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

Ian Ballard, Eric M. Miller, Steven T. Piantadosi, Noah D. Goodman, Samuel McClure

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 or by using explicit reasoning. We tested if the 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 and are inconsistent with RL prediction error. We also find 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 when people learn using explicit reasoning.

Original languageEnglish (US)
Pages (from-to)3965-3975
Number of pages11
JournalCerebral Cortex
Volume28
Issue number11
DOIs
StatePublished - Nov 1 2018

Keywords

  • Bayesian modeling
  • categorization
  • dopamine
  • fMRI
  • feedback

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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