فرآیندهای عصبی در مدولاسیون تقلید توسط اعضاء گروه اجتماعی و ابراز هیجانی درگیر شده

Abstract People often spontaneously engage in copying each other's postures and mannerisms, a phenomenon referred to as behavioral mimicry. Social psychology experiments indicate that mimicry denotes an implicit affiliative signal flexibly regulated in response to social requirements. Yet, the mediating processes and neural underpinnings of such regulation are largely unexplored. The present functional magnetic resonance imaging (fMRI) study examined mimicry regulation by combining an automatic imitation task with facial stimuli, varied on two social-affective dimensions: emotional expression (angry vs happy) and ethnic group membership (in- vs out-group). Behavioral data revealed increased mimicry when happy and when out-group faces were shown. Imaging results revealed that mimicry regulation in response to happy faces was associated with increased activation in the right temporo-parietal junction (TPJ), right dorsal premotor cortex (dPMC), and right superior parietal lobule (SPL). Mimicry regulation in response to out-group faces was related to increased activation in the left ventral premotor cortex (vPMC) and inferior parietal lobule (IPL), bilateral anterior insula, and mid-cingulate cortex (MCC). We suggest that mimicry in response to happy and to out-group faces is driven by distinct affiliative goals, and that mimicry regulation to attain these goals is mediated by distinct neuro-cognitive processes. Higher mimicry in response to happy faces seems to denote reciprocation of an affiliative signal. Higher mimicry in response to out-group faces, reflects an appeasement attempt towards an interaction partner perceived as threatening (an interpretation supported by implicit measures showing that out-group members are more strongly associated with threat). Our findings show that subtle social cues can result in the implicit regulation of mimicry. This regulation serves to achieve distinct affiliative goals, is mediated by different regulatory processes, and relies on distinct parts of an overarching network of task-related brain areas. Our findings shed new light on the neural mechanisms underlying the interplay between implicit action control and social cognition.

. Introduction Imagine yourself in a conversation with a friend, or even somebody you have just met. You laugh and have a good time and then you might come to notice that you're sitting in the same position: you both have your legs crossed and lean forward in your chair. In many social interactions, individuals unconsciously align their body postures or mannerisms to each other. This engagement in behavioral mimicry has been termed the Chameleon effect (Chartrand & Bargh, 1999), referring to the chameleon-like way in which interaction partners “merge” with their social surroundings (Chartrand and Bargh, 1999, Chartrand and Lakin, 2013, Heyes, 2011 and Lakin and Chartrand, 2003). The Chameleon effect has been ascribed multiple socially beneficial functions, such as affiliating and bonding with others (Lakin and Chartrand, 2003, Lakin et al., 2003 and Stel and Vonk, 2010), stabilizing group cohesiveness (Lakin, et al., 2003), and enhancing prosocial behavior (Van Baaren et al., 2004 and Van Baaren et al., 2009). Moreover, contextual factors such as liking of the interaction partner (Stel et al., 2010), or the goal to affiliate with him or her (Lakin & Chartrand, 2003), have been shown to enhance behavioral mimicry. Conversely, decreased mimicry has been observed in situations in which it is advantageous to inhibit mimicry (Brass et al., 2001 and Spengler et al., 2009), such as disliking an interaction partner (Stel, et al., 2010) or not wanting to affiliate with him or her (Johnston, 2002). Mimicry thus seems to be regulated in a versatile fashion to different affiliative motives. The present study aimed to identify the (neural) processes engaged in such a flexible regulation of mimicry, in order to gain a better understanding of the role of mimicry in the implicit regulation of social interaction. To this end, we investigated whether and how mimicry of arbitrary finger lifting movements is modulated by salient social signals, i.e., the emotional expressions (happy vs angry) and group-membership (in- vs out-group) of putative interaction partners. However, behavioral mimicry has thus far mostly been studied by social psychologist, using naturalistic paradigms, which usually manipulated or measured the frequency of mimicking acts in interactions between a participant and a confederate (Chartrand and Bargh, 1999, Lakin et al., 2008, Stel et al., 2010, Stel and Vonk, 2010, Van Baaren et al., 2004 and Van Baaren et al., 2009). While such naturalistic paradigms have high ecological validity, they suffer from a number of limitations. For one, they are limited in their ability to experimentally control social cues relevant for social interactions, such as emotion displays or eye contact. Secondly, measuring the frequency of mimicry provides only a crude quantification of the extent of behavioral mimicry. Also, behavioral measures alone are limited in identifying the underlying processes regulating mimicry. While neural measures would be more informative in this respect, naturalistic paradigms are hardly suitable for use in neuroimaging experiments, which usually require repeated trials, and whose measurement constraints mostly preclude the investigation of naturalistic social interaction. Automatic imitation paradigms have therefore been proposed as laboratory models of mimicry (Heyes, 2011), providing an intriguing possibility to study the neural bases of chameleon-like mimicry effects to varying social cues (Heyes, 2011, Klapper et al., 2014, Wang and Hamilton, 2012, Wang and Hamilton, 2014, Wang and Hamilton, 2015, Wang et al., 2011 and Wang et al., 2011). Central to automatic imitation paradigms is the notion that the mere observation of a movement triggers motor resonance processes that facilitate the execution of this very movement (Brass, Bekkering, Wohlschläger, & Prinz, 2000). The label “automatic”, in this context, refers to the fact that the perception-action link operates independently of the explicit intentions of the individual exerting it, as participants are instructed to respond to a number cue (e.g., with a finger-lifting movement (Brass et al., 2000)), but are “automatically” influenced by a simultaneously displayed movement (e.g., a congruent or incongruent finger-lifting movement) acting as a distractor irrelevant to the task at hand (Heyes, 2011). Notably, there is consistent evidence that situational and contextual variables implicitly modify automatic imitation (Grecucci et al., 2011, Klapper et al., 2014, Leighton et al., 2010, Wang and Hamilton, 2012, Wang and Hamilton, 2014, Wang and Hamilton, 2015, Wang et al., 2011 and Wang et al., 2011). For instance, automatic imitation has been shown to be modulated by pro-versus antisocial primes (Leighton et al., 2010 and Wang and Hamilton, 2015), the social status of the interaction partner (Wang & Hamilton, 2012), or the occurrence of direct eye-contact (Wang et al., 2011 and Wang et al., 2011). Studies by Losin, Iacoboni, Martin, Cross, and Dapretto (2012) & Losin, Cross, Iacoboni, and Dapretto (2014) have investigated the modulation of conscious imitation (i.e., instructed imitation of gestures) by group-membership. Importantly, the results suggest that it is the implicit perception of the out-group's social status and not ethnic1 similarity per se which modulates conscious imitation and underlying neural processes (Losin et al., 2012 and Losin et al., 2014). This is vital to notice, as it suggests that it might be first and foremost implicit perceptions and the relevance of the stimuli for one's own current social interest that are crucial for guiding mimicry in a goal-directed fashion. Overall, the stark analogy with the malleability of mimicry in the Chameleon effect renders automatic imitation paradigms a promising tool to investigate mimicry's versatility in implicitly supporting and improving social interaction. As we are using automatic imitation as a laboratory model of mimicry, we will furthermore refer to automatic imitation assessed with this paradigm as mimicry. The term mimicry effect will henceforth be used to refer specifically to the difference in reaction times from incongruent versus congruent trials in such automatic imitation paradigms. Due to their high experimental control and the repeated measurement over multiple trials automatic imitation paradigms also enable studies of mimicry with neuroimaging methods. This allows assessments of the neuro-cognitive mechanisms underpinning the regulation of mimicry in different social contexts and in response to different social signals. In this way some central questions on how the modulation of mimicry is implemented functionally can be addressed. For instance, as proposed by Heyes (2011), the mimicry response could be altered on the one hand by enhanced attention to the task-irrelevant hand stimuli, thus modulating mimicry via input modulation. On the other hand, social-cognitive variables might alter mimicry via modulating its overt behavioral output (i.e., output modulation) (Heyes, 2011). Conducting a functional neuroimaging study might thus allow us to also shed more light on the involvement of in- and output modulation in the course of tailoring mimicry to affiliative goals. Neuroimaging studies on this topic have thus far mainly focused on understanding the phenomenon of automatic imitation itself (Brass et al., 2005, Catmur and Heyes, 2011, Catmur and Heyes, 2013, Catmur et al., 2011, Cooper et al., 2013, Sowden and Catmur, 2015 and Spengler et al., 2009). In these studies, the processes underlying mimicry regulation are typically assessed by contrasting incongruent with congruent trials. In this way it has been shown that mimicry regulation using the imitation inhibition task mentioned above has repeatedly revealed increased activation of the medial prefrontal cortex (mPFC) and the temporo-parietal junction (TPJ) (Brass and Heyes, 2005, Brass et al., 2009, Spengler et al., 2010 and Spengler et al., 2009). Only few studies have investigated the neural processes associated with the regulation of mimicry when presented with different social signals (e.g., Klapper et al., 2014, Wang and Hamilton, 2015, Wang and Hamilton, 2014 and Wang et al., 2011). For instance, the modulation of mimicry when priming participants with pro- and anti-social words has been associated with activation changes in the anterior medial prefrontal cortex (amPFC) (Wang & Hamilton, 2015). Moreover, modulation of mimicry while an “interaction partner” was presented who was either engaging in direct or averted eye-contact was associated with a network comprising the mPFC, the inferior frontal gyrus (IFG) and the superior temporal sulcus (STS) – which all showed higher activation in the direct-gaze condition (Wang and Hamilton, 2012 and Wang et al., 2011). These studies provide important first insights into the psychological and neural mechanisms of the regulation of mimicry. Nevertheless, given the remarkable flexibility of mimicry, different behavioral goals evoked through relevant information in the social environment may engage different social-cognitive and behavior (mimicry) regulation processes. This should be reflected in distinct neural pathways. Direct eye-gaze in a social interaction represents an important social signal, as it may express affiliative intent (Wang et al., 2011 and Wang et al., 2011). Beside eye contact, emotional expressions likewise are highly salient social cues which might enhance or temper mimicry, respectively. A smile, for example, inherently signals affiliative intention, whereas an angry facial expression rather indicates dominance motives (Bourgeois and Hess, 2008, Van Kleef et al., 2004 and Van Kleef et al., 2010). Hence mimicry should be enhanced in response to smiling others, and decreased when confronted with an angry interaction partner. Another powerful factor in human social life is belonging to a cohesive and functional social group (Dunbar, 2012, Dunbar and Shultz, 2010 and Machin and Dunbar, 2011). To signal and to reciprocate affiliation, in order to stabilize social bonds, mimicry might therefore be enhanced more towards in-group members, while out-group members might be imitated less. This has indeed been demonstrated previously (Van der Schalk et al., 2011 and Yabar et al., 2006). On the other hand, some studies have shown opposite effects, suggesting that negative social signals may enhance mimicry. For instance, Grecucci et al. (2011) have shown enhanced mimicry to stimuli with non-social negative valence. Lakin et al. (2008) demonstrated enhanced mimicry after experiencing social rejection, which was interpreted as an attempt to achieve the affiliative goal of regaining social inclusion. As mentioned above, Losin et al. (2012) & Losin et al. (2014) have demonstrated that implicit perceptions of an out-group of low social status modulates conscious imitation and associate neural activations. Thus, implicit social perceptions of an out-group and their relevance for current affiliative goals might also guide mimicry (i.e., unconscious imitation). More specifically, mimicry as an affiliative signal might be up-regulated in response to negative social cues to soothe potentially harmful conflicts (de Waal, 1986, de Waal, 2003 and Keltner et al., 1997). It has been reported for other primates that, depending on the context, affiliative signals, such as embracing or kissing, may reflect attempts to soothe a potential conflict. Primates seem to display these affiliative behaviors to reduce aggression or prevent a potentially harmful encounter, as an alternative to engaging in withdrawal or fighting behavior (Keltner et al., 1997, de Waal, 1986 and de Waal, 2003). In many western countries, Black out-group members are implicitly perceived as posing enhanced (physical) threat (Amodio, 2004, Amodio, 2008, Amodio et al., 2008, Amodio et al., 2004 and Neuberg and Cottrell, 2008). This might in particular be the case if they display anger. This notion seems to be confirmed by previous behavioral results from our group (Rauchbauer et al., 2014 and Rauchbauer et al., 2015). In a series of experiments with more than 180 participants, we revealed enhanced mimicry towards angry Black out-group members, as compared to angry in-group members. Based on these considerations, we suggest that the implicit perception of angry Black out-group members as threatening might lead to an up-regulation of mimicry for the affiliative goal of appeasement (Rauchbauer et al., 2014 and Rauchbauer et al., 2015). That is, rather than eliciting reduced mimicry, reflecting a withdrawal or fight signal, angry out-group members might evoke an up-regulation of mimicry which serves to appease the interaction partner and to de-escalate a potential conflict. These observations suggest that the fine-tuning of mimicry (as an affiliative response) might be even more delicate than previously shown. Moreover, the question emerges how such adaptively tailored modulation of mimicry to distinct affiliative goals, that is, reciprocation of affiliative intent on the one, and appeasement of a potentially threatening encounter on the other hand, is achieved on a process level, and how this is implemented and reflected on the neural level. The aim of the present study was, therefore, to investigate whether and how mimicry (in this case mimicry of arbitrary finger-lifting movements) is modulated by social-affective variables such as emotion displays and group membership of a putative “interaction partner”. To investigate the behavioral and neuro-cognitive processes associated with this modulation, we used an automatic imitation paradigm while participants underwent functional magnetic resonance imaging (fMRI). We extended the imitation inhibition task developed by Brass et al. (2000), which we, in the context of our design, will refer to as mimicry task, by adding social-affective stimuli (social-affective mimicry task (SAMT)). To this end, we presented finger-lifting movements simultaneously with face stimuli differing in ethnic group membership (White/European Caucasian and Black/African-American) and emotional expression (happy and angry). Thus it is important to point out, that we did not measure facial mimicry itself, but were interested in how mimicry of arbitrary finger-lifting movements (carrying no inherent affective or social information) were modulated by task-irrelevant social-affective stimuli. In line with the notion that mimicry might be used as an affiliative signal which is flexibly regulated to varying social requirements, we expected mimicry of the task-irrelevant finger lifting movements to be higher in response to happy facial expressions (signaling affiliative intent) than to angry ones (signaling non-affiliative intent). As for the effects of group-membership previous evidence (reviewed above) suggested an open hypothesis: mimicry might either be enhanced towards in-group members, reflecting affirmation of group bonds, or towards out-group members, in an attempt to appease a potentially threatening interaction partner. Moreover, as our design enabled us to explore interactions between emotion displays and group membership, we predicted based on previous findings ( Rauchbauer et al., 2014 and Rauchbauer et al., 2015) that mimicry should be strongest in response to happy in-group faces, in line with its function to reciprocate affiliative intent, and speculated that it would be stronger in response to angry out-group faces as compared to angry in-group faces, in serving an implicit appeasement function. In the SAMT enhanced mimicry to different social-affective cues is reflected in a uniform measure, which is the magnitude of the mimicry effect. This, however, might not reflect the distinct underlying processes involved in goal-directed regulation of mimicry. Specifically, reciprocation of affiliation and appeasement serve distinct affiliative goals. The former might aim to ensure continuative positive social interaction with the other ( Bourgeois and Hess, 2008, Hess and Fischer, 2013 and Van Kleef et al., 2010), whereas the latter might aim to soothe a potentially negative social exchange ( Keltner et al., 1997). Thus, such distinct types of goal-directed mimicry regulation might potentially be underpinned by distinct processes and neural mechanisms. This could include attentional and sensorimotor, as well as behavior regulation processes. Moreover, social-cognitive processes involved in representing and actively discerning the representation of others' internal states from one's own current state, as self-other distinction, might be engaged in the regulation of mimicry. These processes may operate at different stages, involving both the modification of sensory input and of behavioral output (see Heyes, 2011). We predicted that these processes should be reflected in the engagement of distinct neural networks. First, the right TPJ has been assigned a crucial role in self-other distinction, which in the present context is the active distinction of one's own motor representations or intentions from those related to observed actions (Brass et al., 2001, Brass et al., 2005, Brass et al., 2009, Santiesteban et al., 2012, Spengler et al., 2009 and Spengler et al., 2010). Causal evidence from a tDCS-study reports that excitatory tDCS stimulation of the TPJ enhances self-other distinction in the imitation inhibition task (Santiesteban et al., 2012). The TPJ has further been shown to be engaged in the distinction of self-generated movements from those of a human, as opposed to a non-human agent (Klapper et al., 2014). Thus, the TPJ might be of particular importance in an experimental manipulation where salient social signals, as in our case, interfere with one's task requirements, requiring a higher effort to disambiguate one's own actions from those of others, in order to better comply with task demands. The mPFC is another area that might play a role in mimicry regulation, as overlapping neural activation in social-cognition tasks (investigating mentalizing and self-referential judgments) and in the imitation inhibition task have been shown (Spengler et al., 2009). Moreover, the mPFC has been linked to the modulation of mimicry behavior by gaze processing and social context (Wang and Hamilton, 2012, Wang and Hamilton, 2015 and Wang et al., 2011), and in tasks that require integrating one's own actions with those of out-group members (specifically African-American) (Amodio, 2008 and Amodio et al., 2006). In the context of controlling mimicry by eye contact (gaze processing), it is interesting to note that the mPFC has been reported (by means of dynamic causal modeling) to modulate sensory input to the STS and the IFG. Second, taking into account contextual information in order to regulate mimicry in a goal-directed fashion suggests the involvement of behavior regulation processes sensitive to social needs. Behavioral and homeostatic regulation, in particular in the social domain, have been repeatedly linked to a network including the anterior insula (AI) and the mid-cingulate cortex (MCC; e.g., Lamm and Singer, 2010, Medford and Critchley, 2010 and Shackman et al., 2011). In the present context, these areas might constitute an in- and output module aimed at the regulation of mimicry. The AI, the input-module, codes sensory multi-modal information of the social environment and signals the need for behavioral regulation to the output-module, the MCC. Moreover, recent evidence specifically reports involvement of the (AI) and anterior parts of the MCC in the control of mimicry (Cross, Torrisi, Losin, & Iacoboni, 2013). Third, modulation of sensorimotor processes might allow us to clarify how modifications of mimicry are accompanied by changes in attentional processing of the primary task (the number cue) versus the (task-irrelevant) hand stimulus. Since evidence on the malleability of mimicry by salient social cues is scarce (with the exception of e.g., Wang and Hamilton, 2012, Wang and Hamilton, 2014, Wang and Hamilton, 2015 and Wang et al., 2011), hypotheses about the specific roles of these processes in modifying mimicry in response to our social-affective context manipulation were open. More in general, in line with the different presumed functions of mimicry in either reciprocating affiliative intent, or in appeasing a threatening interaction partner, we predicted that there is no unitary process engaged in all kinds of mimicry regulation, but that distinct processes, engaging different neural networks, underlie mimicry's flexibility.

5. Conclusion Our study demonstrates that automatic imitation paradigms are a valid tool to investigate the influence of social-affective cues on mimicry. Our results show that subtle manipulations of such social-affective cues significantly affect both behavioral and neural measures of mimicry. Crucially, our findings suggest that the regulation of mimicry is not a unitary phenomenon. Depending upon the affiliative goals, it may be supported by distinct social-cognitive, behaviorally regulative, and sensorimotor processes. Thus, the present study confirms the notion that despite its automaticity, mimicry is a highly context-sensitive and implicitly modifiable motor response, and provides further evidence on how the regulation of this response is supported by distinct neuro-cognitive processes.