Bayesian integration in sensorimotor learning.

@article{citeulike:93509, abstract = {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.}, address = {Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK. konrad@koerding.de}, author = {K\"{o}rding, K. P. and Wolpert, D. M. }, citeulike-article-id = {93509}, doi = {http://dx.doi.org/10.1038/nature02169}, issn = {1476-4687}, journal = {Nature}, keywords = {bayesian, human, probabilistic, psychophysics, sensorimotor\_learning}, month = {January}, number = {6971}, pages = {244--247}, posted-at = {2005-02-11 21:45:13}, priority = {2}, title = {Bayesian integration in sensorimotor learning.}, url = {http://dx.doi.org/10.1038/nature02169}, volume = {427}, year = {2004} }

TY - JOUR ID - citeulike:93509 L3 - citeulike-article-id:93509 TI - Bayesian integration in sensorimotor learning. JF - Nature VL - 427 IS - 6971 SP - 244 EP - 247 AD - Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK. konrad@koerding.de SN - 1476-4687 N2 - 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. KW - bayesian KW - human KW - probabilistic KW - psychophysics KW - sensorimotor_learning AU - Körding, KP AU - Wolpert, DM PY - 2004/01/15/ UR - http://dx.doi.org/10.1038/nature02169 ER -