Revealing Behavioural Synchronization in Humans and Other Animals: Why Individuals Mirror Others [1st ed. 2023] 3031484487, 9783031484483

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Angélique Lamontagne Florence Gaunet

Revealing Behavioural Synchronization in Humans and Other Animals

Revealing Behavioural Synchronization in Humans and Other Animals

Angélique Lamontagne • Florence Gaunet

Revealing Behavioural Synchronization in Humans and Other Animals Why Individuals Mirror Others

Angélique Lamontagne Laboratoire de Psychologie Cognitive (UMR 7290) CNRS – Aix-Marseille University Marseille Cedex 03, France

Florence Gaunet Laboratoire de Psychologie Cognitive (UMR 7290) CNRS – Aix-Marseille University Marseille Cedex 03, France

Association Agir pour la Vie Animale (AVA) Cuy-Saint-Fiacre, France

ISBN 978-3-031-48448-3    ISBN 978-3-031-48449-0 (eBook) https://doi.org/10.1007/978-3-031-48449-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

Behavioural synchronization is a social process that can be observed every day among humans. In daily life, people tend to copy the behaviours of others, spontaneously and involuntarily. When engaged in conversations, such as when you’re speaking with others, if your conversation partners suddenly shift their gaze in a specific direction, you may have an irrepressible urge to look in the same direction and you will have turned your head before even thinking about why you performed this gesture. This automatic copying is the basis of behavioural synchronization, which consists of doing the same thing, in the same place, at the same time, as others. If you sit in a public park and watch people walking around, you will quickly notice that people walking side by side swing their legs at the same rhythm; this is locomotor synchronization, i.e. behavioural synchronization applied to locomotor behaviour. Behavioural synchronization is a fundamental component of social cognition, certainly more marked in relatives and within groups temporally aside for a specific reason. However, it is not specific to humans: all social species exhibit behavioural synchronization. Note, for example, how starlings give us an impressive show at sunset: these birds form flocks of hundreds of individuals, flying around in a fascinating ballet, before perching all together in the bushes. Behavioural synchronization can even be observed at the interspecific level. Indeed, recent research has shown that pet dogs synchronize their walking pace with that of their owner. Behavioural synchronization is thus a key cognitive ability in social interactions and it is universal in social species. Therefore, what cognitive processes does it rely on? Moreover, it is undeniable that individuals do not synchronize all the time, nor on any other individual. Thus, what are the social factors that modulate behavioural synchronization and how do they act? In this review, we address these questions by exploring the neurophysiological basis and cognitive properties of behavioural synchronization in humans and animals, at the intraspecific and interspecific levels. We also study the mechanisms that enhance or inhibit behavioural synchronization and identify the social factors that modulate it through these mechanisms. Marseille Cedex 03, France 

Angélique Lamontagne Florence Gaunet v

Acknowledgements

This work was funded by the National Association for Research and Technology, the Agir pour la Vie Animale (AVA) association, the National Centre for Scientific Research, and Aix-Marseille University. This book originated from initial discussions with Dr Thierry Bedossa on the origin of the behaviour of a pet: a disease or the owner’s social influence. We thank him and Charlotte Duranton who considerably contributed to experimental aspects, Thierry Legou for engineering contributions to record behaviours of both dogs and owners, and Fannie Fabre for the extension of the topic regarding attenuation and adaptation to global warming. Competing Interests The authors have no conflicts of interest to declare. Ethics Approval Not applicable.

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Contents

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I ntroduction of Behavioural Synchronization��������������������������������������    1 1.1 A Subset of Interpersonal Behavioural Alignment ��������������������������    1 1.2 A Form of Social Entrainment����������������������������������������������������������    4 1.2.1 Definition of Entrainment ����������������������������������������������������    4 1.2.2 Locomotor Entrainment��������������������������������������������������������    5 1.2.3 Is Entrainment to a Musical Rhythm Specific to Humans? ��������������������������������������������������������������������������    6 1.2.4 Gaze Following ��������������������������������������������������������������������    6 References��������������������������������������������������������������������������������������������������    7

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 isuomotor Approach of Behavioural Synchronization����������������������   11 V 2.1 Visual Perception of Action��������������������������������������������������������������   11 2.2 Ideomotor Theory ����������������������������������������������������������������������������   13 2.2.1 Correspondence Between Perception and Action ����������������   13 2.2.2 Towards a Motor Representation of Action��������������������������   14 References��������������������������������������������������������������������������������������������������   15

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 europhysiological and Cognitive Bases of Behavioural N Synchronization����������������������������������������������������������������������������������������   19 3.1 Neural Circuits of the Perception of Actions������������������������������������   19 3.2 Neural Circuits Related to Temporal Processes��������������������������������   20 3.3 Mirror Neurons ��������������������������������������������������������������������������������   20 3.3.1 Mirror Neurons in Non-human Primates������������������������������   20 3.3.2 Mirror Neurons in Humans��������������������������������������������������   22 3.4 Mirror Neurons and Motor Resonance ��������������������������������������������   23 3.5 Mirror Neurons and Motor Experiences ������������������������������������������   24 3.5.1 Origin and Development of Mirror Neurons������������������������   24 3.5.2 Influence of Motor Expertise on the Activation of Mirror Neurons ����������������������������������������������������������������   26 3.6 From Motor Resonance to Motor Contagion������������������������������������   27 References��������������������������������������������������������������������������������������������������   28

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 ognitive Properties of Behavioural Synchronization ������������������������   33 C 4.1 Motor Interference����������������������������������������������������������������������������   33 4.2 Inhibition of Motor Contagion����������������������������������������������������������   35 4.3 Social Facilitation Contributes to Motor Contagion ������������������������   37 4.4 Sensorimotor Processes and Motor Contagion ��������������������������������   37 4.4.1 Sensory Modalities for Behavioural Synchronization����������   37 4.4.2 Sensorimotor Processes and Motor Contagion at the Group Level����������������������������������������������������������������   39 4.5 From Interbrain Neural Synchronization to Behavioural Synchronization��������������������������������������������������������������������������������   40 4.5.1 Behavioural Studies��������������������������������������������������������������   41 4.5.2 Neurophysiological Studies in Adults and Infants����������������   42 4.6 Behavioural Synchronization and Hormones ����������������������������������   44 4.6.1 Oxytocin, a Biomarker of Affiliation������������������������������������   44 4.6.2 Dopamine, Serotonin, and Endorphin, Biomarkers of Reward Circuits��������������������������������������������   46 4.6.3 Cortisol, a Biomarker of Stress��������������������������������������������   46 4.6.4 Happy Hormones, Brain Activity, and Behavioural Synchronization��������������������������������������������������������������������   47 4.7 Behavioural Synchronization and Mutual Social Attention System������������������������������������������������������������������������������   48 References��������������������������������������������������������������������������������������������������   49

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Social Functions of Mirror Neurons, Motor Resonance and Motor Contagion������������������������������������������������������������������������������   57 5.1 Motor Imitative Responses Enable Learning������������������������������������   57 5.2 Action Recognition and Understanding��������������������������������������������   57 5.3 Action Anticipation��������������������������������������������������������������������������   60 5.4 Understanding Intentions and Mental States������������������������������������   61 5.5 Reducing Psychological Distance����������������������������������������������������   64 5.6 Behavioural Synchronization Induces Prosociality��������������������������   65 References��������������������������������������������������������������������������������������������������   67

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 ocial Modulators of Behavioural Synchronization ����������������������������   73 S 6.1 Factors Related to the Observer��������������������������������������������������������   73 6.1.1 Effect of Attentional State����������������������������������������������������   73 6.1.2 Effect of Interindividual Distance����������������������������������������   75 6.1.3 Effect of Affiliation ��������������������������������������������������������������   77 6.1.4 Effect of Willingness to Be Like Other or to Belong to a Group/Social Comparison ����������������������������   79 6.2 Factors Related to the Observed Individual(s)����������������������������������   81 6.2.1 Effect of Leadership��������������������������������������������������������������   81 6.2.2 Effect of Social Referencing ������������������������������������������������   86

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6.2.3 Effect of Social Influence, the Case of Humans ������������������   87 6.2.4 Effect of Visuomotor Profile of Social Agent ����������������������   88 6.3 Modulation of the Spread of Synchronized Behaviours at the Group Level����������������������������������������������������������������������������   90 6.3.1 The Joining Process��������������������������������������������������������������   90 6.3.2 Effect of Group Spatial Configuration and Size on Behavioural Synchronization������������������������������������������   96 References��������������������������������������������������������������������������������������������������   98 7

 onclusion: Behavioural Synchronization, a Pillar of Social C Cognition��������������������������������������������������������������������������������������������������  109 References��������������������������������������������������������������������������������������������������  113

Index������������������������������������������������������������������������������������������������������������������  115

Chapter 1

Introduction of Behavioural Synchronization

1.1 A Subset of Interpersonal Behavioural Alignment In daily life, people tend to align their behaviours to those of people around them: when they clap their hands in rhythm or match their posture with peers (Shamay-­ Tsoory et al. 2019). This behavioural alignment can happen explicitly, when soldiers do a military march for example, or outside of conscious awareness and intent, when mates coordinate their walking pace for example (Chartrand and Lakin 2013; Heyes 2011; Néda et al. 2000). As a matter of fact, studies have shown that individuals tend to involuntarily copy the behaviours of others (Chartrand and Bargh 1999; Shockley et al. 2003). This interactional behavioural alignment is defined as ‘the reciprocal matching of behaviour, postures, facial or vocal expression to the interaction partner’ (Rauchbauer and Grosbras 2020). It is acknowledged that brain processes follow the general principle of optimization in neural computation (Koban et al. 2019), which involves selecting the least energy-intensive behavioural response from a set of alternatives (Laughlin and Sejnowski 2003). Indeed, the brain tends to conserve resources through mechanisms that optimize representations of the social environment (Friston 2010). When individuals are attentive to each other, share a common goal, are familiar, or are want to affiliate (see Sect. 6.1.3), aligning one’s behaviour with that of others minimizes behavioural effort by decreasing the distance between actions produced by self and those produced by others. Behavioural synchronization thus constitutes energy optimization, as it costs the brain little in terms of process strategy (Koban et al. 2019). In other words, synchronized behaviour results in synchronized neural representations for self- and other-generated behaviour, which are reinforced as they are in line with the brain’s general tendency to compress information and to reduce prediction errors (Friston 2010; Laughlin and Sejnowski 2003). However, it is important to note that during social interactions, behavioural alignment is not always the least energy-consuming response; this is the case during the numerous © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0_1

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situations when individuals have divergent goals. In this case, the behavioural alignment inhibition circuits are activated (see Sect. 4.2). In social interactions for which behavioural alignment is associated with less effort and energy consumption, it leads to a reduction of the differences between motor representation of self and others and it is therefore rewarding (Keller et al. 2014). The term motor representation refers here to the cerebral mechanisms that allow, on the one hand, the implementation of an intentional and adapted motricity, whether it is actually performed or imagined; and on the other hand, the anticipation of the sensory, postural, and spatial consequences of this motricity (Bidet-Ildei et al. 2011). Thus, in light of the optimization principle, Shamay-Tsoory et  al. (2019) proposed that the different levels of behavioural alignment involve a feedback loop with three components (see Fig. 1.1): firstly, a detection system for levels of gap between self and others; secondly, an enforcement system regulating alignment; and finally, a reward system signalling an optimal inter-individual gap, reflecting the fact that the individual is socially aligned with others. Detection of a gap activates the alignment system, while detection of no gap activates the reward system (Shamay-Tsoory et al. 2019). Thus, behavioural alignment is a cognitive process that relies on the ability to perceive the presence of behavioural nonalignment deriving from a noncongruent behavioural response between partners, and on the ability to adjust the noncongruent response to achieve alignment. It has indeed been shown that when alignment is achieved, brain areas associated with positive reward are

Fig. 1.1  Feedback loop involved in behavioural alignment, following the general principle of optimization

1.1  A Subset of Interpersonal Behavioural Alignment

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activated (Palagi and Cordoni 2020). This activation is potentially related to the sense of satisfaction that occurs when people experience connectedness (Shamay-­ Tsoory et al. 2019). The positive feelings provided by this activation make the interaction beneficial (Palagi and Cordoni 2020). Interpersonal behavioural alignment must be distinguished from interpersonal coordination. Both refer to the production of similar behaviours at approximately or exactly the same time (Chartrand and Lakin 2013). However, interpersonal behavioural alignment is automatic, spontaneous, and mostly unconscious, unlike the interpersonal coordination mechanism which is intentional and conscious. Interpersonal coordination is observed during a joint action for example (Rauchbauer and Grosbras 2020). According to Sebanz et al. (2006), joint action is a social interaction in which multiple individuals coordinate their actions across space and time to achieve a specific goal, when two individuals coordinate their movements to move a heavy object for example (Rinott and Tractinsky 2021). Interpersonal behavioural alignment encompasses mimicry, behavioural synchronization and automatic imitation (Rauchbauer and Grosbras 2020). Although the boundaries between these different processes are blurred, behavioural synchronization must be distinguished from the other mechanisms leading to behavioural alignment (Rinott and Tractinsky 2021). Mimicry is a form of interpersonal alignment in which a person unwittingly copies the behaviours of another person with a slight temporal delay of 3 to 6 s (Chartrand and Bargh 1999; Chartrand and Lakin 2013; Fuhrmann et al. 2015). Automatic imitation involves individuals performing the same action to achieve the same goal (Fuhrmann et al. 2015). Finally, behavioural synchronization occurs when two or more individuals perform the same behavioural patterns in a very short period of time (almost no delay) and in the same space (Rinott and Tractinsky 2021). Behavioural alignment is present in many social species; in canids for instance, interacting partners exhibiting playful signals during a play session are very quickly copied by their conspecifics (Palagi et  al. 2015). Behavioural synchronization is a spontaneous congruent and rapid response (Palagi and Cordoni 2020) and is divided into three components: temporal synchronization, activity synchronization, and location synchronization. Temporal synchronization is defined as switching actions at the same time (i.e., in less than 3 s of delay), regardless of the nature of the actions (Duranton and Gaunet 2015, 2016), enabling the smoothness of the interaction (Bernieri et al. 1994). For instance, the synchronization of dogs’ walk on that of humans has been deemed to take place in less than 3 s (Duranton et al. 2017a, b). Activity synchronization is defined as performing the same action at the same time (Chartrand and Bargh 1999; Duranton and Gaunet 2015). Location synchronization corresponds to the fact of being spatially close during the interaction (Duranton and Gaunet 2016). From now on, when we talk about behavioural synchronization, we will refer to the definition with the three components explained above. When we talk about motor or social synchronization, we will refer to the temporal and activity components of behavioural synchronization.

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1  Introduction of Behavioural Synchronization

Behavioural synchronization and mimicry are ubiquitous behaviours in everyday social life. Both consist in performing identical or very similar behaviours, but distinctions between these two behavioural processes exist (Lakin 2013). They differ in their temporal dimension, with the importance of timing being a central aspect of behavioural synchronization, but not of mimicry (Shamay-Tsoory et  al. 2019). Indeed, behavioural synchronization refers to the temporally matched behaviours of interacting partners, contrarily to mimicry based on a temporal gap between partners’ actions (Lakin 2013). Synchronized individuals mutually adjust to each other with acute temporal accuracy (Sebanz et al. 2006). Mimicry involves a temporal lag of at least 3 s (Lakin 2013; Rinott and Tractinsky 2021). For example, an individual exhibits mimicry if he or she follows the same path previously chosen by antother individual, or copies another individual’s facial expression with a time delay (Lakin 2013). Behavioural synchronization is also distinguished from mimicry by its rhythmic nature. Indeed, behavioural synchronization concerns mostly rhythmic behaviours, two individuals having the same walking rhythm for example, or two people each holding a pendulum and swinging it at the same rate (Louwerse et al. 2012). Mimicry does not necessarily concern rhythmic behaviours, but the delayed copying of a gesture, posture, or attitude (Lakin 2013). According to Shamay-­ Tsoory et al. (2019), the different forms of social alignment are underpinned by a common set of brain networks that promote positive interactions between individuals (Shamay-Tsoory et al. 2019). Thus, behavioural synchronization, automatic imitation, and mimicry rely on the same neurocognitive mechanisms and are modulated by the same social factors.

1.2 A Form of Social Entrainment Behavioural synchronization occurs in various contexts; two or more social agents are systematically involved. A classification of different types of behavioural synchronization according to types of social entrainment has thus been proposed (Cacioppo et al. 2014). Orchestral entrainment involves individuals synchronizing to an external rhythm; unilateral entrainment involves one person adjusting his or her behaviour to that of another in the dynamics of a leader-follower interaction; and reciprocal entrainment involves individuals mutually adjusting their behaviours to each other (Bente and Novotny 2020; Cacioppo et al. 2014; Hove and Risen 2009).

1.2.1 Definition of Entrainment Entrainment is ubiquitous in the living world, whenever at least two biological systems exhibiting periodic behaviour are coupled (Bente and Novotny 2020). The periodic behaviour can be any repetitive behaviour, such as breathing, walking gait, dance steps, etc. (Earl and Strogatz 2003). Two rhythmic behaviours that interact

1.2  A Form of Social Entrainment

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with each other eventually harmonize into a common rhythm, which is called entrainment (Clayton 2012). Social entrainment results in a spatiotemporal behavioural correspondence between individuals in relation to rhythmic cues (Fuhrmann et al. 2015). A model of coupled oscillators has been proposed to explain the behavioural synchronization resulting from inter-individual entrainment. According to this model, mutually adjusting oscillators may oscillate synchronously (Kuramoto 1975). Entrainment is much studied in music, indeed the rhythm of music can act as an external pacemaker to which dancers and musicians are entrained, and they synchronize to the same tempo (Rinott and Tractinsky 2021). Entrainment can result in synchronized, in-phase movement, as observed when two individuals walk side by side and both simultaneously advance their right foot, or in anti-phase movement, where a similar rhythm is maintained but alternated between partners, for example, when two people are walking side by side and one person advances their right foot while the other advances their left (Cirelli et al. 2014; Lakin 2013). In both cases, there is a phase lock in the periodic behaviour of the partners involved (Bente and Novotny 2020). Identical rhythm does not imply identical movement, for example, dancers can exhibit high temporal alignment, changing actions simultaneously, without exhibiting the exact same movement: one may jump while the other waves his  or her hands (Rinott and Tractinsky 2021). Interestingly, dancers performing synchronous but not identical movements later remember more information about each other than those dancing at different rhythms (Woolhouse et al. 2016). Rhythmic entrainment involves the ability to generate a rhythm and to entrain body movements to that rhythm (Brown 2022). The ability of non-human animals to entrain their body movements to an external rhythm or to their conspecifics is the subject of a growing body of behavioural and neural research (Merchant and Honing 2014; Patel et al. 2009). Dance, for example, is a social interaction involving the entrainment of body movements. It relies on the ability of individuals to synchronize with instantaneous accuracy to a shared, steady rhythm (Lameira et al. 2019). At the motor level, the ability to synchronize to a musical rhythm engages multiple neural circuits and involves a periodic motor response to complex sound sequences (Patel et al. 2009). The ability to entrain may come from the universal propensity of individuals to converge in their body movements and postures (Brown 2022). During a conversation, for example, interlocutors spontaneously adjust their posture to that of the other, and there is also convergence in facial expression and speech prosody (Brown 2022).

1.2.2 Locomotor Entrainment Almost all animal species exhibit rhythmic locomotor patterns, including bipedalism, quadrupedalism, flight movements, or swimming (Brown 2022). One theory is that the predictable sounds of locomotion, limb contact with the ground for example, may promote synchronous behaviours (Larsson 2014). In the example of

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bipedalism entrainment, a study in chimpanzees showed that when one individual initiates a bipedal locomotor movement, it quickly induces synchronization in its conspecifics’ locomotion from the first or second step (Lameira et al. 2019). This locomotor synchronization persists when individuals encounter obstacles or change their direction, and when an individual accelerated or slowed its pace, their partner adjusted their own pace with instantaneous accuracy. This locomotor entrainment is predictable and non-random, with each individual alternately acting as a pacemaker (Lameira et al. 2019).

1.2.3 Is Entrainment to a Musical Rhythm Specific to Humans? The ability of auditory-motor entrainment, i.e., the alignment of movements to a musical rhythm, seems to be at first almost specific to humans, with some exceptions in the animal world (Schachner et al. 2009). Motor entrainment on auditory stimuli has indeed been described in a few species, including sulphur-crested cockatoos, roosters, macaques, and California sea lions, all of which were raised in an anthropized environment (Brown 2022; Patel et al. 2009; Schachner et al. 2009). For example, a sulphur-crested cockatoo was able to synchronize its actions to a musical beat, spontaneously adjusting the tempo of its head movements to stay in sync with the rhythm of the music (Patel et al. 2009). It should be noted that the cockatoo’s motor sequences synchronized to the musical rhythm were not simple copies of movements typically present in its natural behavioural repertoire: these birds do not synchronize their head movements with auditory signals in their natural environment (Patel et al. 2009). This suggests the ability of entrainment to a musical rhythm in this species. Auditory-motor entrainment involves a link between auditory and motor representations, in the same way as vocal mimicry. Auditory-motor entrainment has thus been proposed as a byproduct of selection for vocal mimicry, and consequently only species capable of mimicking sounds would have the ability to entrain their movements to a musical rhythm (Schachner et al. 2009). In other words, the ability of birds to entrain to a musical rhythm would be a byproduct of their capacity for vocal imitation (Schachner et al. 2009). Nevertheless, this assumption may be questioned, as species that are less flexible in vocal imitation, such as sea lions, have also been shown to be capable of motor entrainment to an external auditory rhythm (Cook et al. 2013). Musical entrainment is thus more widespread in the animal kingdom than previously assumed.

1.2.4 Gaze Following Gaze following is the ability to detect the direction of another individual’s gaze and then look at the spatial point towards which the observed individual’s gaze is directed (Itakura 2004; Schaffer et al. 2020). Joint visual attention is gaze following

References

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in which the spatial point is a target of attention, such as an object (Itakura 2004). Gaze following is therefore a form of visual entrainment that provides the observer with information about the environment, the location of a food source or predators, for example, and more generally a source of information. This skill is highly adaptive and guides the behaviour of individuals (Schaffer et al. 2020; Shepherd 2010). In humans, gaze following appears from an early age, with this ability fully developed in 18-month-old infants (see Del Bianco et al. 2019 for a review). Gaze following has also been extensively studied in non-human animals (see Itakura 2004 and Schaffer et  al. 2020 for reviews). Gaze following between conspecifics has thus been demonstrated in great apes (Bräuer et al. 2005; Tomasello et al. 1998), monkeys (Emery et al. 1997), and domestic ungulates (Schaffer et al. 2020). The ability of animals to follow the direction of a human’s gaze has also been studied in many species. It has thus been reported that primates (Anderson et al. 1996; Itakura and Tanaka 1998; Povinelli and Eddy 1996; Tomasello et al. 1999), birds (Bugnyar et al. 2004), and some domestic animal species such as dogs (Bräuer et al. 2004; Duranton et al. 2017c; Hare et al. 2002; Met et al. 2014; Miklösi et al. 1998), cats (Pongrácz et al. 2019), and domestic ungulates (McKinley and Sambrook 2000; Schaffer et al. 2020) have the ability to visually follow human gaze.

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Chapter 2

Visuomotor Approach of Behavioural Synchronization

2.1 Visual Perception of Action Behavioural synchronization necessitates two processes to occur (Heggli et al. 2019). The first process is perceiving the behaviour of the partner, because if one does not perceive the other’s action by any modality, behavioural synchronization can’t occur (De Guzman et al. 2016). The second process is producing the actions leading to interpersonal motor synchronization, involving the motor system. In socio-cognitive neuroscience, the term action in a broad sense refers to any type of motor behaviour, and in a narrow sense action refers to goal-directed motor behaviours (Rizzolatti et  al. 2001). Humans interact with their environment in two ways (Herwig et  al. 2007). Indeed, an action is produced either in response to a stimulus in the environment, or with the goal of achieving the intended effect on the environment (see Fig. 2.1). Stimulus-based actions are related to stimulus-response learning, whereas intention-based actions are related to action-effect learning (Herwig et al. 2007). For non-human animals, to be synchronized, it is necessary to be able to perceive and discriminate the actions; for humans, the meaning of those actions might also be detected. In any case, it requires a high-performance visual system that is sensitive to action detection and able to discriminate actions quickly and accurately even under highly impoverished perceptual conditions (Bidet-Ildei et al. 2011). In animals, the ability of visual discrimination has been studied using animation sequences of light dots representing biological movements (Johansson 1973). For example, after extensive training, cats were able to discriminate a sequence of dots depicting biological motion (i.e., a cat walking) from a sequence of dot that did not (Blake 1993). Discrimination of biological versus non-biological motion has been studied in many other species such as baboons (Parron et al. 2007), chimpanzees (Tomonaga 2001), rats (MacKinnon et  al. 2010), dogs (Delanoeije et  al. 2020; Eatherington et al. 2019; Ishikawa et al. 2018; Kovács et al. 2016), pigeons (Dittrich et al. 1998), and chickens (Vallortigara et al. 2005). In dogs for example, three studies showed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0_2

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Fig. 2.1  Illustration of the two types of actions produced by an individual in interaction with his environment. (a) An action can be produced in response to an external stimulus, when an individual moves to avoid a falling object for example. (b) An action can be produced intentionally to obtain an effect on the environment, such as picking up an object in the environment

that dogs spent more time looking at a light figure representing a human biological motion in frontal view compared to a light stimulus representing a random non-­ biological motion (Delanoeije et al. 2020; Ishikawa et al. 2018; Kovács et al. 2016). According to these studies, dogs are more attentive to human biological motion compared to non-biological motion. However, in the study by Eatherington et al. (2019), dogs were not attentive to light figures representing human walkers. The authors suggest that despite dogs’ intense and long-standing exposure to human movement, they are not systematically sensitive to the two-dimensional depictions of bipedal motion cues: they need more perceptual cues to recognize human locomotion. Other paradigms, such as the two-choice discrimination task, have been used to study visual discrimination abilities in dogs (Byosiere et al. 2018; Milgram et al. 1994; Range et al. 2008), and after a training phase, dogs were able to visually discriminate stimuli based on their size, shape, or biological movement .

2.2  Ideomotor Theory

13

In humans, some brain visual regions are activated only when observing an individual performing an action and directing his/her gaze towards the target of the action (Jellema et  al. 2000). These brain regions thus combine sensitivity to the direction of gaze and to the observation of the performed action. The human visual system is thus sensitive to the identification of observed actions and to spatiotemporal cues that are specific to motor anticipation rules, to predict an action yet to occur within a predefined sequence (Bidet-Ildei et al. 2011). For example, observing the movement of grasping an object predicts its subsequent use, such as moving or throwing the object (Louis-Dam et al. 2000). Visual information is thus a critical precursor to the development of synchronous behaviours (Lakin 2013).

2.2 Ideomotor Theory 2.2.1 Correspondence Between Perception and Action Perception and action have long been considered to be underpinned by distinct and independent neural processes. The notion that action is intrinsically coupled to perception first goes back to 1890 with James’ ideomotor theory of action (see Fig. 2.2).

Fig. 2.2  Ideomotor theory: the idea of an action is the basis for its execution. The perception of an action activates the perceptual effects related to the action, and the motor representation of the action, which promotes the execution of the action. Thus, the perception of the action and the intention to perform the action share a common representational format

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This theory suggests the existence of a correspondence between the visually observed actions and the motor system, based on the assumption that the visual idea of an action serves as the basis for its initiation (Greenwald 1970; James 1890). The activation of sensory representations of the action induces the evocation of the execution of the action (James 1890). A key aspect of the theory lies in the existence of associations between action and its perceptual effects (Bunlon 2015). Action control is related to the anticipated representation of the perceptual effects of the action, thus predicting the consequences of this action (Elsner and Hommel 2001; Greenwald 1970). The overlapping brain areas supporting respectively action perception and action execution supports this theory. More precisely, the fact that some brain regions, notably the left inferior frontal cortex and the right superior parietal lobule, are activated during both the execution of a movement and the observation of that movement is an argument in favour of this theory (Gazzola and Keysers 2009; Iacoboni et al. 1999). Additional evidence bolstering this theory includes the notion that perceptual development is constrained by also motor development, also motor difficulties typically lead to visual-perceptual difficulties, and the visual perception of human movement activates brain structures known for their involvement in movement production (Bidet-Ildei et al. 2011).

2.2.2 Towards a  Motor Representation of Action Consistent with the ideomotor theory, action is coded first in terms of perceptual effects, and the perception of actions is thus the result of an interaction between the perceptual systems and the motor system (James 1890). According to this approach, the recognition of actions is not the result of simple visual learning but depends to a large extent on motor representation and even on the explicit knowledge individuals have regarding their own motor abilities (Bidet-Ildei et  al. 2011). The action-­ perception coupling automatically activates representations corresponding to the execution of the observed action (Shamay-Tsoory et al. 2019). In other words, the visual perception of an action triggers spontaneously and automatically the activation of its motor representation in the observer (Rizzolatti et al. 2001). Activation of the motor representation of the action can be related to the intention to perform the action, but can also originate from the perception of the action performed by other individuals. In humans, the term intention refers to the ‘why’ of an action (Iacoboni et al. 2005). When an individual holds an object for example, the intention of the agent performing this action is what he/she will do with the object: throw it away, put it somewhere else, give it to another individual, etc. When an individual wants to perform an action, he or she activates the representation of the effects produced by that action, this triggers the execution of the movement associated with those effects (Bunlon 2015; Koch et  al. 2004). According to the ideomotor theory, observing an action performed by others results in the observer in the activation of perceptual processes similar to those involved when he or she performs the action (Bidet-Ildei et  al. 2011; Grèzes et  al. 2003). Consequently, the

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perceived actions and the performed actions share a common representational format (Prinz 1997). It has thus been suggested that the actions of multiple individuals are represented together in the motor system as shared representations (Cracco and Brass 2018; Hommel et  al. 2001; Prinz 1997). In this sense, the correspondence between the observation of an action and the internal motor representation of this action underlies behavioural synchronization. Iacoboni et  al. (1999) tested this hypothesis by measuring brain activity via functional magnetic resonance imaging (fMRI) of human subjects observing or copying finger movements. The results showed an increase in brain activity in the left inferior frontal cortex and right superior parietal lobule when performing the movement and when observing the same movement. These brain neuroimaging results therefore support the ideomotor theory. Behavioural studies are also congruent with the ideomotor theory. A study conducted in humans showed that observing finger movements affected the execution of these movements, proving the influence of perception on motor response (Brass et  al. 2001). Another study showed that the reaction time to catch an object was shorter when a picture showing the purpose of the action was presented to participants beforehand (Craighero et al. 2002). The execution of an action is thus facilitated when it is preceded by a visual perception of that action, which is mediated by the activation of the internal representation of the action. This activation of the motor representation of the action following its perception is present from an early age, as suggested by Sommerville et al. (2005) and Hauf et al. (2007), who showed a strong link between the perception of an action and its execution in infants aged 9–12 months.

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Milgram NW, Head E, Weiner E, Thomas E (1994) Cognitive functions and aging in the dog: acquisition of nonspatial visual tasks. Behav Neurosci 108:57–68. https://doi. org/10.1037/0735-­7044.108.1.57 Parron C, Deruelle C, Fagot J (2007) Processing of biological motion point-light displays by baboons (Papio papio). J Exp Psychol Anim Behav Process 33:381–391. https://doi. org/10.1037/0097-­7403.33.4.381 Prinz W (1997) Perception and action planning. Eur J Cogn Psychol 9:129–154. https://doi. org/10.1080/713752551 Range F, Aust U, Steurer M, Huber L (2008) Visual categorization of natural stimuli by domestic dogs. Anim Cogn 11:339–347. https://doi.org/10.1007/s10071-­007-­0123-­2 Rizzolatti G, Fogassi L, Gallese V (2001) Neurophysiological mechanisms underlying the understanding and imitation of action. Nat Rev Neurosci 2:661–670. https://doi.org/10.1038/35090060 Shamay-Tsoory SG, Saporta N, Marton-Alper IZ, Gvirts HZ (2019) Herding brains: a core neural mechanism for social alignment. Trends Cogn Sci 23:174–186. https://doi.org/10.1016/j. tics.2019.01.002 Sommerville JA, Woodward AL, Needham A (2005) Action experience alters 3-month-old infants’ perception of others’ actions. Cognition 96:B1–B11. https://doi.org/10.1016/j. cognition.2004.07.004 Tomonaga M (2001) Visual search for biological motion patterns in chimpanzees (Pan troglodytes). Psychologia 44:46–59 Vallortigara G, Regolin L, Marconato F (2005) Visually inexperienced chicks exhibit spontaneous preference for biological motion patterns. PLoS Biol 3:e208. https://doi.org/10.1371/journal. pbio.0030208

Chapter 3

Neurophysiological and Cognitive Bases of Behavioural Synchronization

3.1 Neural Circuits of the Perception of Actions The visual perception of movement involves not only the areas responsible for visual processing, but also motor areas, previously considered to be involved only in the production of action (Di Pellegrino et  al. 1992; Rizzolatti and Craighero 2004). Specifically, the perception of an action relies on specific neural networks including brain structures known to play a role in the organization of motor skills (Blakemore and Frith 2005; Rizzolatti and Sinigaglia 2007). According to neuroimaging studies, these structures include areas common to movement production and perception as well as areas specific to movement perception, specifically the superior temporal sulcus, cerebellum, lingual gyrus, amygdala, right temporal cortex, inferior parietal lobule, and the extra-striate body area (Grossman and Blake 2002; Peuskens et  al. 2005). Observing the actions performed by others systematically engages this set of brain regions to a greater extent than observing other categories of visual movements such as non-biological motion. This set of bilateral brain areas consistently engaged during observation of others’ action is the Action Observation Network (AON) (Grosbras et al. 2012). Neurophysiological studies conducted in humans have confirmed the involvement of these motor regions in the visual perception of human movements, suggesting that the perception and execution of human movements are the result of a functional interaction between perception and motor skills (Bidet-Ildei et al. 2011). This interaction relies on neural networks connecting primary sensory areas, fronto-parietal areas, and premotor cortex. It implies the existence of neurons engaged during the observation and execution of motor acts: the mirror neurons (Caspers et al. 2010; Grosbras et al. 2012).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0_3

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3.2 Neural Circuits Related to Temporal Processes The temporal aspect is a key component of behavioural synchronization, as it involves a very close temporal coupling between the actions of the interacting partners (Rauchbauer and Grosbras 2020). Thus, brain mechanisms related to temporal processing might also be involved in detecting event timing and rhythmic properties of actions (Coull et al. 2011; Merchant et al. 2013). A study in macaques showed the existence of two groups of neurons responsible for quantifying intentional timing (Merchant et al. 2013). There is a close relationship between these brain circuits and the neural networks involved in motor processes (Rauchbauer and Grosbras 2020). In particular, the striatum and supplementary motor area may be involved in behavioural synchronization, as studies indicate that these areas are involved in time perception as well as the production of timed or coordinated motor actions (Coull et al. 2011; Merchant et al. 2013).

3.3 Mirror Neurons Mirror neurons are involved in both the organization of perceptual and motor acts, allowing the generation of real or imagined actions (Bidet-Ildei et al. 2011). The discovery of mirror neurons has provided insight into the physiological process underlying the ideomotor theory (Lakin 2013). Interpersonal motor synchronization is therefore not simply underpinned by perceptual coding of others’ behaviours, but by motor coding through the activation of mirror neurons (Fuhrmann et al. 2015).

3.3.1 Mirror Neurons in Non-human Primates Mirror neurons were originally discovered in area F5 of premotor cortex in non-­ human primates by single-cell recordings (di Pellegrino et al. 1992; Gallese et al. 1996). In monkeys, the areas of the superior temporal sulcus and F5 contain neurons that respond to the observation of biological actions. These two areas are linked to the inferior parietal lobule, called Brodmann’s area (Rizzolatti et  al. 2001). Subsequently, neuroimaging studies have found mirror neurons in other brain areas, including the inferior parietal lobule, primary motor cortex, and dorsal premotor cortex (Bonini 2017; Dushanova and Donoghue 2010; Tkach et al. 2007). Mirror neurons were first described as visuomotor neurons that are activated when performing an object-directed action, like grasping or holding, but also when observing another individual, conspecific or human, performing the same class of actions (Rauchbauer and Grosbras 2020). As an illustration (see Fig.  3.1), these neurons are active when a monkey grasps an apple but also when the monkey observes a hand grasping the apple (Rizzolatti et al. 1996). It should be noted that

3.3  Mirror Neurons

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Fig. 3.1  Activation of mirror neurons when performing an action, holding an apple for example, and when observing this action performed by another individual

mirror neurons are activated even in the absence of a complete visual description of the action, and 80% of mirror neurons become active during the execution of the action in the dark (Bonini et al. 2010). For example, a study in monkeys showed that mirror neurons are activated when the subject saw the entire action (grasping or holding an object), but also when part of the action was hidden by a screen (seeing the object and the hand but not seeing the entire interaction) (Filion et al. 1996). The first studies indicated that mirror neurons responded selectively to the actions of others and not to visually presented objects, tool actions, or nonbiological movements, regardless of the distance between the observed action and the observer (Di Pellegrino et al. 1992; Gallese et al. 1996; Rizzolatti et al. 1996). Indeed, the early findings were that these neurons were triggered only when performing or observing goal-directed actions towards an object or a third party (Rizzolatti and Craighero 2004; Rizzolatti et al. 2001). According to these studies, the activation of mirror neurons would therefore require an interaction between a biological effector and an object, and mirror neurons would not respond to the observation of an agent mimicking an action (without the object present) or to the observation of the object alone (without mimicking). Rizzolatti et al. (2001) also found that the type of object did not influence the response of the mirror neurons, they discharged in the same way for the grasping of a piece of food or a solid object (Rizzolatti and Craighero 2004). Subsequent studies have extended the criteria of mirror neurons activation. In fact, neurons in area F5 respond to both observed actions and visually presented objects (Bonini et al. 2014), to actions performed with a tool (Rochat et al. 2010), and to non-biological objects in motion (Albertini et al. 2021). F5 neurons are categorized according to their properties. Thirty per cent of mirror neurons are strictly

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congruent: they show a strict correspondence between the visual and motor coding of an action and the execution of that action, so they fire during the observation and execution of the same action, holding a specific object for example. Fifteen per cent of mirror neurons are broadly congruent, they have a generalized response to the observation and execution of actions not necessarily identical but having the same goal, grasping something for example. Thirty per cent of mirror neurons are triggered during the execution of an action or during observation of another action related to that action, for example when a monkey grabs a piece of food and when an experimenter puts the piece of food in front of the monkey. These neurons are called logically related mirror neurons (di Pellegrino et al. 1992; Rizzolatti et al. 2001; Rizzolatti and Craighero 2004). Further, 25% of F5 neurons respond when observing the actions of others but not when performing the action, they are called mirror-like neurons (Gallese et al. 1996).

3.3.2 Mirror Neurons in Humans Neuroimaging data have provided evidence for the existence of mirror neurons in humans similar to those present in monkeys (Gallese 1998). The first study in humans was performed by measuring the electrical activity of the brain, namely by electroencephalography (EEG) (Gastaut and Bert 1954). It revealed two resting rhythms located in the alpha frequency range (8–13 Hz): a posterior alpha rhythm, present only when the sensory systems are not activated, and a central mu rhythm, present only during motor rest. The authors have shown that the mu rhythm is not only inhibited during active movement, but also when an action is observed (Gastaut and Bert 1954). Another EEG study showed a significant decrease in oscillations in the alpha frequency band during observation or execution of finger movements (Cochin et al. 1999). Other studies have measured the magnetic activity of the brain (magnetoencephalography or MEG) and have shown a decrease in the central mu rhythm in sensorimotor areas when manipulating an object and when observing the manipulation of the same object, suggesting that the mu rhythm reflects the activity of motor and mirror neurons (Bimbi et al. 2018; Salenius et al. 1997; Virji-Babul et al. 2008). There is thus a very strong similarity between the rhythms evoked over the motor cortex for both action execution and observation (Rauchbauer et al. 2020). These neurophysiological indicators are now consensual to reflect the activity of mirror neurons. Transcranial magnetic stimulation (TMS) studies showed that observing an action automatically activates the premotor cortex, with a direct somatotopic correspondence between visual stimuli of body parts and corresponding movements. In fact, the increase in motor evoked potentials concerned only the group of muscles usually engaged during the execution of the observed movement (Baldissera et al. 2001; Fadiga et al. 1995; Gangitano et al. 2001; Maeda et al. 2002; Strafella and Paus 2000). These neurophysiological experiments thus provided evidence of the link between action observation and the activation of cortical areas involved in

3.4  Mirror Neurons and Motor Resonance

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motor control in humans, since the same motor circuits are recruited during the execution or observation of an action (Gallese 1998; Rizzolatti et  al. 2001). Moreover, TMS studies have shown that the temporal course of cortical activation during the observation of an action follows that of the execution of the action, whether for goal-directed actions, also called transitive movements or for non-goal-­ directed actions, also called intransitive movements (Fadiga et  al. 1995; Maeda et al. 2002). The activation of human mirror neurons is specific to the observation of biological movements (Grèzes et al. 2001). Only biomechanically possible actions activate the mirror neurons, and observation of actions performed by a biological agent elicits much greater activity than when performed by an artificial agent (Perani et al. 2001; Tai et al. 2004). Research then focused on the anatomical location of the mirror neurons in the human brain. In a fMRI study, human subjects were asked to either observe an action, imitate an action after observing it, or perform a movement after observing an action (Iacoboni et al. 1999). The results showed that when imitating an action, activation of three cortical areas, the left inferior frontal cortex, the right anterior parietal region, and the right parietal operculum, was higher than when performing a movement different from the observed action. The right parietal operculum was not activated in the observation alone task. Other brain neuroimaging studies additionally showed that observing an action resulted in activation in the ventral and dorsal premotor cortex, superior temporal sulcus, inferior and superior parietal lobule, supplementary motor area, medial temporal lobe of the anterior part of Broca’s area (Di Pellegrino et al. 1992; Gallese et al. 1996; Gallese 1998; Molenberghs et al. 2012; Mukamel et al. 2010). These cortical areas are thus involved in the mirror system, coding for both observed and performed motor actions. A more extensive mirror system involves additional regions, such as the primary somatosensory cortex, primary motor cortex, and middle frontal cortex (Pineda 2008).

3.4 Mirror Neurons and Motor Resonance According to the principle of action-perception coupling, the perception of an action implies a correspondence between the visual representation of the action and its motor representation, which involves mirror neurons (Rauchbauer and Grosbras 2020; Rizzolatti and Craighero 2004). The discovery of mirror neurons and motor activity during action observation in primates are thus generally considered to support motor theories of action-perception coupling. When an individual observes an action performed by another person, the motor representation of this action is activated in the brain of the observer; the neurons of the premotor cortex that represent this action are activated (i.e., motor resonance, Rizzolatti and Craighero 2004). Motor resonance thus reflects the activation of the motor system during the observation of an action (Uithol et al. 2011). This notion is adopted from physics and is used to describe at least two systems oscillating at the same frequency and in the same phase (Viviani 2002). In the neurocognitive domain, it is claimed that the

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motor system resonates in the sense that premotor neurons fire at the same frequency and in the same phase as other neurons (Uithol et al. 2011). There is now strong evidence from neuroscience that motor resonance involves the translation of observed actions into motor programs (Brass and Heyes 2005). Through motor resonance, the neurons responsible for the motor representation of the action are activated simultaneously in the observer and the individual performing the action. This simultaneous activation leads to two situations: either the perceived action is interpreted, in this sense motor resonance allows to recognize the perceived action or its outcome, or the perceived action is immediately and automatically reproduced by the observer (Bidet-Ildei et  al. 2011). Motor resonance is thus the sensorimotor basis of imitative behaviours (Fabbri-Destro and Rizzolatti 2008).

3.5 Mirror Neurons and Motor Experiences 3.5.1 Origin and Development of Mirror Neurons Sensorimotor experience plays a key role in the adaptation of brain structures dedicated to the behaviour of others (Bonini et al. 2022). However, the ontogenetic origin of mirror neurons is still debated, and two hypotheses are proposed: either mirror neurons are the product of neurophysiological mechanisms such as sensorimotor associative learning, or they are innate (Bonini et al. 2022). –– First hypothesis: mirror neurons are developed through sensorimotor experience The ideomotor theory implies a causal link between the characteristics of an observed action and its execution (Bunlon 2015). When individuals perform an action, they learn the effects of that action and in this way a repertoire of action-­ effect associations is created (Bunlon 2015). Thus, according to the action-­perception coupling suggested in the ideomotor theory, to be able to synchronize to an observed action, individuals have to map it to their own motor repertoire and to link it to their representation of the effect of this action. The individuals’ perceptions depend on their internal experiential and functional representations (Coello and DelevoyeTurrell 2007). According to Proffitt (2006), ‘Perceptions are embodied; they connect the body and goals to opportunities and costs of action in the environment’. In this sense, perceptual experience and control of motor actions are limited by the individual’s motor representations, and an action is performed only if the individual has the functional capabilities to perform it (Coello and Delevoye-­Turrell 2007). Mirror neurons have been suggested to underly the ability to directly map observed actions to one’s own motor repertoire (Gallese et  al. 2004). The perception of a behaviour automatically activates the observer’s motor representations of this behaviour (Rauchbauer and Grosbras 2020), this mechanism is therefore modulated by the observer’s motor repertoire (Thornton and Knoblich 2006). Consequently, motor resonance occurs during the observation of motor acts that are part of the observer’s behavioural repertoire, as the activation of mirror neurons following the

3.5  Mirror Neurons and Motor Experiences

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perception of the action is limited to actions already present in the observer’s behavioural repertoire (Bouquet et al. 2011; Stevens et al. 2000). In humans, the motor representation of biological actions seems to start developing after the first months of life and the efficiency of the activation of mirror neurons increases during child development. Indeed, 4-month-old infants show the same motor cortex response for actions performed by an artificial agent as for those performed by a human (Grossmann et  al. 2013), and motor engagement during action observation in 6-month-olds was greater for live stimuli (Shimada and Hiraki 2006). Moreover, a functional near-infrared spectroscopy (fNIRS) study conducted on 6–7-month-old infants showed an activation of the premotor and sensorimotor cortices during both observation and execution of action, but this activation is less pronounced compared to that observed in 9-month-olds (Shimada and Hiraki 2006). In another study, 14-month-old infants were in the presence of an adult experimenter who performed a head gesture in a way that allowed the infants to match or not match that movement to their own motor repertoire (Paulus et al. 2011). More precisely, the adult model touched a light with her head either having her hands free, hidden in a blanket, raised or holding a ball. Head touch with raised hands is not part of infants’ motor repertoire, as they can’t raise their arms and lean their head forward without being held. The number of infants who imitated the head action differed between conditions: infants reproduced the experimenter’s gesture only when the action could match the infants’ own motor repertoire. In another study, EEG in 3-year-old infants showed a specific decrease in alpha-band oscillations (7.5–12.5 Hz) during the performance or observation of graphic gestures (Fecteau et al. 2004). Thus, the coupling between the systems of perception and action execution in humans are observable several months after birth (Marshall et al. 2010). This has been interpreted as an early but not innate functionality of mirror neurons (Cochin et al. 2001; Fecteau et  al. 2004; Lepage and Théoret 2006). Furthermore, the level of motor development exerts an influence on the level of perceptual development, and mechanisms related to mirror neurons are experience-dependent (Bidet-Ildei et al. 2011; Marshall and Meltzoff 2014). Activation of mirror neurons is indeed less pronounced in 7- to 10-year-olds compared to adults (Morales et  al. 2019). Further, there is evidence of the involvement of mirror neurons in the recognition of actions from one’s own motor repertoire at the interspecific level. A fMRI study compared human subjects observing humans or animals (monkey and dog) performing oral actions that are both within and outside the human observer’s motor repertoire (Buccino et al. 2004). The results show very small differences in the activation of mirror neurons for actions belonging to the observer’s motor repertoire (e.g., biting), suggesting that they were mapped onto the observer’s own motor system. In contrast, actions that were not part of the observer’s motor repertoire and thus could not be reproduced, such as a human perceiving a dog’s bark, produced no activation in the frontal lobe. It suggests that these actions were not recognized through motor processes but rather through visual processes (Buccino et al. 2004). Another study has shown that one- and two-month-old puppies are able to synchronize their locomotion with both familiar and unfamiliar humans (Duranton et al. 2022). Moreover, this locomotor synchronization is modulated by motor experiences: one-month-old

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puppies have less developed motor cortex and weaker synchronization with humans than 2-month-old puppies; and by perceptual experiences: two-month-old puppies have interacted more with humans than one-month-old puppies. These findings suggest the existence of an interspecific motor resonance between dogs and humans, which appears at an early age in dogs and is modulated by sensorimotor experiences. –– Second hypothesis: mirror neurons are innate and modulated by experience The alternative hypothesis states that mirror neurons are an evolutionary adaptation for action understanding. According to this hypothesis, mirror neurons are innate and are facilitated through motor experience (Del Giudice et al. 2009). Thus, motor experience may not always be necessary to activate the motor representation of an action. For example, a study aimed to assess perception of biological motion in observers with early restrictions in body movement (Pavlova 2003). Point-light walking figures were presented to participants who differed in their locomotion ability, ranging from normal ability to complete walking disability. The participants had to judge whether they recognized a biological movement or not. The results of this study showed that subjects born with locomotor disabilities were capable of perceptual discrimination of locomotor movements, even though this capacity was weaker than that present in healthy subjects. According to this study, motor experience is not always essential for action identification, suggesting the existence of compensatory mechanisms to compensate for the lack of contribution of the motor system in action-perception coupling. For example, it is possible that subjects with motor pathology develop representations of actions solely based on their visual experience (Bidet-Ildei et al. 2011). In two other studies, EEG brain responses were recorded in 4- to 16-month-old infants during the observation of actions within (crawling or hand reaching) or without (walking) their motor repertoire (Van elk et al. 2008; Virji-Babul et al. 2012). In both studies, results showed that infants had motor resonance to all types of actions in the sensorimotor brain regions. It suggests that the infants may have a basic, experience-independent sensorimotor mechanism optimized to perceive all human actions that is modulated by experience.

3.5.2 Influence of Motor Expertise on the Activation of Mirror Neurons Matching the actions of others with one’s own motor repertoire facilitates the performance of a motor act during observation (Paulus et al. 2011). The involvement of mirror neurons therefore depends in part on a link between sensory inputs and previous motor experiences (Bidet-Ildei et al. 2011). Specifically, by repeatedly performing an action, the motor representation and sensory consequences of the action become refined and coupled, as suggested by the associative sequence learning theory (Roberts et al. 2016). According to this theory, the link between visual and motor representations of action is established by the correlated experience of observing and performing the same action, and the development of mirror neurons

3.6  From Motor Resonance to Motor Contagion

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depends on this link (Heyes 2001, 2011). In this sense, the activation of mirror neurons is modulated by the level of motor expertise of the observer. The role of motor expertise in mirror neuron activation has been investigated in studies comparing motor resonance between experts and non-experts and in studies showing that motor learning affects perception (Casile and Giese 2006; Catmur et al. 2007; Marshall et al. 2009; Press et al. 2007). For example, an fMRI study aimed to compare the brain activity of expert and naive dancers during the observation of a dance step (Calvo-Merino et al. 2005). The results show similar activity to that present during intentional motor acts, but this activity was higher for expert dancers than for naive individuals. Similar results were found when comparing expert versus amateur athletes (Gray et al. 2007).

3.6 From Motor Resonance to Motor Contagion Action-related sensory input not only evokes neural activity in motor pathways, but also implicitly affects motor behaviour. Indeed, we have seen earlier that observing an action activates the neurons coding for the execution of this action, and it makes the observer more likely to perform the action (Chartrand and Bargh 1999; Dijksterhuis and Bargh 2001; Iacoboni et  al. 1999). When the observation of an action leads to the immediate reproduction of that action by the observer, it is referred to as motor contagion (Roberts et al. 2016). In other words, motor contagion corresponds to the execution of a motor movement following the observation of that same movement (see Fig.  3.2), so the behaviour is propagated from one individual to another (Blakemore and Frith 2005). Motor contagion is the product of the mirror system, through the simultaneous activation of motor representations associated with the execution of observed movements (Blakemore and Frith 2005). It concerns represented and memorized behaviours or actions. For some authors, motor contagion does not only concern goal-directed actions, but it is more general and corresponds to the immediate reproduction of perceived behaviours, even if they are not directed towards a goal (Blakemore and Frith 2005). In this sense, behavioural synchronization is considered to be directly related to motor contagion: when an individual observes an action, the motor representation of this action is automatically activated through action-perception coupling, the motor neurons related to this action are activated through motor resonance, and the individual performs the action through motor contagion if it is not inhibited. Indeed, motor resonance is required to achieve motor contagion, if it is inhibited due to willingness of the observer or interferes with the activation of other representations, motor contagion cannot take place or may be affected (Knuf et al. 2001; Wilson and Knoblich 2005). When motor contagion occurs, the execution of the simultaneous action by the observer and the person initially performing the action leads to behavioural synchronization (Takeuchi et al. 2018; Uithol et al. 2011). Note that motor contagion is also referred as visuo-motor contagion, social contagion, or social influence.

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Fig. 3.2  Illustration of the concepts of motor resonance, motor contagion, and behavioural synchronization

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Dijksterhuis A, Bargh JA (2001) The perception-behavior expressway: automatic effects of social perception on social behavior. In: Advances in experimental social psychology. Elsevier, Amsterdam, pp 1–40 Duranton C, Courby-Betremieux C, Gaunet F (2022) One- and two-month-old dog puppies exhibit behavioural synchronization with humans independently of familiarity. Animals (Basel) 12:3356. https://doi.org/10.3390/ani12233356 Dushanova J, Donoghue J (2010) Neurons in primary motor cortex engaged during action observation. Eur J Neurosci 31:386–398. https://doi.org/10.1111/j.1460-­9568.2009.07067.x Fabbri-Destro M, Rizzolatti G (2008) Mirror neurons and mirror systems in monkeys and humans. Physiology 23:171–179. https://doi.org/10.1152/physiol.00004.2008 Fadiga L, Fogassi L, Pavesi G, Rizzolatti G (1995) Motor facilitation during action observation: a magnetic stimulation study. J Neurophysiol 73:2608–2611. https://doi.org/10.1152/ jn.1995.73.6.2608 Fecteau S, Carmant L, Tremblay C et  al (2004) A motor resonance mechanism in children? Evidence from subdural electrodes in a 36-month-old child. Neuroreport 15:2625–2627. https://doi.org/10.1097/00001756-­200412030-­00013 Filion CM, Washburn DA, Gulledge JP (1996) Can monkeys (Macaca mulatta) represent invisible displacement? J Comp Psychol 110:386–395. https://doi.org/10.1037/0735-­7036.110.4.386 Fuhrmann D, Ravignani A, Marshall-Pescini S, Whiten A (2015) Synchrony and motor mimicking in chimpanzee observational learning. Sci Rep 4:5283. https://doi.org/10.1038/srep05283 Gallese V (1998) Mirror neurons and the simulation theory of mind-reading. Trends Cogn Sci 2:493–501. https://doi.org/10.1016/S1364-­6613(98)01262-­5 Gallese V, Fadiga L, Fogassi L, Rizzolatti G (1996) Action recognition in the premotor cortex. Brain 119:593–609. https://doi.org/10.1093/brain/119.2.593 Gallese V, Keysers C, Rizzolatti G (2004) A unifying view of the basis of social cognition. Trends Cogn Sci 8:396–403. https://doi.org/10.1016/j.tics.2004.07.002 Gangitano M, Mottaghy FM, Pascual-Leone A (2001) Phase-specific modulation of cortical motor output during movement observation. Neuroreport 12:1489–1492. https://doi. org/10.1097/00001756-­200105250-­00038 Gastaut HJ, Bert J (1954) EEG changes during cinematographic presentation (moving picture activation of the EEG). Electroencephalogr Clin Neurophysiol 6:433–444. https://doi. org/10.1016/0013-­4694(54)90058-­9 Giudice MD, Manera V, Keysers C (2009) Programmed to learn? The ontogeny of mirror neurons. Dev Sci 12:350–363. https://doi.org/10.1111/j.1467-­7687.2008.00783.x Gray R, Beilock SL, Carr TH (2007) “As soon as the bat met the ball, I knew it was gone”: outcome prediction, hindsight bias, and the representation and control of action in expert and novice baseball players. Psychon Bull Rev 14:669–675. https://doi.org/10.3758/BF03196819 Grèzes J, Fonlupt P, Bertenthal B et al (2001) Does perception of biological motion rely on specific brain regions? NeuroImage 13:775–785. https://doi.org/10.1006/nimg.2000.0740 Grosbras M-H, Beaton S, Eickhoff SB (2012) Brain regions involved in human movement perception: a quantitative voxel-based meta-analysis. Hum Brain Mapp 33:431–454. https://doi. org/10.1002/hbm.21222 Grossman ED, Blake R (2002) Brain areas active during visual perception of biological motion. Neuron 35:1167–1175. https://doi.org/10.1016/S0896-­6273(02)00897-­8 Grossmann T, Cross ES, Ticini LF, Daum MM (2013) Action observation in the infant brain: the role of body form and motion. Soc Neurosci 8:22–30. https://doi.org/10.1080/1747091 9.2012.696077 Heyes C (2001) Causes and consequences of imitation. Trends Cogn Sci 5:253–261. https://doi. org/10.1016/S1364-­6613(00)01661-­2 Heyes C (2011) Automatic imitation. Psychol Bull 137:463–483. https://doi.org/10.1037/a0022288 Iacoboni M, Woods RP, Brass M et al (1999) Cortical mechanisms of human imitation. Science 286:2526–2528. https://doi.org/10.1126/science.286.5449.2526

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Rizzolatti G, Sinigaglia C (2007) Mirror neurons and motor intentionality. Funct Neurol 22:205–210 Rizzolatti G, Fadiga L, Gallese V, Fogassi L (1996) Premotor cortex and the recognition of motor actions. Cogn Brain Res 3:131–141. https://doi.org/10.1016/0926-­6410(95)00038-­0 Rizzolatti G, Fogassi L, Gallese V (2001) Neurophysiological mechanisms underlying the understanding and imitation of action. Nat Rev Neurosci 2:661–670. https://doi. org/10.1038/35090060 Roberts JW, Katayama O, Lung T et  al (2016) The modulation of motor contagion by intrapersonal sensorimotor experience. Neurosci Lett 624:42–46. https://doi.org/10.1016/j. neulet.2016.04.063 Rochat MJ, Caruana F, Jezzini A et  al (2010) Responses of mirror neurons in area F5 to hand and tool grasping observation. Exp Brain Res 204:605–616. https://doi.org/10.1007/ s00221-­010-­2329-­9 Salenius S, Schnitzler A, Salmelin R et  al (1997) Modulation of human cortical rolandic rhythms during natural sensorimotor tasks. NeuroImage 5:221–228. https://doi.org/10.1006/ nimg.1997.0261 Shimada S, Hiraki K (2006) Infant’s brain responses to live and televised action. NeuroImage 32:930–939. https://doi.org/10.1016/j.neuroimage.2006.03.044 Stevens JA, Fonlupt P, Shiffrar M, Decety J (2000) New aspects of motion perception: selective neural encoding of apparent human movements. Neuroreport 11:109–115. https://doi. org/10.1097/00001756-­200001170-­00022 Strafella AP, Paus T (2000) Modulation of cortical excitability during action observation: a transcranial magnetic stimulation study. Neuroreport 11:2289–2292. https://doi. org/10.1097/00001756-­200007140-­00044 Tai YF, Scherfler C, Brooks DJ et al (2004) The human premotor cortex is “mirror” only for biological actions. Curr Biol 14:117–120. https://doi.org/10.1016/j.cub.2004.01.005 Takeuchi T, Ikudome S, Unenaka S et  al (2018) The inhibition of motor contagion induced by action observation. PLoS One 13:e0205725. https://doi.org/10.1371/journal.pone.0205725 Thornton IM, Knoblich G (2006) Action perception: seeing the world through a moving body. Curr Biol 16:R27–R29. https://doi.org/10.1016/j.cub.2005.12.006 Tkach D, Reimer J, Hatsopoulos NG (2007) Congruent activity during action and action observation in motor cortex. J Neurosci 27:13241–13250. https://doi.org/10.1523/ JNEUROSCI.2895-­07.2007 Uithol S, van Rooij I, Bekkering H, Haselager P (2011) Understanding motor resonance. Soc Neurosci 6:388–397. https://doi.org/10.1080/17470919.2011.559129 Van Elk M, Van Schie HT, Hunnius S et  al (2008) You’ll never crawl alone: neurophysiological evidence for experience-dependent motor resonance in infancy. NeuroImage 43:808–814. https://doi.org/10.1016/j.neuroimage.2008.07.057 Virji-Babul N, Moiseev A, Cheung T et al (2008) Changes in mu rhythm during action observation and execution in adults with down syndrome: implications for action representation. Neurosci Lett 436:177–180. https://doi.org/10.1016/j.neulet.2008.03.022 Virji-Babul N, Rose A, Moiseeva N, Makan N (2012) Neural correlates of action understanding in infants: influence of motor experience. Brain Behav 2:237–242. https://doi.org/10.1002/brb3.50 Viviani P (2002) Motor competence in the perception of dynamic events: a tutorial. In: Prinz W, Hommel B (eds) Common mechanisms in perception and action: attention and performance XIX. Oxford University Press, Oxford, pp 406–442 Wilson M, Knoblich G (2005) The case for motor involvement in perceiving conspecifics. Psychol Bull 131:460–473. https://doi.org/10.1037/0033-­2909.131.3.460

Chapter 4

Cognitive Properties of Behavioural Synchronization

4.1 Motor Interference Through motor contagion, a subject is led to perform the observed action. But what if the observer has to perform another action than the one observed? A crucial discovery in cognitive neuroscience was that observing an agent performing an action interferes with the execution of another action (Blakemore and Frith 2005; Bouquet et al. 2007, 2011; Capa et al. 2011; Kilner et al. 2003). Several studies have indeed shown that the execution of an arm movement is disrupted by the simultaneous observation of a person performing an incongruent arm movement (Bouquet et al. 2007; Kilner et al. 2003; Marshall et al. 2010). Performing a movement while perceiving a different movement thus requires inhibition of the perceived movement to perform the target movement (Rauchbauer and Grosbras 2020). The perception of the movement activates the observer’s motor representation of that movement, which interferes with the concurrent execution of the performed movement in terms of timing and pattern, such as the direction of the performed movement (Blakemore and Frith 2005; Kilner et al. 2003). Motor responses are thus facilitated by congruent observed actions and hindered by incongruent observed actions (Brass et  al. 2001; Cracco and Brass 2018; Cracco et al. 2019). Neurophysiological studies have shown this interference between the observation of an action and the preparation and execution of a different action, supporting the action-perception coupling and the ideomotor theory (Blakemore and Frith 2005; Bouquet et al. 2007; Brass et al. 2000, 2001; Capa et al. 2011; Kilner et al. 2003; Roberts et al. 2016). Motor interference arises from the activation of the mirror system, so it is sensitive to the prior sensorimotor experience of the observed action. In an experiment, two groups of participants observed the same incongruent movement, but only one of the two groups had previously received visuomotor practice of the incongruent action. Motor interference induced by action observation was significantly stronger in terms of delay of execution among individuals with short-term visuomotor experience of the observed action (Capa et al. 2011). In another experiment, participants © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0_4

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were trained to perform an alternative action to the one they were observing, closing their hand in response to observing hands opening, for example, and motor interference was inhibited by this short-term training (Roberts et al. 2016). These results indicate that even brief visual-motor experience of an action can affect motor resonance (Marshall et al. 2010). Importantly, motor interference is modulated by properties of the observed movement, such as the existence of a goal, the trajectory, or the biological or non-­ biological nature of the movement (Bouquet et al. 2007, 2011). Indeed, motor interference is stronger when the observed movement is goal-directed than when the observed movement is not goal-directed (Bekkering et  al. 2000; Bouquet et  al. 2007, 2011; Gowen et al. 2008; Kilner et al. 2003; Stanley et al. 2007). When individuals are asked to copy an action produced by a model, they then tended to imitate the ends (goals) rather than the means (movements) (Bekkering et al. 2000). It suggests that the action-perception coupling is facilitated when observing goal-directed versus non-goal-directed actions (Bouquet et  al. 2011). The biological or non-­ biological nature of the movement also has an effect on motor interference (Kilner et al. 2007). For example, participants were asked to perform arm movements while observing either a human or a robot performing the same arm movements (congruent trials) or different movements (incongruent trials) (Kilner et al. 2003). The participants’ movements were only perturbed by the observation of the incongruent movements performed by the human agent; thus, there was no motor interference for the incongruent movements performed by the robotic arm. It has been shown that the perception of biological motion induces activity in the motor system that interferes with the execution of different motor actions (Blakemore and Frith 2005; Kilner et  al. 2003). The perception of biological movement thus depends on the activity of the motor system (Jeannerod 2001). In this sense, motor systems resonate only when perceiving biological motion (Capa et al. 2011), since the observation of non-biological motion cannot be mapped to the observer’s motor representations (Bouquet et al. 2007; Capa et al. 2011; Kilner et al. 2007). Moreover, extrastriate lesions can affect the ability to perceive non-biological movements, such as dot movements, without affecting the perception of biological movements, such as moving human agents (McLeod 1996). The brain processing of biological motion relies on specific brain areas, such as the superior temporal sulcus of the cortex in humans or the superior temporal polysensory area in macaques (Grossman et  al. 2004). However, other studies have shown motor interference for nonbiological movements. Bouquet et al. (2007) report that the execution of an arm movement was affected by the observation of a moving dot representing biological or non-­ biological motion. Another study in adults showed that observing the movements of a wooden hand induced greater motor interference when participants had previously watched an animated wooden hand video (Müller et  al. 2011). Finally, participants of another experiment had to observe a hand movement and its shadow cast either facing the same direction (congruent movements) or a different direction (incongruent movements) (Badets et  al. 2013). Next, participants were asked to copy the hand or shadow movement, and in both cases, interference was noted for incongruent trials. Thus, observing a shadow initiates motor interference (Badets et al. 2013).

4.2  Inhibition of Motor Contagion

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Motor interference is present as early as childhood. In a behavioural study, 4-year-olds were asked to move a stylus in a certain direction on a graphics tablet screen while watching a video of a model moving his or her arm in the same direction or in a different direction than the stylus the child manipulated (Marshall et al. 2010). In the incongruent movement condition, i.e., the child’s movement was interfered by the model’s movement. Observation of an incongruent action thus interferes with the performance of another action in children. This motor interference was more pronounced when the model was a child of the same age than when it was an adult (Marshall et al. 2010). The effect of the biological or non-biological nature of the movement on motor interference has also been studied in children. In a behavioural study, children  exhibited a higher degree of synchronization in their drumming movements with a human agent compared to a robot (Kirschner and Tomasello 2009). In another study, children with an average age of 5 years were asked to move a stylus on a graphics tablet while watching a video showing a previously animated or non-animated teddy bear performing biologically (human-­ actuated) or non-biologically (machine-actuated) arm movements (Saby et  al. 2011). The authors hypothesized that motor interference would be stronger when the previously animated bear was performing a biological movement. Motor interference between stylus and bear movements occurred when children observed the previously animated bear in the nonbiological motion condition and when they observed the previously inanimate bear in the biological motion condition. For the authors, these results highlight the importance of the link between prior experience with an agent and the behavioural response to that agent in another context (Saby et al. 2011).

4.2 Inhibition of Motor Contagion When observing an action, several behavioural responses are possible for the observer. The observer can copy the observed action, avoid doing it or perform a complementary or alternative action (Bonini et al. 2022). Motor synchronization to the actions of others is indeed not always adaptive and there are many everyday situations in which motor contagion is undesirable or unwanted and must be prevented: when individuals have non-convergent goals for instance (Brass et  al. 2001; Takeuchi et al. 2018). The perception of an action thus does not necessarily lead to a mimetic response in the observer. Control of the activation of the motor representation of the action is therefore necessary to avoid unwanted motor contagion (Brass et al. 2001). Ubaldi et al. (2015) asked participants to observe an action and produce a counter-imitative response. This TMS study showed that when observing an action, a two-phase response occurs in the observer’s motor system. Indeed, an early sensory and imitative neurophysiological response occurs in the motor cortex within 150  ms of the onset of the observed action. Then, another motor cortex response is evoked later, approximately 300 ms after the stimulus onset. It is this later response that leads to the production of a non-imitative behavioural response

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(Ubaldi et al. 2015). This study demonstrates flexibility allowing for a voluntary, counter-imitative response to the observed action. This two-step response is in agreement with the dual-process accounts of cognition (Jenkins 2019), where one set of processes is thought to be relatively fast, automatic, and intuitive (‘System 1’), whereas another is thought to be relatively slow, controlled, and deliberative (‘System 2’). It also aligns with Cracco et  al.’s study (2022). According to this study, subjects tend to follow the gaze direction of the individuals they are observing, and sometimes change their gaze direction in a second step. The authors thus put forward a two-stage model: the perception of another individual performing an action first leads to an automatic tendency to imitate, before top-down processes reinforce or inhibit this automatic imitation depending on the interpretation of the social situation. There are functional brain mechanisms involved in these responses. The mechanisms underlying motor contagion indeed facilitate congruent imitative responses and inhibit inappropriate or irrelevant automatic imitative responses (Fuhrmann et  al. 2015). In fact, premotor neurons are often inhibited during the observation of an action (Kraskov et al. 2009) and some neurons activated during the encoding of information about others’ actions play a role in preventing unwanted movements, suggesting the existence of ‘mirror interneurons’ (Ferroni et al. 2021). The mirror system is thus not reduced to a one-to-one sensory correspondence between the action of others and the observer’s motor plan for that action: the observer’s perceptive signals activate pyramidal tract neurons with mirror properties and efference copies of these pyramidal tract neurons may activate the mirror interneurons, which inhibit premotor neurons activity (Bonini et al. 2022). Therefore, it is not the mirror neurons sensu stricto that reflect the behaviour of others, but the ensemble of a variety of cell classes distributed in brain regions devoted to self-­ related processes. This mirror system plays a fundamental and evolutionarily conserved role in social learning and behavioural synchronization (Bonini et al. 2022). Inhibitory mechanisms prevent motor coding by mirror neurons, thus avoiding irrelevant imitation in certain contexts. However, in some cases, these inhibitory mechanisms are overcome. As a result, the mirror system is no longer inhibited, resulting in automatic mimetic response to the observed action (Fuhrmann et  al. 2015). Neuroimaging studies conducted to understand the cortical mechanisms underlying the inhibition of inappropriate motor responses found that it is thought to be a function of the frontal lobes. Indeed, a lesion of the anterior-inferior part of the frontal lobe in human patients resulted in a behavioural dysfunction called echopraxia, triggering automatic motor imitation of observed actions (Fuhrmann et al. 2015). The patients then presented difficulties in inhibiting imitative responses (Vendrell et al. 1995). These inappropriate motor responses occurred due to the lack of a suppression mechanism in the middle and inferior frontal cortex. These cortical areas are thus involved in the regulation of motor contagion (Cho et al. 2009). In a study conducted in healthy individuals, participants were asked to perform a predefined finger movement in response to an observed congruent or incongruent finger movement (Brass et  al. 2001). Even in this task where response selection precedes the observed motor action, motor inhibition in the case of the incongruent movement was detected, with involvement of the participants’ prefrontal cortex.

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These results indicate that the preponderance of the imitative response is high but may be inhibited by prefrontal cortical activation (Brass et al. 2001). The tendency to synchronize or not therefore depends on the mirror system and inhibitory mechanisms, which allow one’s own actions to be distinguished from those of others (Brass et al. 2005).

4.3 Social Facilitation Contributes to Motor Contagion The term social facilitation was first used by Allport in 1924, who observed that the mere presence of another person influenced an individual’s performance in executing a movement. Cottrell (1968) added that the mere presence of another individual was not enough to influence the individual’s performance: the other individual must also be involved in the task. Therefore, research on social facilitation has been classified into two experimental paradigms: audience effects and co-action effects. Audience effects involve a comparison of an individual’s performance in executing an action when alone and when observed by passive spectators (Zajonc and Sales 1966). Results from studies of audience effects are mixed: some authors report a decrease in performance in the presence of a passive audience through social loitering (Moore 1917; Burri 1931), while others indicate an improvement in performance in the presence of an audience (Tripplett 1898). In other words, being in groups is sometimes daunting, sometimes galvanizing, depending on the context. Co-action effects involve a comparison of an individual’s performance in executing an action when alone and when surrounded by other individuals performing the same action (Zajonc 1965). Results from studies of co-action effects show that the presence of another person performing the same action increases the actor’s overall level of dynamism and has small to moderate effects on motor performance (Zajonc 1965). The co-action effects linked to social facilitation thus participate in motor contagion. Indeed, when an individual observes another individual performing a behaviour, the observer is likely to perform the behaviour through motor resonance, and the co-action effects improve motor performance (Guerin and Innes 1984; Landers et al. 1978; Livingston et al. 1974).

4.4 Sensorimotor Processes and Motor Contagion 4.4.1 Sensory Modalities for Behavioural Synchronization Action-perception coupling results from a link between the visual and motor systems (Bouquet et al. 2007). It has been suggested that motor resonance is based on automatic action-perception coupling in sensorimotor areas (Palagi and Cordoni 2020). Sensorimotor processes thus contribute to motor contagion (Cracco and

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Brass 2018). Indeed, under ecological conditions, interacting partners can synchronize using multisensory cues, such as visual, auditory, and haptic cues, creating a link between the sensory perception and the motor components of behavioural synchronization (Brown 2022). In humans, when two people walk side by side, behavioural synchronization is achieved more easily when they have sensory information about their partner, whether it is through visual modality – seeing the other walking, auditory – hearing the sound of shoes, or tactile – holding hands (Zivotofsky et al. 2018). Miyata et al. (2017) asked human dyads (two-member groups) to follow the rhythm of a metronome, with the option to see or not see each other. Behavioural synchronization was better when both individuals could see each other. In another study, participants were asked to synchronize their finger tapping with visual and auditory stimulation sequences (Hove et al. 2013). According to this study, motor contagion was better for moving visual stimuli compared to stationary visual stimuli and was worse for frequency-modulated sounds compared to simple beeps (Hove et al. 2013). In another experiment, two individuals tapped their fingers synchronously and then the information perceived by the other was suppressed: the partners could no longer see or hear each other. The participants did not return to their natural tapping rhythm, they kept the rhythm they had reached together, the authors speak of ‘attraction remnants’ (Oullier et al. 2008). In animals, a study was conducted with Japanese macaques: two individuals were positioned facing each other and were asked to press a button (Nagasaka et al. 2013). The authors not only noted that both individuals ended up pressing the button at the same rate, but at the sensory level, the partner’s visual information induced a higher degree of temporal synchronization compared to auditory information. Furthermore, temporal synchronization was even higher when individuals had access to both visual and auditory cues from their counterparts. Finally, haptic contact is also a sensory cue for behavioural synchronization, as it significantly amplifies the strength of motor contagion between interacting partners (Brown 2022). The importance of haptic contact was demonstrated in an observational study involving a chimpanzee dyad during synchronous bipedal walking (Lameira et al. 2019). During this locomotor action, the follower animal maintained its hand on the back of the other animal, creating a haptic channel for motor contagion. When tactile information was available, this physical connection resulted in stronger behavioural synchronization than visual or auditory information (Felsberg and Rhea 2021). The close physical contact between partners, in addition to visual and auditory cues, are therefore physiological regulators for maintaining behavioural synchronization during the action (Brown 2022). To sum up, motor contagion is mediated by sensory perceptions, such as visual, haptic, or auditory information. Conversely, the restriction of sensory information, for example, if one cannot see or hear the other’s movement, leads to a decrease in the degree of behavioural synchronization (see Fig. 4.1) (Heggli et al. 2019; Nessler and Gilliland 2009). It indicates that the transfer of perceptual information plays a fundamental role in interpersonal behavioural synchronization, confirming a link between the brain processes of action and perception (Repp and Su 2013; Schmidt and O’Brien 1997). Alterations in sensory information therefore affect behavioural synchronization, nevertheless, it remains relatively robust to these sensory changes.

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Fig. 4.1  Perceptual modalities of action influence the degree of behavioural synchronization

For example, behavioural synchronization is weaker when individuals can hear but cannot see each other, but it persists despite the absence or attenuation of one or more sensory modalities (Nessler and Gilliland 2009).

4.4.2 Sensorimotor Processes and Motor Contagion at the Group Level Interestingly, not only the actions of another agent are represented in the motor system, but the actions of multiple agents are, and identical actions of multiple individuals are represented together onto the motor system, leading to an imitative motor response (Cracco and Brass 2018; Cracco et al. 2015, 2016; Ramenzoni et al. 2014). According to a TMS study, the observation of several agents performing the same action leads to an increase in corticospinal excitability (Cracco et al. 2016). Each separate movement activates a distinct motor representation, and multiple observed actions are represented simultaneously in the motor system. Motor activation during action observation reflects the combined input of the different actions observed (Cracco and Brass 2018). This is consistent with research on face perception, which indicates that an observer in the presence of a group does not process each face individually, but rather processes an average face among all faces (Haberman and Whitney 2009). In another study, the authors asked participants to perform a finger abduction task while they observed between one and four hands showing the same or different movements (Cracco and Brass 2018). As the number of observed hands increases, motor synchronization improves, there is thus a sensorimotor basis for social group contagion (Cracco and Brass 2018). Furthermore, the improvement in motor synchronization as a function of the number of hands follows a linear curve when the observed hands perform different movements, and an asymptotic curve when the observed hands perform identical movements. At the sensorimotor level, at least three mechanisms may explain this ceiling effect of the group size effect for large groups (Cracco and Brass 2018). The first is a saturation of motor activation as the input to the motor system increases, due to non-linear neural responses or limited processing capacity. The second is a saturation of the

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motor response speed by physical limitations of the observer. The third is a strategic control mechanism causing saturation of timing. The results of Cracco and Brass’s study (2018) are instead in favour of a strategic control mechanism regulating the motor response by adjusting the response threshold according to the number of movements observed. This mechanism would aim to prevent involuntary imitative responses (Cracco and Brass 2018). Indeed, a high response threshold would prevent premature responses, allowing cognitive processes to influence or not the motor response. From this perspective, social group contagion is explained as an interaction between sensorimotor and interpretive processes that determine interpersonal motor synchronization (Cracco and Brass 2018).

4.5 From Interbrain Neural Synchronization to Behavioural Synchronization Behavioural synchronization is associated with interbrain neural synchronization, i.e., the synchronization of the neuronal activity of the interacting partners. Neuronal activity interacts in a complex manner with the rhythms of the internal and external environment through the phenomenon of ‘neuronal entrainment’ (Lakatos et  al. 2019). Interbrain neuronal synchronization is a form of neuronal entrainment that occurs when the perceived sensory information pertains to a social agent. Studies using brain imaging have shown inter-brain coupling in partners engaged in a behavioural synchronization task (Rauchbauer and Grosbras 2020). For example, EEG studies conducted on dyads and groups have shown several oscillatory bands including the alpha/mu band, considered as the electrophysiological marker of the movement observation and execution (Dumas et  al. 2010). Studies using fNIRS have shown inter-brain coupling in premotor cortices during a finger-tapping synchronization task (Holper et  al. 2012). A recent study showed a causal effect of interbrain neural synchronization on behavioural synchronization by recording simultaneously the activity of two side-by-side individuals with transcranial stimulation: 20  Hz in-phase stimulation facilitated the establishment of interpersonal behavioural synchronization in a finger tapping task (Novembre et al. 2017). fMRI studies also showed simultaneous neural activation of participants during a synchronized task. For example, when two people performed a task involving synchronous hand movement, greater activity was observed in cortical areas of each interaction partner (Cacioppo et  al. 2014). Another study investigated interbrain neural synchronization within dyads during a hand movement synchronization task (Dumas et al. 2010). The results indicate that behavioural synchronization between individuals correlates with the emergence of an interbrain neural synchronization network in the alpha-mu band between the right centro-parietal regions of the two interacting partners, with these bands being proposed as a neuromarker of social interaction and as a neural correlate of mirror neurons (Dumas et al. 2010). It has been proposed that the regulation of behavioural synchronization is underpinned by endogenous oscillators. The alpha and mu bands are the neural reflection of several aspects

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of social interaction, such as behavioural synchronization and anticipation of others’ actions (Dumas et  al. 2010). The temporoparietal junction is presented as a brain region with a central role in social interactions. It is consistently activated in sociocognitive processes involving attentional orientation, discrimination between self and other, and in the temporal analysis of visuomotor processing (Dumas et al. 2010). In a neurodynamic framework, the temporoparietal regions of interacting individuals synchronize and appear to be a key functional centre in the interindividual brain network (Dumas et al. 2010). Interbrain neural synchronization is more likely to emerge during naturalistic social interactions. Thus, interbrain neural synchronization has also been studied in social contexts. During interactions between 5- to 9-year-old children and their caregivers, studies using fNIRS have shown that high interbrain neural synchronization between children and adults correlated with cooperative performance and predicted successful problem solving (Nguyen et al. 2020; Reindl et al. 2018). In an EEG study conducted in high school students, interbrain neural synchronization between group members in a classroom was correlated with the level of engagement and enjoyment reported by students (Dikker et al. 2017). Furthermore, this study in teenagers showed that social priming through eye contact increased interbrain neural synchronization within student pairs. Other studies showed that the ability to look at each other and smile enhances interbrain neural synchronization (Leong et al. 2017) and behavioural synchronization (Piazza et al. 2020). For example, facing the partner increases interbrain neural synchronization compared to a back-to-­ back interaction (Jiang et al. 2012). These social cues thus enhance interbrain neural synchronization and serve as triggers for behavioural synchronization (Gvirts and Perlmutter 2020). Furthermore, a hyperscanning study measured interbrain neural synchronization during a social interaction between two individuals and showed better interbrain synchronization in temporoparietal regions for dyads composed of familiar individuals compared to dyads of strangers (Kinreich et  al. 2017). This interbrain neural synchronization was not present outside of social interaction, and was associated with behavioural synchronization, particularly through gaze activity. This study provides a link between interbrain neural synchronization and the degree of familiarity between interacting partners (Kinreich et al. 2017). Interbrain neural synchronization is thus a social marker of the relationship between individuals and of the quality of their interactions (Rauchbauer and Grosbras 2020).

4.5.1 Behavioural Studies Many bird and mammal species demonstrate behavioural synchronization to audio, visual, and social stimuli (Fuhrmann et  al. 2015). In chimpanzees, for example, behavioural synchronization occurs during exhibition of nut-cracking skills. Analyses of videos in which these behaviours are exhibited show body alignment between individuals, and the observer’s behaviours are synchronized to those of the model individual (Fuhrmann et  al. 2015; Marshall-Pescini and Whiten 2008).

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Behavioural synchronization in the execution of an established behaviour allows for adaptive behaviour modification and the social transmission of new behaviours from a model to an observer (Huber et al. 2018). In this sense, behavioural synchronization is an adaptive tool in the social learning of new behaviours (Fuhrmann et al. 2015; Huber et al. 2018). Also in macaques, it has been shown that individuals can influence the temporal characteristics of their conspecifics’ movements (Nagasaka et al. 2013). In a laboratory setting, Japanese macaques were trained to press a button, and the authors noted that when two individuals were face-to-face when performing this task, temporal synchronization between partners emerged, characterized by a change in the rhythm of each individual’s button-pressing behaviour towards a shared tempo (Nagasaka et al. 2013). The degree of temporal synchronization depended on the partners, suggesting a link between the quality of the social bond and temporal synchronization (Nagasaka et al. 2013). In humans, behavioural synchronization is observed in various everyday situations that can easily be reproduced in experimental settings. Classic examples of behavioural synchronization include side-by-side walking (Sylos-Labini et al. 2018; Van Ulzen et al. 2008), rocking (Demos, et al. 2012), rowing (Cohen et al. 2010), dancing (Reddish et al. 2014), clapping after a successful performance (Néda et al. 2000), and handshaking (Melnyk and Hénaff 2019). In experiments requiring relatively simple movement, behavioural synchronization is found to be a stable pattern. For example, a study showed that when two people sit side-by-side in rocking chairs, both partners initially rock at their own individual pace and then spontaneously and unintentionally synchronize their chair rocking (Richardson et al. 2007). Another study in humans focused on the behavioural synchronization of facial expressions (Sato and Yoshikawa 2007). It was shown that when individuals observed an angry expression, they tended to lower their eyebrows, a prototypical sign of an angry expression. Similarly, when individuals observed an expression of joy, they tended to pull the corner of their lips, a precursor of a smile. The authors showed that these changes occurred less than 900 ms after the onset of the facial expression observation (Sato and Yoshikawa 2007). Furthermore, naive participants identified the emotional expression of the observers, it corresponded to the facial expression that the observers had observed. There is thus a spontaneous and rapid behavioural synchronization of facial expressions, which functions both as a form of intra-individual processing and as inter-individual communication (Sato and Yoshikawa 2007).

4.5.2 Neurophysiological Studies in Adults and Infants In addition to behavioural evidence, neurophysiological studies have been conducted to directly assess how humans synchronize their behaviours to that of others (Hasson and Frith 2016; Oullier et al. 2008; Marshall et al. 2010). The first brain imaging studies only scanned one individual at a time, making it difficult to study

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the emergence of behavioural synchronization between individuals during a social interaction (Dumas et al. 2010). New techniques, such as hyperscanning, make it possible to simultaneously record the brain activities of several interacting individuals. Indeed, hyperscanning is the simultaneous recording of multiple brains based on fMRI, fNIRS, or EEG. This technology allows to measure the inter-cerebral coupling of neural activations associated with social interactions. In contrast to classical approaches that assess the brain activity of only one of the interacting agents, hyperscanning allows further investigation of the mechanisms involved in human interactions, and thus the mutual influence of individuals during social interactions (Dumas et  al. 2010; Gvirts and Perlmutter 2020). In an EEG study for example, human dyads were asked to perform synchronized finger tapping (Tognoli et  al. 2007). Two simultaneous EEG oscillatory components were found. The authors suggested that the EEG component during phase-locked synchronization was linked to mirror neuron activation, whereas the component during unsynchronized tapping was linked to mirror neuron inhibition. This study revealed a neuromarker of behavioural synchronization, called the phi complex. This neuromarker was detected in the right centroparietal area for each subject. In another EEG study, pairs of guitarists were asked to play using the rhythm of a metronome, and phase synchronization was found in the theta frequency range between the frontal areas of pairs of individuals (Lindenberger et al. 2009). In another experiment, Rodriguez et al. (1999) asked human subjects to observe a visual stimulus and indicate as quickly as possible whether they perceived a face or a meaningless shape. Recordings of the subjects’ brain electrical activity showed a difference in the timing of gamma oscillations between the face perception and the meaningless shape perception. Gamma oscillations, which encompass oscillatory neuronal discharges within the frequency range of 30–80 Hz, have been documented to be associated with cognitive processes in humans and other animal species (Rodriguez et al. 1999). In this study, only the face perception condition elicited a three-part temporal pattern covering the entire duration of the task, from stimulus presentation to motor response (Rodriguez et al. 1999). First, a significant increase in synchrony associated with the first gamma response for the face perception condition occurred between 200 and 260  ms after visual stimulus presentation, followed by a marked decrease in synchrony at 500 ms, marking the transition from the moment of perception to the motor response, and a final increase in synchrony at reaction time. This final increase was present for both face perception and meaningless shape perception. The desynchronization between perception and motor response reflects a process of active uncoupling of the underlying neural ensembles that is required to move from one cognitive state to another (Rodriguez et al. 1999). The ability to synchronize one’s behaviours to those of others is reportedly higher when observing an interaction between two people than when observing a single person or two people acting independently. An fMRI study measured the brain activity of 29 participants while they observed the execution of gestures produced by two right hands (Cracco et al. 2019). Results indicated that brain activity in the premotor and parietal motor cortex was higher when two hands performed two different gestures than when a single hand performed a single gesture.

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Furthermore, the observation of two different gestures compared to two identical gestures activated brain areas related to motor conflict, and this activity was correlated with parietal motor activity.

4.6 Behavioural Synchronization and Hormones 4.6.1 Oxytocin, a Biomarker of Affiliation A close relationship is characterized by repeated interactions involving shared activities and the manifestation of specific behaviours for these interactions to take place (Feldman 2007; Feldman et al. 2011). Social bonding refers to the development of a close relationship (Gangestad and Grebe 2017). Affiliation is a type of close relationship involving parent and offspring or siblings (Feldman 2012). Attachment is a form of affiliation that is specifically found in humans (Feldman 2007). The development of affiliation is supported by the secretion of oxytocin (Atzil et al. 2014; Feldman 2012; Feldman et al. 2011). This pituitary neuropeptide is indeed involved in social-cognitive processes in mammals (Goodson and Thompson 2010), and is an indispensable component in the development of affiliation in animals and attachment in humans (Champagne et al. 2004; Feldman 2012). Mammalian females, for example, express a unique set of specific behaviours upon delivery, enabling adequate maternal care and promoting offspring survival (Feldman 2007; Niedenthal 2007). This maternal behavioural repertoire is based on eye contact with the offspring, vocalizations such as high-pitched and soft voice, physical contact such as licking in canids for example, and the appropriate and synchronous adaptation of the offspring’s behaviours (Barrett and Fleming 2011; Feldman and Eidelman 2004). As a result, a bond between mother and offspring is constructed through sensory, motor, and behavioural perceptions. Social bonding includes neurobiological and behavioural adaptation between affiliated individuals, which results in synchronous behaviours and increased oxytocin levels (Champagne et al. 2004; Feldman 2012; Hofer 1994; Ulmer-Yaniv et al. 2016). Thus, over time, affiliated individuals form a close relationship. In humans, for example, behavioural synchronization between parents and child applies to gaze behaviours, smiles, tone of voice, etc. (Feldman 2012). Studies in rodents and humans have shown that the degree of behavioural synchronization between parents and their offspring is positively correlated with the density of oxytocin receptors in parents and offspring (Apter-Levi et  al. 2014; Feldman et  al. 2011, 2013; Olazábal and Young 2006). Furthermore, in humans, high levels of oxytocin are associated with many affectionate contacts produced by parents and with gaze synchronization. Moreover, the duration of gaze synchronization predicts oxytocin levels in parents and youngsters. The ability of parents to respond synchronously to their offspring’s behaviour is therefore related to oxytocin (Apter-Levi et al. 2014). Parents’ synchronized behaviours on the offspring help to develop the child’s cognitive and affective abilities and

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provide a model for future social relationships. More precisely, behavioural synchronization between parents and infants between 3 and 9 months of age has a significant impact on the development of the infant’s social skills; it prepares the infant for future social interactions (Apter-Levi et al. 2014; Feldman 2007; Feldman et al. 2013). In this way, individuals rely on early synchronous experiences for successful social interactions with others (Feldman 2007). Oxytocin is thus a neurohormonal substrate of behavioural synchronization (see Fig.  4.2), which promotes social bonding between individuals (Feldman 2012). Intranasal administration of oxytocin in humans influences social cognition by modulating neural activity related to trusting behaviour (Baumgartner et al. 2008), and improves behavioural synchronization (Arueti et al. 2013). In dogs for instance, oxytocin administration extends the duration of playful interactions (Palagi et al. 2015). Authors have investigated whether the level of oxytocin influences contagious yawning in dogs, but the results did not validate this hypothesis (Kis et al. 2020). At the interspecific level, when dogs and their owners look at each other, the oxytocin levels of both increase, which promotes behavioural synchronization between the two and improves the quality of their relationship (Nagasawa et  al. 2015). Intranasally administered oxytocin increases gaze behaviour in dogs towards their owners (Nagasawa et al. 2015). Another study showed that oxytocin promoted positive social behaviours in dogs towards humans and conspecifics (Romero et al. 2014).

Fig. 4.2  Behavioural synchronization and hormones. Hormone markers of affiliation and reward circuits enhance behavioural synchronization, and behavioural synchronization triggers these biomarkers in feedback. Stress hormone decreases the degree of behavioural synchronization, and less synchronized individuals have higher cortisol levels. Biomarkers of reward circuits activate oxytocin, leading to a reduction in cortisol levels and a stronger behavioural synchronization

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4.6.2 Dopamine, Serotonin, and Endorphin, Biomarkers of Reward Circuits Beta-endorphin is an endogenous opioid involved in multiple reinforcement and reward-related processes, and its secretion increases during positive social interactions (Curley and Keverne 2005; Keverne et al. 1989; Odendaal and Meintjes 2003; Ulmer-Yaniv et al. 2016). In humans, beta-endorphin levels are higher for dyads of affiliated individuals, such as a parent and child or a couple, compared to dyads of strangers, and beta-endorphin levels are correlated with the level of behavioural synchronization between partners (Schneiderman et al. 2012). During sports activity, for example, synchronized movements between athletes result in an increase in endorphins that is more pronounced than when athletes are alone (Cohen et  al. 2010). Behavioural synchronization allows for the achievement of common goals (Valdesolo and DeSteno 2011). The emergence of behavioural synchronization thus leads to a rewarding feeling, and once achieved, it activates mechanisms related to reward (see Fig. 4.2) (Bente and Novotny 2020). Higher beta-endorphin and behavioural synchronization in affiliated individuals induce more pleasant social interactions, which further promotes attachment between partners (Ulmer-Yaniv et  al. 2016). The effects of beta-endorphin on behavioural synchronization are mediated by their impact on oxytocin (see Fig. 4.2). Thus, periods of affiliative bond formation are linked to increased activity in the systems underlying affiliation and reward. These systems become tightly coupled, promoting behavioural synchronization (Ulmer-Yaniv et al. 2016). The dopaminergic and serotonergic systems are also involved in behavioural synchronization via their effect on oxytocin. Indeed, oxytocin neurons are reciprocally connected with dopaminergic and serotonergic neurons, this interconnection plays a key role in approach behaviours and the development of social bonds (Feldman 2012; Melis and Argiolas 2011). In this sense, interactions between dopamine and oxytocin, as well as serotonin and oxytocin, modulate neural circuits involved in affiliative behaviours, which in turn impacts behavioural synchronization (Apter-Levi et al. 2014).

4.6.3 Cortisol, a Biomarker of Stress When partners involved in a social interaction are anxious, stress-related mechanisms are activated in each partner (Atzil et al. 2011). Similarly, one individual’s stress can impact the stress state of another individual (Liu et  al. 2013). This is reflected in cortisol levels, which is a biomarker of physiological stress. In humans, partners in a couple synchronize their daily variation in cortisol levels and mutually influence their stress levels. A change in cortisol level in one partner consequently leads to a similar change in the other (Liu et al. 2013). Couples who experience little stress have more stable cortisol levels than those who regularly experience stress.

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Daily variation in cortisol levels is therefore associated with the quality of the relationship between dyad partners (Liu et al. 2013). For example, elevated capillary or salivary cortisol in stressed mothers is linked to elevated cortisol in their children, this may alter the quality of the relationship between mother and child (Tarullo et al. 2017). Mother-child dyads with high cortisol levels have fewer synchronous interactions than dyads with lower cortisol levels (Tarullo et al. 2017). Likewise, dyads with low levels of behavioural synchronization experience greater physiological stress (Tarullo et al. 2017). At the interspecific level, a study found a correlation of cortisol levels between dog and owner, reflecting interspecific synchronization of physiological expressions of stress (Sundman et al. 2019). Cortisol levels are correlated with oxytocin levels (see Fig. 4.2). Higher levels of oxytocin between partners are indeed  associated with reduced cortisol levels (Hegadoren et al. 2009; Light et al. 2005). Also, administration of oxytocin to dyads during conflict results in reduced cortisol levels (Ditzen et al. 2009).

4.6.4 Happy Hormones, Brain Activity, and Behavioural Synchronization In a social interaction involving behavioural synchronization, there is a temporal concordance between neural activation, hormonal release, and behavioural response. Indeed, partners who synchronize their behaviour show neural activation in areas rich in happy hormones, including oxytocin, dopamine, serotonin, and endorphin (Feldman 2012; Scheele et al. 2013; Young and Wang 2004). An EEG study has shown that oxytocin affects brain activity during social interaction. When a dyad performed a synchronized action, greater interbrain synchronization of alpha band neuronal oscillations was present compared to a non-synchronized action. Furthermore, increased interbrain synchronization of the alpha band predicted better interpersonal behavioural synchronization in all participants (Mu et al. 2016). An fMRI study conducted in humans measured the brain implications of mothers’ synchronized behaviours with their child and showed that mothers who engage extensively in synchronous interactions with their child exhibit increased brain activity in the left amygdala, the nucleus accumbens and regions included in the mirror system (Atzil et al. 2011). The level of activation of these brain regions also predicts the level of behavioural synchronization. Furthermore, the study revealed a correlation between the level of plasma oxytocin and activations of these brain regions in mothers who synchronize highly to their child. These regions are functionally linked to reward circuits, emotion modulation networks, and recognition of others’ mental states. These activations may reflect the mother’s ability to better understand their child’s intentions and desires and respond with synchronized behaviour (Atzil et al. 2011). Behavioural synchronization underlies the development of affiliative bonds, consequently detecting behavioural synchronization is important for social bond formation (Atzil et al. 2014). This study therefore examined the relationship between the mother’s level of behavioural synchronization on

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their child and the mother’s ability to detect behavioural synchronization in others. The results showed that recognition of behavioural synchronization involves activations in the dorsal anterior cingulate cortex, fusiform, cuneus, inferior parietal lobule, supplementary motor area, and left nucleus accumbens, and that the mother’s behavioural synchronization on their child correlates with the dorsal anterior cingulate cortex response to detection of behavioural synchronization. This region is activated during parent-child behavioural synchronization and during the detection of behavioural synchronization in others, so it plays a particular role in participating in a synchronous exchange and detecting behavioural synchronization (Atzil et al. 2014). To sum up, the effects of hormones and interbrain neural systems related to stress, affiliation and reward, modulate individuals’ ability to synchronize to others and affiliate socially (Atzil et al. 2011; Feldman 2012).

4.7 Behavioural Synchronization and Mutual Social Attention System The mutual social attention system is crucial for social interactions. It is activated for each of the interacting agents, and the coupling between the agents’ mutual social attention system directs attention towards the interaction and enhances behavioural synchronization (Gvirts and Perlmutter 2020). Conversely, behavioural synchronization facilitates social interactions and improves the ability to focus on the interaction by supporting the mutual social attention system (Gvirts and Perlmutter 2020). Behavioural synchronization indeed allows for a greater allocation of attention to the interaction. As a result, interbrain neural synchronization increases as a function of direct involvement of agents in social interaction (Bonini et al. 2022). The link between behavioural synchronization and mutual social attention is thus bidirectional and forms a feedback loop (Gvirts and Perlmutter 2020). In humans, the fact that deficits in social alignments are observed in conditions associated with deficits in social attention, such as autism spectrum disorders, further supports the association between the mutual social attention system and behavioural synchronization (Gvirts and Perlmutter 2020). The activation of the mutual social attention system leads to a sense of reward which reinforces behavioural synchronization. In humans, the more interaction is perceived as beneficial, the stronger the mutual social attention (Davidesco et al. 2023). Thus, neurochemical mechanisms may support the bidirectional link between mutual social attention and behavioural synchronization. Notably, hormones such as oxytocin and dopamine have a central role in the reward circuit and may underly the activation of this feedback loop (Dichiara and Bassareo 2007). Indeed, through their influence on reward circuits, oxytocin and dopamine are likely to regulate the mutual social attention system (Shamay-­ Tsoory and Abu-Akel 2016). According to Gvirts and Perlmutter (2020), achieving behavioural synchronization triggers activation of the reward system and activates the mutual social attention system through these hormones.

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Chapter 5

Social Functions of Mirror Neurons, Motor Resonance and Motor Contagion

5.1 Motor Imitative Responses Enable Learning When learning new motor skills, the first steps are attempts to reproduce the movements of a model, and mirror neurons facilitate this learning (Gallese, 1998). In this sense, mirror neurons enable the acquisition of new social behaviours and therefore facilitate cultural and behavioural habits transmission (Fuhrmann et  al. 2015). In humans, transcranial magnetic stimulation of the frontal gyrus improved vocal imitation and motor synchronization in the participants (Hogeveen et  al. 2014; Restle et  al. 2012), whereas inhibitory stimulation of the parietal lobule or frontal gyrus reduced participants’ performance in an imitative task (Newman-Norlund et  al. 2007). Furthermore, lesioning cortical areas deemed to include mirror neurons, such as the postcentral gyrus, parietal lobules, intraparietal sulcus, and inferior frontal cortex, results in impaired goal-directed imitation ability (Binder et al. 2017; FrenkelToledo et al. 2016). It is therefore agreed that mirror neurons facilitate motor synchronization and have a social function in motor learning (Rizzolatti et al. 2001).

5.2 Action Recognition and Understanding Action recognition is necessary for survival in social species, particularly for interacting with conspecifics. Recognition of locomotor behaviours has been studied in pigeons via three-dimensional models depicting locomotion of various animal species, including walking and running (Asen and Cook 2012). The procedure involved a go/no go task, with half of the pigeons learning to peck when observing running models and the other half when observing walking models. The subjects learned to recognize these locomotor behaviours and transferred this learning to a variety of digital animal models. These results suggest that these birds are able to recognize © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0_5

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locomotor behaviours of model animals (Asen and Cook 2012). Concerning goal-­ directed actions, the ability of animals to recognize the outcome of an action is controversial. Some studies have shown that some animal species, such as non-­ human primates and dogs, are able to recognize goal-directed actions (Call et al. 2004; Marshall-Pescini et al. 2014; Phillips et al. 2009; Santos and Hauser 1999). In Marshall-Pescni et al. study (2014), dogs were trained to observe a human or artificial agent interacting with an object. Then, during the test phase, the agent either interacted with the object located at a new location or interacted with another object. Dogs spent more time watching the human agent interact with the new object than with the first object in a different location; this result was not found for the non-­ human agent. Thus, like children and non-human primates, dogs looked longer at the action with a new goal than at the first action at another location, but only for the biological agent. The authors interpreted this result as the dogs’ ability to perceive human action as goal-directed, as they were more attentive to the goal of the human action (type of object) than to its location (Marshall-Pescini et al. 2014). In Piotti and Kaminski’s (2016) study, dogs were able to recognize an object a human was looking for and they pointed the location of that object. In another study, dogs observed a conspecific manipulating a rod with their paw (non-rational action) or with their mouth (rational action) to access a reward (Range et al. 2007). The demonstrator dogs using their paw either held a ball in their mouth or not. Observer dogs manipulated the rod with their paw only when the demonstrator dogs did not have a ball in their mouth. When the demonstrators used their paw with their mouth free, the observer dogs used their mouth. This result can be interpreted as a discrimination of the outcome of an action; nevertheless, the dogs’ performance in these tasks can be explained by low-level mechanisms, the distracting effect of the presence of the ball for example (Heyes 2014). In humans, two theories have been proposed to explain action recognition and understanding: the visual hypothesis and the direct-matching hypothesis (see Fig.  5.1). According to the visual hypothesis, the activity of object- and motion-­sensitive brain visual regions is sufficient to perceive the effects of the observed action (Rizzolatti et al. 2001). Thus, action recognition relies on the visual analysis of all the constituents of the action. The visual analysis of an agent performing the action, the context and the object on which the action is performed would allow the observer to understand and recognize the action (Buccino et  al. 2004). According to this hypothesis, the neural substrates involved in action recognition would be the extrastriate visual areas, the inferotemporal lobe and the superior temporal sulcus region (Buccino et  al. 2004). This theory is in line with the ecological approach to visual perception in animals. According to this approach, originally proposed by Gibson (2014), visual perception depends directly on visual information from the stimulus, without integration of this information by high-level cognitive processes. Memory and related processes would then play no role in perception (Norman 2002). According to this approach, visual perception is processed in a single step, sensory information is sufficient, and matching the representations related to the action is not necessary (Norman 2002; Rodrigues et al. 2020).

5.2  Action Recognition and Understanding

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Fig. 5.1  The two theories explaining the recognition of the action: the visual hypothesis (a) and the direct-matching hypothesis (b)

The second theory, the direct-matching hypothesis, holds that action recognition occurs through the correspondence between the observer’s perception of the action and the observer’s own motor representation of that action. Thus, the observation of the action would activate the same neural structures in the observer as those involved in the execution of this action (Buccino et al. 2004). This correspondence between observation and execution of the action would allow the observer to understand the observed action. This theory is consistent with the cognitive approach to visual perception in animals. According to this approach, which is less parsimonious than the ecological approach, the processing of perceived visual information necessarily involves the activation of high-level cognitive processes (Bruce et al. 2003). The direct-matching hypothesis states that perception is processed in several stages: sensory stimulation is intrinsically insufficient, cognitive processes come into play, and the perception of an action activates internal representations of that action (Norman 2002; Rodrigues et al. 2020). These theories are the subject of intense debate. Nevertheless, the discovery of mirror neurons provides strong neurophysiological support for the direct matching theory. There is now a consensus that the mirror system plays a role in action recognition (Buccino et al. 2004; Gallese et al. 2004; Gallese and Sinigaglia 2011; Heyes and Catmur 2021; Iacoboni et al. 2005; Rizzolatti and Craighero 2004). Some mirror neurons are involved in the discrimination of specific actions; for example, some neurons are activated when observing an object grasping action, regardless of the nature of the object, but are not activated for another type of action (Iacoboni et al. 2005). These properties indicate a mechanism of action recognition by mirror neurons. According to Rizzolatti and Craighero (2004), there is action understanding when the visual representation of the action is matched with the motor representation of the action. The perceived action is integrated into a corresponding motor

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program, allowing the understanding of the motor program of others (Blakemore and Decety 2001; Jeannerod 2001; Keysers and Gazzola 2006; Rizzolatti and Craighero 2004). In this sense, the action-perception coupling allows the understanding of actions. There would then be a process of simulation, as the observer simulates the action in order to understand it (Uithol et al. 2011). By activating the motor representation  of the action, observers understand the action because they know its outcome (Gallese et al. 2004). The visual perception is in this way linked to personal experience through motor resonance (Brass and Heyes 2005; Jeannerod 2001; Prinz 1997; Rizzolatti and Craighero 2004). However, some authors argue that the activation of motor neurons during the observation of an action is not related to the understanding of the action but only to motor preparation (Rizzolatti et al. 2001). They suggest that the involvement of cortical areas including mirror neurons in action understanding is limited to low-level processing of observed actions, such as recognition of an action and discrimination of the type of movement observed, e.g., grasping, holding, mouthing, but not to high-level processing, such as inferring the intentions of others from their observed actions (Heyes and Catmur 2021; Iacoboni et  al. 2005; Thompson et  al. 2019). Nevertheless, a Positron Emission Tomography study conducted on humans contradicts this argument: when the participants observed an action and had to understand it or to copy it, the activation of motor neurons was stronger for participants who had to understand the action compared to those who had to imitate  it, in favour thus of the implication of mirror neurons in action understanding. In this sense, the mechanism underlying behavioural synchronization is a key feature in non-verbal communication, as it allows the observer to understand the actions of others, thus facilitating social interactions (Bidet-Ildei et al. 2011; Jellema et al. 2000).

5.3 Action Anticipation Individuals must be able to anticipate each other’s behaviours in order to stay synchronized (Lakin 2013). Action anticipation is thus important for social species living in complex environments. Several studies have focused on the ability to anticipate actions in animals (Santos and Hauser 1999; Rochat et al. 2008; Völter et al. 2020). Santos and Hauser (1999) showed that the white-crested tamarin used the orientation of a human agent’s head to predict the human’s action with an object. Anticipation can also be analysed using gaze activity. Anticipatory gaze is a gaze directed towards the future target of the action before it is reached (Völter et  al. 2020). For example, an eye-tracking study examined motion tracking and anticipatory gaze in dogs (Völter et al. 2020). Subjects watched a video showing two players throwing a Frisbee back and forth repeatedly. The dogs followed the horizontal movements of the Frisbee, and after several throws, they anticipated the destination of the object by looking at the catcher before the object was received (Völter et al. 2020). As dogs gained experience, their movement tracking became anticipatory gaze. Another study showed that macaques looked longer at goal-directed human

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actions when they were unexpected, such as taking a detour to grab an object rather than taking the shortest route. This result was interpreted as an ability to recognize and anticipate the goal of an action (Rochat et al. 2008). Anticipatory gaze has been extensively studied in humans. Individuals watching dynamic scenes like a tennis match anticipate players’ movements by fixing the ball’s bounce point before it reaches it (Mital et al. 2011). Prediction of goal-directed actions is present in humans from an early age (Cannon and Woodward 2012) and humans predict the actions and movements of their conspecifics based on their past experiences (Flanagan and Johansson 2003). Anticipation requires active monitoring of the actions of the interaction partners, in order to predict their actions and reactions (Brown 2022). The motor system is capable of simultaneously representing multiple observed actions, suggesting that mirror processes are used to simultaneously predict the outcomes of multiple co-actors’ actions to achieve behavioural synchronization (Cracco et al. 2019). Bonini et al. (2022) indicate that the mirror system processes sensory information to update the observers’ motor plans, allowing subjects to anticipate rather than simply react to the observed actions of others. Besides, perceptive anticipation tasks activate motor representation in the observer at a higher level than discrimination or detection tasks (Bidet-Ildei et al. 2011). This representation is useful to the observers as it enables them to anticipate the future actions of the individuals being observed, whether in a cooperative or threatening context, and to adapt their responses precisely and appropriately (Gallese 1998). The observers understand the goal of the observed action, and by predicting the outcome of the action, their motor system is activated (Blakemore and Frith 2005). In this sense, motor contagion occurs when observers can predict the action outcome based on the perception of this action (Takeuchi et al. 2018). Motor contagion is therefore used to predict the actor’s next action, this theory is supported by the fact that motor contagion is enhanced when the observer is able to anticipate the outcome of the action (Takeuchi et al. 2018). In this sense, the degree of motor contagion is influenced by the level of predictability of the observed action. fMRI studies aimed to establish the role of the mirror system and motor contagion in action anticipation (Rao et  al. 1997; Schubotz et  al. 2000). These studies revealed that different areas of the lateral premotor cortex, such as the frontal opercular cortex, were involved in sensory triggered motor preparation and in perceptual anticipation.

5.4 Understanding Intentions and Mental States Assigning an intention corresponds to inferring a subsequent goal to the execution of the action (Iacoboni et al. 2005). For example, if an individual observes another individual grasping an object, he or she recognizes the nature of the action, but also what the other individual wants to do with the object, i.e., his or her immediate intention or goal related to the object (Iacoboni et al. 2005). Two theories are proposed regarding the mechanisms underlying mental states or thought reading in humans: the simulation theory and the theoretical theory (Gallese 1998). According

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to the simulation theory, the mental states of others are represented by adopting their perspective: by following or matching their states with states resonant of one’s own. In this sense, mind-reading involves the ability of the observer to simulate the observed action to replicate the observed agent’s mental state and predict their mental processes. According to the theoretical theory, mental states are represented as postulates derived from a basic, naive inferred theory. In this sense, mind-reading is a detached activity (Gallese 1998). In non-human animals, the attribution of intentions is very controversial (Birch et al. 2020; Heyes 2014; Krupenye and Call 2019). It has been studied using the unwilling vs unable paradigm. This paradigm compares subjects’ responses to an agent’s intentional or unintentional behaviour. This paradigm has demonstrated the ability to discriminate between intentional and unintentional behaviour in 18-month-­ old human infants (Meltzoff 1995) and non-human primates (Call et  al. 2004; Phillips et al. 2009; Santos and Hauser 1999). This paradigm has also been tested in dogs. Dogs were exposed to a human agent who withheld a reward, either by not giving them voluntarily, or involuntarily by dropping it or not being able to give it due to the presence of an obstacle (Schünemann et al. 2021). Dogs reacted differently under these experimental conditions: they expressed attentional cues such as stopping tail wagging and they waited longer before approaching the reward when the experimenter voluntarily withheld it compared to an involuntarily withhold. This result was interpreted as the dogs being confused when the experimenter did not want to give them the reward. When the experimenter could not give them the reward, due to clumsiness or the presence of the obstacle, the dogs sat or lay down, as if they were not disturbed/surprised by the agent’s actions sequence. The authors concluded that the dogs were able to distinguish intentional from unintentional human behaviours (Schünemann et al. 2021). Nonetheless, the attribution of intentions in animals is controversial, as recognizing or discriminating a goal-directed action does not necessarily imply an understanding or representation of the other’s mental states (Marshall-Pescini et al. 2014), but can be based on a low-level cognitive process previously learned (Gaunet and Deputte 2011). Understanding the outcome of actions is a necessary condition for understanding intentions and attributing mental states (Marshall-Pescini et al. 2014). Some studies in humans have sought to identify the role of mirror neurons in encoding the intention of the agent performing an action. This ability emerges by the end of the first year of life (Carpenter et  al. 2002; Woodward 1998). Some authors indicate that mirror neurons are involved in high-level processing of the action, such as the activation of shared mental representations, allowing for ‘understanding from within’ (Nagasaka et al. 2013; Rizzolatti et al. 2001). The processing of social behaviours activates neural circuits involved in the simulation of motor action and in higher cognitive functions such as mental state inference (Spunt and Lieberman 2013). Mental state inference is a psychological process involving the mentalization system, enabling the subject to explain observed actions in terms of the actors’ mental states (Spunt and Lieberman 2013). According to this view, motor contagion emerges through interindividual interactions, which implies a cognitive component, allowing the establishment of a mental representation (Brown 2022). This

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representation reflects the behaviour of others and provides insight into their mental states (Cracco and Brass 2018). The mirror system thus plays a role in the process of matching self-representations with others’ mental states, such as their perceptions, beliefs, and goals (Brass and Heyes 2005; Chartrand and Bargh 1999; Chartrand and Van Baaren 2009; Heyes and Catmur 2021; Rizzolatti and Craighero 2004). Understanding the mental states of others involves activation of brain regions that underly mentalization processing, such as the medial prefrontal cortex and posterior cingulate gyrus (Whitfield-Gabrieli et al. 2011). A study showed that when observing an action, brain activity was different if there was an intention to understand or copy the action (Decety et al. 1997). In another study, individuals observed videos showing grasping actions either without context – grasping a mug; or with a context – grasping a mug in a scene containing mugs and tea; or with an intention associated with the action  – cleaning the mug or drinking its contents (Iacoboni et al. 2005). Brain activity was recorded by fMRI, and in situations with an intention associated with grasping actions, an increase in brain activity was noticed in regions where hand actions are represented, including the inferior frontal gyrus and the adjacent area of the ventral premotor cortex. According to this study, these areas of premotor mirror neurons are actively involved in understanding action-related intentions. Thus, the role of mirror neurons in action coding is more complex than previously shown and extends from action recognition to intention coding. Logically related neurons (see Sect. 3.3.1) are triggered by the observation of an action (e.g., placing an object on a table) and by the execution not of the same action but of another action functionally related to the observed one (e.g., grasping the object). These neurons are part of a neural chain coding the intentions of others. In this sense, the attribution of intentions is thus performed by the motor system in an automatic way (Iacoboni et al. 2005). This process called mind-reading or mentalization mediates motor contagion (Gallese 1998) and involves the brain’s mentalization network, such as the temporo-parietal junction and posterior cingulate cortex (Chauvigné et al. 2018). This automatic, non-conscious process then allows for the attribution of a mental state to another person, reading another’s mind in order to predict their feelings and behaviours (Gallese 1998). This mechanism provides an explanation for the bystander effect, in which individuals are less likely to help when the number of passive bystanders increases (Cracco and Brass 2018). Indeed, a neuroimaging study showed that participants’ motor cortex activity decreased when the number of passive individuals increased. These results suggest that participants embodied the mental state of the spectators, causing them to inhibit motor responses (Hortensius and De Gelder 2014). It has been suggested that motor resonance allows individuals to tune their minds, which may promote social relationships (Baimel et al. 2015). For instance, human parents synchronize to their babies, smiling along with them, and infer mental states from observed behaviour (Atzil et al. 2014). The shared representation of motor action engages the mind in thinking about the mental states of others, individuals then become aware of what their interaction partners perceive (Baimel et al. 2015). In this sense, motor resonance is associated with theory of mind, which involves the ability to reason about one’s own and others’ mental

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states (Baimel et al. 2015). From this perspective, the mirror neurons underly the motor theory of empathy as they play a role in empathizing with others (Carr et al. 2003; Iacoboni et al. 2005; Leslie et al. 2004; Fabbri-Destro and Rizzolatti 2008). More precisely, according to this theory, emotions are expressed through actions or bodily expressions, and when observing behavioural manifestations of emotions, the emotional brain network is recruited and allows for the alignment or misalignment of behaviours with those of others (Bonini et al. 2022). Thus, the recognition of the emotions of others involves the internal representation of the emotional expressions. Nevertheless, this theory is criticized because empathy does not rely exclusively on the motor representation of bodily expressions. According to De Vignemont and Singer (2006), the empathic response activates somatosensory cortex and limbic and paralimbic structures, and not necessarily motor circuits. These authors thus suggest that the empathic response does not necessarily involve the activation of mirror neurons (De Vignemont and Singer 2006; Hickok 2009).

5.5 Reducing Psychological Distance Embodied cognition theory suggests that the mind and body are connected, supporting the idea that cognition, thoughts, learning, memory, and perception are linked to bodily experiences (Dourish 2001). Thus, bodily experiences play an integral role in cognition and emotion (Meier et al. 2012). This is supported by the so-called shared reality theory, which considers that when individuals interact, they develop a set of inner states together consisting of shared thoughts and feelings (Rossignac-­Milon et al. 2021). At the neurocognitive level, one hypothesis is that synchronous motor activation and motor resonance allow for mental state inference and prediction of others’ behaviours, which improves cooperation and helps reduce psychological distance (Hove 2008; Vacharkulksemsuk and Fredrickson 2012). To lift and carry a heavy object, for example, partners must be synchronized, which requires their minds and bodies to perceive each other to successfully complete the task. Thus, motor resonance leads to proximity of representations of self and other (Baimel et al. 2015; Laughlin and Sejnowski 2003). By representing the mental states of others, the distinction between self and other is blurred, leading to an improvement in the quality of the interaction (Roberts et  al. 2016) and a reduction in the complexity of the interaction (Bente and Novotny 2020). Reducing psychological distance then optimizes the energy expended during a social interaction (Bente and Novotny 2020).

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5.6 Behavioural Synchronization Induces Prosociality Social beings have a strong tendency to synchronize their behaviour with those of conspecifics (Epple and Alveario 1985; Range et al. 2007). Behavioural synchronization is sometimes interpreted as an adaptation to the natural environment (Nagasaka et al. 2013). It would then have no direct and immediate benefit at the individual scale, but would be of benefit for the whole group, when a herd of prey flees a predator for example. Behavioural synchronization of learned behaviours is then a generalization of this adaptive mechanism to the social environment (Nagasaka et al. 2013). It helps maintaining group cohesion (Conradt and Roper 2005; Bonanni and Cafazzo 2014). Prosociality refers to behaviours that are intended to benefit others (Tennie et al. 2016). Synchronized individuals are more attentive to the interaction, and it leads to prosociality, as it increases the tendency of agents to help each other afterwards (Gvirts and Perlmutter 2020). In humans, behavioural synchronization induces increased sympathy and quality of relationships between individuals (Hove and Risen 2009). Behavioural synchronization facilitates the building of social rapport (Nagasaka et  al. 2013). After adopting rhythmic movements together for a few minutes (walking, dancing, clapping, or greeting), synchronized individuals are more likely to exhibit prosocial behaviours towards each other and report feelings of trust, collaboration, cooperation, and willingness to help (Hove and Risen 2009; Lumsden et  al. 2012; Miles et  al. 2009; Paladino et  al. 2010; Rabinowitch and Meltzoff 2017). Synchronous group-level movements, such as a military march, or a dance troupe, constitute social interactions in which individuals are able to develop a sense of unity and common cause for collective action (Brown 2022). The link between prosociality and behavioural synchronization has been found within intraspecific interactions in humans, dogs, baboons, and chimpanzees (Davila-Ross et al. 2011; Mancini et al. 2013; Persson et al. 2018). The link between prosociality and behavioural synchronization is bidirectional, as behavioural synchronization generates affiliative relationships and cooperation, and conversely prosocial attitudes enhance the degree of behavioural synchronization (Leighton et al. 2010). In humans, the prosocial consequences of behavioural synchronization are present from an early age. 3–5-year-old children are more helpful after being copied (Carpenter et al. 2013), and 14-month-old infants rocked to a musical beat are more likely to help an experimenter pick up an object when the experimenter had previously synchronized to the rocking than when the experimenter had not (Cirelli et al. 2014). These prosocial consequences of behavioural synchronization are not limited to the partners in the interaction, they extend to the people around those partners, increasing cooperative and altruistic behaviours (Reddish et al. 2013; Valdesolo and DeSteno 2011). Behavioural synchronization thus binds people together (Chartrand and Bargh 1999). Social attitudes modulate behavioural synchronization through their effect on inhibitory mechanisms (see Sect. 4.2) (Leighton et al. 2010). A prosocial attitude for example leads to distractibility and impairment of the ability to distinguish

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oneself from others, and promotes motor synchronization, whereas a more negative attitude promotes the inhibitory mechanisms (Leighton et al. 2010). Furthermore, individuals with a prosocial attitude, resulting in increased visual attention to their mates as mentioned previously, spontaneously synchronize with a partner to a greater extent than those with a more egocentric orientation (Chartrand and Bargh 1999; Leighton et al. 2010; Lakin and Chartrand 2003; Lumsden et al. 2012; van Baaren et al. 2009). Alternatively, behavioural synchronization is also modulated by self-focus. In humans, individuals who perceive themselves as unique and independent synchronize less to their peers than individuals who feel connected to others (van Baaren et al. 2003). When observing an action, activation of the motor system involved in shared representation is lower for individuals who feel self-centred compared to individuals who feel connected to others (Schütz-Bosbach et al. 2009). Activation of shared representations during action observation may therefore be inhibited by self-focus, thus preventing motor contagion (Spengler et al. 2010). A study showed that motor contagion is inhibited by self-focus during action observation: when athletes focused on themselves, motor contagion was lower than when they focused on other players (Takeuchi et al. 2018). At the interspecific level, a study looking at interactions between chimpanzees living in a zoo and visitors found that the two species exhibited behavioural synchronization for 10% of the actions produced (Persson et  al. 2018). Interactions exhibiting some form of behavioural synchronization lasted significantly longer than those that did not (Persson et al. 2018). Other studies showed that macaques and capuchins are more attentive, interact more, and exhibit more affiliative behaviours with humans who synchronize to them (Paukner et al. 2005, 2009). In a study involving pet dogs in the presence of two unknown experimenters, one synchronizing to the dog, the other performing a predefined path, it was shown that dogs have a social preference for the person who synchronized to them (Duranton et al. 2019). An hypothesis suggests that during the course of evolution, synchronized behaviours were developed with the aim of creating large-scale social bonds. Behavioural synchronization would therefore be adaptive as it would enable coordination and collaboration between individuals (Bente and Novotny 2020). This social function would be derived from the evolution and selection of smaller-scale functional mechanisms. The prosocial benefits of behavioural synchronization would come from dyadic interactions, and then the physiological effects would have been developed on a larger scale, enabling synchronized behaviours on a collective scale (Lameira et al. 2019). Finally, it should be noted that the positive effects of behavioural synchronization on social interaction are sometimes limited. For example, depending on the task, behavioural synchronization does not always result in improved performance (Wallot et al. 2016). There is a trade-off between the social benefits of behavioural synchronization and the cost of time lost to other activities, such as foraging (McDougall and Ruckstuhl 2018). When a behavioural adjustment is not expected, it does not necessarily lead to a sense of closeness (Hale and Hamilton 2016). To our knowledge, there are no studies on the conditions under which individuals operate against this behavioural display.

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Chapter 6

Social Modulators of Behavioural Synchronization

6.1 Factors Related to the Observer 6.1.1 Effect of Attentional State Attentional state is a key determinant of behavioural synchronization. During a social interaction, individuals spontaneously and systematically adjust their behaviours in response to those of their partners. This continuous mutual adaptation results in behavioural synchronization to which all involved individuals contribute (Dumas et al. 2010). Behavioural synchronization facilitates communication, but to sustain it, beyond perceiving the behaviour of others, a certain level of attention between the interacting partners is necessary (Bunlon 2015; Bente and Novotny 2020). To be synchronized, individuals must direct their attention to each other (see Sect. 4.7) and any behaviour that increases attention towards an individual has the potential to facilitate behavioural synchronization (McDougall & Ruckstuhl 2018). Therefore, individuals must be attentive to each other and use each other’s body cues to synchronize (Rinott and Tractinsky 2021). The effect of attentional state on behavioural synchronization has been extensively studied in humans and animals (Brass et al. 2001; Leighton et al. 2010). In chimpanzees and white-faced capuchins, for example, group members appear to pay attention to the speed of their partners and use this information to move together (Leca et al. 2003); speed thus seems to inherently convey information. Observers spontaneously direct their attention to individuals performing a synchronized action, which is not necessarily the case when observing individuals that are not synchronized (Baimel et al. 2015; Rinott and Tractinsky 2021). Very importantly, behavioural synchronization is stronger when individuals are more attentionally focused on their partners (see Sect. 4.7). When beginning a movement, animals vocalize to attract the attention of their mates, thereby indicating their willingness to move (Radford 2004). Visual attention allows for synchronization of behaviours, thereby © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0_6

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maintaining cohesion within the group (Dunbar and Lehmann 2013). In a study conducted in humans, when two participants walked side by side while performing a task involving a simple cognitive load, such as hearing a story, locomotor synchronization was higher than when they performed a task involving a high cognitive load, such as paying close attention and retaining the content of the story. This result indicates that task complexity induces increased attention towards the concurrent task of locomotion, which decreases the attentional resources available for the partner, and consequently decreases the strength of behavioural synchronization (Zivotofsky et al. 2018). The direction of attention is mainly associated with the direction of gaze, and this association is learned early in life (Shepherd 2010). In humans, from the first year of life, babies learn that they are more successful at reaching an object when they look at it. Thus, focusing attention on a target by directing the gaze towards that target is part of the behavioural repertoire from infancy (Rizzolatti et  al. 2001). Directing one’s gaze towards another individual therefore implies that one is paying attention to that individual (Palagi and Cordoni 2020), so when visual information is exchanged between partners, behavioural synchronization can occur (Nessler and Gilliland 2009). An experiment was conducted in humans to identify the effect of visual attention on the degree of behavioural synchronization (Richardson et  al. 2007). Participants were seated in rocking chairs, either next to each other or facing each other, and had to rock their chairs either at their own pace or in intentional coordination. They could only make eye contact in the face-to-face condition. Results showed that spontaneous or intentional behavioural synchronization between partners was enhanced when they faced each other (Richardson et  al. 2007). In another study, dyads were each asked to swing a pendulum either while looking at the other’s pendulum or while looking away (Schmidt et  al. 2007). Spontaneous synchronization of pendulum swings occurred more frequently when partners looked at each other’s pendulum than when they looked away. The observed synchronization remained unaffected by whether or not participants could communicate with each other (Schmidt et al. 2007). These results indicate that behavioural synchronization is mediated by the visual attentional state and the degree to which an individual detects visual information about another agent’s movements. At the interspecific level, recent studies have shown that dogs are able to engage in complex interactions with humans, particularly through eye contact, and are sensitive to their owner’s attentional state (Call et al. 2003; Horn et al. 2013; Virányi et al. 2004). Eye contact is an important component of owner-dog interactions. To obtain an inaccessible food item for example, dogs use visual cues, including the owner’s availability to make eye contact, and increase their visual communication behaviours when eye contact is made with the owner (Savalli et al. 2016; Udell et al. 2011). Thus, dogs are able to differentiate between an attentive person, whose gaze is available to make eye contact, and an inattentive person, who looks away and is not available to make eye contact with the dog (Savalli et  al. 2016). In another experiment, dogs observed the hiding of a toy or a treat, followed by the return of their uninformed owner to the room (Gaunet and Deputte 2011). The results showed that the dogs optimally positioned themselves relative to the height of the target and

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Fig. 6.1  Dog’s direction of attention linked to dog’s locomotor synchronization with the owner. Dogs focus their attention on their owner to synchronize their locomotor behaviour with that of their owner

their owner’s line of sight, and alternated their gaze between the target and their owner. These behaviours suggest that dogs are able to draw the owner’s attention to the target’s location (Gaunet and Deputte 2011). Dogs are thus able to adapt their behaviours based on their owner’s attentional state (see also Gaunet and Massioui 2014). In addition, dogs regularly direct their gaze towards their owner to synchronize their locomotor behaviour with their owner (see Fig.  6.1) (Duranton et  al. 2017a, 2018a), highlighting the link between attentional state and interindividual synchronization at the interspecific level.

6.1.2 Effect of Interindividual Distance Proxemics corresponds to the study of the role of distances in interindividual interactions (Gorrini et al. 2016). This topic is directly related to behavioural synchronization, since location synchronization, one of the three components of behavioural synchronization, involves spatial proximity between individuals at a given time. Behavioural synchronization thus depends on interindividual distance. Experimentally, the effect of interindividual distance on behavioural synchronization was first demonstrated in static settings for methodological reasons, with still individuals facing each other (Costa 2010). However, non-verbal behaviours have dynamic properties, making it imperative to incorporate this dynamism into the study of interindividual distance. This is particularly crucial because proxemic behaviours during movements lead to different spatial configurations compared to those identified in static settings (Costa 2010). During a walk for example,

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individuals are not face-to-face, resulting in a reduction in interindividual distance and thus promoting social interactions (Costa 2010). In many bird and mammal species living in groups, behavioural synchronization is stronger when individuals are physically closer (McDougall and Ruckstuhl 2018). In sheep, spatially close individuals synchronize their behaviours more than distant individuals (McDougall and Ruckstuhl 2018). Similarly, in marsupials, the degree of behavioural synchronization increases with spatial proximity (Pays et al. 2007). In horses and lemurs, the spread of behaviours within the social group depends on interindividual distances: the closer individuals are, the faster behaviours spread (Briard et al. 2021; Sperber et al. 2019). In Tibetan macaques, contagion of social behaviours such as scratching is correlated with spatial distance between individuals, as it occurs more frequently between individuals within 1 m (Zhang et al. 2022). The effect of spatial proximity on motor contagion is an important aspect of group life as it helps to maintain group cohesion and allows synchronization of group members’ activities (Zhang et al. 2022). In humans, for small groups, individuals walk side-by-side at an average distance of less than 1 m, maintaining spatial cohesion that allows communication (Gorrini et al. 2016). For groups of more than four individuals, the spatial configuration of the group changes (see Sect. 6.3.2), which affects interindividual distance (Gorrini et al. 2016). The group then tends to fragment into smaller units of triads or dyads to keep interindividual distance low (Costa 2010). Besides, physically close individuals are more likely to synchronize, and behavioural synchronization maintains proximity between individuals (Dunbar and Shultz 2010). Behavioural synchronization and interindividual proximity are thus mutually reinforcing: interindividual proximity increases the degree of behavioural synchronization between individuals, and behavioural synchronization increases interpersonal proximity, this two-way reinforcement contributes to the stability of social interactions (Lakin et  al. 2003). Interpersonal distance is also modulated by the relationship between individuals: the more affiliated they are, the closer they interact, suggesting that reduced peripersonal space is the result of increased social attraction (Costa 2010). In humans for example, when walking on the street, couples have a lower interpersonal distance than co-worker dyads (Zanlungo et al. 2017). This link between social relationship and interindividual distance is perceived by individuals outside of the interaction. In a behavioural study, participants were asked to observe interacting individuals and estimate their level of relationship (Wellens and Goldberg 1978). When interacting partners were spatially close, observers estimated that they had a closer relationship than when they were farther apart. At the interspecific level, a study focusing on behavioural synchronization between chimpanzees living in a zoo and visitors showed that both species exhibited more behavioural synchronization when they were physically close (Persson et  al. 2018). Studies of dog locomotor synchronization with humans have also demonstrated the link between physical proximity and social familiarity. Pet dogs indeed spend more time within 1  m of their owners compared to shelter dogs and their caretakers, which is explained by a closer relationship between pet dogs and their owners compared to shelter dogs and their caretakers (Duranton et al. 2017b).

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6.1.3 Effect of Affiliation 6.1.3.1 The Most Affiliated Individuals Are the Most Synchronized Another important social modulator of behavioural synchronization is the level of affiliation between interacting partners. Behavioural synchronization is a universal social ability; individuals are able of synchronizing to any congener, whether familiar or not. However, social relationships influence individuals’ propensity to synchronize, as affiliative bonds involve familiarity-based interactions that enhance the perception of closeness, security, and increase the degree of behavioural synchronization (Feldman 2015). Thus, individuals synchronize more to those with whom they are affiliated compared to non-affiliated or unfamiliar individuals (Briard et al. 2021; Shamay-Tsoory et al. 2019). Conversely, if the observer does not have a close relationship/bond (see Sect. 4.6.1) with the observed individual, behavioural synchronization is attenuated (van Baaren et  al. 2009). In general, individuals with strong social bonds move together to stay in close proximity to each other (Krueger et al. 2014). In primates and dogs, for example, the degree of behavioural synchronization is correlated with the frequency of affiliative interactions (Krueger et al. 2014). Dogs spend more time close to affiliated congeners compared to unfamiliar congeners (Duranton 2020). In Tibetan macaques, behavioural synchronization is stronger between affiliated individuals, and it strengthens social relationships (Zhang et al. 2022). In humans, individuals with close relationships, such as a couple or a mother and child, tend to synchronize more than individuals who do not know each other (Julien et  al. 2000). Regarding the mother-infant dyad, studies show that behavioural synchronization develops towards the end of the first year of life, allowing the infant to prepare for more complex social interactions (Tunçgenç et al. 2015). In addition, it is well known that parents reciprocally copy their infant’s behaviours. Co-regulatory processes therefore occur during early social interactions, facilitating the maintenance of social interaction and reflecting the quality of the interaction between the two members of the dyad (Verde-Cagiao et al. 2022). At the interspecific scale, dog-human behavioural synchronization depends on the level of affiliation between them: dogs synchronize their behaviours more to their owner than to an unfamiliar person, and shelter dogs exhibited activity synchronization and temporal synchronization with their caretakers at a lower rate than pet dogs with their adult owners (Duranton and Gaunet 2015, 2018; Duranton et al. 2017b, 2019a; Gaunet et al. 2014; Palagi and Cordoni 2020). Affiliation thus plays a fundamental role in behavioural synchronization, but it has not been studied in all social species. In horses, for example, the association between social bonding and the level of behavioural synchronization has not been yet reported (Krueger et al. 2014). Even for third-party observers, behavioural synchronization is perceived to be related to the level of relationship between individuals. Simply observing synchronized individuals induces the feeling that individuals are close and constitute a social unit (Lakens 2010). Participants of a behavioural experiment observed two

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people walking with varying degrees of behavioural synchronization and were asked to estimate the level of relationship between them. Ratings indicate that the strongest relationships were associated with the most synchronized walkers (Miles et al. 2009). At the interspecific level, a study showed that the perception of dogs’ behavioural synchronization with their owners was assessed accurately, even for non-experts in canine ethology (Duranton et al. 2018b). 6.1.3.2 Willingness to Affiliate or to Achieve a Common Goal Modulates Behavioural Synchronization in Humans The degree of behavioural synchronization is modulated by the level of concern for the other, and affiliation develops with behavioural synchronization (Feldman 2012). In humans, behavioural synchronization is inhibited when individuals are at odds, have divergent goals, or are unwilling to affiliate (see Sect. 4.2). Behavioural synchronization occurs during a social interaction with a shared intentionality, a common goal, or a desire to create a social bond (Gvirts and Perlmutter 2020). Synchronization is indeed enhanced when persons feel close (have shared opinions or beliefs, for example), wish to create or strengthen their bond, or have a desire to achieve a common goal, during a cooperative task for example (Gvirts and Perlmutter 2020). Behavioural synchronization is stronger when participants are  engaged in a collaborative action compared to a non-interactive or non-cooperative social activity (Gvirts and Perlmutter 2020). Additionally, an affiliation goal, whether conscious or not, is associated with higher behavioural synchronization than when there is no affiliation goal (Lakin and Chartrand 2003; van Baaren et al. 2009). Behavioural synchronization then induces perception of social proximity between individuals (Shamay-Tsoory et al. 2019). The chameleon effect refers to the tendency of individuals to unconsciously change their behaviour to match that of the people with whom they interact (Chartrand and Bargh 1999), alluding to the way chameleons change colour to adapt to their environment (Rauchbauer and Grosbras 2020). This mechanism occurs outside of consciousness, and without any intention to imitate. A classic example is when a person involuntarily copies the postures and facial expressions of another person (Rinott and Tractinsky 2021). The chameleon effect creates an affiliation between individuals (Lakin et al. 2003). The relationship between partners or the desire to create affiliation increases the degree of behavioural synchronization and individuals may be motivated to synchronize to strengthen the social bond between them, without seeking to learn a new behaviour (Charafeddine et  al. 2021; Chartrand and Bargh 1999; Lakin and Chartrand 2003; Over and Carpenter 2013). Behavioural synchronization thus helps individuals develop their skills and preferences and  it strengthens their social bonds (Charafeddine et al. 2021). Conversely, if individuals do not want to bond with each other, they will unconsciously try to prevent behavioural synchronization (van Baaren et al. 2009).

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6.1.3.3 Behavioural Synchronization Is a Cue to Similarity That Promotes Affiliation in Humans and Non-human Animals Individuals tend to align their behaviours and preferences with those similar to them. In humans for example, young children (4–5  years old) align their toy or activity preferences with those of their peers, even if it means changing their initial preference to align with their peers (Marinho 1942; Shutts et al. 2009). In a mirror way, behavioural synchronization induces a perception of similarity between interacting partners, as they perceive themselves as united, which leads to subsequent affiliative behaviours (Cirelli et al. 2014). Behavioural synchronization, as an index of self-similarity, is present from early life (Leclère et al. 2014; Xavier et al. 2018). From birth, infants witness synchronous and asynchronous interpersonal interactions. By age 1, infants become active social agents, understand that others’ behaviour can be goal-directed, and develop the ability to engage in coordinated interpersonal activities (Cirelli et al. 2014). They are thus able to interpret social interactions, they prefer interaction partners who move synchronously and show spontaneous helping behaviour for example (Cirelli et  al. 2014; Tunçgenç et  al. 2015). Behavioural synchronization, as well as other cues to self-similarity, such as mother tongue, therefore contributes to affiliative behaviours and social preference from an early age (Cirelli et al. 2014). A study showed similar results in dog-human interactions, as dogs exhibited a social preference for unfamiliar humans who synchronized their locomotion with them compared to unfamiliar humans who did not (Duranton et al. 2019b).

6.1.4 Effect of Willingness to Be Like Other or to Belong to a Group/Social Comparison Behavioural synchronization is an automatic and uncontrolled process involving the mutual entrainment of at least two individuals (Bente and Novotny 2020). It involves perceptual interaction through sound, touch, or sight, as well as the establishment of a mental connection and social attachment between individuals (Alderisio et  al. 2017). Behavioural synchronization is thus sometimes communicative. For example, in the case of an empathic response during a social interaction, copying a social partner’s facial expression conveys the message ‘I feel your pain’ (Bavelas et al. 1986). In other contexts, it conveys information about achievement or competition: ‘I can do that too’ or relative status: ‘I admire you’. However, one of the most important messages that can be conveyed through behavioural synchronization is ‘I am like you’ or, at the group level, ‘I am one of you’ (Meltzoff 1995; Over and Carpenter 2013). Behavioural synchronization is closely related to cooperativeness and perceived sense of group entitativity (Bente and Novotny 2020). It is even considered as a central mechanism of group formation and collaboration. Behavioural synchronization at the group level indeed allows for the maintenance of a certain

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cohesion between individuals (Petit and Bon 2010). In humans, the sense of group membership is important because members of a group feel close, and behavioural synchronization is higher among them than with individuals outside the group (Shamay-Tsoory et al. 2019). Behavioural synchronization is thus a relevant cue to indicate in some way that the individuals belong to the group with which they interact. Without behavioural synchronization, social groups would dissolve, so it acts as a glue that holds groups together (Dunbar and Shultz 2010; Duranton and Gaunet 2016; Gautrais et al. 2007; Lakin et al. 2003). When human synchronize their behaviours with those of others, they develop feelings of sympathy, closeness, connection, commitment, and similarity towards others (Baimel et al. 2015; Rinott and Tractinsky 2021). When individuals perform the same task asynchronously, these feelings are not found (Baimel et  al. 2015). From childhood, individuals synchronize their behaviour with others to communicate their similarity and thus establish their position within a group (Over and Carpenter 2013). Behavioural synchronization is therefore related to the need to belong to the group and the desire to affiliate with others. This is present from early childhood, so it partially explains the pressure felt to fit in and be like the other group members, at school for instance. It also explains some aspects of intergroup behaviours, including why individuals within the samegroup tend to synchronize more with each other than with out-group members (Fiske 2019). In-group members cooperate more than out-group individuals and are likely to share equivalent norms or preferences. Norms are the behaviours or choices most expected in a group context, distinguishing the group from other social groups (Charafeddine et  al. 2021; McDonald and Crandall 2015). However, some authors report that the prosocial effects of behavioural synchronization also extend to intergroup relationships (Miles et al. 2011). More precisely, the authors asked persons to perform a rhythmic action together with a member of the same or a different group. Results showed that synchronizing with a person from a different group promoted the feeling of proximity between individuals, thus reducing social boundaries. These effects may be mediated by an increased perception of interpersonal similarity through the display of behavioural synchronization (Rabinowitch and Knafo-Noam 2015; Valdesolo and DeSteno 2011). Social alignment reinforces the overlap of shared representations in the perception and execution of those behaviours. This overlap between self and other leads to social cohesion (Lang et al. 2017; Tarr et al. 2014), also extending to out-group members and helping to overcome group boundaries (Rauchbauer and Grosbras 2020). Finally, when individuals feel concerned about others, feel close to or dependent on others, or want to affiliate with others, they are more likely to align behaviours with others. According to the optimal distinctiveness theory, individuals seek to balance the desire to be different from others and the desire to belong and feel similar to others (Brewer 1991). When individuals feel too distinct from others, they will thus tend to synchronize with others to regain this balance (van Baaren et al. 2009).

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6.2 Factors Related to the Observed Individual(s) 6.2.1 Effect of Leadership 6.2.1.1 Definition of Leadership and Distinction Between Leader and Initiator When individuals synchronize their behaviours, two mechanisms take place: –– Mutual adaptation: The interacting individuals adapt mutually and continuously their behaviour, incorporating perceptual information from one another. In this case, individuals tend to minimize the difference between their action and perceptual feedback (Heggli et al. 2019). –– Leadership: One or few individuals present less behavioural plasticity than the others and thus become leaders. In this case, the action-perception coupling is stronger in the followers, they tend to minimize the difference between their action and perceptual feedback to a greater extent than leaders (Heggli et al. 2019). The presence of a leader is another factor modulating behavioural synchronization. Leadership is defined as a form of social interaction in which one or several individuals, labelled and recognized as leaders, initiate group activities and entrain one or more followers (Bourjade et al. 2015). Leadership occurs when not all members of the group have the same degree of influence on the behaviour of their conspecifics. Consequently, the identity of the individual attempting to initiate a group activity influences the outcome of that attempt. An initiator may try to start a new activity but may not necessarily succeed in triggering a joining process: the success of their start attempts greatly varies (Petit and Bon 2010). A leader is the focal point of the group and followers are more attentive to the position and activity of the leader than to those of other conspecifics (Leca et al. 2003). The leader-follower dynamic implies that followers naturally synchronize their behaviours to those of the leader: they do the same activities and go to the same place and at the same time. When starting a new activity, a leader is quickly followed and copied by other group members and consequently, followers synchronize their behaviours with each other (Rinott and Tractinsky 2021). When no leadership is present, any individual can initiate a group activity (Holekamp et al. 2000). There is then a regular exchange of the roles of initiator and follower: group members alternately initiate or follow the actions of others. Behavioural synchronization then results from simple rules (Dumas et al. 2010). In self-organizing systems, such as groups of insects or flocks of birds, behavioural synchronization results from interactions between spatially close individuals: each individual takes into account the position of its neighbour to adjust its direction. Recent research has shown that in species without leaders, the success of initiation of collective synchronous movements is attributed to any group member, as any individual can be the initiator of a group movement (Petit and Bon 2010). The success in being followed is generally measured by the number of followers. There is

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no consensus regarding the success of a start attempt: it is deemed successful when there is at least one (Trillmich et al. 2004) or three (Leca et al. 2003) followers, or half the group (Erhart and Overdorff 1999). 6.2.1.2 Leader Emergence Recent research in humans analysed how behavioural synchronization emerges between individuals. Studies showed that influencing signals flow during social interactions in part via non-verbal social information (Rands et al. 2003) and the spontaneous emergence of the leader is a crucial factor directing the emergence of behavioural synchronization. In an experiment involving participants in a joint arm movement action, it was found that specific leadership behavioural patterns emerged spontaneously and increased behavioural synchronization performance (Calabrese et al. 2021). More specifically, the participants were all in the same room and had to simultaneously swing a pendulum or their finger. Time series analysis of participants’ movement positions revealed three patterns of leader emergence: a pattern where the leader, i.e., the most influential member of the group, is the person who is faster than the others, thus moving ahead of all the others; a pattern where the leader is the slowest, lagging behind all the others; and a pattern where leadership is shared between the two individuals who frame the movement of the group, i.e., one moving ahead and one lagging behind the other participants. The authors draw an analogy between these patterns of leadership emergence and the existing configurations in everyday life. Leaders are thus either individuals who move ahead of the rest of the group, as can be seen in rowing competitions; or those who stay behind the group, as can be seen in cycling road races, or those who surround the group, as in track cycling competitions. The three patterns of leadership emergence are represented in Fig. 6.2. Thus, the leader is not necessarily the person who moves in front of the group. This observation has also been made in baboon, where the leader is not necessarily at the front of the group when it moves (Byrne 2000; Stueckle and Zinner 2008). In animals, social species living in groups often face situations in which decision-­ making is necessary: the direction to take when moving, the selection of a resting area, a feeding area, a nesting site, etc. In these situations, if all members do not reach a unanimous decision on the same action, the group will split and its members will lose the benefits of living in the group (Rands et  al. 2003). Leadership thus emerges in groups of animals, even if communication does not rely on language. Some studies have focused on how these leaders emerge, and why other individuals follow them (King and Cowlishaw 2009). Leadership emergence is studied in animals in the context of movements that involve multiple individuals or in the initiation of new activities (Bourjade et al. 2015). Leadership can emerge from simple rules of thumb, e.g., ‘always forage when your resources fall below a threshold or when the other animal is foraging’. In small groups, the animals with the lowest energy reserves start foraging first and are then followed by the rest of the group (King and Cowlishaw 2009). In more complex social groups, leadership emerges

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Fig. 6.2  The three patterns of leader emergence found in humans. The leader, in orange and brown, is either in front of the other individuals (a), or behind (b), or several leaders emerge and surround the other individuals (c)

either in a non-shared manner, when only one animal makes the decision, or in a shared manner, when several individuals make the decision, and the other animals follow (Conradt and Roper 2005). Shared leadership exists in honeybees (Seeley and Buhrman 2001), hamadryas baboons (Stueckle and Zinner 2008), musk oxen (Ihl and Bowyer 2011), red deer (Conradt and Roper 2003), bottlenose dolphins (Lewis et al. 2011), red-fronted lemurs (Pyritz et al. 2011), and horses (Bourjade et al. 2009; Krueger et al. 2014). The existence of shared leadership in social species has been shown to be a necessary and sufficient condition for guiding and protecting groups of animals. Shared leadership has a functional advantage: the existence of different but cooperating leaders improves the ability of group members to cooperate and increases their performance in dangerous or unpredictable contexts (Rands et al. 2003). Shared leadership allows group members to benefit from each other’s information and practices, and reduce the energy expenditure and risks associated with non-synchronous movement (Bourjade et al. 2015; Peterson et al. 2002). In canids, shared leadership is often observable (Holekamp et al. 2000). In a study of free-ranging dogs for example, the leader was defined as the first dog to move in a direction while being followed by at least two companions within 10 minutes. The authors found that dogs formed stable packs with a shared leadership, as leaders started synchronous collective movements (Bonanni et al. 2010). Leadership thus contributes to the social functioning of groups in social species. 6.2.1.3 Characteristics of the Leader Leaders have different properties which are detailed below. –– Leaders are the oldest or the most experienced. Leaders are the oldest and most experienced members of the group. In humans for example, children are more likely to follow individuals who are more competent

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in a given activity, or those who exhibit a greater ability to achieve their goal (Charafeddine et al. 2021). In sheep, individuals are more likely to synchronize their vigilance behaviours to that of older individuals (McDougall and Ruckstuhl 2018). In nesting species, parents typically make decisions about hunting, foraging, and direction of travel, and are followed by their offspring (Mech 1999). Older individuals have been part of the group for longer, so they take initiative to move or forage in the most profitable areas of the territory and it is possible that young individuals follow more experienced animals by motor contagion, as they display a directed movement (Mech 1999; Petit and Bon 2010; Peterson et al. 2002). According to these authors, experienced group members play an important role in guiding less experienced or inexperienced members. In horses for instance, younger individuals follow the movements of older ones. Krueger et  al. (2014) suggested that young individuals benefit from the greater experience of older individuals, as the information provided by the latter may be more reliable than that provided by other group members. In baboons, the whole group react instantaneously to vocal signal given by experienced adults, but not to those given by young individuals (Seyfarth and Cheney 2003). In African elephants, leaders are the older individuals, they have more experience and exhibit increased decision-making abilities, in a defence context against a predator for example (McComb et al. 2011). In many species, leaders have relevant information, such as the location of a food source or migration path (Couzin et al. 2005). Within schools of fish, a small number of informed individuals influence the foraging behaviour of the group and the ability of the school to move towards a target. The larger the group, the lower the proportion of informed individuals needed to influence the movement of the school with a given accuracy (Couzin et al. 2005). In experimental studies in sheep, pigs and chimpanzees, individuals trained to move towards a source of food started to move first, they were then followed by their non-informed mates (Held et al. 2000; Menzel 1974; Pillot et  al. 2010). In horses, individual knowing the location of a preferred food approached the food source more steadily and actively than non-informed horses, and were followed by a higher number of group members than their uninformed congeners (Andrieu et al. 2016). Individuals with relevant information about their environment are thus the one who move towards a target place, while others follow; the formers are thus the influencers of the group. –– Leaders have specific physiological needs. Leadership is influenced by individual physiological needs, which depend on variables such as reproductive status, age, and sex (Fischhoff et al. 2007; Petit and Bon 2010). In many mammalian species, most leaders are individuals of one sex. In canids such as dogs, wolves, and jackals, several studies have reported that leaders are more often males than females (Holekamp et al. 2000; Mech 1999; Peterson et al. 2002). Similarly, in some primates such as the mountain gorilla (Watts 2000), chacma baboon (Rowell 1969), and brown capuchin (Boinski et al. 2000), the leader is the highest-ranking male in the hierarchy. In contrast, in the spotted hyena (Boydston et al. 2001), Guatemalan howler monkey (van Belle et al. 2013), white-­ handed gibbon (Barelli et al. 2008), musk oxen (Ihl and Bowyer 2011), lion (Schaller

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1976), and red-fronted lemur (Pyritz et al. 2011), collective behaviours are led by females. An explanation is that females have higher energetic needs than males in these species, so they go for food first, thus leadership by females gives them first access to resources such as food. In species with long gestation and lactation periods, lactating or pregnant females have higher nutritional requirements than other females (Stueckle and Zinner 2008). In plain zebras, for example, lactating females are more likely to lead herd movements than other females, with the advantage for lactating females being first access to water, related to their energetic needs (Fischhoff et al. 2007). –– Leaders are the boldest. In many animal species, leadership is closely related to some aspects of temperament as the personality of leaders distinguishes them from other group members: leaders are more reckless, and followers are shyer (Petit and Bon 2010). In horses, Briard et al. (2015) found that bolder horses initiated movements more often than shy horses, as they were more explorative and independent, and showed less fearfulness. In sticklebacks, bolder fish are more often leading in collective movements and are less responsive to their congeners than shy animals, the latter are less initiative and follow their congeners more faithfully (Harcourt et al. 2009). In sheep, shy individuals remain in close proximity to their mates and bold sheep sometimes go to a great distance from the group and are more likely to be followed (Sibbald et al. 2009). Finally, more socially indifferent animals instigate more often new activities than others (Conradt et al. 2009). –– Leaders have strong relationships with followers. Social relationships among group members modulate leadership (Ramseyer et al. 2009; King and Sueur 2011). Past leader-follower interactions are thus another factor to consider. Individuals are more likely to follow those who previously led them to rewarding places (Galef 1995; Nakayama et al. 2012). In dogs for example, followers have more close interactions with leaders than with other followers (Bonanni et al. 2010). A study in domestic horses showed that individuals socially bonded were more likely to move together (Briard et al. 2015) in agreement with the effect of affiliation and behavioural synchronization earlier presented. However, a study in feral horses found that group members tended to follow individuals of higher social rank rather than individuals with whom they have close social ties (Krueger et  al. 2014). It is possible that a difference in social structure between domestic and feral horses is responsible for this discrepancy in results. Relationship stability influences the potential for consistent leadership. In social groups where individuals have stable relationships, leaders change less often than in groups with loose relationships (Fischhoff et al. 2007). –– Leaders and social rank. In some animal species living in groups, apes for instance, not all individuals have equal access to resources, such as food or partners. There is then a social structure based on a social ranking system, in which some individuals always have access to resources, the high-rank animals, while other individuals have limited access to

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resources, the low-rank animals (Sapolsky 2005). Leadership is linked to social rank in species with a social organization based on a linear ranking system (Couzin et al. 2005). In rhesus gorillas and mountain baboons, for example, leaders are individuals of higher social rank, they more often initiate group activities, are followed by other individuals, and are more often at the front of the group when moving (Bourjade et al. 2015). Followers tend to synchronize their behaviour with that of leaders and follow other group members much less (King and Cowlishaw 2009). In white-faced capuchins, leadership is distributed, and all individuals can initiate collective movement, regardless of their social rank Leca et al. (2003). Nearly half of the group members are consistently successful in recruiting at least three followers. In addition, when an individual initiates a movement, the longer the group has been in a resting phase, the more followers it has (Leca et al. 2003). In horses, results vary between studies: some authors found that horses pay more attention to higher-­ ranking animals, copy their behaviours and follow them when they initiate movements (Krueger et  al. 2014). However, other studies found that horses do not synchronize their behaviour with high-ranked horses more often than with the rest of the herd, so these studies found a lack of correlation between the social rank and initiation success (Bourjade et al. 2009; Briard et al. 2015). To conclude, leaders are characterized by behavioural and temperament traits, specific physiological needs or knowledge about the environment, the location of resources, etc. These characteristics are often intertwined with the social group structure.

6.2.2 Effect of Social Referencing Social referencing occurs when an individual is faced with a new object or an unfamiliar situation and uses information provided by a familiar social referent: the individual alternates his/her gaze between the stimulus and the referent (referential gaze) and adjusts his/her behaviour based on that of the referent (behavioural adjustment) (Walden and Ogan 1988). Social referencing has been extensively studied in human infants from the age of 5 months onwards (see Walden 1991 for a review). When faced with a new object, infants engage in referential gaze and adjust their behaviour based on perceived cues, approaching the object more if the referent has a positive facial expression compared to a negative one. Interestingly, when the referent is an unfamiliar person, infants exhibit the same level of referential gaze as when the referent is their mother; however, behavioural adjustment only occurs when the referent is the infant’s mother (Zarbatany and Lamb 1985). Social referencing in humans thus depends on familiarity. Social referencing has also been studied in animal species, particularly primates. Some studies report the existence of social referencing in chimpanzees (Russell et  al. 1997) and capuchin monkeys (Morimoto and Fujita 2012), while others

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indicate no evidence of social referencing in chimpanzees (Tomonaga et al. 2004) and macaques (Roberts et al. 2008). Social referencing has also been studied at the interspecific level, between pets and humans. Several studies have shown that when pet dogs encounter a new object (Merola et al. 2012), an unknown person (Duranton et al. 2016), or a potentially stressful situation like a veterinary visit (Girault et al. 2022; Helsly et al. 2022), they alternate their gaze between the unknown stimulus and their owner and synchronize their behaviour with that of their owner. As in humans, familiarity plays an important role in dog’s social referencing with humans: if the referent is an unfamiliar person, dogs alternate their gaze between the new stimulus and the referent but do not adjust their behaviour based on the person (Merola et al. 2012). Another study showed that shelter dogs faced with an unknown person in the presence of their caregiver exhibit referential gazes but very little adjust their behaviour based on their caregiver’s behaviour (Duranton et al. 2017b). Regarding other domesticated species, a study conducted with horses showed that they adjust their behaviour based on their caregiver’s reaction when facing a new object (Schrimpf et al. 2020). Studies conducted with domestic cats revealed that when faced with an unknown object, cats display referential gazes towards their owner and to some extent adjust their behaviour based on that of their owner (Galvan and Vonk 2016; Merola et al. 2015). However, the behavioural adjustment of cats is less pronounced than that of dogs towards their owner. The evolutionary history of these two species may explain this difference: the ancestors of cats were more solitary, while the ancestors of dogs lived in packs and were selected during domestication to respond to human social signals, potentially increasing their propensity to synchronize their behaviours with those of humans (Merola et al. 2015).

6.2.3 Effect of Social Influence, the Case of Humans In humans, if one values certain individuals, their ethnicity, group membership, or social status, there is a tendency to synchronize behaviours with those individuals (van Baaren et  al. 2009). From an early age, social influence impacts children’s behaviour. Mumme and Fernald (2003) found that 12-month-old infants avoided interacting with a new object towards which an adult expressed negative affect. In another study, 4-year-olds children watched an experimenter choose one box from two presented, and then the children were asked to choose between the remaining box, not chosen by the experimenter, and a new box. Children tended to choose the new box only when the experimenter had previously examined both boxes, suggesting that in this experimental condition, children had devalued the box that the experimenter had not chosen. It means that after observing the others not choosing an option, the children also rejected that option. When the experimenter chose a box without looking inside, children chose randomly, suggesting that they consider the choices of others to make their own choice, but are also able to discern whether

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people are a reliable source of information (Hennefield and Markson 2016). Note that this is not social referencing, which occurs only with a familiar person (parent, close relatives, or owner for a dog). The level of social influence also varies with the social status of the demonstrator. In 5-year-old children, parents and teachers are role models who have a higher status than other adults, they have a particularly powerful influence on the child’s behavioural synchronization (McGuigan 2013). Indeed, children are able to synchronize their behaviour to that of an adult demonstrator, and even to copy irrelevant actions, namely overimitation (McGuigan 2013). Nevertheless, children’s behavioural synchronization ability is strongly influenced by the status of the model, with high-status models being more copied than lower-status models, such as an unfamiliar person or a person without authority over the child (McGuigan 2013).

6.2.4 Effect of Visuomotor Profile of Social Agent When individuals synchronize to others, their visual perception of the observed behaviour is processed into a motor command (Hülsdünker et al. 2018). They thus adapt their motor response based on the perceived signals, leading to behavioural synchronization (Nessler and Gilliland 2009). Behavioural synchronization occurs via sensory systems, particularly visual information, reflecting the importance of shared information between partners (Felsberg and Rhea 2021). The visuomotor information perceived by the observers impacts their motor response. More precisely, the characteristics of the observed agent’s motor pattern affect behavioural synchronization (Nessler and Gilliland 2009). Notably, when individuals have the same motor characteristics, synchronization is enhanced (Varlet et  al. 2014). In humans, when two individuals have the same natural pace, locomotor synchronization between these individuals is more stable compared to individuals with different natural paces (Bingham et al. 1999). This reflects the effort for individuals to adjust their behaviour to the perceived movements that are faster or slower than their own (Bingham et al. 1999). An individual’s movement speed profile therefore modulates the degree of behavioural synchronization (Varlet et  al. 2014). Similarly, the biomechanical and spatiotemporal properties of movement influence behavioural synchronization (Hove and Keller 2010; Nessler and Gilliland 2009; Varlet et al. 2014). Similar mechanical properties induce stronger behavioural synchronization compared to different mechanical properties (Nessler et  al. 2013). For instance, leg length affects locomotor synchronization in humans (Bertram and Ruina 2001). The walking pace is indeed dependent on leg length, which is why taller individuals move faster, and dyads formed by individuals of different sizes synchronize less easily (Gorrini

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et al. 2016). A study required dyads to walk side by side on treadmills (Nessler and Gilliland 2009). Spontaneous synchronization of step rate occurred for 62% of dyads. In addition, a relationship between leg length difference and the degree of locomotor synchronization was found, as dyads for which the leg length difference between individuals was smaller had better locomotor synchronization (Nessler and Gilliland 2009). In another study, Nessler et  al. (2013) compared locomotor synchronization between pairs of healthy adults without locomotor disorders and with either similar or different walking patterns, i.e., with the same or different stride length and frequency between individuals. Results show that for similar gait patterns, locomotor synchronization occurs more easily and requires fewer adaptation to achieve. Conversely, greater differences in locomotor pattern between partners induce greater modification of their gait to synchronize (Nessler et  al. 2013). Thus, locomotor synchronization requires adaptation of one’s own gait to the locomotor pattern of one’s partner (Issartel et  al. 2007), and interindividual differences in the physical, mechanical, or spatiotemporal properties of their locomotor behaviours impact behavioural synchronization. Nevertheless, individuals mutually adapt their behaviour to synchronize, so even if these changes affect behavioural synchronization, it remains robust (Hove and Keller 2010; Nessler et al. 2013). At the interspecific level, to our knowledge, the effect of locomotor characteristics on behavioural synchronization has been the subject of only one study. This study investigated the locomotor synchronization of pet dogs to the movements of children (Wanser et al. 2021). Children under the age of 15, like the elderly, have up to 40% lower movement speeds compared to healthy adults (Gorrini et al. 2016). This low movement speed compared to adults is explained by the immaturity of the locomotor pattern in children (Gorrini et al. 2016). The results of Wanser et al.’s study (2021) showed that dogs’ locomotor synchronization with the children was weaker than dog’s locomotor synchronization with their owner. Pet dogs were indeed immobile for 41.2% of the time the child was immobile, while Duranton et al. (2018a) found that pet dogs exhibited activity synchronization for 81.8% of the time their adult owner was immobile. Dogs exhibited location synchronization with the child family member for 27.1% of the test duration, while Duranton et al. found location synchronization rates of 72.9% in pet dogs upon adult owners (2018a) and 39.7% (2019a) in shelter dogs upon adult caregivers. There is another case where visuomotor profile of people affects dogs’ behaviour. It concerns the citizens in cities hosting free-ranging dogs. A study reported that dogs followed pedestrians when crossing streets or crossed the streets as a group with their congeners (Capella Miternique and Gaunet 2020). A parsimonious explanation is that the non-threatening and neutral behaviours of people towards dogs allowed them to use citizens as social references for crossing streets.

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6.3 Modulation of the Spread of Synchronized Behaviours at the Group Level 6.3.1 The Joining Process 6.3.1.1 Importance of the Period Preceding the Joining Process Synchronized behaviours seen in collective movements are sometimes preceded by a specific preliminary period (Bourjade et al. 2015; Petit and Bon 2010). The use of on-board devices for gathering animal trajectory data has allowed for the measurement of initiations of new trajectories (Bourjade et  al. 2015). The dynamic of collective movements is thus the result of a two-step process. The preliminary period influences the next collective movement in terms of the number of followers or the speed of joining, while the initial attempt acts as a trigger for the collective movement (Bourjade et  al. 2009; Petit and Bon 2010). Few studies on collective movements in mammals have taken the pre-departure period into account (Briard et al. 2021). In ungulates, before departure, group behaviours change, as the number of active individuals and the degree of behavioural synchronization between group members increase (Briard et  al. 2021; Ramseyer et al. 2009). Also, during parakeet flight preparation, birds synchronize their yawning and stretching before collective movement (Miller et  al. 2012). The motivation state, the behaviour, and the spatial dispersion of animals before departure reflect the readiness of the group to move (Briard et al. 2021; Petit and Bon 2010). In horses, during the preliminary period, the more the number of individuals present occurrences of pre-departure behaviours, the longer the duration of the joining process. Moreover, the spatial location of individuals within the group is important (Gérard et al. 2020). Indeed, in capuchins, sheep and horses, the lower the spatial dispersion of group members, the shorter the duration of the joining process (Bourjade et  al. 2009; Briard et  al. 2021). Briard et al. (2021) found that before departure, the position of horses is linked to the order in which they joined the movement. Specifically, the initiator of the movement is among the individuals closest to the destination of the movement, and individuals close to the initiator, thus to the direction of departure, join the movement earlier. Conversely, the greater the distance between individuals and the direction of departure, the later they join the collective movement (Briard et  al. 2021). In macaques and white-faced capuchin monkey, studies showed that when initiators start from a central position, and are thus visible to all group members, they are more likely to be followed than when they start from a peripheral position (Leca et al. 2003; Sueur and Petit 2008). An initiation starting from a central position likely prevents a disruption of communication within the group and a missed start by some individuals, the collective movement being thus facilitated (Petit and Bon 2010).

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6.3.1.2 Characteristics of the Joining Process The synchronized movements of gregarious animals may result from an initial voluntary change in the behaviour of one or several individuals, followed by joining responses of other group members (Bourjade et al. 2009). In many species, such as fish, ungulates, human and non-human primates, when few individuals initiate a movement in a certain direction, the whole group may quickly follow (Krueger et  al. 2014). Within schools of fish, when a few individuals change direction to avoid a predator or to move towards a food resource for example, this action often prompts nearby individuals to also change their direction. Changes in direction are then quickly propagated by local interactions. The ability to synchronize requires a transfer of information between individuals (Rinott and Tractinsky 2021). Fish that change their direction of movement in response to the direction taken by their close neighbours quickly get information about the presence of a predatory threat or food source (Sumpter et al. 2008). In horses, two different patterns of group movement have been reported: single-bout and multiple-bout movements (Bourjade et  al. 2009). In single-bout movements, all group members rapidly join the first moving horse. In multiple-bout movements, the joining process is longer, especially when the number of decision-makers increases. During horses’ daily movements, groups rarely fail to keep cohesion, and stops and re-starts of first movers induce stops and re-starts of all group members (Bourjade et al. 2009). In humans, behavioural synchronization is a key feature of many group activities. The movements of interacting individuals entrain each other over time, leading to the emergence of specific and stable behavioural synchronization patterns that can be observed in many everyday situations: hand claps are synchronized when an audience claps, locomotor movements are synchronized when a crowd moves, dancers synchronize their motor movements with each other during their performances (Lumsden et al. 2012). 6.3.1.3 Specific Behaviours to Recruit Partners Specific behaviours are exhibited during the initial phase to recruit partners, such as glances, vocalizations, and posture (Gérard et  al. 2020). In canids, the high-­ motivated animals get up after a resting period (see Fig. 6.3), awake the other pack members, and display specific behaviours like running after them or licking them until all the whole pack is active and ready to move (Petit and Bon 2010). In gorillas, macaques, and baboons, behaviours aimed at recruiting group members are characterized by body direction, animals also emit grumbling vocalizations, alternate their gaze between their conspecifics and the direction they are moving, and attract their  congeners’ attention by approaching them or turning towards them (Leca et  al. 2003; Petit and Bon 2010; Sueur and Petit 2008). This is referential communication (Gaunet and Deputte 2011; Gaunet and Massioui 2014; Gaunet et  al. 2022). In these species, individuals that emit vocalizations to recruit other animals are more followed than those that do not (Petit and Bon 2010). In insects, individuals can display behaviours indicating their willingness to move, either

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facing a direction, moving away or using specific postures (Petit and Bon 2010). In African elephants, the member of the group that initiates a movement recruits other individuals by moving to the periphery of the group, raising a leg and making repetitive grunts until the whole group follows (Hedwig et al. 2021). In domestic horses, expressive behaviours include head movements such as raising head, shaking head, and the amount of time spent galloping or trotting. In addition, the more these specific behaviours are repeated, the faster the joining process is (Gérard et al. 2020). The variation of speed also plays a role in the recruitment process, but in a different way depending on species. In gorillas, capuchins, and baboons, the individual attempting to recruit partners moves slowly away from the group, while in macaques and horses, the one initiating the movement moves quickly (Leca et al. 2003; Gérard et al. 2020; Sueur and Petit 2008). In sheep, when an individual informed of the location of food moves quickly away from the group, a following response by other group members is observed, but the recruitment process is passive, as there are no gaze, vocalization, or herding behaviours. The rapid movement of the informed animal may be sufficient to trigger a following response by the other group members (Pillot et al. 2010). 6.3.1.4 The Spread of Behaviours Within the Group Leads to Behavioural Synchronization According to Petit and Bon (2010), the behaviours produced to recruit partners may reflect the initiator’s attempt to start a collective movement, or an internal motivation to be followed. The motivation level, measured as the repetition of these specific behaviours, is further known to play a significant role in social / motor contagion (McVey et al. 2018; Petit and Bon 2010; Sueur and Petit 2008). This hypothesis is supported by the fact that the individuals exhibiting these behaviours repeat them as long as they are not followed by a sufficient number of conspecifics or by their preferred congeners (Ramseyer et  al. 2009). These behaviours produced by the first individuals may reflect their motivation to move, and can be perceived as an attractive feature by the other group members (Andrieu et al. 2016). In other words, the behaviour of one or more individuals may transmit a kind of impulse to the other group members, enhancing their motivation and increasing their probability to do the same behaviour (Gérard et al. 2020). As more and more animals perceive and reproduce the cues or signals displayed by their conspecifics, a chain reaction of behaviours spreads throughout the rest of the group (see Fig.  6.3) (Gérard et  al. 2020). Through the perception of the behaviours of other individuals, the propagation of behaviours leads to collective synchronized behaviours. First, few individuals adjust their activity according to the information obtained from their neighbour. A positive feedback loop then occurs: the more individuals synchronize, the stronger their influence on other individuals, leading to a propagation and amplification of synchronized behaviours on a larger scale (see Fig. 6.3) (Moussaid et al. 2009). Behaviours then become contagious between individuals in a social group (Chartrand and Bargh 1999; Chartrand and van Baaren 2009). This (visuo)

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Fig. 6.3  Steps of the joining process

motor/social contagion is based on non-verbal communication and behavioural synchronization between members of the group (Cracco and Brass 2018). (Visuo) motor/social contagion allows the spread of a synchronized behaviour performed by

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several individuals (Petit and Bon 2010). This propagation process is widespread and observable in many social species. It is observed in humans for instance when individuals in an audience synchronize their clapping rhythm. Audience members interact through auditory signals and each individual communicates his or her clapping rhythm to his or her neighbours and acquires information about the rhythm adopted by others around. In this way, a common clapping frequency and phase emerges from the interaction between people without any external rhythm being imposed (Moussaid et al. 2009). Similar processes are involved and lead to the synchronization of light flashes in some firefly species (Buck and Buck 1968). Another example is the contagion of synchronized behaviours during periods of vigilance in group-living prey species (Pays et al. 2009). Sheep for instance display vigilance behaviour by raising their heads to scan the environment, with their gaze directed successively in different directions (McDougall and Ruckstuhl 2018). This behaviour performed by one individual influences the vigilance behaviour of a neighbouring individual, leading to a propagation of vigilance behaviour within the group. As a result, individual episodes of vigilance do not appear to be independent of each other; instead, periods of collective vigilance are observed (Pays et al. 2007). 6.3.1.5 Example of a Contagious Behaviour: Contagious Yawning Contagious behaviours spread spontaneously and do not necessarily need to be learned (Miller et al. 2012; Zentall 2003). This contagion is rapid, automatic, and relies on social and neural mechanisms of behavioural synchronization (Palagi and Cordoni 2020). The spread of behaviours has been extensively studied through the contagion of yawning, i.e., yawning occurring within a few minutes of the observation of a yawn. Yawning exists in many animal species, suggesting that this behaviour is phylogenetically ancient (Gallup 2022). In contrast, contagious yawning occurs only in social species, suggesting a more recent phylogenetic origin for yawning contagion (Anderson 2020; McDougall and Ruckstuhl 2018; Massen and Gallup 2017). The function associated with yawning is subject to several hypotheses. Yawning is consistently triggered during states of low arousal and vigilance (Guggisberg et al. 2007; Kasuya et al. 2005). According to Gallup and Meyers (2021), yawning has a function on group vigilance, as it influences conspecifics behaviours by providing information about the yawner’s reduced vigilance, leading to an increase in the observer’s vigilance. This hypothesis is supported by neuroimaging studies that indicate that seeing or hearing another individual yawning activates brain regions involved in attentional allocation to visual search and vigilance, such as the prefrontal cortex (Arnott et al. 2009; Nahab et al. 2009) and the superior temporal sulcus (Tsurumi et al. 2019). Another hypothesis is that contagious yawning triggers the replication of this action pattern in observers (Massen and Gallup 2017; Palagi et al. 2009; Silva et al. 2012), by involving mirror neurons (Haker et al. 2013). Contagious yawning would then play a role in synchronizing group behaviours (Deputte 1994: de Waal and

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Preston 2017; Massen et al. 2015; Miller et al. 2012). Yawning naturally clusters during collective movement transitions (Deputte 1994). In lions, for example, motor synchronization is more frequent when contagious yawning is present than when no yawning is observed, and yawning contagion increases the likelihood of observers to replicate the motor patterns of the action of yawners (Casetta et al. 2021). This synchronization may provide benefits to group members, facilitating greater social cohesion among group members (Duranton and Gaunet 2016; Gallup 2022). Nonetheless, other studies indicate that yawning may be clustered due to similar physiological and circadian rhythms among group members, not by social influence (Campbell and Cox 2019; Gallup 2022; Miller et al. 2012). Further studies are thus needed to examine the influence of yawn contagion on motor synchronization. Authors agree that contagious yawning exists in various primate species including chimpanzees and bonobos, and in canids including dogs and wolves, both in captivity and in the wild (Campbell and Cox 2019; Palagi et al. 2014). However, results are mixed regarding the effect of factors such as affiliation and empathy on yawning contagion (Gallup 2022; Massen et  al. 2015). For example, studies show that in chimpanzees and bonobos, yawning contagion most often originates from yawning by higher-ranking individuals (Palagi et al. 2014; Massen et al. 2015). In primates and canids, some studies of yawning contagion in experimental settings or based on observations of natural interactions show that yawning is more contagious between familiar individuals than between strangers (Palagi et al. 2009, 2014; Romero et al. 2014). However, other studies indicate that yawning contagion is not related to the level of familiarity in these species (Madsen and Persson 2013; Nielands et al. 2020; Massen et al. 2015). More studies are necessary to clarify context facilitating yawning contagion. Contagious yawning is not innate. In chimpanzees, for example, individuals under the age of 3 years do not exhibit contagious yawning (Madsen and Persson 2013). In humans, infants as young as 5  months of age can distinguish yawning from other types of mouth movements (Tsurumi et al. 2019) but yawning is thought to develop from the age of 5 years (Anderson 2020). Listening to a story in which a character yawns repeatedly or watching a video with several repeated yawns does not elicit a contagious yawn in children under 5 years of age, whereas it does elicit at least one contagious yawn in children 5–11 years of age (Greco and Baenninger 1991). Children under 5 months of age are capable of spontaneous yawning but are not sensitive to contagious yawning, with this sensitivity developing during childhood, until it reaches a sensitivity similar to that of adults around the age of 9–11 years (Anderson 2020). Yawning contagion is not only present at the intraspecific scale. Indeed, animals are also sensitive to human yawning. Several studies report that pet dogs, for example, yawn in response to seeing or hearing repeated yawns from an unfamiliar human (Romero et al. 2013; Silva et al. 2012), but not in response to conspecifics (Harr et al. 2009). The effect of social factors has also been studied at the interspecific level. Dogs are no more sensitive to the contagious yawning of a human who behaves prosocially towards them than to the yawning of a human who exhibits antisocial behaviours (Anderson 2020). Furthermore, puppies younger than

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7 months of age are not sensitive to human-produced yawning (Madsen and Persson 2013). These results imply that there has been a selective pressure favouring the significance of paying attention to human cues during the process of domestication (Gallup 2022).

6.3.2 Effect of Group Spatial Configuration and Size on Behavioural Synchronization Behavioural synchronization exists in all social species and at many scales, ranging from finger movements within a dyad, i.e., two individuals, to large collective movements, such as a migrating herd of buffalo or a large crowd of people acting together (Rinott and Tractinsky 2021). Nevertheless, it is important to distinguish scales in group dynamics, as small group dynamics may not result from the same rules of social organization that underly large groups (Giardina 2008). Within small groups, all individuals can interact with each other, whereas at larger scales only local interactions, i.e., between neighbours, can take place (Conradt and Roper 2005). Many studies consider that groups respond to the same characteristics regardless of their size, but the principles of local interactions found in large groups may not apply to small groups (Petit and Bon 2010). These aspects must be considered when studying the modulation of behavioural synchronization as a function of group size (Giardina 2008; Petit and Bon 2010). Group formation is universal in the animal kingdom: insect colonies, schools of fish, herds of wildebeest, etc. For individuals to maximize the benefits and minimize the costs of living in a group, they must be at least partially synchronized in their activities and directions of movement, otherwise group cohesion would be compromised (King and Cowlishaw 2009). Group members therefore synchronize their activities, feed collectively, and move together, which may be a strategy for protection from predators, competition with other conspecific groups, reproduction, and information exchange (Leca et al. 2003). As an illustration, studies of free-living dogs in a variety of environments have shown that these animals form stable social groups of 2–12 individuals, and group activities, such as territory defence, are coordinated by synchronized behaviour characterized by a tendency to do what other group members are doing via some degree of mutual stimulation (Boitani and Ciucci 1995). Wolves also form stable packs of a few individuals, 2–11 on average. The pack is the basic unit of the population, most alone wolves do not remain so for long, as they eventually form their own pack (Fuller et al. 2003). In humans, research in cognitive psychology has shown that the degree of behavioural synchronization depends on the number of agents observed (Cracco and Brass 2018; Cracco et al. 2015). For example, Milgram et al. (1969) measured the frequency with which passersby on a busy street copied groups of one to fifteen people looking towards a high window (Milgram et al. 1969). As the size of the group of people looking up at the window increased, passersby were more likely to

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direct their gaze upward. This amplification effect in behavioural synchronization as a function of group size follows a logarithmic curve, so the amplification is greater when comparing small groups than when comparing large groups. For example, the difference in the degree of synchronization is greater when comparing a group of 5 people to a group of 3 people, than when comparing a group of 10 people to a group of 5 people. In other words, as group size increases, the incremental effect of increasing group size on (visuo)motor or social contagion is weaker (Milgram et al. 1969). In another study, participants were asked to perform a finger abduction task while they observed between one and four hands showing the same or different movements (Cracco and Brass 2018). As the number of observed hands increased, behavioural synchronization improved. Cracco et al. (2022) also studied gaze following as a function of group size. They showed that gaze following increased as the number of people in the group increased. Two hypotheses could explain this effect of group size on gaze following. It is possible that larger groups provide stronger triggers in the observer’s motor system, this would be a bottom-up mechanism (Cracco et al. 2015, 2016, 2019; Cracco and Brass 2018). Another possibility is that large groups influence gaze behaviour because they induce reasoning in the subject: when many people look in the same direction, they must be looking at something important, this would be a top-down mechanism (Milgram et al. 1969). In the street, up to 70% of pedestrians walk in groups. Groups composed of two to four members are the most frequent, while groups of more than five people are rare. Social interactions between walkers generate specific spatial configurations that influence group dynamics (Nicolas and Hassan 2021). Members of dyads typically walk abreast, that is, in a line perpendicular to the direction of walking (Moussaïd et al. 2010; Zanlungo et al. 2017). Groups of three individuals also walk abreast, but the person in the middle tends to stand 20 cm behind the other two individuals, so the line formed by the group becomes slightly curved (Costa 2010; Moussaïd et al. 2010). From 4 to 5 individuals, the linear formation of the walk turns into a V- or U-shaped pattern (Zanlungo et al. 2017). One would expect these groups to have more of an inverted V pattern, as observed in migratory birds. These V- and U-shaped spatial configurations are the result of each walker’s tendency to find a comfortable walking position that allows for social exchanges with other group members (Moussaïd et al. 2010; Zanlungo et al. 2017). These configurations influence movement speed: in an environment without obstacles, as group size increases, the group moves more slowly (Moussaïd et al. 2010). For example, dyads walk 30% slower than single individuals (Gorrini et al. 2016). This decrease in speed maintains spatial cohesion and facilitates interactions between neighbours (Gorrini et al. 2016; Moussaïd et al. 2010; Zanlungo et al. 2017). While these group configurations are robust, they are subject to variations. In larger groups, individuals lose the ability to interact with all group members as distance hinders communication. Consequently, when group members are too far apart to communicate, they tend to disperse, focusing their interactions on those in their immediate vicinity, ultimately leading to the formation of smaller subgroups consisting of two to three individuals (Costa 2010). In other contexts, when the group is in the middle of a dense crowd for example, the group configuration becomes

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more constrained, group members then tend to align themselves in a single file, resembling the movement of ants forming columns  when moving (Helbing et al. 2005).

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Chapter 7

Conclusion: Behavioural Synchronization, a Pillar of Social Cognition

The purpose of this review was to examine the neurocognitive processes underlying behavioural synchronization in humans and animals and to identify the social factors that modulate it (see Fig. 7.1). Behavioural synchronization is a form of behavioural alignment that occurs when individuals do the same thing at the same place at the same time. Action-perception coupling is the neurophysiological basis of behavioural synchronization. Indeed, the perception of a behaviour performed by a third party activates in the observer the sensory processing of visually, auditory, or tactually perceived information. This results in the activation of motor representations of the behaviour in the observer, and the activation of mirror neurons. By motor resonance, the interacting agents then show an interbrain neural synchronization, which can lead to behavioural synchronization by motor contagion. Fortunately, this synchronization does not occur all the time and with any individual. Inhibitory mechanisms indeed prevent motor resonance from occurring in inappropriate situations or when it is unnecessary, when affiliation is not wanted for instance. Hormones are indicators of the degree of behavioural synchronization. Some social factors, intrinsic or extrinsic to the observer, lift this inhibition and activate the mirror neurons. The environmental context and the internal state of observers influence how observed actions are represented in their own motor system. In particular, the attentional state and the distance between the observer and the observed individual modulate action-perception coupling. Similarly, leadership, affiliation, the number of model agents, and the visuomotor profile of the model agents also modulate motor activation. Behavioural synchronization activates the reward circuits and the mutual attention system in each agent, and a feedback loop is established, thus reinforcing behavioural synchronization. Finally, synchronization has various social functions, such as maintaining social cohesion, strengthening social bonds, but also more complex functions such as recognizing and anticipating action or understanding the mental states of others, at least in humans. A peculiar case must be noted. To date, behavioural synchronization has been studied mainly at the intraspecific level. Behavioural synchronization has been © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0_7

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Fig. 7.1  Behavioural synchronization: modulating social factors, synthetic model of the neurocognitive basis, and social implications

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studied at the interspecific level mainly during human-dog interaction. Although dogs are common in human societies, they have a peculiar and specific position around humans. Recent studies have shown that dogs are able to synchronize their behaviours with humans. However, the neurocognitive basis of interspecific behavioural synchronization has been poorly studied to our knowledge. Future studies should thus focus on understanding the mechanisms underlying motor resonance in dogs and determining the social modulators of interspecific behavioural synchronization. This is of major interest for understanding interspecific interactions and properties of mirror neurons, especially whether or not they are innate (phylogeny), and the establishment of the activation period during development (ontogeny). In summary, the functioning of the mirror neuron system offers insights into the behaviours exhibited by both human and non-human animals. It sheds light on why certain behaviours recur or vary within a species or across generations, such as within a family. Additionally, it helps explain how the behaviours of both humans and animals convey non-verbal information that observers can perceive and interpret. As we have described, behavioural synchronization plays a pivotal role in various interactions, making it a potent mechanism that individuals can employ to adjust and influence these interactions. Below, we delve into three specific domains where this synchronization comes into play. Behavioural contagion is a valuable tool for societal challenges, in promoting pro-environmental behaviours within the context of addressing the climate crisis for instance. Individuals tend to mimic observed behaviours, which means they can imitate pro-environmental actions they witness, such as using low-emission transportation, reducing meat consumption, minimizing waste, conserving water, or installing solar panels (Babutsidze and Chai 2018; Cialdini and Jacobson 2021; Kwasny et al. 2022; Wolske et al. 2020). When a reference model is available to demonstrate a new behaviour, people can adopt that behaviour by emulating the model, thereby increasing individuals’ propensity to take action in response to the climate crisis. A wide range of behaviours has been shown to be enhanced when artificial ‘spectators’ are strategically positioned to model behaviour to others, such as reducing littering in public spaces (Cialdini et al. 1990), signing a petition (Bégin 1978), choosing reusable packaging (Dorn and Stöckli 2018), or composting food waste (Sussman et al. 2013). Moreover, it has been found that the observation of two models interacting is even more effective than one (Aronson and O’Leary 1982; Sussman et al. 2013). Sussman and Gifford (2013) demonstrated that two models correctly disposing of their cafeteria waste had an impact on onlookers. In another study, signs were placed in a university gym locker room urging people to conserve water and energy by turning off the water while soaping up (Aronson and O’Leary 1982). When there was also a model performing the appropriate behaviour, the effect on other students’ behaviour was greater, and it was even greater if two models displayed the appropriate behaviour (Aronson and O’Leary 1982). Therefore, by educating future role models through university students, a positive change can be more firmly established. Finally, a different type of field observation indicates that the size of a group of people has an impact on littering behaviour. Indeed, the larger the group of people, the less waste these individuals are likely to leave behind

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(Al-Mosa et al. 2017). It has been suggested that factors such as social disapproval can influence littering behaviour, so people are less likely to do so when they are part of a larger group (Bator et al. 2011; Meeker 1997). Finally, social comparison, through modelling and using more conceptual interventions targeting social norms through campaigns, and financial approaches were the most effective tools for promoting pro-environmental behaviours, while information and feedback were the least effective. Social influence and behavioural contagion can also be applied to societal challenges related to behavioural change for health, such as diabetes, obesity, eating disorders, and lack of physical activity. In fact, many campaigns are conducted to change health-related behaviours (such as increasing fruit and vegetable consumption or increasing physical activity levels), but these interventions are based on information and reflection to persuade individuals to change their behaviours (Marteau et al. 2012). However, human behaviour is not motivated by the consequences of action but is rather guided by automatic processes triggered by environmental stimuli. Interventions targeting these automatic behaviours would thus be more effective. Notably, within a community, individuals tend to adopt similar behaviours. Numerous studies have demonstrated the effect of social influence on health-related behaviours (see Latkin and Knowlton 2015 for a review). Observing peers consuming fruits and vegetables and engaging in physical activity encourages individuals to adopt a healthy lifestyle (Cialdini and Jacobson 2021; Cruwys et al. 2015; Van der Put and Ellwardt 2022). Social influence is therefore an intervention tool for promoting sustainable health behaviour change (Emmons et  al. 2007). Social influence in changing health behaviours applies not only to humans but also between humans and pet animals. Specifically, owning a pet can increase people’s levels of physical activity (Sjögren et al. 2011). Machová et al. (2019) reported that horse owners engage in more physical activity than owners of other animals. Regarding dog ownership, results are mixed: some authors report higher physical activity among dog owners (Brown and Rhodes 2006), while others suggest that dog owners have the same level of activity as non-owners (Machová et al. 2019). These mixed results could be explained by the fact that some owners have multiple animals, not necessarily just one dog. Indeed, the more animals owners have, the higher their level of physical activity increases (Machová et al. 2019). Lastly, understanding the cognitive processes of behavioural contagion sheds light on the importance of copying behaviour in pathophysiology. For instance, stress is highly contagious (Jackson et al. 2005; Botvinick et al. 2005). It is known that babies have elevated cortisol levels when their parents are stressed, as do students when they have highly stressed teachers (Botvinick et al. 2005). The contagion of stress is also evident at the interspecific level, between pet dogs and their owners: dogs’ stress level largely mirrors that of their owners (Sundman et al. 2019). Knowledge of stress contagion mechanisms enables the implementation of appropriate interventions to improve individuals’ well-being. It is also applicable to pain contagion: in humans and animals, perceiving pain in others activates brain areas sensitive to personal pain (Budell et al. 2015). Sensitivity to another’s pain is stronger among familiar individuals. In other words, when familiar people are together,

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they exhibit increased pain behaviours or pain assessments compared to when they are alone (Martin et al. 2015). For example, a pet dog may be presented at a veterinary clinic for pain or lameness when, in fact, the dog is copying the owner’s behaviour, who themselves have pain or lameness. It is therefore essential to consider the possibility of behavioural contagion when providing care for both animal and human patients. Behavioural synchronization is undeniably a pillar of social cognition, as it is observed in myriad everyday situations in both humans and nonhuman animals. To give just a few examples, this ubiquitous phenomenon is observable in social gatherings such as meetings or a family/friend meal, where individuals often synchronize their behaviour with each other. In professional settings, team members frequently synchronized their prosody with that of their manager, enhancing communication and collaboration. Within the confines of a shared household, people naturally synchronize their routines and habits. Pets exhibit behavioural synchronization, with their owners and with other pets in the household, as illustrated in Walt Disney’s film ‘The Ugly Dachshund’. Behavioural synchronization extends to the universal patterns of crowd movement in humans and non-human animals, facilitating efficient navigation and serving as a crucial mechanism for species survival. In essence, aligning to others embodies a fundamental cognitive mechanism that fosters cohesion, communication, and shared experiences, highlighting its paramount role in our interconnected world.

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Index

A Action anticipation, 60, 61 Action Observation Network, 19 Action-perception, 14, 23, 24, 26, 27, 33, 34, 37, 60, 81, 109 Action recognition, 57–60, 63 Action understanding, 26, 57–60 Affiliation, 44, 46, 48, 77, 78, 85, 95, 109 Anticipation, 2, 13, 41 Anticipatory gaze, 60, 61 Attachment, 44, 79 Attention, 6, 7, 48, 66, 73, 74, 86, 91 Attentional state, 73–75, 109 Automatic imitation, 3, 36 B Baboon, 11, 65, 82–84, 86, 91, 92 Behavioural alignment, 1–3, 109 Behavioural change, 112 Behavioural synchronization, 1, 3–5, 11–15, 20, 27, 33–48, 60, 61, 65–66, 73–98, 109–113 Biological movements, 11, 12, 23, 26, 34, 35 Birds, 6, 7, 41, 57, 76, 81, 90, 97 Bonobo, 95 Brain activity, 15, 27, 43, 47, 63 Buffalo, 96 C Canids, 3, 44, 83, 84, 91, 95 Capuchin, 66, 73, 84, 86, 90, 92 Cat, 7, 11, 87 Chameleon effect, 78

Chickens, 11 Chimpanzee, 6, 11, 38, 41, 65, 66, 73, 76, 84, 86, 87, 95 Cockatoo, 6 Collective movements, 83, 85, 86, 90, 92, 95, 96 Congruent movement, 34, 36 Contagious yawning, 45, 94–96 Cortisol, 46, 47, 112 D Decision-making, 82, 84 Deer, 83 Direct-matching hypothesis, 58, 59 Dogs, 3, 7, 11, 12, 25, 26, 45, 47, 58, 60, 62, 65, 66, 74–79, 83–85, 87, 89, 95, 96, 111–113 Dolphin, 83 Domestication, 87, 96 Dopamine, 46–48 E EEG, 22, 25, 26, 40, 41, 43, 47 Elephant, 84, 92 Embodied cognition, 64 Emotion, 47, 64 Endorphin, 46, 47 Entrainment, 4–7, 40, 79 F Face perception, 39, 43 Firefly, 94

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Lamontagne, F. Gaunet, Revealing Behavioural Synchronization in Humans and Other Animals, https://doi.org/10.1007/978-3-031-48449-0

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116 Fish, 84, 85, 91, 96 fMRI, 23, 25, 27, 40, 43, 47, 61, 63 fNIRS, 25, 40, 41, 43 G Gaze following, 6, 7, 97 Gibbon, 84 Goal-directed actions, 21, 23, 27, 34, 58, 60–62, 79 Gorilla, 84, 86, 91, 92 Great apes, 7, 85 Group cohesion, 65, 76, 80, 91, 95, 96, 109 Group membership, 80, 87 Group size, 39, 96–98 Group spatial configuration, 75, 76, 96–98 H Honeybee, 83 Horse, 76, 77, 83–87, 90–92, 112 Hyena, 84 Hyperscanning, 41, 43 I Ideomotor theory, 13–15, 20, 24, 33 Incongruent movement, 33–36 Infant, 7, 15, 25, 26, 45, 62, 65, 77, 79, 86, 87, 95 Insects, 81, 91, 96 Intention, 14, 47, 60–64, 78 Interbrain neural synchronization, 40, 41, 48, 109 Interpersonal coordination, 3 J Jackal, 84 Joining process, 81, 90–96 L Leader-follower, 81, 85 Leadership, 81–86, 109 Lemurs, 76, 83, 85 Lion, 84, 95 Locomotion, 5, 6, 12, 25, 26, 57, 74, 79 M Macaque, 6, 20, 34, 38, 42, 60, 66, 76, 77, 87, 90–92

Index Mammals, 41, 76, 84, 90 Marsupials, 76 MEG, 22 Mental state inference, 62, 64 Mental states, 47, 61–64, 109 Mimicry, 3, 4, 6 Mirror interneurons, 36 Mirror neurons, 19–27, 36, 40, 43, 57–66, 94, 109, 111 Mirror system, 23, 27, 33, 36, 37, 47, 59, 61, 63 Monkeys, 7, 20–22, 25, 84 Motor contagion, 27, 28, 33, 35–40, 57–66, 76, 84, 92, 109 Motor development, 14, 25 Motor experiences, 24–27, 33 Motor inhibition, 36 Motor interference, 33–35 Motor learning, 27, 57 Motor preparation, 60, 61 Motor repertoire, 24–26 Motor representation, 2, 6, 14–15, 23–27, 33–35, 39, 59–61, 64, 109 Motor resonance, 23–24, 26–28, 34, 37, 57–66, 109, 111 Motor system, 14, 15, 23–26, 34, 37, 39, 61, 63, 66, 97, 109 Motor theory of empathy, 64 Musk oxen, 83, 84 Mutual adaptation, 73, 81 Mutual social attention, 48, 109 Mutual social attention system, 48 N Neuroimaging studies, 19, 20, 23, 36, 63, 94 Neurophysiological studies, 19, 33, 42 O Optimal distinctiveness theory, 80 Optimization, 1, 2 Overimitation, 88 Oxytocin, 44–48 P Pain contagion, 112 Parakeet, 90 Perceptual feedback, 81 Pig, 84 Pigeon, 11, 57 Positron Emission Tomography, 60

Index Primates, 7, 20, 23, 58, 62, 77, 84, 86, 91, 95 Pro-environmental behaviours, 111, 112 Prosociality, 65–66 R Rat, 11 Referential communication, 91 Reward system, 2, 48 Rhythm, 1, 4–6, 22, 38, 40, 42, 43, 94 Rodents, 44 Rooster, 6 S Sea lion, 6 Self-similarity, 79 Sensorimotor representations, 2, 26 Sensorimotricity, 2 Sensory perception, 38 Serotonin, 46, 47 Shared mental representation, 62, 80 Sheep, 76, 84, 85, 90, 92, 94 Simulation theory, 61, 62 Social alignment, 4, 48, 80 Social bond, 42, 44–47, 66, 77, 78, 109 Social facilitation, 37 Social factors, 4, 95, 109, 110 Social group contagion, 39, 40, 93, 97 Social influence, 27, 87–88, 95, 112 Social interaction, 1–3, 5, 40, 41, 43, 45–48, 60, 65, 66, 76–79, 81, 82, 97 Social learning, 36, 42

117 Social modulation, 45, 65 Social rank, 85, 86 Social referencing, 86–88 Stickleback, 85 Stress contagion, 112 T Theoretical theory, 61, 62 Theory of mind, 63 TMS, 22, 23, 35, 39 U Ungulates, 7, 90, 91 Unwilling vs. unable paradigm, 62 V Vigilance behaviour, 84, 94 Visual hypothesis, 58 Visual perception, 14, 15, 19, 58–60, 88 Visual system, 11, 13, 37 Visuomotor profile, 88–89, 109 W White-crested tamarin, 60 Wildebeest, 96 Wolf, 84, 95, 96 Z Zebra, 85