Context-dependent memory decay is evidence of effort minimization in motor learning: a computational study

Abstract

Recent theoretical models suggest that motor learning includes at least two processes: error minimization and memory decay. While learning a novel movement, a motor memory of the movement is gradually formed to minimize the movement error between the desired and actual movements in each training trial, but the memory is slightly forgotten in each trial. The learning effects of error minimization trained with a certain movement are partially available in other non-trained movements, and this transfer of the learning effect can be reproduced by certain theoretical frameworks. Although most theoretical frameworks have assumed that a motor memory trained with a certain movement decays at the same speed during performing the trained movement as non-trained movements, a recent study reported that the motor memory decays faster during performing the trained movement than non-trained movements, i.e., the decay rate of motor memory is movement or context dependent. Although motor learning has been successfully modeled based on an optimization framework, e.g., movement error minimization, the type of optimization that can lead to context-dependent memory decay is unclear. Thus, context-dependent memory decay raises the question of what is optimized in motor learning. To reproduce context-dependent memory decay, I extend a motor primitive framework. Specifically, I introduce motor effort optimization into the framework because some previous studies have reported the existence of effort optimization in motor learning processes and no conventional motor primitive model has yet considered the optimization. Here, I analytically and numerically revealed that context-dependent decay is a result of motor effort optimization. My analyses suggest that context-dependent decay is not merely memory decay but is evidence of motor effort optimization in motor learning.

Speed-dependent and mode-dependent modulations of spatiotemporal modules in human locomotion extracted via tensor decomposition

“…In this case, the third factor t k,r can illustrate how spatiotemporal modules are modulated depending on adaptation, learning, development, or rehabilitation. Because several studies have focused on both adaptation [27][28][29][30][31][32][33][34][35] and spatiotemporal modules [2][3][4][5][6] in detail but only separately, the relationship between those concepts has been investigated in only a few studies 10,16 . Investigating the link between motor adaptation and spatiotemporal modules via tensor decomposition may be a promising direction for future work.…”

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome

“…Although we relied on simple logistic regression, more complicated methods, such as mixture models [12][13][14][15] , kernel techniques 16 , and deep learning 17 , could also be applied. Linear regression has several advantages, including that it is related to motor primitive, a conventional model of motor control and learning [18][19][20][21][22][23] . Although most conventional models of motor learning focus on continuous outcome values, our method can also be applied to motor learning with categorical outcomes.…”

Larger, but not better, motor adaptation ability inherent in medicated Parkinson’s disease patients revealed by a smart-device-based study

“…Although the state-space model [38][39][40][41] is a popular method to quantify the adaptation effects in motor adaptation, it is not appropriate in the current study. In this framework, the task error should be minimized.…”