Bayesian inference in motor learning
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.
A person uses Bayesian inference to create an estimate that is a weighted combination of his current sensory information and his previous knowledge, or prior.
Another key part of Bayesian inference is that the estimate will be closer to the physical state suggested by sensory information if the senses are more accurate and will be closer to the state of the prior if the sensory information is more uncertain than the prior.
Alternatively, if one were familiar with one's opponent but were playing in foggy or dark conditions that would hamper sight, sensory information would be less certain and one's estimate would rely more heavily on previous knowledge.
Translating this into the language of motor learning, the prior represents previous knowledge about the physical state of the thing being observed, the likelihood is sensory information used to update the prior, and the posterior is the nervous system's estimate of the physical state.
In some cases, the cursor is shifted a small distance away from the actual hand position to test how the person responds to changes in visual feedback.
This form of adaptation holds true only when the shift is small compared to the distance the person has to reach to hit the target.
Bayesian inference can also be applied to the way humans combine information about changes in their environment from multiple senses without any consideration of prior knowledge.
Additionally, Bayesian inference has been found to play a part in adaptation of postural control.
In one study, for example, subjects use a Wii Balance Board to do a surfing task in which they must move a cursor representing their center of pressure (COP) on a screen.
[8] The Wii surfer got visual information about his/her COP from clouds of dots similar to the one shown in the reaching section.
[10] However, to date, no studies have determined if humans adapt their gates using Bayesian inference or not.
Some adaptation studies do not support the application of Bayesian inference to motor learning.
[3][4] This indicates that, while Bayesian inference is used in adaptation, it is limited in that much previous experience is necessary to develop an influential prior.
