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Optimal Schedules in Multitask Motor Learning. Neural Comput 2016 Apr;28(4):667-85

Date

02/19/2016

Pubmed ID

26890347

Pubmed Central ID

PMC6555556

DOI

10.1162/NECO_a_00823

Scopus ID

2-s2.0-85005959757 (requires institutional sign-in at Scopus site)   11 Citations

Abstract

Although scheduling multiple tasks in motor learning to maximize long-term retention of performance is of great practical importance in sports training and motor rehabilitation after brain injury, it is unclear how to do so. We propose here a novel theoretical approach that uses optimal control theory and computational models of motor adaptation to determine schedules that maximize long-term retention predictively. Using Pontryagin's maximum principle, we derived a control law that determines the trial-by-trial task choice that maximizes overall delayed retention for all tasks, as predicted by the state-space model. Simulations of a single session of adaptation with two tasks show that when task interference is high, there exists a threshold in relative task difficulty below which the alternating schedule is optimal. Only for large differences in task difficulties do optimal schedules assign more trials to the harder task. However, over the parameter range tested, alternating schedules yield long-term retention performance that is only slightly inferior to performance given by the true optimal schedules. Our results thus predict that in a large number of learning situations wherein tasks interfere, intermixing tasks with an equal number of trials is an effective strategy in enhancing long-term retention.

Author List

Lee JY, Oh Y, Kim SS, Scheidt RA, Schweighofer N

Author

Robert Scheidt BS,MS,PhD Associate Professor in the Biomedical Engineering department at Marquette University




MESH terms used to index this publication - Major topics in bold

Adaptation, Physiological
Animals
Computer Simulation
Humans
Learning
Models, Theoretical
Motor Activity