Uncertainty and reward histories have distinct effects on decisions after wins and losses.

ABSTRACT: Intelligent behavior necessitates an adaptive integration of feedback. It is well-known that animals asymmetrically learn from positive and negative feedback. While asymmetrical learning is a robust behavioral effect, the latent computations behind how animals represent their environments and use this to differentially weight wins and losses is poorly understood. Here we tested whether and how uncertainty and reward history modulate the weights placed on wins and losses using a behavioral data set collected in rats. We propose a reinforcement learning model that integrates uncertainty history via an unsigned average reward prediction error and a separate subjective reward history component. We showed that in a dynamic probabilistic reversal learning task with blocks of variable reward pred

SUBMITTER: Kalhan S 

PROVIDER: S-EPMC12916781 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature