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Related Experiment Videos

Learned changes in the complexity of movement organization during multijoint, standing pulls

W A Lee1, J L Patton

  • 1Program in Physical Therapy, Northwestern University Medical School, Chicago, IL 60611-2814, USA. wlee@casbah.acns.nwu.edu

Biological Cybernetics
|November 14, 1997
PubMed
Summary

The central nervous system does not simplify bouncing pull movements to a basic inverted pendulum model with practice. Instead, it learns a more complex strategy involving dynamic controls and intersegmental interactions for better balance.

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Area of Science:

  • Motor control
  • Human movement science
  • Biomechanics

Background:

  • The central nervous system (CNS) aims to organize multijoint movements efficiently.
  • A common hypothesis suggests learning simplifies movements to a conservative, one degree of freedom (DF) inverted pendulum model.
  • This model is proposed to facilitate achieving goals like precise force production and balance maintenance.

Purpose of the Study:

  • To test if the CNS learns to organize multijoint 'bouncing pull' movements to resemble a one DF inverted pendulum model after practice.
  • To evaluate changes in center of mass (CMAP) motion, phase plane symmetry, and parameter constancy with practice.

Main Methods:

  • Ten subjects practiced a bouncing pull task for 5 days, applying forces between 20-80% of maximum.

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  • Kinematic, force plate, and pulling force data were collected.
  • Analysis focused on CMAP trajectory fit to the inverted pendulum model, phase plane symmetry, and parameter constancy.

    • Main Results:

    • Practice did not significantly improve the fit between actual and model-simulated CMAP motion, except for moderate forces.
    • Phase plane symmetry and parameter constancy did not improve; differences in CMAP position and speed increased.
    • Anterior shift in CMAP position and greater ankle torque differences indicated separate learning of force production and balance recovery.

    Conclusions:

    • The CNS does not organize multijoint bouncing pulls like a simple, conservative inverted pendulum model.
    • Learning involves a more complex organization utilizing time-varying controls and intersegmental dynamics.
    • Future models may require additional degrees of freedom and time-varying parameters to fully explain the learned movement strategy.