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The Impact of Emotional Expression on the Association Between Sensorimotor Simulation and Empathic Accuracy

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The Impact of Emotional Expression on the Association Between Sensorimotor Simulation and Empathic Accuracy The Impact of Emotional Expression on the Association between Sensorimotor Simulation and Empathic Accuracy Master’s Thesis Presented to The Faculty of the Graduate School of Arts and Sciences Brandeis University Department of Psychology Jennifer Gutsell, Advisor In Partial Fulfillment of the Requirements for the Degree Master of Arts in Psychology by Tong Lin August 2019 Copyright by Tong Lin © 2019 ABSTRACT The Impact of Emotional Expression on the Association between Sensorimotor Simulation and Empathic Accuracy A thesis presented to the Department of Psychology Graduate School of Arts and Sciences Brandeis University Waltham, Massachusetts By Tong Lin Understanding other people’s emotions is one of the most important things people do in daily life. Multiple systems are involved in this process including sensorimotor simulation, the inhibition of activity in sensorimotor networks (Wood, Rychlowska, & Niedenthal, 2016). Sensorimotor simulation has been studied in simple tasks such as reading a narrative or looking at pictures, but it is unclear how this simulation-related brain activity might play out in daily interactions that are far more complex than the experience of research participants in traditional social neuroscience studies that use non-naturalistic stimuli, nor do we know its impact on empathic accuracy. Therefore, we explored people’s ability to understand another’s emotions by combining a face-to-face interaction with a reliable index of sensorimotor simulation, which is mu suppression, an 8–13 Hz band-range of the electroencephalogram (EEG) over the motor cortex (Ulloa, & Pineda, 2007) in this study. In the experiment, we recorded EEG as participants took turns sharing one positive and one negative experience with their partner while the other listened passively. They later watched video recordings of the shared experience and rated the iii emotion expressed by themselves and their partner. Correlations between self– and partner– ratings served as an estimate of empathic accuracy, the extent to which they understand each other’s emotions. Following the experience sharing task, participants watched their partner repeatedly squeezing a ball, and EEG mu suppression during the observation of the partner’s ball squeezing served as a measure of sensorimotor simulation. We hypothesized that stronger mu suppression would be correlated with higher empathic accuracy and that this association would be moderated by emotional expression displayed in conversations, such that having more emotional expression would be associated with a stronger correlation between mu suppression and empathic accuracy. We did not find a correlation between mu suppression and empathic accuracy, but we found evidence supporting a positive correlation between emotional expressions and empathic accuracy. iv List of Tables TABLE PAGE Table 1 20 Table 2 25 Table 3 28 Table 4 30 v List of Figures Figure PAGE Figure 1 17 Figure 2 24 Figure 3 25 Figure 4 26 Figure 5 32 Figure 6 32 vi The Impact of Emotional Expression on the Association between Sensorimotor Simulation and Empathic Accuracy Empathy is an important ability that allows us to perceive and respond (Decety, 2016) to the emotional states of others. It plays a crucial role in interpersonal interaction, which was illustrated by Barack Obama who told the Northwestern University graduates at 2006 Commencement that people should try to “put [themselves] in someone else’s shoes” and “to see the world through those who are different from [them]” (Obama, 2016). Furthermore, impairments in empathy could result in severe problems in social function, such as those seen in autism spectrum disorders and psychopathy (Zaki, Weber, Bolger, & Ochsner, 2009). However, the exact mechanisms that underlie people’s ability to accurately empathize are still under debate: Studies have linked facial mimicry, which is the tendency to imitate others’ emotional facial expressions (Achaibou, Pourtois, Schwartz, & Vuilleumier, 2008) to empathic accuracy, which is defined as the observer’s ability to correctly infer the target’s emotion (Hess, & Blairy, 2001). Other studies have linked sensorimotor simulation, a mechanism that activates similar neural networks in perceivers as they would if they were to perform the same action or experience the same emotions themselves (Zaki et al., 2012), and empathic accuracy (Ong, Zaki, Perry, & Ong, 2018, preprint), but to our knowledge, no study has looked at how both emotional expression and sensorimotor simulation might interact to affect empathic accuracy. Therefore, the goal of the current study is to look at neural and behavioral indicators of the simulation of another’s emotional states, and to relate them to empathic accuracy. 1 Experience Sharing, its Precursors and Consequences Characteristics of empathy can be divided into three mental categories: (1) experience sharing: “vicariously sharing targets’ internal states”, (2) mentalizing: understanding the targets’ mental states, and (3) prosocial concern: “expressing motivation to improve targets’ experiences” (Zaki, & Ochsner, 2012). The category of interest of this study is experience sharing, which is often tied to sensorimotor simulation (Zaki et al., 2012). Previous research has identified factors that contribute to the degree to which individuals share the experience of others: The target- related factors include similarity (Gutsell, & Inzlicht, 2012), familiarity (Khalil, 2002), past experience (Stinson, & Ickes, 1992), learning (Preston, & Waal, 2002) and emotional expressivity (Zaki, Bolger, & Ochsner, 2009); situational factors include material costs (Zaki, 2014); perceiver related factors include personality traits (Avenanti, Minio-Paluello, Bufalari, & Aglioti, 2009) and the socioeconomic status of the perceivers (Kraus, Côté, & Keltner, 2010). Yet no study has examined the effect of the perceivers’ emotional expressivity on their ability to share the experiences of others. Sensorimotor simulation is one of the brain mechanisms involved in experience sharing (Ong et al., 2018, under review). The association between sensorimotor simulation and different components in empathy has been explored. For example, sensorimotor simulation is associated with perspective-taking (Woodruff, Martin, & Bilyk, 2011), an aspect of trait empathy that allows individuals to understand situations from another point of view (Galinsky, Maddux, Gilin, & White, 2008), and plays an important role in the social learning of reactions to pain (Avenanti, Bueti, Galati, & Aglioti, 2005). Experience sharing can influence social understanding through its interplay with mentalizing (Kanske, Böckler, Trautwein, Parianen Lesemann, & Singer, 2016). For instance, research using a measure of empathic ability and propensity that separates 2 empathy and Theory of Mind, showed that experience sharing could inhibit mentalizing in emotionally intense situations (Kanske et al., 2016). The neurological investigation of sensorimotor simulation can be traced back to the discovery of mirror neurons in macaque monkeys. In the experiment that examined a macaque monkey’s premotor area, mirror neurons responded both when the monkey performed a particular action and when it observed the same action (Gallese, & Goldman, 1998). Later studies also found sensorimotor simulation in response to emotional faces (Moore, Gorodnitsky, & Pineda, 2012) and tasks that require empathy (Pineda & Hecht, 2009). Most of the existing research on the relationship between experience sharing and empathy has focused on trait empathy or state empathy, but it is also important to look at empathic accuracy because it is the aspect of empathy that includes both the state of the empathic perceiver and the state of the target, and examines the extent to which people accurately empathize with the target at the moment (Zaki et al., 2008). Moreover, experience sharing is usually examined through self-report (such as Atkins, Uskul, & Cooper, 2016; Ali, Amorim, & Chamorro-Premuzic, 2009; Clark, Winkielman, & McIntosh, 2008), or assessed on the neural level through vicarious neural activation in relevant sensorimotor, pain, or emotional areas (such as Fabi, & Leuthold, 2018; Cheng, Chen, & Decety, 2014; Oberman, Hubbard, McCleery, Altschuler, Ramachandran, & Pineda, 2005). The targets of empathy in these studies are images or videos of facial expressions, hands in painful situations, or similarly artificial stimuli, rather than actual interaction partners, which compromises in mundane realism. Using similar methods, a previous study found supporting evidence for a positive association between experience sharing, indexed through EEG mu suppression (a 8–13 Hz band-range of the electroencephalogram over the motor cortex (Ulloa et al., 2007), and empathic accuracy where 3 participants watched videos of other people sharing their stories and rated the feelings of the targets (Ong et al., preprint). However, given that the social situation in this study was somewhat artificial it is still unclear how experience sharing might affect empathic accuracy in a more realistic environment during which the perceiver also interacts and communicates with the target. Finally, on a behavioral level experience sharing sometimes is revealed in vicarious emotional facial expressions and has been investigated using facial electromyography (EMG). For instance, facial EMG activity showed that people with high trait empathy are more reactive to emotional faces in comparison with individuals with low trait empathy (Dimberg,
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