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Inter-dependence between mathematically independent variability components in human multi-finger force control.

Yushin Kim1, Kyung Koh2, Jae Kun Shim3

  • 1Department of Health Administration & Healthcare, Cheongju University, Cheongju, South Korea.

Neuroscience Research
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

Motor variability components, though mathematically independent, are behaviorally linked in multi-finger force control. Online and offline variability showed positive correlations across subjects, impacting human movement control.

Keywords:
FingersForce controlHierarchical variabilityMotor performanceMotor synergyMotor variability

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Area of Science:

  • Neuroscience
  • Biomechanics
  • Human Motor Control

Background:

  • Human movement control exhibits variability due to inherent uncertainties in neural and sensory systems.
  • Previous research identified methods to quantify independent components of motor variability in redundant systems.
  • The behavioral independence of these mathematically distinct variability components across individuals remained unexplored.

Purpose of the Study:

  • To investigate the inter-relations between mathematically independent motor variability components.
  • To examine behavioral dependencies among these components in multi-finger force control.
  • To determine if variability components scale dependently across subjects.

Main Methods:

  • Utilized the hierarchical variability decomposition (HVD) model to quantify variability components.
  • Measured online task-relevant variance (onTRV), online task-irrelevant variance (onTIV), offline task-relevant variance (offTRV), and offline task-irrelevant variance (offTIV).
  • Analyzed correlations between these components across 19 healthy subjects performing force production tasks.

Main Results:

  • Online and offline motor variability components demonstrated a positive correlation across subjects.
  • Task-relevant and task-irrelevant variability components did not show significant correlations.
  • These findings suggest a dependent scaling, particularly between online and offline variability, among individuals.

Conclusions:

  • Mathematically independent motor variability components exhibit dependent behavioral relationships among subjects.
  • The scaling of online and offline variability components is notably interdependent.
  • This suggests a unified behavioral control strategy influencing distinct variability measures in multi-finger force control.