Stabilization of kinematic variables in the control of bimanual pointing movements

J. Laczko, S. Jaric, Mark Latash

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

Human voluntary movements face a problem of kinematic redundancy: The number of degrees of freedom for the peripheral mechanical apparatus (e.g., a limb) is higher than the number of variables necessary to describe movement execution. Thus, there is an infinity of different ways to execute a given motor task. The recently developed Uncontrolled Manifold (UCM) hypothesis suggests that the central nervous system (CNS) generates solutions such that important task related variables are selectively stabilized. Each motor task is associated with stabilizing a time series of a task variable. At each instant, the CNS selects, in the state space of elements participating in the task, a manifold (UCM) corresponding to a fixed value of the selected task variable. We study a planar bimanual task, when one hand moves a target and the other hand moves a pointer that must reach the target. We hypothesized that the stabilized task variable was the vectorial difference of the pointertip and the target. The 6 dimensional state space was defined by "joint configuration vectors" whose elements were intersegmental joint angles (shoulder, elbow and wrist in both arms). The subjects repeated the movements 15 times, and the movements were recorded by a movement analysis system. Then, the subjects practiced the movements (300 trials). After practice 15 trials were recorded again. We computed the variance of the joint configurations before and after practice. Six joint rotations affected the 2 dimensional task variable. The UCM corresponding to this variable is 4-dimensional, while the subspace of the state space that is orthogonal (ORT) to the UCM is 2-dimensional. The variance within the UCM was larger than in the ORT conforming to the UCM hypothesis. After practice the joint variance decreased and the drop in the component of variance that did not affect the task variable was larger than the drop of the other component. Thus, practice lead to more stable time courses of the task variable and of the corresponding joint configuration.

Original languageEnglish (US)
Pages1256-1260
Number of pages5
StatePublished - Jan 1 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period7/15/017/19/01

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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