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Learning to Learn: Modeling the time-course of visuomotor adaptation

Authors
Tejas Savalia
University of Massachusetts, Amherst ~ Psychological and Brain Sciences
Rosemary Cowell
University of Massachusetts, Amherst, United States of America
David Huber
University of Massachusetts, Amherst ~ Psychological and Brain Sciences
Abstract

Across 640 training trials, participants using a computer tablet learned to move acursor that had its movement direction rotated by 90 degrees relative to onscreen visual feedback. These training trials involved either an all-at-once "sudden" rotation to 90 degrees starting at trial 1 or a "gradual" rotation in nine separate increments of 10 degrees. Similar prior work found a larger detrimental aftereffect when transferring back to no rotation following gradual adaptation training. Wereplicated this effect and crossed these conditions with a speed/accuracy emphasis manipulation. To characterize the nature of learning during training, we applied a simple two-parameter learning model to trial-by-trial errors in motion direction. One parameter captured the learning rate, reflecting trial-to-trial adjustments based on the difference between predicted and observed rotation. The other parameter captured memory, reflecting a tendency to use the estimate of rotation from the previous trial. This simple model captured individual differences, speed/accuracy emphasis, and subtle differences between the sudden and gradual trainingregimes. Furthermore, the model correctly predicted transfer performance for the gradual condition. However, it grossly over-estimated transfer errors for the sudden condition. We hypothesize that participants in the sudden condition learned that the mapping between movements and visual feedback can abruptly change (i.e., a change of environment, rather than visuomotor adaptation), allowing themto quickly adopt a new visuomotor mapping in the transfer phase when the rotation was removed. This learning-to-learn in the sudden condition may reflect model-based forms of reinforcement learning, in contrast to trial-and-error model-free learning.

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