Modular decomposition in visuomotor learning.
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.
Ghahramani
Z
Z
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139, USA. zoubin@cs.toronto.edu
Wolpert
D M
DM
D005246:Feedback; D006225:Hand / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009043:Motor Activity / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*