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Practice reduces task relevant variance modulation and forms nominal trajectory.

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The brain learns internal models for motor control, shifting from feedback to feedforward strategies during movement learning. This study shows how motor variability is reduced by developing stereotyped trajectories.

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Area of Science:

  • Neuroscience
  • Motor Control
  • Computational Neuroscience

Background:

  • Humans exhibit complex motor skills despite redundant degrees of freedom.
  • Motor variability is often modulated by online feedback control strategies.
  • The brain is hypothesized to learn internal models for feedforward control using nominal trajectories.

Purpose of the Study:

  • To investigate the interplay between feedback and feedforward control during motor learning.
  • To elucidate the relative contributions of different control schemas in motor adaptation.
  • To examine trajectory variance in spatial and temporal domains to understand control strategies.

Main Methods:

  • Subjects learned reaching movements with multiple via-points.
  • Hand trajectory variance in spatial and temporal domains was analyzed.
  • A computational model simulating noise in nominal trajectories and motor commands was employed.

Main Results:

  • Hand trajectories became stereotyped with reduced task-relevant variance modulation during learning.
  • Variance reduction correlated strongly with the velocity profile, not always task constraints.
  • The computational model successfully reproduced observed variance modulation, supporting internal nominal trajectories.

Conclusions:

  • Motor learning involves a shift towards feedforward control based on learned nominal trajectories.
  • Feedback control's influence decreases as feedforward control becomes dominant after practice.
  • The brain utilizes a combination of feedforward and feedback mechanisms, with feedback engaged when necessary.