Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control

Area of Science:

• Motor control
• Neuroscience
• Biomechanics

Background:

• Motor behaviors rely on both reactive and proactive control mechanisms.
• Neuromusculoskeletal systems face noise and delays, requiring robust control strategies.
• Muscle co-contraction stabilizes motor commands against internal noise.

Purpose of the Study:

• To extend stochastic optimal control to include environmental uncertainty.
• To investigate the relationship between task uncertainty and anticipatory muscle co-contraction.
• To determine if proactive co-contraction stabilizes movements under external disturbances.

Main Methods:

• Analytical derivation and numerical simulations of neuromusculoskeletal models.
• Stochastic optimal open-loop control modeling.
• Single-joint pointing task experiment with externally applied wrist torque.

Main Results:

• A predictive relationship between task uncertainty and optimal anticipatory co-contraction was identified.
• Experimental results confirmed the model's predictions.
• Increased environmental uncertainty led to increased muscle co-contraction.

Conclusions:

• Environmental uncertainty drives the need for impedance control through proactive muscle co-contraction.
• Proactive co-contraction stabilizes motor behaviors when reactive control is insufficient.
• This highlights the crucial role of anticipatory mechanisms in adaptive motor control.