title: Inferring visuomotor priors for sensorimotor learning.
abstract: Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations--the mapping between actual and visual location of the hand--during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior.
authors.affiliation: Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom. ejat3@cam.ac.uk
authors.forename: Edward J A
authors.initials: EJ
authors.lastname: Turnham
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Braun
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Wolpert
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003201:Computers; D056228:Feedback, Sensory / Q000502:physiology*; D006225:Hand; D006801:Humans; D048088:Informatics / Q000379:methods; D007858:Learning; D008962:Models, Theoretical; D009048:Motor Skills; D009068:Movement; D016011:Normal Distribution; D011597:Psychomotor Performance; D012371:Robotics; D012984:Software