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.
Nature 386 :392-395 (1997) postscript.