Bayesian Inference in Motor Learning

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HandWiki. Bayesian Inference in Motor Learning. Encyclopedia. Available online: https://encyclopedia.pub/entry/31526 (accessed on 17 October 2026).

HandWiki. Bayesian Inference in Motor Learning. Encyclopedia. Available at: https://encyclopedia.pub/entry/31526. Accessed October 17, 2026.

HandWiki. "Bayesian Inference in Motor Learning" Encyclopedia, https://encyclopedia.pub/entry/31526 (accessed October 17, 2026).

HandWiki. (2022, October 27). Bayesian Inference in Motor Learning. In Encyclopedia. https://encyclopedia.pub/entry/31526

HandWiki. "Bayesian Inference in Motor Learning." Encyclopedia. Web. 27 October, 2022.

Bayesian Inference in Motor Learning

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Bayesian inference is a statistical tool that can be applied to motor learning, specifically to adaptation. Adaptation is a short-term learning process involving gradual improvement in performance in response to a change in sensory information. Bayesian inference is used to describe the way the nervous system combines this sensory information with prior knowledge to estimate the position or other characteristics of something in the environment. Bayesian inference can also be used to show how information from multiple senses (e.g. visual and proprioception) can be combined for the same purpose. In either case, Bayesian inference dictates that the estimate is most influenced by whichever information is most certain.

adaptation prior knowledge proprioception

References

Körding, K. P., & Wolpert, Daniel M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences (Vol. 10, pp. 319–326).
Lee, PM. (2004). Bayesian Statistics: An Introduction. Oxford University Press: London.
Körding, K. P., & Wolpert, Daniel M. (2004). Bayesian integration in sensorimotor learning. Nature 427:244-7.
Wei, K., Kording, K. (2009). Relevance of error: what drives motor adaptation? J Neurophysiol 101:655-64.
Kording, K.P., Ku, S., Wolpert, D.M. (2004). Bayesian Integration in force estimation. J Neurophysiol 92:3161-5.
Van Beers, R.J., Wolpert, D.M., Haggard, P. (2002). When feeling is more important than seeing in sensorimotor adaptation. Current Biology 12:834-7.
Plooy, A., Tresilian, J.R., Mon-Williams, M., Wann, J.P. (1998). The contribution of vision and proprioception to judgements of finger proximity. Experimental Brain Research 118(3):415-20.
Stevenson, I.H., Fernades, H.L., Vilares, I., Wei, K., Kording, K.P. (2009). Bayesian integration and non-linear feedback control in a full-body motor task. PLoS Comp Biol 5(12):1-9.
Vasudevan, E.V., Bastian, A.J. (2010). Split-belt treadmill adaptation shows different functional networks for fast and slow human walking. J Neurophysiol 103:183-191.
Crowell, H.P., Milner, C.E., Hamill, J., Davis, I.S. (2010). J Orthopaedic & Sports Physical Therapy 40(4):206-13.
Scheidt, R.A., Dingwell, J.B., Mussa-Ivaldi, F.A. (2001). Learning to move amid uncertainty. J Neurophysiol 86:971-85.

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