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Nature. 1997 Mar 27;386(6623):392-5.

Modular decomposition in visuomotor learning.

Author information

1
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139, USA. zoubin@cs.toronto.edu

Abstract

The principle of 'divide-and-conquer' the decomposition of a complex task into simpler subtasks each learned by a separate module, has been proposed as a computational strategy during learning. We explore the possibility that the human motor system uses such a modular decomposition strategy to learn the visuomotor map, the relationship between visual inputs and motor outputs. Using a virtual reality system, subjects were exposed to opposite prism-like visuomotor remappings-discrepancies between actual and visually perceived hand locations- for movements starting from two distinct locations. Despite this conflicting pairing between visual and motor space, subjects learned the two starting-point-dependent visuomotor mappings and the generalization of this learning to intermediate starting locations demonstrated an interpolation of the two learned maps. This interpolation was a weighted average of the two learned visuomotor mappings, with the weighting sigmoidally dependent on starting location, a prediction made by a computational model of modular learning known as the "mixture of experts". These results provide evidence that the brain may employ a modular decomposition strategy during learning.

PMID:
9121554
DOI:
10.1038/386392a0
[Indexed for MEDLINE]

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