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Jamil Zaki
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Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2016) 28 (9): 1270–1282.
Published: 01 September 2016
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Neuroscientific studies of social cognition typically employ paradigms in which perceivers draw single-shot inferences about the internal states of strangers. Real-world social inference features much different parameters: People often encounter and learn about particular social targets (e.g., friends) over time and receive feedback about whether their inferences are correct or incorrect. Here, we examined this process and, more broadly, the intersection between social cognition and reinforcement learning. Perceivers were scanned using fMRI while repeatedly encountering three social targets who produced conflicting visual and verbal emotional cues. Perceivers guessed how targets felt and received feedback about whether they had guessed correctly. Visual cues reliably predicted one target's emotion, verbal cues predicted a second target's emotion, and neither reliably predicted the third target's emotion. Perceivers successfully used this information to update their judgments over time. Furthermore, trial-by-trial learning signals—estimated using two reinforcement learning models—tracked activity in ventral striatum and ventromedial pFC, structures associated with reinforcement learning, and regions associated with updating social impressions, including TPJ. These data suggest that learning about others' emotions, like other forms of feedback learning, relies on domain-general reinforcement mechanisms as well as domain-specific social information processing.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2015) 27 (7): 1412–1426.
Published: 01 July 2015
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Obesity contributes to 2.8 million deaths annually, making interventions to promote healthy eating critical. Although preliminary research suggests that social norms influence eating behavior, the underlying psychological and neural mechanisms of such conformity remain unexplored. We used fMRI to investigate whether group norms shift individuals' preferences for foods at both behavioral and neural levels. Hungry participants rated how much they wanted to eat a series of healthy and unhealthy foods and, after each trial, saw ratings that ostensibly represented their peers' preferences. This feedback was manipulated such that peers appeared to prefer each food more than, less than, or as much as participants themselves. After a delay, participants rerated each food. Participants' second ratings shifted to resemble group norms. Initial consensus, as compared to disagreement, with peers produced activity in the nucleus accumbens, a region associated with reward prediction errors. Furthermore, the strength of this activity predicted the extent to which participants' ratings conformed to peer ratings, suggesting that the value associated with consensus drives social influence. Ventromedial prefrontal cortex (vMPFC), a region associated with value computation, initially responded more strongly to unhealthy, as compared to healthy, foods. However, this effect was “overwritten” by group norms. After individuals learned their peers' preferences, vMPFC responses tracked the popularity, but not the healthfulness, of foods. Furthermore, changes in vMPFC activity tracked social influence over behavioral ratings. These data provide evidence that group norms can shift food preferences, supporting the use of norms-based interventions to promote healthy eating.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2013) 25 (6): 834–842.
Published: 01 June 2013
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Functional imaging has become a primary tool in the study of human psychology but is not without its detractors. Although cognitive neuroscientists have made great strides in understanding the neural instantiation of countless cognitive processes, commentators have sometimes argued that functional imaging provides little or no utility for psychologists. And indeed, myriad studies over the last quarter century have employed the technique of brain mapping—identifying the neural correlates of various psychological phenomena—in ways that bear minimally on psychological theory. How can brain mapping be made more relevant to behavioral scientists broadly? Here, we describe three trends that increase precisely this relevance: (i) the use of neuroimaging data to adjudicate between competing psychological theories through forward inference, (ii) isolating neural markers of information processing steps to better understand complex tasks and psychological phenomena through probabilistic reverse inference, and (iii) using brain activity to predict subsequent behavior. Critically, these new approaches build on the extensive tradition of brain mapping, suggesting that efforts in this area—although not initially maximally relevant to psychology—can indeed be used in ways that constrain and advance psychological theory.