Reinforcement learning signals predict future decisions

Michael X. Cohen, Charan Ranganath

Research output: Contribution to journalArticle

200 Scopus citations

Abstract

Optimal behavior in a competitive world requires the flexibility to adapt decision strategies based on recent outcomes. In the present study, we tested the hypothesis that this flexibility emerges through a reinforcement learning process, in which reward prediction errors are used dynamically to adjust representations of decision options. We recorded event-related brain potentials (ERPs) while subjects played a strategic economic game against a computer opponent to evaluate how neural responses to outcomes related to subsequent decision-making. Analyses of ERP data focused on the feedback-related negativity (FRN), an outcome-locked potential thought to reflect a neural prediction error signal. Consistent with predictions of a computational reinforcement learning model, we found that the magnitude of ERPs after losing to the computer opponent predicted whether subjects would change decision behavior on the subsequent trial. Furthermore, FRNs to decision outcomes were disproportionately larger over the motor cortex contralateral to the response hand that was used to make the decision. These findings provide novel evidence that humans engage a reinforcement learning process to adjust representations of competing decision options.

Original languageEnglish (US)
Pages (from-to)371-378
Number of pages8
JournalJournal of Neuroscience
Volume27
Issue number2
DOIs
StatePublished - Jan 10 2007

Keywords

  • Decision-making
  • Dopamine
  • ERN
  • Event-related potential
  • Reinforcement learning
  • Reward prediction error

ASJC Scopus subject areas

  • Neuroscience(all)

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