When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active...

Area of Science:

• Neuroscience
• Motor Control
• Robotics

Background:

• Movement planning traditionally focuses on minimizing motor costs like inaccuracy and effort.
• Optimal movement generation often relies on feedback controllers for error correction.
• Sensory feedback quality can be influenced by the movement itself, a factor often overlooked.

Purpose of the Study:

• To investigate if movement planning incorporates state-dependent sensory feedback reliability.
• To determine if humans account for how movement affects sensory information when planning trajectories.
• To explore the role of active sensing and feedforward control in movement planning.

Main Methods:

• Developed an optimal control model incorporating state-dependent sensory feedback.
• Designed a human experiment where hand visibility (sensory feedback) was modulated based on movement velocity.
• Analyzed participants' hand movement trajectories to assess adaptation to altered sensory feedback.

Main Results:

• Participants adapted to state-dependent visibility perturbations by producing curved hand movements.
• Observed adaptation patterns align with predictions from an optimal controller that accounts for sensory noise.
• Movement trajectories indicated subjects actively modulated their movements to optimize sensory information.

Conclusions:

• Movement planning is sensitive to sensory costs, not just motor costs.
• Optimal movement control integrates feedforward commands alongside feedback, especially with state-dependent sensory information.
• Humans exhibit sophisticated control strategies that actively manage sensory feedback during movement.