Multiple timescales and uncertainty in motor adaptation

Konrad P. Körding, Joshua B. Tenenbaum, Reza Shadmehr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Our motor system changes due to causes that span multiple timescales. For example, muscle response can change because of fatigue, a condition where the disturbance has a fast timescale or because of disease where the disturbance is much slower. Here we hypothesize that the nervous system adapts in a way that reflects the temporal properties of such potential disturbances. According to a Bayesian formulation of this idea, movement error results in a credit assignment problem: what timescale is responsible for this disturbance? The adaptation schedule influences the behavior of the optimal learner, changing estimates at different timescales as well as the uncertainty. A system that adapts in this way predicts many properties observed in saccadic gain adaptation. It well predicts the timecourses of motor adaptation in cases of partial sensory deprivation and reversals of the adaptation direction.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages745-752
Number of pages8
StatePublished - Dec 1 2007
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: Dec 4 2006Dec 7 2006

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period12/4/0612/7/06

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Körding, K. P., Tenenbaum, J. B., & Shadmehr, R. (2007). Multiple timescales and uncertainty in motor adaptation. In Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference (pp. 745-752). (Advances in Neural Information Processing Systems).