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Research Article

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning

Lusha Zhu, Kyle E. Mathewson, and Ming Hsu
PNAS first published January 18, 2012; https://doi.org/10.1073/pnas.1116783109
Lusha Zhu
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Kyle E. Mathewson
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Ming Hsu
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  • For correspondence: mhsu@haas.berkeley.edu
  1. Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved December 20, 2011 (received for review October 13, 2011)

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Abstract

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents’ beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

  • game theory
  • neuroeconomics
  • computational modeling
  • functional MRI

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: mhsu{at}haas.berkeley.edu.
  • Author contributions: L.Z., K.E.M., and M.H. designed research; L.Z., K.E.M., and M.H. performed research; L.Z. contributed new reagents/analytic tools; L.Z. and M.H. analyzed data; and L.Z., K.E.M., and M.H. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1116783109/-/DCSupplemental.

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Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning
Lusha Zhu, Kyle E. Mathewson, Ming Hsu
Proceedings of the National Academy of Sciences Jan 2012, 201116783; DOI: 10.1073/pnas.1116783109

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Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning
Lusha Zhu, Kyle E. Mathewson, Ming Hsu
Proceedings of the National Academy of Sciences Jan 2012, 201116783; DOI: 10.1073/pnas.1116783109
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