Literature DB >> 15795132

Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk.

Christopher Trepel1, Craig R Fox, Russell A Poldrack.   

Abstract

Most decisions must be made without advance knowledge of their consequences. Economists and psychologists have devoted much attention to modeling decisions made under conditions of risk in which options can be characterized by a known probability distribution over possible outcomes. The descriptive shortcomings of classical economic models motivated the development of prospect theory (D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk. Econometrica, 4 (1979) 263-291; A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5 (4) (1992) 297-323) the most successful behavioral model of decision under risk. In the prospect theory, subjective value is modeled by a value function that is concave for gains, convex for losses, and steeper for losses than for gains; the impact of probabilities are characterized by a weighting function that overweights low probabilities and underweights moderate to high probabilities. We outline the possible neural bases of the components of prospect theory, surveying evidence from human imaging, lesion, and neuropharmacology studies as well as animal neurophysiology studies. These results provide preliminary suggestions concerning the neural bases of prospect theory that include a broad set of brain regions and neuromodulatory systems. These data suggest that focused studies of decision making in the context of quantitative models may provide substantial leverage towards a fuller understanding of the cognitive neuroscience of decision making.

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Mesh:

Year:  2005        PMID: 15795132     DOI: 10.1016/j.cogbrainres.2005.01.016

Source DB:  PubMed          Journal:  Brain Res Cogn Brain Res        ISSN: 0926-6410


  59 in total

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5.  Functional dissociations of risk and reward processing in the medial prefrontal cortex.

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Review 6.  Reinforcement learning, conditioning, and the brain: Successes and challenges.

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7.  Quantifying mechanisms of cognition with an experiment and modeling ecosystem.

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8.  Recurrent, robust and scalable patterns underlie human approach and avoidance.

Authors:  Byoung Woo Kim; David N Kennedy; Joseph Lehár; Myung Joo Lee; Anne J Blood; Sang Lee; Roy H Perlis; Jordan W Smoller; Robert Morris; Maurizio Fava; Hans C Breiter
Journal:  PLoS One       Date:  2010-05-26       Impact factor: 3.240

9.  At what stage of neural processing does cocaine act to boost pursuit of rewards?

Authors:  Giovanni Hernandez; Yannick-André Breton; Kent Conover; Peter Shizgal
Journal:  PLoS One       Date:  2010-11-30       Impact factor: 3.240

10.  Ecological expected utility and the mythical neural code.

Authors:  Jerome Feldman
Journal:  Cogn Neurodyn       Date:  2009-09-04       Impact factor: 5.082

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