Journal
YEAR IN COGNITIVE NEUROSCIENCE
Volume 1224, Issue -, Pages 22-39Publisher
BLACKWELL SCIENCE PUBL
DOI: 10.1111/j.1749-6632.2011.05965.x
Keywords
Bayesian models; uncertainty; graphical models; psychophysics; neural representations
Categories
Funding
- NATIONAL CENTER FOR RESEARCH RESOURCES [UL1RR025741] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [K12GM088020] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R01NS063399, P01NS044393, R01NS057814] Funding Source: NIH RePORTER
- NCRR NIH HHS [UL1 RR025741] Funding Source: Medline
- NIGMS NIH HHS [K12GM088020, K12 GM088020] Funding Source: Medline
- NINDS NIH HHS [P01 NS044393-08, 1R01NS063399, R01 NS063399, R01NS057814, P01 NS044393, R01 NS063399-02, R01 NS057814, R01 NS063399-01A2] Funding Source: Medline
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Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.
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