Model behavior in unpredictable visual environments with coactivation

Evaluate how feedforward impedance-planning or stochastic optimal open-loop control models that implement muscle co-contraction handle unpredictable visual environments—such as random visuomotor rotations—where muscle coactivation emerges and increased stiffness may be detrimental, and determine whether these models can account for fast feedback-based corrections.

Background

Some modeling frameworks posit coactivation primarily as feedforward impedance modulation without facilitating feedback. The authors highlight a gap for unpredictable visual environments, where coactivation emerges and increased stiffness may hinder mobilization.

They explicitly state it is unclear how such models would handle these scenarios, prompting an open modeling problem.