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February 18, 2009Journal of Neuroscience284 citationsOpen Access

Neural Response to Reward Anticipation under Risk Is Nonlinear in Probabilities

MHMing HsuIKIan KrajbichCZChen Zhao

Key Points

  • The study aims to investigate how neural responses to expected rewards vary in relation to probabilities under risk, challenging traditional theories.
  • Used functional magnetic resonance imaging (fMRI) to measure striatal activity during valuation of monetary gambles.
  • Examined the correlation between individual decision-making patterns and striatal activity.
  • Analyzed behavioral anomalies in the context of prospect theory.
  • Neural responses in the striatum showed nonlinearity in probabilities, consistent with prospect theory predictions.
  • Increased nonlinearity in decision-making correlated with higher striatal activity among subjects.
  • Results imply that neural encoding of reward is influenced by probability distortions.

Abstract

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.

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Cite This Study

Hsu et al. (2009) studied this question.

synapsesocial.com/papers/6a125b9992637892a9a65d00https://doi.org/10.1523/jneurosci.5296-08.2009
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