LAUSR

People learn about themselves from social feedback, but desires for coherence and positivity constrain how feedback is incorporated into the self-concept. We developed a network-based model of the self-concept and embedded it in a reinforcement-learning framework to provide a computational account of how motivations shape self-learning from feedback. Participants (N = 46 adult university students) received feedback while evaluating themselves on traits drawn from a causal network of trait semantics. Network-defined communities were assigned different likelihoods of positive feedback. Participants learned from positive feedback but dismissed negative feedback, as reflected by asymmetries in computational parameters that represent the incorporation of positive versus negative outcomes. Furthermore, participants were constrained in how they incorporated feedback: Self-evaluations changed less for traits that have more implications and are thus more important to the coherence of the network. We provide a computational explanation of how motives for coherence and positivity jointly constrain learning about the self from feedback, an explanation that makes testable predictions for future clinical research.