Computational model of motor learning and perceptual change

Area of Science:

• Motor control and learning
• Neuroscience
• Robotics

Background:

• Force-field learning is a common paradigm for studying motor learning in arm reaching movements.
• Recent findings indicate that somatosensory perception changes are associated with motor learning.
• Persistent deviations in hand trajectory after perturbation removal suggest underlying perceptual shifts.

Purpose of the Study:

• To propose a computational model explaining the link between motor learning and perceptual changes.
• To investigate the computational mechanisms underlying motor adaptation and its perceptual consequences.
• To model how adjustments in movement planning affect hand trajectory and perceived hand position.

Main Methods:

• Development of a motor control and learning scheme using a simplified two-link system.
• Representation of motor learning as adjustments in desired joint-angular trajectories.
• Mathematical proof of convergence using a Lyapunov-like lemma and validation via computer simulations.

Main Results:

• The proposed model demonstrates that adjustments in desired trajectories influence perceived hand position.
• Computer simulations confirm that perceptual changes can result from motor learning-induced planning adjustments.
• The model explains why hand trajectories may not immediately return to baseline after perturbation removal.

Conclusions:

• Perceptual changes are integral to motor learning, influencing movement control.
• The proposed computational model provides a framework for understanding the interplay between motor planning, execution, and perception.
• This research highlights the adaptive nature of the sensorimotor system in response to learning and perturbations.