Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 108, Issue 1, Pages 55-60Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1014938108
Keywords
functional MRI; striatum; instruction; computational modeling; prediction error
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Funding
- James S. McDonnell Foundation
- National Institute of Mental Health [MH 080756, MH 084081]
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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.
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