Modeling Variation in Empathic Sensitivity Using Go/No-Go Social Reinforcement Learning.

ABSTRACT: Recent advances in computational behavioral modeling can help rigorously quantify differences in how individuals learn behaviors that affect both themselves and others. But social learning remains understudied in the context of understanding individual variation in social phenomena like aggression, which is defined by persistent engagement in behaviors that harm others. We adapted a go/no-go reinforcement learning task across social and non-social contexts such that monetary gains and losses explicitly impacted the subject, a study partner, or no one. We then quantified participants' (n = 61) sensitivity to others' rewards, sensitivity to others' losses, and the Pavlovian influence of expected outcomes on approach and avoidance behavior. Results showed that subjects learned in respo

SUBMITTER: O'Connell K 

PROVIDER: S-EPMC9537390 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature