Emotional contagion of anger is automatic: An evolutionary explanation

Transcription

1 182 British Journal of Social Psychology (2016), 55, The British Psychological Society Brief report Emotional contagion of anger is automatic: An evolutionary explanation Janice R. Kelly*, Nicole E. Iannone and Megan K. McCarty Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA Emotional contagion the transfer of emotions between people is thought to occur automatically. We test the prediction, based on evolutionary psychology, that negative, threat-related emotions transfer more automatically than positive emotions. We introduce a new paradigm for investigating emotional contagion where participants are exposed to videos of faces that morph from neutral to angry or happy expressions. Participants watched these videos under high or low cognitive load. Participants reported more happiness in the happy condition than the anger condition and more anger in the anger condition than the happy condition, supporting our new paradigm. Participants in the happy condition were significantly happier under low compared with high load. Participants were equally angry in high and low load conditions. Emotional contagion is defined as the transfer of emotions from one person to another (Hatfield, Cacioppo, & Rapson, 1992, 1994). This study explores a hypothesis concerning emotional contagion derived from an evolutionary perspective (Spoor & Kelly, 2004) that the contagion of negative, threat-related emotions occurs more automatically than for positive, non-threat-related emotions. Importantly, we explore this hypothesis in the context of a novel paradigm for producing emotional contagion. Emotional contagion There is growing recognition of the importance of the affective life of groups (Kelly & Barsade, 2001; Kelly, Iannone, & McCarty, 2014; Kelly & Spoor, 2012). Drawing on an evolutionary perspective, Spoor and Kelly (2004) argued that affective states in groups were vital in coordinating early group activities by communicating information and facilitating group bonding. Given these important functions, they suggested that group members developed mechanisms of affect regulation to support these functions. A number of researchers have delineated such affect regulation processes in groups (George, 2002; Kelly & Barsade, 2001; Spoor & Kelly, 2004), with emotional contagion being one primary mechanism. Emotional contagion is key to communication in groups because it facilitates the rapid transfer of information. A number of different mechanisms have been proposed for this catching of other s emotions. For example, when a facial expression of another is *Correspondence should be addressed to Janice R. Kelly, Department of Psychological Sciences, Purdue University, 703 Third Street, West Lafayette, IN 47907, USA ( kellyjr@purdue.edu). DOI: /bjso.12134

2 Emotional contagion of negative emotions 183 observed, the observer is thought to come to actually experience the other s emotion through facial feedback mechanisms when the expression is mimicked (Strack, Martin, & Stepper, 1988), through other mimicry processes (Elfenbein, 2014), or through more deliberate mechanisms based on social comparison processes (Gump & Kulik, 1997; Sullins, 1991). Mirror neurons have also been suggested as a possible mechanism (Bastiaansen, Thioux, & Keyers, 2009; Rizzolatti & Craighero, 2004), although this claim is controversial (Heyes, 2010; Hickok & Hauser, 2010). There is abundant evidence that individuals emotionally converge when exposed to affective stimuli. For example, in a diary study of nurses, Totterdell, Kellett, Teuchmann, and Briner (1998) found a significant association between the mood of individual nurses and the collective mood of their work group. In a laboratory setting, Neumann and Strack (2000) used affectively evocative interpersonal stimuli (e.g., tone of voice) to create a congruent mood experience in participants. Other evidence exists for the convergence of emotional experience in developmental, clinical, social, and psychophysiological psychology (see Hatfield et al., 1994, for a review). Mechanisms for producing emotional contagion in the laboratory vary, but often involve the time and resource consuming procedure of training confederates to portray a particular emotion. For example, Barsade (2002) trained confederates to display positive or negative affective states when interacting with na ıve others. Sy, Cote, and Saavedra (2005) induced positive or negative moods in leaders by having them watch funny or serious video clips prior to leading group members through a tent building exercise. A goal of the present research was to develop and test a novel, less labour-intensive method for examining emotional contagion in the laboratory whereby na ıve participants are exposed to a series of brief videos that show male and female faces morphing from neutral to valenced expressions. In our initial test of this paradigm, we examine both the positive expression of happiness and the negative expression of anger. We chose happiness and anger as both are higher arousal emotions (Russell, 1980, 1991). Although more specifics of the procedure are detailed in the method section, the capacity of this procedure to produce emotional contagion effects provides our first set of predictions. Participants who are exposed to expressions that morph from neutral to happy will report more happiness compared with participants exposed to expressions that morph from neutral to angry. In contrast, participants who are exposed to expressions that morph from neutral to angry will report more anger compared with participants exposed to expressions that morph from neutral to happy. The emotional contagion of negative, threat-related emotions There is evidence that emotional contagion may be facilitated for negative, threat-related emotions in particular. Because of the important information value they communicate, Spoor and Kelly (2004) suggested that negative, threat-related emotions may be more contagious than positive, non-threat-related emotions. Negative emotions may communicate information regarding factors external to the group. For example, a fear expression on the part of a single group member may quickly signal danger to the entire group, allowing the group time to respond appropriately to the threat. Alternatively, positive expressions may signal that the external environment is safe. Therefore, emotion expressions in groups may be evolutionarily adaptive, enabling groups to respond appropriately to their environments. This evolutionary explanation suggests that sharing negative, threat-related emotions may be particularly adaptive for groups because they signal external threats.

3 184 Janice R. Kelly et al. This proposition is supported by several converging lines of research. Negative affective states communicate potential threats in the environment and are more likely to receive attention (Rolls, Cowey, & Bruce, 1992). Baumeister, Bratslavsky, Findenauer, and Vohs (2001) also argue that negative emotions have greater impact than positive emotions. These findings suggest that negative acts have a strong impact and are more likely to spread quickly among group members. Indeed, research on contagious depression suggests that negative affective states may be transmitted more easily than positive affect (Joiner, 1994; Katz, Beach, & Joiner, 1999). Spoor and Kelly (2009) also found better agreement in negative rather than positive affective states within interacting dyads. Within work teams, Bartel and Saavedra (2000) found the strongest intragroup agreement with respect to unpleasant mood states. All of these findings are consistent with Rozin and Royzman s (2001) review on negativity dominance and contagion, which pulls together research illustrating negativity dominance across a wide variety of research areas. Thus, negative affective states appear more easily contagious than positive affective states. In the present research, we go one step further and suggest that the communication of negative, threat-related emotions is so critical that their contagion is an especially automatic process. An automatic process is capable of occurring without the need for attention or without drawing on general processing resources. Although the communication of positive affect is also important to the key group processes of communication and group bonding (Kelly et al., 2014; Spoor & Kelly, 2004), it is less proximal to the group s survival and therefore may be more susceptible to moderation by contextual factors, such as cognitive load. We test the automaticity of emotional contagion by examining its operation under high vs. low concurrent cognitive load (Bargh & Tota, 1988). If emotional contagion is automatic, it should produce effects regardless of cognitive load. However, a less automatic process would interact with cognitive load, such that it would produce greater effects in low load conditions. Specifically, we examine the contagion of happiness and anger. These discrete emotions are similar in arousal but differ in valence, with anger being clearly threat-related (Barsade, 2002; Russell, 1980, 1991; Spoor & Kelly, 2004). This leads to our second prediction. We expect that cognitive load will moderate emotional contagion only for participants in the happy emotion condition, such that these participants will experience greater happiness in low than in high load conditions. Given the special evolutionarily adaptive benefits of negative, threat-related emotion transfer, we expect participants to experience similar levels of anger regardless of load. Method Participants and design A total of 182 undergraduate students (92 female) participated in exchange for partial course credit. Four participants under the age of 18 were removed from analyses, as we did not have IRB approval for minors. Further, 18 participants were removed from analyses for reporting below the mean (M = 5.41, SD = 1.10) on fluency in the English language. 1 This resulted in 160 participants (78 female; M age = 19.42). Participants were 1 As we did not have a measure of country of birth, fluency in the English language was the best way to determine those students who differ from the majority of our sample culturally. There is evidence that it is more difficult to recognize facial expressions interculturally (Elfenbein & Ambady, 2002; Weathers, Frank, & Spell, 2002). If it is more difficult for individuals to recognize these emotions, they may be less likely to experience emotional contagion. Therefore, we chose to examine a more culturally homogenous sample.

4 Emotional contagion of negative emotions 185 mostly White (77.5%), 3.8% Black, 4.4% Hispanic, 10% Asian, and 4.4% omitted. The design was a 2 (emotion condition: happy vs. angry) 9 2 (cognitive load: low vs. high) 9 2 (emotion measure: happiness vs. anger) mixed design with emotion measure as a within-subjects variable. Procedure Participants were randomly assigned to emotion and load conditions. They were told that the purpose of the study was to investigate emotion recognition ability. First, participants were told to familiarize themselves with videos of morphing faces. Participants viewed fourteen videos of faces that over a 6-s period morphed from neutral to either happy or angry expressions (see Heuer, Lange, Isaac, Rinck, & Becker, 2010, for more information on these morphed expressions). 2 Seven of these videos depicted a male target, and seven of these videos depicted a female target. Participants were then told that they would see the same videos again, but that this time they should press the space bar at the first moment they notice that the target was clearly expressing an emotion. Participants viewed the same fourteen videos twice more while engaging in this emotion recognition task. Thus, over the course of the experiment participants viewed each of fourteen videos three times. The videos continued playing for the full 6-s even after participants pressed the space bar, such that each participant spent the same amount of time viewing the videos. In fact, the emotion recognition task was a cover story designed to provide the participants with a plausible reason for watching these same videos multiple times. Instead, viewing these videos was intended to induce congruent emotional experiences in participants. The computer did not record the recognition time. Participants were asked to perform a concurrent task intended to manipulate cognitive load (Conway & Gawronski, 2013) while watching these videos. Specifically, participants were asked to memorize and report a password. In the high cognitive load condition, each password consisted of eight characters, including at least one upper-case letter, lowercase letter, number, and punctuation mark (e.g., n63#m1q). In the low cognitive load condition, the passwords consisted of a one-character number (e.g., 8). Participants had 20-s to memorize each password. After watching seven videos, participants were asked to report the password and were then given a new password to memorize. This task occurred during both the familiarization and emotion recognition portions of the video task. Thus, participants reported a total of six passwords throughout the experiment. After completing the concurrent video and password tasks, participants completed our emotion measures. They were asked how angry, happy, and irritated they felt on a scale from 1 (not at all)to7(extremely) and how positive they felt overall on a scale from 1 (very negative) to 7(very positive). Although these were the variables of interest, we embedded them within a few additional emotion terms (e.g., sad, alert). We created an angry emotion composite by averaging participants anger and irritation ratings (a =.79). We created a happy emotion composite by averaging participants happiness and positivity ratings (a =.86). Finally, participants completed a cognitive load manipulation check. They completed three items from the State Ego Depletion Scale on a scale from 1 (not true)to7(very true): 2 Happy and angry faces were selected because they are both considered to be high arousal emotions. In support of this, we included alert as a filler emotion term. There is no significant difference in alertness between the happy (M = 4.58, SD = 1.49) and anger (M = 4.63, SD = 1.36) conditions, F(1, 156) = 0.03, p =.87.

5 186 Janice R. Kelly et al. My mental energy is running low, Right now, it would take a lot of effort for me to concentrate on something, and I can t absorb any more information (Ciarocco, Twenge, Muraven, & Tice, 2010). These three items were averaged to form a composite (a =.80). Participants completed demographic questions, were debriefed, and thanked for their participation. Results Analysis We analysed the results using a 2 (emotion condition: angry vs. happy) 9 2 (load: high vs. low) between-subjects ANOVA or a 2 (emotion condition: angry vs. happy) 9 2 (load: high vs. low) 9 2 (emotion measure: angry composite vs. happy composite) mixedsubjects ANOVA with emotion measure as the within-subjects variable. All significant effects are reported in the text. Significant interactions were probed with follow-up ANOVAs. Complete reporting of all effects are in Tables 1 and 2. Manipulation checks Load manipulation check A two-way ANOVA produced a significant main effect of cognitive load on the cognitive load manipulation check, F(1, 156) = 23.63, p <.001, g 2 p =.13. Participants in the high load condition (M = 3.26, SD = 1.36) reported more depletion than participants in the low load condition (M = 2.28, SD = 1.20). Emotion manipulation check A three-way repeated-measures ANOVA produced a significant interaction between emotion condition and emotion measure, F(1, 156) = 7.83, p =.01, g 2 p =.05. When looking at each emotion measure separately by emotion condition, there was a significant effect of emotion condition on the anger composite, F(1, 158) = 5.26, p =.02, g 2 p =.03. Table 1. Results of cognitive load manipulation check analyses Effect Load Main Effect F(1, 156) = 23.63, p <.001 Emotion Condition Main Effect F(1, 156) = 0.55, p =.46 Load 9 Emotion Condition Interaction F(1, 156) = 0.21, p =.65 Table 2. Results of self-reported emotion analyses Effect Load Main Effect F(1, 156) = 1.13, p =.92 Emotion Condition Main Effect F(1, 156) = 0.011, p =.29 Emotion Measure Main Effect F(1, 156) = , p <.001 Load 9 Emotion Condition Interaction F(1, 156) = 3.62, p =.06 Load 9 Emotion Measure Interaction F(1, 156) = 0.29, p =.59 Emotion Condition 9 Emotion Measure Interaction F(1, 156) = 7.83, p =.01 Load 9 Emotion Condition 9 Emotion Measure Interaction F(1, 156) = 5.24, p =.02

6 Emotional contagion of negative emotions 187 Participants in the angry condition reported more anger (M = 2.53, SD = 1.45) than participants in the happy condition (M = 2.05, SD = 1.12). There was also a significant effect of emotion condition on the happy composite, F(1, 158) = 5.66, p =.02, g 2 p =.04. Participants in the happy condition reported more happiness (M = 4.93, SD = 1.03) than participants in the angry condition (M = 4.49, SD = 1.29). These results suggest that our new method for producing emotional contagion was successful. Emotion and load The predicted three-way interaction between emotion condition, load, and emotion measure was significant, F(1, 156) = 5.24, p =.02, g 2 p =.03. We tested our specific predictions by looking at the load 9 emotion measure interaction separately for each emotion condition. Within the angry condition, the two-way interaction between load and emotion measure was not significant, F(1, 84) = 1.32, p =.25 (M = 2.54, SD = 1.56, for the angry/low load participants and M = 2.52, SD = 1.35, for the angry/high load participants on the anger composite; M = 4.19, SD = 1.37, for the angry/low load participants and M = 4.77, SD = 1.16, for the angry/high load participants on the happy composite). However, within the happy condition, the two-way interaction between load and emotion measure was significant, F(1, 72) = 5.26, p =.03, g 2 p =.07. These findings are in line with our predictions that the contagion of negative, threat-related emotions is automatic and therefore not moderated by cognitive load. 3,4,5 To further probe the two-way interaction within the happy condition, we examined the effects of load for each emotion measure using one-way ANOVAs. Within the happy condition, the effect of load was significant for the happy composite, F(1, 72) = 5.88, p =.02, g 2 p =.08. Participants in the happy/high load condition reported less happiness (M = 4.67, SD = 1.00) than participants in the happy/low load condition (M = 5.23, SD = 1.00). However, the effect of load on the anger composite was not significant, F(1, 72) = 2.40, p =.13 (M = 2.24, SD = 1.23 and M = 1.84, SD = 0.96 for high and low load conditions, respectively). Thus, we found that participants in the happy emotion condition experienced more emotional contagion when they were under low load than when they were under high load, but that participants in the angry emotion condition experienced emotional contagion regardless of load. 6 Discussion In this paper, we introduced a novel method for inducing emotional contagion in the laboratory through the use of videos of faces that morph from neutral to valenced 3 These results remain entirely the same when we use the single item measures of anger and happiness rather than the composites. 4 As expected, when we look at number of passwords that were incorrect, we find a significant main effect of load condition, such that participants entered more incorrect passwords in the high load condition (M = 1.61, SD = 1.59) than the low load condition (M = 0.23, SD = 1.00), F(1, 156) = 42.95, p <.001, g 2 p =.22. There was no significant main effect of emotion condition nor was there a significant interaction between emotion condition and load, ps >.38. Importantly, however, when we include number of passwords incorrect as a covariate in our main analyses, the results remain entirely the same. 5 When gender is added as a factor in analyses, the two-way interaction between emotion condition and emotion measure and the three-way interaction between emotion condition, load, and emotion measure remain significant, supporting both our manipulation of anger and happiness and our main hypothesis. 6 The results of analyses retaining participants who are less fluent in English are essentially identical to the reported analyses. Results of all omnibus analyses that are significant in the reported sample remain significant in the total sample. In addition, all omnibus analyses that are non-significant in the reported sample remain not significant with the total sample, ps >.212.

7 188 Janice R. Kelly et al. expressions. In addition, we examined the hypothesis, derived from an evolutionary perspective, that the contagion of negative, threat-related emotions occurs relatively automatically, whereas the contagion of positive, non-threat-related emotions is more susceptible to context effects. We show clear evidence of the utility of our paradigm for producing emotional contagion, supported by the significant two-way interaction between emotion condition and emotion measure. Participants who viewed faces morphing from neutral to happy reported significantly greater happiness than did participants who viewed faces morphing from neutral to angry. Conversely, participants who viewed faces morphing from neutral to angry reported significantly greater anger than did participants who viewed faces morphing from neutral to happy. We also provided evidence that the contagion of negative, threat-related emotions, or more specifically anger, occurs especially automatically. Although previous research has found that negative emotions are more easily contagious (Joiner, 1994; Rozin & Royzman, 2001), to our knowledge, this is the first study to examine the automaticity of the process. We found that for participants who were exposed to angry faces, cognitive load had no effect on the amount of anger that they reported. Those who were exposed to happy faces under the conditions of high cognitive load reported less happiness than those exposed to happy faces under the conditions of low cognitive load. Thus, emotional contagion occurred more automatically for anger compared with happiness. Strengths, weaknesses, and future directions One strength of this research is the less labour-intensive method for producing emotional contagion. As yet, we have only examined the contagion of the discrete emotions of happiness and anger. Future research should examine a larger pool of discrete emotions, such as sadness, disgust, and fear. Future research should also examine whether low arousal emotions are similarly contagious to high arousal emotions. An important advantage to this method for inducing emotional contagion is that it might allow for the examination of a number of potential moderators of emotional contagion. In this study, we examined moderation in terms of cognitive load. For other emotions, one might examine moderators that are related to how attentional processes are directed at emotion recognition (Gump & Kulik, 1997). We might also explore whether people respond differently to male and female faces and whether males and females respond differently to different emotions. The relative ease and resource efficiency of this paradigm would allow for the examination of a wide range of additional variables. A potential weakness of using our specific combination of emotions and load manipulation is the possibility that participants felt irritation in the high load condition and that the combination of irritation and anger in the high load/anger condition led to the appearance that anger is transferred automatically. However, our data speak against this possibility. In particular, although we find the predicted three-way interaction between emotion condition, load, and emotion measure, we do not find a significant overall twoway interaction between load and emotion measure (p =.59). This finding means that participants in the high load conditions are not reporting greater anger than happiness compared with the low load conditions. We also found no main effect of load condition on the angry composite alone (p =.42), demonstrating no difference in anger/irritation overall based on load condition.

8 Emotional contagion of negative emotions 189 This is also, to our knowledge, the first study to directly test the assumption that emotional contagion occurs automatically (Hatfield et al., 1994), with one exception. Neumann and Strack (2000, Experiment 3) report an investigation of the automatic transfer of happy and sad moods and, in contrast with the current work, find no difference in the automaticity of their transfer. There are many methodological differences between the Neumann and Strack (2000) experiment and our own, including type of emotional stimuli (faces vs. tone of voice), stimulus gender (both males and females vs. males only), participant gender (both males and female vs. females only), etc. Importantly, Neumann and Strack (2000) were interested in the transference of positive or negative generalized mood rather than of specific emotions, and threat-related negative emotions in particular. The fact that we find a relative difference in how automatically transferred our threatrelated emotion of anger is compared to the non-threat positive emotion of happiness, may be due to the higher degree of discrimination provided by our design and manipulations. That is, the manipulation of specific emotions may provide greater signal strength and thus may be a more sensitive test of the degree to which automatic processing may have occurred. In fact, we also show that emotional contagion occurred for both happiness and anger. However, given that our current understanding of automaticity is becoming less categorical and more nuanced/continuous (Dijksterhuis, 2013), we importantly show that anger is relatively more automatically transferred than happiness. 7 We might expect, however, that not all negative emotions would be equally automatically transferred. From an evolutionary perspective (Spoor & Kelly, 2004), only negative emotions that signal threat or danger are likely to be particularly facilitated. For example, anger and fear are likely to signal a direct threat in the environment. Sadness, on the other hand, is experienced more as a sense of loss or sorrow and is not as closely tied to external threat. We would therefore expect anger and fear to be more automatically transferred than sadness. Furthermore, we should note that these hypotheses were derived from an evolutionary framework specific to a group context. Spoor and Kelly (2004) posit that emotional contagion is an emotion regulation mechanism that arose in order for rapid communication to occur in groups. There may be contextual boundaries where such rapid communication would be dysfunctional, such as a parent not wanting to escalate a situation by catching the anger of a child. There also may be cases where it would be maladaptive to catch the emotion of an outgroup member and where a complementary emotional reaction may be more advantageous. Future research may explore these possibilities. Conclusion This paper tests the assumption of the automaticity of emotional contagion and finds evidence for the greater automaticity of the contagion of negative, threat-related emotions specifically anger. We suggest that attentional resources may be more important for the contagion of some emotions over others, and we offer a more efficient paradigm for inducing emotions that may allow more easily for an examination of these processes. 7 In addition, given the difference in power between the two studies a between-subjects design with 80 participants verses a mixed design with 160 participants we would urge caution in the interpretation of Neumann and Strack s (2000) null effect that cognitive load failed to disrupt the contagion of positive emotions.

9 190 Janice R. Kelly et al. Acknowledgements The authors would like to thank Kathrin Heuer, Wolf-Gero Lange, Linda Isaac, Mike Rinck, and Eni S. Becker for use of their morphed emotion faces. References Bargh, J. A., & Tota, M. E. (1988). Context-dependent automatic processing in depression: Accessibility of negative constructs with regard to self but not others. Journal of Personality and Social Psychology, 54, doi: / Barsade, S. G. (2002). The ripple effect: Emotional contagion and its influence on group behavior. Administrative Science Quarterly, 47, doi: / Bartel, C., & Saavedra, R. (2000). The collective construction of work group moods. Administrative Science Quarterly, 45, doi: / Bastiaansen, J. A., Thioux, M., & Keyers, C. (2009). Evidence for mirror systems in emotions. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 364, doi: /rstb Baumeister, R. F., Bratslavsky, E., Findenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5, doi: / Ciarocco, N., Twenge, J. M., Muraven, M., & Tice, D. M. (2010). The state self-control capacity scale: Reliability, validity, and correlations with physical and psychological stress. Unpublished manuscript. Conway, P., & Gawronski, B. (2013). Deontological and utilitarian inclinations in moral decisionmaking: A process dissociation approach. Journal of Personality and Social Psychology, 104, doi: /a Dijksterhuis, A. (2013). Automaticity. In D. E. Carlston (Ed.), The Oxford handbook of social cognition (pp ). New York, NY: Oxford University Press. Elfenbein, H. A. (2014). The many faces of emotional contagion: An affective process theory of affective linkage. Organizational Psychology Review, 4, doi: / Elfenbein, H. A., & Ambady, N. (2002). On the universality and cultural specificity of emotion recognition: A meta-analysis. Psychological Bulletin, 128, doi: / George, J. M. (2002). Affect regulation in groups and teams. In R. G. Lord, R. J. Klimoski & R. Kanfer (Eds.), Emotions in the workplace: Understanding the structure and role of emotions in organizational behavior (pp ). San Francisco, CA: Jossey-Bass. Gump, B. B., & Kulik, J. A. (1997). Stress, affiliation, and emotional contagion. Journal of Personality and Social Psychology, 72, doi: / Hatfield, E., Cacioppo, J., & Rapson, R. L. (1992). Primitive emotional contagion. In M. S. Clark (Ed.), Review of personality and social psychology: Emotion and social behavior (pp ). Newbury Park, CA: Sage. Hatfield, E., Cacioppo, J., & Rapson, R. L. (1994). Emotional contagion. New York, NY: Cambridge University Press. Heuer, K., Lange, W. G., Isaac, L., Rinck, M., & Becker, E. S. (2010). Morphed emotional faces: Emotion detection and misinterpretation in social anxiety. Journal of Behavior Therapy and Experimental Psychiatry, 41, doi: /j.jbtep Heyes, C. (2010). Where do mirror neurons come from? Neuroscience & Biobehavioral Reviews, 34, doi: /j.neubiorev Hickok, G., & Hauser, M. (2010). (Mis)understanding mirror neurons. Current Biology, 20, R593 R594. doi: /j.cub

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