Modular decomposition in visuomotor learning

Nature. 1997 Mar 27;386(6623):392-5. doi: 10.1038/386392a0.


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.

MeSH terms

  • Feedback
  • Hand / physiology
  • Humans
  • Learning / physiology*
  • Models, Neurological
  • Motor Activity / physiology
  • Psychomotor Performance / physiology*
  • Visual Perception / physiology*