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Cited by 372

The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.

Motor Synergies and the Equilibrium-Point Hypothesis

Open access
Latash, Mark L.·2010·Motor Control

Abstract

The article offers a way to unite three recent developments in the field of motor control and coordination: (1) The notion of synergies is introduced based on the principle of motor abundance; (2) The uncontrolled manifold hypothesis is described as offering a computational framework to identify and quantify synergies; and (3) The equilibrium-point hypothesis is described for a single muscle, single joint, and multijoint systems. Merging these concepts into a single coherent scheme requires focusing on control variables rather than performance variables. The principle of minimal final action is formulated as the guiding principle within the referent configuration hypothesis. Motor actions are associated with setting two types of variables by a controller, those that ultimately define average performance patterns and those that define associated synergies. Predictions of the suggested scheme are reviewed, such as the phenomenon of anticipatory synergy adjustments, quick actions without changes in synergies, atypical synergies, and changes in synergies with practice. A few models are briefly reviewed.

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Crossref · 10.1123/mcj.14.3.294

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Metadata

Authors
Latash, M. L.
Venue
Motor Control
Published
2010-07
Volume
14
Issue
3
Pages
294-322
ISSN
  • 1087-1640 (ISSN-L)
  • 1543-2696
OpenAlex
W39842680
Cited by
372
References
96
FWCI
8.69
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Motor Synergies and the Equilibrium-Point Hypothesis. https://app.cochat.ai/papers/10.1123/mcj.14.3.294
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Latash, M. L. (2010). Motor Synergies and the Equilibrium-Point Hypothesis. Motor Control. https://doi.org/10.1123/mcj.14.3.294

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.