Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning

Abstract

Humans naturally group the world into coherent categories defined by membership rules. Rules can be learned implicitly by building stimulus-response associations using reinforcement learning (RL) or by using explicit reasoning. We tested if striatum, in which activation reliably scales with reward prediction error, would track prediction errors in a task that required explicit rule generation. Using functional magnetic resonance imaging during a categorization task, we show that striatal responses to feedback scale with a surprise signal derived from a Bayesian rule-learning model. We also find that striatal feedback responses are inconsistent with RL prediction error and demonstrate that striatum and caudal inferior frontal sulcus (cIFS) are involved in updating the likelihood of discriminative rules. We conclude that the striatum, in cooperation with the cIFS, is involved in updating the values assigned to categorization rules, rather than representing reward prediction errors.