How instructed knowledge modulates the neural systems of reward learning

Jian Li, Mauricio R. Delgado, Elizabeth A. Phelps

Research output: Contribution to journalArticlepeer-review

Abstract

Recent research in neuroeconomics has demonstrated that the reinforcement learning model of reward learning captures the patterns of both behavioral performance and neural responses during a range of economic decision-making tasks. However, this powerful theoretical model has its limits. Trial-and-error is only one of the means by which individuals can learn the value associated with different decision options. Humans have also developed efficient, symbolic means of communication for learning without the necessity for committing multiple errors across trials. In the present study, we observed that instructed knowledge of cue-reward probabilities improves behavioral performance and diminishes reinforcement learning-related blood-oxygen level-dependent (BOLD) responses to feedback in the nucleus accumbens, ventromedial prefrontal cortex, and hippocampal complex. The decrease in BOLD responses in these brain regions to reward-feedback signals was functionally correlated with activation of the dorsolateral prefrontal cortex (DLPFC). These results suggest that when learning action values, participants use the DLPFC to dynamically adjust outcome responses in valuation regions depending on the usefulness of action-outcome information.

Original languageEnglish (US)
Pages (from-to)55-60
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume108
Issue number1
DOIs
StatePublished - Jan 4 2011

Keywords

  • Computational modeling
  • Functional MRI
  • Instruction
  • Prediction error
  • Striatum

ASJC Scopus subject areas

  • General

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