Risk-sensitivity in Bayesian sensorimotor integration

Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory e...

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Bibliographic Details
Published in:PLoS computational biology 2012-09, Vol.8 (9), p.e1002698-e1002698
Main Authors: Grau-Moya, Jordi, Ortega, Pedro A, Braun, Daniel A
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Bayesian statistical decision theory
Bias
Biology
Brain - physiology
Computational biology
Computer Simulation
Cues
Decision making
Decision Making - physiology
Economic models
Expected values
Feedback, Sensory - physiology
Humans
Models, Neurological
Models, Statistical
Pattern Recognition, Visual - physiology
Risk Assessment
Sensorimotor integration
Studies
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Summary:Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1002698