Bayesian integration in sensorimotor learning

• Published in: Nature (London) Vol. 427; no. 6971; pp. 244 - 247
• Main Authors: Körding, Konrad P., Wolpert, Daniel M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.01.2004; Nature Publishing; Nature Publishing Group
• Subjects: Activity levels. Psychomotricity; Bayes Theorem; Bayesian analysis; Biological and medical sciences; Brain - physiology; Carbon; Dissolved inorganic carbon; Ecosystems; Feedback; Female; Fingers - physiology; Food webs; Fundamental and applied biological sciences. Psychology; Humanities and Social Sciences; Humans; Learning; Learning - physiology; letter; Male; Motor ability; Motor Skills - physiology; Movement; multidisciplinary; Normal Distribution; Organic matter; Particulate organic carbon; Photic Stimulation; Photosynthesis; Predator-prey interactions; Primary production; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychomotor activities; Psychomotor Performance - physiology; Science; Science (multidisciplinary); Sensory perception; Zooplankton; Bayes estimation; Human; Acquisition process; Vision; Body movement; Perception; Cognition; Probabilistic model; Motor learning
• ISSN: 0028-0836; 1476-4687; 1476-4687
• DOI: 10.1038/nature02169

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 1 , 2 , 3 , 4 . 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 5 , 6 , 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 4 , 5 . The central nervous system therefore employs probabilistic models during sensorimotor learning.