Modular decomposition in visuomotor learning

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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 learning1–3. 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"1. These results provide evidence that the brain may employ a modular decomposition strategy during learning.