Reward skewness coding in the insula independent of probability and loss.

Christopher J Burke, Philippe N Tobler
Author Information
  1. Christopher J Burke: Laboratory for Social and Neural Systems Research, Dept. of Economics, Univ. of Zurich, Blümlisalpstrasse 10, 8006 Zürich, Switzerland. christopher.burke@econ.uzh.ch

Abstract

Rewards in the natural environment are rarely predicted with complete certainty. Uncertainty relating to future rewards has typically been defined as the variance of the potential outcomes. However, the asymmetry of predicted reward distributions, known as skewness, constitutes a distinct but neuroscientifically underexplored risk term that may also have an impact on preference. By changing only reward magnitudes, we study skewness processing in equiprobable ternary lotteries involving only gains and constant probabilities, thus excluding probability distortion or loss aversion as mechanisms for skewness preference formation. We show that individual preferences are sensitive to not only the mean and variance but also to the skewness of predicted reward distributions. Using neuroimaging, we show that the insula, a structure previously implicated in the processing of reward-related uncertainty, responds to the skewness of predicted reward distributions. Some insula responses increased in a monotonic fashion with skewness (irrespective of individual skewness preferences), whereas others were similarly elevated to both negative and positive as opposed to no reward skew. These data support the notion that the asymmetry of reward distributions is processed in the brain and, taken together with replicated findings of mean coding in the striatum and variance coding in the cingulate, suggest that the brain codes distinct aspects of reward distributions in a distributed fashion.

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MeSH Term

Adult
Cerebral Cortex
Conditioning, Classical
Corpus Striatum
Decision Making
Economics, Behavioral
Female
Gambling
Gyrus Cinguli
Humans
Magnetic Resonance Imaging
Male
Probability Learning
Reward
Uncertainty
Young Adult

Word Cloud

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