The synergic control of multi-finger force production: stability of explicit and implicit task components

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• 作者：Sasha Reschechtko ; Vladimir M. Zatsiorsky ; Mark L. Latash
• 关键词：Synergy ; Grip force ; Shear force ; Uncontrolled manifold hypothesis
• 刊名：Experimental Brain Research
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：235
• 期：1
• 页码：1-14
• 全文大小：
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Neurosciences; Neurology;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1106
• 卷排序：235

文摘

Manipulating objects with the hands requires the accurate production of resultant forces including shear forces; effective control of these shear forces also requires the production of internal forces normal to the surface of the object(s) being manipulated. In the present study, we investigated multi-finger synergies stabilizing shear and normal components of force, as well as drifts in both components of force, during isometric pressing tasks requiring a specific magnitude of shear force production. We hypothesized that shear and normal forces would evolve similarly in time and also show similar stability properties as assessed by the decomposition of inter-trial variance within the uncontrolled manifold hypothesis. Healthy subjects were required to accurately produce total shear and total normal forces with four fingers of the hand during a steady-state force task (with and without visual feedback) and a self-paced force pulse task. The two force components showed similar time profiles during both shear force pulse production and unintentional drift induced by turning the visual feedback off. Only the explicitly instructed components of force, however, were stabilized with multi-finger synergies. No force-stabilizing synergies and no anticipatory synergy adjustments were seen for the normal force in shear force production trials. These unexpected qualitative differences in the control of the two force components—which are produced by some of the same muscles and show high degree of temporal coupling—are interpreted within the theory of control with referent coordinates for salient variables. These observations suggest the existence of two classes of neural variables: one that translates into shifts of referent coordinates and defines changes in magnitude of salient variables, and the other controlling gains in back-coupling loops that define stability of the salient variables. Only the former are shared between the explicit and implicit task components.