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WIREs Cogn Sci
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Bayesian approaches to sensory integration for motor control

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Abstract 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

(a) Possible sources of noise in the motor system. Motor commands possess noise and result in uncertain movements. Visual and proprioceptive senses too, contain inherent uncertainties. (b) Bayesian integration of a prior and likelihood. The prior, denoted with the green curve, represents the probability of a state, x. The likelihood, denoted with the red curve, represents the probability of observing the data, o, given x. The posterior is the probability that x is the state, given our observation, o. (c) Bayes' rule applied to cue combinations is the mathematical analog, only instead of using a prior and likelihood, we integrate two likelihoods. Here, for simplicity we assume the two observations are conditionally independent given the state, and a flat prior distribution over x.

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(a) Two structural beliefs of the world. If two cues are adequately coincident, subject's perceive them as having a common cause (the green box), a phenomenon typified through ventriloquism. If the cues are disparate in time or space, subjects perceive them as having independent causes (the red box). (b) Subject's belief of a common cause. As the spatial disparity of two cues, a light flash and a tone, is experimentally controlled the belief in a common cause can be manipulated.

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(a) Typical procedure for optimally estimating the world's state in modern control theory. A model of the world, combined with a motor command is used to estimate a predicted state (and its observation), through a prior distribution (labeled in green). Observations of the world dictate the likelihood of a particular observation given the current world state (labeled in red). A Kalman filter is used to make a Bayesian update of our belief in the world's current state (labeled in blue). (b) This process of Bayesian inference repeats itself at each time step, using the posterior from one time step, as the prior for the following time step. (c) Motor adaptation can be framed as an analogous update procedure. Our prior belief in muscle properties (e.g., muscle strength, labeled in green) is integrated with our observed motor errors (labeled in red) to update estimates in our muscle properties.

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