Facilitative effects of use-dependent learning on interlimb transfer of visuomotor adaptation in a person with congenital mirror movements

Jo, Yeongsin; Javidialsaadi, Mousa; Wang, Jinsung

doi:10.1016/j.humov.2022.102973

Cited by 2 publications

(1 citation statement)

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“…A third intriguing hypothesis is that passive training engaged both a strategic explicit system that can be guided by instruction (Taylor et al, 2014;Mazzoni & Krakauer, 2006;McDougle & Taylor, 2019), and an implicit system that adapts without our conscious awareness (Avraham et al, 2020;Miyamoto et al, 2020;Javidialsaadi and Wang, 2020;Wang et al, 2019;Jo et al, 2022). Might an interplay between these two systems be related to the time-dependent error sensitivity patterns observed during rotation learning?…”

Section: Discussionmentioning

confidence: 99%

A conversion from slow to fast memory in response to passive motion

Javidialsaadi

Wang

2021

Preprint

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When the same perturbation is experienced consecutively, learning is accelerated on the second attempt. This savings is a central property of sensorimotor adaptation. Current models suggest that these improvements in learning are due to changes in the brain's sensitivity to error. Here, we tested whether these increases in error sensitivity could be facilitated by passive movement experiences. In each experimental group, the robot moved the arm passively in the direction that solved the upcoming rotation, but no visual feedback was provided. Then, following a break in time, participants adapted to a visuomotor rotation. Prior passive movements substantially improved motor learning, increasing total compensation in each group by approximately 30%. Similar to savings, a state-space model suggested that this improvement in learning was due to an increase in error sensitivity, but not memory retention. Thus, passive memories appeared to increase the motor learning system's sensitivity to error. However, some features in the observed behavior were not captured by this model, nor by similar empirical models, which assumed that learning was due a single exponential process. When we considered the possibility that learning was supported by parallel fast and slow adaptive processes, a striking pattern emerged; whereas initial improvements in learning were driven by a slower adaptive state, increases in error sensitivity gradually transferred to a faster learning system with the passage of time.

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