Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making

Tom Schönberg, Nathaniel D. Daw, Daphna Joel, John P. O'Doherty

Research output: Contribution to journalArticle

232 Scopus citations

Abstract

The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

Original languageEnglish (US)
Pages (from-to)12860-12867
Number of pages8
JournalJournal of Neuroscience
Volume27
Issue number47
DOIs
StatePublished - Nov 21 2007

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Keywords

  • Associative learning
  • Basal ganglia
  • Computational models
  • Instrumental conditioning
  • Prediction errors
  • fMRI

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