Linking motor learning to function approximation: Learning in an unlearnable force field

Opher Donchin, Reza Shadmehr

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in their reaching trajectories to various targets as they learn an internal model. Using a framework from function approximation, we argue that the sequence of errors should reect the process of gradient descent. If so, then the sequence of errors should obey hidden state transitions of a simple dynamical system. Fitting the system to human data, we find a surprisingly good fit accounting for 98% of the variance. This allows us to draw tentative conclusions about the basis elements used by the brain in transforming sensory space to motor commands. To test the robustness of the results, we estimate the shape of the basis elements under two conditions: in a traditional learning paradigm with a consistent force field, and in a random sequence of force fields where learning is not possible. Remarkably, we find that the basis remains invariant.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001
PublisherNeural information processing systems foundation
ISBN (Print)0262042088, 9780262042086
StatePublished - 1 Jan 2002
Externally publishedYes
Event15th Annual Neural Information Processing Systems Conference, NIPS 2001 - Vancouver, BC, Canada
Duration: 3 Dec 20018 Dec 2001

Publication series

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

Conference

Conference15th Annual Neural Information Processing Systems Conference, NIPS 2001
Country/TerritoryCanada
CityVancouver, BC
Period3/12/018/12/01

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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