Behavioral Social Neuroscience Seminar

Predicting other peoples' beliefs, desires, and intentions is a primary function of human cognition and is essential for survival in our complex social world. To do this efficiently and successfully, we must form lasting representations of individuals and social groups based on information we receive through personal and vicarious experience. My research is focused on developing a computational account of the neurocognitive mechanisms through which we learn about other people, make social predictions, and are influenced by social biases. To achieve this, I employ a multidisciplinary approach, integrating a wide range of techniques from cognitive neuroscience, social psychology, neuroeconomics, computational modeling of learning and decision-making, and clinical psychology. My theoretical model of social learning and decision-making treats social group biases as a set of initial guesses (akin to Bayesian priors) that inform our social decision-making when we lack specific information about a person with whom we are interacting. Using these priors as a starting point, we form and update our mental representation of a person (as well as their social group) on the basis of observed behavior. I will present behavioral and neural data on the influence of race bias on trust estimations, as well as the computational processes through which we learn about individuals' traits and intentions (i.e., theory of mind), and how these processes might be disrupted in individuals with social impairments (e.g. Autism Spectrum Disorder). These results suggest that while many common processes support learning about social and non-social entities, there also exist neural computations unique to social learning.