Opinion | Published:

A distributed, hierarchical and recurrent framework for reward-based choice

Nature Reviews Neuroscience volume 18, pages 172182 (2017) | Download Citation

Abstract

Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this Opinion article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex. This account also suggests that certain correlates of value are emergent rather than represented explicitly in the brain.

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Acknowledgements

The authors are grateful to many colleagues with whom interactions have shaped ideas within this article, in particular T. Behrens, T. Blanchard, S. Kennerley, J. Pearson, S. Piantadosi, M. Platt, M. Rushworth and T. Seeley. The authors thank H. Barron, A. de Berker, R. Dolan, M. A. Noonan and T. Seow for comments on a previous draft of the manuscript. L.T.H. is supported by a Sir Henry Wellcome Fellowship from the Wellcome Trust (098830/Z/12/Z). B.Y.H. is supported by a US National Institutes of Health award (DA037229).

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  1. Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK.

    • Laurence T. Hunt
  2. Department of Brain and Cognitive Sciences, University of Rochester, 309 Meliora Hall, Rochester, New York 14618, USA.

    • Benjamin Y. Hayden

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The authors declare no competing financial interests.

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Correspondence to Laurence T. Hunt or Benjamin Y. Hayden.

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