Bayesian integration in sensorimotor learning

When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in...

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Bibliographic Details
Published in:Nature 2004-01, Vol.427 (6971), p.244-247
Main Authors: Körding, Konrad P, Wolpert, Daniel M
Format: Article
Language:English
Subjects:
Activity levels. Psychomotricity
Bayes Theorem
Bayesian analysis
Biological and medical sciences
Brain - physiology
Feedback
Female
Fingers - physiology
Fundamental and applied biological sciences. Psychology
Humans
Learning
Learning - physiology
Male
Motor ability
Motor Skills - physiology
Movement
Normal Distribution
Photic Stimulation
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychomotor activities
Psychomotor Performance - physiology
Sensory perception
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Summary:When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
ISSN:0028-0836
1476-4687
DOI:10.1038/nature02169