CNS learns stable, accurate, and efficient movements using a simple algorithm

We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates preci...

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Veröffentlicht in:The Journal of neuroscience Jg. 28; H. 44; S. 11165
Hauptverfasser: Franklin, David W, Burdet, Etienne, Tee, Keng Peng, Osu, Rieko, Chew, Chee-Meng, Milner, Theodore E, Kawato, Mitsuo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 29.10.2008
Schlagworte:
Adaptation, Physiological - physiology
Adult
Algorithms
Central Nervous System - physiology
Female
Humans
Learning - physiology
Male
Movement - physiology
Postural Balance - physiology
Psychomotor Performance - physiology
ISSN:1529-2401, 1529-2401
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Zusammenfassung:We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1529-2401
1529-2401
DOI:10.1523/JNEUROSCI.3099-08.2008