Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict.

ABSTRACT: Learning to predict action outcomes in morally conflicting situations is essential for social decision-making but poorly understood. Here we tested which forms of Reinforcement Learning Theory capture how participants learn to choose between self-money and other-shocks, and how they adapt to changes in contingencies. We find choices were better described by a reinforcement learning model based on the current value of separately expected outcomes than by one based on the combined historical values of past outcomes. Participants track expected values of self-money and other-shocks separately, with the substantial individual difference in preference reflected in a valuation parameter balancing their relative weight. This valuation parameter also predicted choices in an independent costly help

SUBMITTER: Fornari L 

PROVIDER: S-EPMC9988878 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature