journal.pone.0055885.g005 non-social emotional pictures [6], emotional body expressions [48,49] and auditory stimuli [51–53], nor why they are occasionally incongruent with the attended signals [23]. Moreover, our results are clearly at odds with the predictions that can be derived from a motor mimicry perspective, i.e. that participants should either display congruent RFRs to others’ angry faces, irrespective of the direction of attention of the emitter [28] or display less mimicry when directed at the observer as anger conveys non-ambiguous signals of non-affiliative intentions [29,37]. Yet, the present early RFRs elicited in all experimental conditions, including neutral bodies (Level 1), also rule out the possibility that they reflect emotional processes only and suggest that RFRs could partly result from a mere orienting response to the apparition of the stimuli and/or the observer’s cognitive effort [54] to decode an emotional expression in the absence of facial information and/or the appraisal of goal-obstructiveness [54,55]. Also, as the present findings were revealed using body expressions of anger only, we cannot simply rule out that motor-mimicry processes would occur under other experimental circumstances, nor specify how motor, emotional and appraisal processes might interact. Further experiments are thus needed to determine whether the present results can be generalized to a wider range of emotions as well as whether (and to what extent) both motor and affective processes operate when facial information is available [18]. To conclude, we not only demonstrate that the corrugator supercilii muscle can be triggered in response to angry expressions but extend these findings to dynamic bodies. The present findings corroborate the emotional readout framework and further suggest that rapid facial reactions reflect the appraisal of the context and its self-relevance which varies as a function of the emitter’s direction of attention and the intensity of his/her anger. Supporting Information Table S1 Mean (SEM) recognition rate. (DOC) Table S2 Mean (SEM) intensity ratings of feelings (DOC) Table S3 Mean (SEM) data from the Corrugator activity submitted to a repeated measures ANOVA using Target of Attention (Self or Other), Level of Emotion (1, 2, 3, 4) and Time Windows (10) as within-subject factors. (DOC) Table S4 Mean activity (SEM) between 300 and 700 ms for the Corrugator muscle region submitted to a repeated measures ANOVA with within-subject factors of Target of Attention (Self or Other) and Level of Emotion (1, 2, 3, 4). (DOC) Table S5 Mean (SEM) data from the zygomatic activity submitted to a repeated measures ANOVA using Target of Attention (Self or Other), Level of Emotion (1, 2, 3, 4) and Time Windows (10) as within-subject factors. (DOC) Acknowledgments We are grateful to Sylvie Berthoz (INSERM U669 & IMM) for administrative supports and to the anonymous referees for their constructive comments. 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PLOS ONE | www.plosone.org 7 February 2013 | Volume 8 | Issue 2 | e55885 ñ½ññ½½½ò½ññ§§§§§§§§ ½½ òòò¼ ¿§§§§§ § §§§§§§ó§§§§§§ §§§§§§§§§§§§ §² §§§ §§§§ §² §§§§§ §§§§§§§¼ ¼§§§ § §§§§ §§§§§§ §§§ §§ §§§§§§ §§§§ §§ § §§§¬§§§ §§ §§§§§§§§§§§ §§§§ ò§§§ó §§ §§ §§¬§§§ §§ §§§ó §§§§§§§§ §§§§§ ò§§§ §§ §§§óò ¿§§ §§²§ §§§§§§§§§ §§§§§§§§§§§§§ §§§§§§ §§§§§§ §§§ §§§§§§§§§ §§ §§§§§§§ §§§§§§§§§§§§ §§§§§ §§§§ §§§§ §§§§§§§§ §§ §§§§§§§ §§§§§§§ §§§§ òò§§§§§ §§ ò§§§§§§§§ §§ §§ò ççïçóò ¬§§§§§§§§§ó §§§§§§ §§ §§§§ §§§§ §¬§§§§§§ §§§ §§§§§§§§ §§§§ §§§§§§§§§§§§§§§§ §§§§§§ §§§§§§§§ §§§ §§§§§ §§§§§§ §§§§§ §§§§ §§§§§ ò§§§ §§§ §§§ò§§§ó §§§§§§§§§§§§§§ò ¼§ §§§ §§§§§ §² §§§ §§§§§§§§ §§ §§§§§§§§§§§§§§§§ó §§ §§§§§§§§ §§§§ § ²½½½½²²²½½²½½ §§§§§§§§§§§§ §§§§§§ §§§§§§ §§§§§§§§§§§§§ò §§§§§§§§§§§§§ §§§§§§§ §§§§§§§§§§§§ó §§ §§§§ §²²§§ § §§§§§§ §§§§§§§§§ó §§§§§§§§§ §§§ §§§§§§§§§ §² §§§§§§§§§§§§ §§ ²§§§§ §² §§²§§§§§§§ §§§§§§§ §§§ §§§ §§§ò§§§ §§§§§§§§§§§§ò ק § §§§§§§§ §§§§§ó §§§§§§ó §§§ §§§§§ §² §§§ §§§§§§ §§§ §§§§§ §§§§§§§ §§§ 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§§ §§ò §§§§§§§ § §§§§§§§ ²§§ §§§§§§ó§§§§§§ §§§§§§§§§§§§ ²§§§ §§§§§ §§ §§§ §¬§§§§§ §§§ ²§§ó §§§§§§ §§§§§§§§§§ ²§§ §§§§§§ó§§§§§§ §§§§§ §§§§§§§§§¼ ïò ¿§§ §§§§§§ §§§§§§ §§ §§§§§§§§§ §§ §§§§§§§§ §§§§§§ò ¿§§§ §§ §§§§§§§ §§ §§§ §§§§§§§§§ §§§§§§§§§§ §§ §§§§§ §§§§§§§§§§ §§§§§§§§§ §§ ¿§§§§§§§§ òççççóó §§§§§ §§ §§§ §§§§§§ §§§§§§§ò çò ¿§§§ §§§§§§§§§§ §§ §§§§§§ §§ §§§ §§§§§§§§§§ §² §§§§§§ §²²§§§§§§§§ò óò §§§§§ §²²§§§§§§§§ §§§§§§ ²§§§ §§§ §§§§§§§§§§§ §§§§§§§§ §² §§§ §§§§§§ §§§§ §§ §§§§§§§§§§§ §§ §§§ §§§ §§§§§§ò îò ¿§§§ §§±§§§§§ §§§§ §§§ §§§§§§ §§§ §§§§§§§§ §§§ §§§§§ §§ § §§§¬§§§ó §§§§§ §§ §§§§ §§§§ §§§§§§§§§§ §§§ §§§§§ §§ §§ §§§§§ò ק§§§§§ó §§§ §§§§§ §§§§§ §§§§ §§ §§§§§§§§§ §§ §²²§§§§§§ §§§§§§ §§§§§§§§§§§ò §§§§§§§§ §§ §§ò §§§§§§ §§§§ §§§§§§ §§§§§§§§§§§ §§§§§§§§ §§§§§§§§§§§ §§§§§§§ §§§² §§§ §§§§§ò ¿§§ §§§§§§§§§ §² §§§§§§§§§§§ §§ §§§§§§§ §§ §§§ §§§§§§§§ §§§§§§ §² §§²§§§§ §§§ §§§§§ § §§§§§§§ §§§§ §§ Guillaume DEZECACHE Studies on emotional propagation in humans: the cases of fear and joy Etudes sur la propagation émotionnelle chez l’humain : les cas de la peur et de la joie Les psychologues de la foule des 19e et 20e siècles nous ont légué l’idée que les émotions sont si contagieuses qu’elles peuvent conduire un grand nombre d’individus à rapidement et spontanément adopter une même émotion. L’on pense par exemple aux situations de panique de foule, où, en l’absence de coordination centrale, des mouvements de fuite collective sont susceptibles d’émerger. Les travaux présentés dans cette thèse se proposent d’étudier la propagation de deux émotions considérées comme particulièrement contagieuses, la peur et la joie. Leur propagation est étudiée à deux niveaux d’analyse : d’abord, au niveau proximal (la question du "comment"), je discute les mécanismes potentiels permettant à l’émotion de se propager en foule ; aussi, je soulève la question du bien-fondé de considérer la transmission émotionnelle comme un processus de contagion. Dans un second temps, au niveau d’analyse évolutionnaire ou ultime (la question du "pourquoi"), je pose la question de savoir pourquoi les individus de la foule ont ainsi l’air de partager leur états émotionnels de peur et de joie avec leurs voisins. A ce propos, je présente une étude montrant que la transmission de la peur peut être facilitée par la propension du système cognitif humain à moduler l’intensité des réactions faciales liées à la peur, en fonction de l’état informationnel de leurs congénères. Ces résultats suggèrent que les réactions faciales spontanées de peur ont pour fonction biologique la communication, à autrui, d’information cruciale pour la survie. Pour finir, je discute les implications de ces travaux pour notre compréhension plus générale des liens entre émotions et comportement de foule. Transmission émotionnelle ; contagion émotionnelle ; communication émotionnelle ; peur ; joie ; psychologie de la foule Crowd psychologists of the 19th and 20th centuries have left us with the idea that emotions are so contagious that they can cause large groups of individuals to rapidly and spontaneously converge on an emotional level. Good illustrations of this claim include situations of crowd panic where largemovements of escape are thought to emerge through local interactions, and without any centralized coordination. Our studies sought to investigate the propagation of two allegedly contagious emotions, i.e., fear and joy. This thesis presents two theoretical and two empirical studies that have investigated, at two different levels of analysis, the phenomenon of emotional propagation of fear and joy: firstly, at a proximal level of analysis (the how-question), I discuss the potential mechanisms underlying the transmission of these emotions in crowds, and the extent to which emotional transmission can be considered analogous to a contagion process. Secondly, at an evolutionary/ultimate level of analysis (the why-question), I ask why crowd members seem to be so inclined to share their emotional experience of fear and joy with others. I present a study showing that the transmission of fear might be facilitated by a tendency to modulate one’s involuntary fearful facial reactions according to the informational demands of conspecifics, suggesting that the biological function of spontaneous fearful reactions might be communication of survival-value information to others. Finally, I discuss the implications of these studies for the broader understanding of emotional crowd behavior. Emotional transmission; emotional contagion; emotional communication; fear; joy; crowd psychology 159