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Thur, Oct 28

Speaker: Nicolas Schweighofer (University of Southern California)

Title: Dual-adaptation supports a parallel architecture of motor memory

Abstract: Although our understanding of the mechanisms underlying motor adaptation has greatly benefited from previous computational models, the architecture of motor memory is still uncertain. We investigated the architecture of human motor memory by systematically testing possible architectures via a combination of simulations and of a dual visuo-motor adaptation experimental paradigm. We found that only one parsimonious model can account for both previous motor adaptation data and our dual-task adaptation data: a fast process that contains a single state is arranged in parallel with a slow process that contains multiple states switched via contextual cues. Our result suggests that during motor adaptation fast and slow processes are updated simultaneously from the same motor learning errors.

In recent work, we have used our model to account for the role of short-term memory in the contextual interference (CI) tasks in individuals post-stroke. In the CI effect, intermixing the learning of different tasks via random schedules reduces performance during training, but enhances long-term retention compared to blocked schedules.  We showed that individuals at least 3 months post-stroke exhibited slower change in performance during training and less forgetting after 24 hours in the random condition than in the blocked condition, thus reproducing the CI effect. Furthermore, as predicted by our model, our results indicated that individuals with low spatial working memory exhibited little forgetting after either random or blocked schedules.  We thus propose that the CI effect is due, at least in part, to greater forgetting in short-term memory between trials of the same task during random schedules than during blocked schedules.