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Experimental Methods to Study Human Postural Control
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Published on: September 11, 2019

Movement stability under uncertain internal models of dynamics.

F Crevecoeur1, J McIntyre, J-L Thonnard

  • 1Center for Systems Engineering and Applied Mechanics, Université catholique de Louvain, Louvain-la-Neuve, Brussels, Belgium.

Journal of Neurophysiology
|June 18, 2010
PubMed
Summary

The central nervous system (CNS) adjusts movement speed and accuracy when internal models are uncertain during motor learning. This strategy stabilizes movements despite sensory noise and feedback delays.

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Area of Science:

  • Neuroscience
  • Motor Control
  • Biomechanics

Background:

  • Sensory noise and feedback delays can destabilize on-line movement control.
  • Internal models predict motor action consequences but can be inaccurate during motor learning or exposure to novel dynamics.
  • A strategy is needed to maintain movement stability when predictions are unreliable.

Purpose of the Study:

  • To test the hypothesis that individuals adapt speed and accuracy constraints to stabilize movements under uncertain internal models.
  • To investigate how the central nervous system (CNS) adjusts motor control policies in novel dynamic environments.
  • To examine the role of internal model accuracy in movement variability.

Main Methods:

  • Subjects performed discrete arm rotations in a manipulandum under short-term weightlessness (0 g) to necessitate updating limb dynamics.
  • Grip force adjustments were measured to assess changes in grip force/load force coupling.
  • Movement kinematics (speed, velocity profiles, accuracy) were analyzed and compared with optimal feedback control simulations.

Main Results:

  • Grip force tuning to load force variations decreased during initial exposure to 0 g, indicating altered internal predictions.
  • Movements became slower with asymmetric velocity profiles and target undershooting during the learning period.
  • Simulations supported the hypothesis, showing that adjusting movement objectives (cost function) reduces variability caused by internal prediction noise.

Conclusions:

  • The CNS modifies movement objectives to enhance stability when internal models are uncertain.
  • Adaptation of speed and accuracy constraints is a key strategy for preserving movement stability in novel environments.
  • This study provides evidence for a flexible control policy that prioritizes stability over precision when facing dynamic uncertainty.