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Cortical substrates for exploratory decisions in humans

Naturevolume 441pages876879 (2006) | Download Citation

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Abstract

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this ‘exploration–exploitation’ dilemma1, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning2 (RL) theory, indicates that a dopaminergic3,4, striatal5,6,7,8,9 and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical1 and ethological10 perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

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Acknowledgements

We thank J. Li, S. McClure, B. King-Casas and P. R. Montague for sharing their unpublished data on exploration, and Y. Niv, Z. Gharamani and C. Camerer for discussions. Funding was from a Royal Society USA Research Fellowship (N.D.), the Gatsby Foundation (N.D., P.D.), the EU BIBA project (N.D., P.D.), and a Wellcome Trust Programme Grant (J.O.D., R.D.).

Author information

Author notes

    • John P. O'Doherty

    Present address: Division of Humanities and Social Sciences, California Institute of Technology, 1200 East California Boulevard, Pasadena, California, 91125, USA

  1. Nathaniel D. Daw and John P. O'Doherty: *These authors contributed equally to this work

Affiliations

  1. Gatsby Computational Neuroscience Unit, University College London (UCL), Alexandra House, 17 Queen Square, WC1N 3AR, London, UK

    • Nathaniel D. Daw
    •  & Peter Dayan
  2. Wellcome Department of Imaging Neuroscience, UCL, London, 12 Queen Square, WC1N 3BG, UK

    • John P. O'Doherty
    • , Ben Seymour
    •  & Raymond J. Dolan

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Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Corresponding authors

Correspondence to Nathaniel D. Daw or John P. O'Doherty.

Supplementary information

  1. Supplementary Notes

    This file contains Supplementary Methods, Supplementary Discussion and Supplementary Tables 1–5. (PDF 371 kb)

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https://doi.org/10.1038/nature04766

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