title: Bayesian integration in force estimation.
abstract: When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, termed prior information. Bayesian integration defines the way in which these two sources of information should be combined to produce an optimal estimate. To investigate whether subjects use such a strategy in force estimation, we designed a novel sensorimotor estimation task. We controlled the distribution of forces experienced over the course of an experiment thereby defining the prior. We show that subjects integrate sensory information with their prior experience to generate an estimate. Moreover, subjects could learn different prior distributions. These results suggest that the CNS uses Bayesian models when estimating force requirements.
authors.affiliation: Institute of Neurology, Sobell Department of Movement Neuroscience, University College London, London WC1N 3BG, UK. konrad@koerding.de.
authors.forename: Konrad P
authors.initials: KP
authors.lastname: Körding
authors.forename: Shih-pi
authors.initials: SP
authors.lastname: Ku
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Wolpert
mesh_terms: D001499:Bayes Theorem; D002490:Central Nervous System / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009043:Motor Activity / Q000502:physiology*; D009068:Movement / Q000502:physiology