Bayesian Models of Motor Control

M. Berniker*, K. Kording

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

Motor control is fundamental to the nervous system: only through our movements do we interact with the world. Successful motor control requires the integration of a myriad of pieces of information. Yet all this information is of an uncertain nature because we only obtain noisy information in a changing world. We are thus faced with the problem of integrating many uncertain pieces of information into a relatively precise estimate of the properties of our bodies and the surrounding world. Bayesian models of motor control formalize the problem of how uncertain information should be integrated and make predictions that often well describe human movement behavior.

Original languageEnglish (US)
Title of host publicationEncyclopedia of Neuroscience
PublisherElsevier Ltd
Pages127-133
Number of pages7
ISBN (Print)9780080450469
DOIs
StatePublished - Jan 1 2009

Keywords

  • Bayesian statistics
  • Cost function
  • Cue combination
  • Feedback control
  • Kalman filters
  • Motor control
  • Normative models
  • Optimal control
  • Psychophysics
  • Sensorimotor integration
  • Stochastic modeling

ASJC Scopus subject areas

  • Neuroscience(all)

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