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Bayesian approaches to sensory integration for motor control
Author(s) -
Berniker Max,
Kording Konrad
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.125
Subject(s) - sensory system , computer science , bayesian probability , control (management) , artificial intelligence , bayesian inference , information integration , machine learning , human–computer interaction , neuroscience , data mining , psychology
The processing of sensory information is fundamental to the basic operation of the nervous system. Our nervous system uses this sensory information to gain knowledge of our bodies and the world around us. This knowledge is of great importance as it provides the coherent and accurate information necessary for successful motor control. Yet, all this knowledge is of an uncertain nature because we obtain information only through our noisy sensors. We are thus faced with the problem of integrating many uncertain pieces of information into estimates of the properties of our bodies and the surrounding world. Bayesian approaches to estimation formalize the problem of how this uncertain information should be integrated. Utilizing this approach, many studies make predictions that faithfully predict human sensorimotor behavior. WIREs Cogni Sci 2011 2 419–428 DOI: 10.1002/wcs.125 This article is categorized under: Neuroscience > Behavior