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Modular decomposition in visuomotor learning

机译：视觉运动学习中的模块化分解

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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.
机译：已提出将“复杂的任务分解为更简单的子任务（分别由一个单独的模块学习）”的“分而治之”的原理，作为学习期间的一种计算策略。我们探讨了人类运动系统使用这种模块化分解策略来学习视觉运动图，视觉输入与运动输出之间的关系的可能性。使用虚拟现实系统，对象从两个不同的位置开始进行相反的棱柱形视觉运动重映射-实际和视觉感知的手位置之间的差异。尽管视觉空间和运动空间之间存在冲突，但受试者还是学会了两个依赖于起点的视觉运动映射，并且将该学习推广到中间的开始位置证明了这两个学习映射的插值。此插值是两个学习的视觉运动贴图的加权平均值，其权重取决于开始位置，该预测是通过模块化学习的计算模型（称为“专家混合”）进行的。这些结果提供了证据，表明大脑在学习过程中可能采用模块化分解策略。

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