Neuronal signals for reward risk in frontal cortex

Authors

  • Wolfram Schultz,

    1. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.
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  • Martin O’Neill,

    1. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.
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  • Philippe N. Tobler,

    1. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.
    2. Social and Neural Systems Laboratory, Department of Economics, University of Zürich, Zürich, Switzerland.
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  • Shunsuke Kobayashi

    1. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom.
    2. Department of Neurology, Fukushima Medical University, Fukushima-shi, Japan
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Wolfram Schultz, Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK. ws234@cam.ac.uk; mo293@cam.ac.uk; philippe.tobler@uzh.ch

Abstract

Rewards can be viewed as probability distributions of reward values. Besides expected (mean) value, a key parameter of such distributions is variance (or standard deviation), which constitutes a measure of risk. Single neurons in orbitofrontal cortex signal risk mostly separately from value. Comparable risk signals in human frontal cortex reflect risk attitudes of individual participants. Subjective outcome value constitutes the primary economic decision variable. The terms risk avoidance and risk taking suggest that risk affects subjective outcome value, a basic tenet of economic decision theories. Correspondingly, risk reduces neuronal value signals in frontal cortex of human risk avoiders and enhances value signals in risk takers. Behavioral contrast effects and reference-dependent valuation demonstrate flexible reward valuation. As a potential correlate, value signals in orbitofrontal neurons adjust reward discrimination to variance (risk). These neurophysiological mechanisms of reward risk on economic decisions inform and validate theories of economic decision making under uncertainty.

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