A Single, Continuously Applied Control Policy for Modeling Reaching Movements with and without Perturbation.

Area of Science:

• Motor control
• Computational neuroscience
• Robotics

Background:

• The interpretation of kinematic features in movement, such as velocity profile peaks, remains debated.
• It is unclear whether these features reflect discrete motor commands or continuous control processes, especially during perturbed movements.

Purpose of the Study:

• To develop and validate an infinite-horizon optimal feedback control model for reaching movements.
• To investigate if a single control policy can explain varying kinematic profiles and responses to target perturbations.

Main Methods:

• An infinite-horizon optimal feedback control model was employed, incorporating control and sensory noise, delays, and Weber's law.
• The model analytically derived a continuous control policy adaptable to target jumps and perturbations.
• Simulations analyzed velocity profiles, submovement generation, and feedback effectiveness.

Main Results:

• The model produced varied velocity profiles and "submovements" from a single policy, without preset movement durations.
• Movement speed and perturbation timing influenced the number of observed submovements.
• The model explained performance during perturbed reaching without target detection and differential feedback effectiveness.

Conclusions:

• Kinematic features like submovements do not necessarily signify discrete motor impulses but can arise from continuous optimal feedback control.
• The findings challenge traditional interpretations of movement control and feedback strategies, suggesting a unified approach.