Modulation of wrist stiffness caused by adaptation to stochastic environment

Abstract

We can catch all sorts of falling objects like eggs or balls. We must predict the dynamical properties of objects to interact with them, but doing so precisely is difficult. The CNS is suggested to modulate the mechanical impedance of the musculoskeletal dynamics to accomplish robust control and overcome variability in environmental dynamics. In this study, we tested the hypothesis that musculoskeletal stiffness increases as the degree of variability in the environment increase through the motor adaptation process. We conducted a ball-catching task experiment in a virtual reality system where the load force of the ball changed every trial and measured the muscle activity in the wrist to estimate its joint stiffness. We found that group level wrist stiffness after adaptation monotonically increased against load force variability. Meanwhile, some participants showed a non-monotonic relationship between wrist stiffness and load force variability. The results of the experiment and computational simulations suggest that the CNS may adapt to a stochastic environment by modulating musculoskeletal stiffness level under the trade-off between movement accuracy and energetic cost.