Neural Averaging in Motor Learning

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Abstract
The capacity for skill development over multiple training episodes is fundamental to human motor function. We have studied the process by which skills evolve with training by progressively modifying a series of motor learning tasks that subjects performed over a 1-mo period. In a series of empirical and modeling studies, we show that performance undergoes repeated modification with new learning. Each in a series of prior training episodes contributes such that present performance reflects a weighted average of previous learning. Moreover, we have observed that the relative weighting of skills learned wholly in the past changes with time. This suggests that the neural substrate of skill undergoes modification after consolidation.