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Title: Human motor learning dynamics in high-dimensional tasks
Conventional approaches to enhance movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model’s convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.  more » « less
Award ID(s):
1940950
PAR ID:
10616846
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
PLOS Computational Biology
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
20
Issue:
10
ISSN:
1553-7358
Page Range / eLocation ID:
e1012455
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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