Neural Response to Reward Anticipation under Risk Is Nonlinear in Probabilities

A widely observed phenomenon in decision making under risk is the apparent overweighting of unlikely events and the underweighting of nearly certain events. This violates standard assumptions in expected utility theory, which requires that expected utility be linear (objective) in probabilities. Mod...

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Bibliographic Details
Published in:The Journal of neuroscience 2009-02, Vol.29 (7), p.2231-2237
Main Authors: Hsu, Ming, Krajbich, Ian, Zhao, Chen, Camerer, Colin F
Format: Article
Language:English
Subjects:
Adult
Brain Mapping
Cognition - physiology
Corpus Striatum - anatomy & histology
Corpus Striatum - physiology
Decision Making - physiology
Female
Gambling - psychology
Humans
Judgment - physiology
Magnetic Resonance Imaging
Male
Neurons - physiology
Neuropsychological Tests
Nonlinear Dynamics
Probability
Probability Learning
Reward
Risk-Taking
Young Adult
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Summary:A widely observed phenomenon in decision making under risk is the apparent overweighting of unlikely events and the underweighting of nearly certain events. This violates standard assumptions in expected utility theory, which requires that expected utility be linear (objective) in probabilities. Models such as prospect theory have relaxed this assumption and introduced the notion of a "probability weighting function," which captures the key properties found in experimental data. This study reports functional magnetic resonance imaging (fMRI) data that neural response to expected reward is nonlinear in probabilities. Specifically, we found that activity in the striatum during valuation of monetary gambles are nonlinear in probabilities in the pattern predicted by prospect theory, suggesting that probability distortion is reflected at the level of the reward encoding process. The degree of nonlinearity reflected in individual subjects' decisions is also correlated with striatal activity across subjects. Our results shed light on the neural mechanisms of reward processing, and have implications for future neuroscientific studies of decision making involving extreme tails of the distribution, where probability weighting provides an explanation for commonly observed behavioral anomalies.
ISSN:0270-6474
1529-2401
DOI:10.1523/JNEUROSCI.5296-08.2009