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 response to punishments and rewards that affected their partner in a way that was computationally similar to how they learned for themselves, consistent with the possibility that social learning engages empathic processes. Further supporting this interpretation, an individualized model parameter that indexed sensitivity to others’ punishments was inversely associated with trait antisociality. Modeled sensitivity to others’ losses also mapped onto post-task motivation ratings, but was not associated with self-reported trait empathy. This work is the first to apply a social reinforcement learning task that spans affect and action requirement (go/no-go) to measure multiple facets of empathic sensitivity.