Citation

Sternad D. Curr. Opin. Behav. Sci. 2018; 20: 183-195.

Affiliation

Department of Biology, Electrical and Computer Engineering and Physics, Center for the Interdisciplinary Study of Complex Systems, Northeastern University, Boston, MA.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.cobeha.2018.01.004

PMID

30035207

PMCID

PMC6051545

Abstract

Mastering a motor skill is typified by a decrease in variability. However, variability is much more than the undesired signature of discoordination: structure in both its distributional properties and temporal sequence can reveal control priorities. Extending from the notion that signal-dependent noise corrupts information transmission in the neuromotor system, this review tracks more recent recognitions that the complex dynamic motor system in its interaction with task constraints creates high-dimensional spaces with multiple equivalent solutions. Further analysis differentiates these solutions to have different degrees of noise-sensitivity, goal-relevance or additional costs. Practice proceeds from exploration of these solution spaces to then exploitation with further channeling of noise. Extended practice leads to fine-tuning of skill brought about by reducing noise. These distinct changes in variability are suggested as a way to characterize stages of learning. Capitalizing on the sensitivity of the CNS to noise, interventions can add extrinsic or amplify intrinsic noise to guide (re-)learning desired behaviors. The persistence and generalization of acquired skill is still largely understudied, although an essential element of skill. Consistent with advances in the physical sciences, there is increasing realization that noise can have beneficial effects. Analysis of the non-random structure of variability may reveal more than analysis of only its mean.

Language: en