This chapter introduces information-theoretic tools — including Shannon entropy and mutual information — as principled metrics for quantifying the regularity, variability, and computational demands of motor behavior, with applications to motor learning and aging research.
Undergraduate/graduate course covering the neural mechanisms of human motor control: sensorimotor loops, predictive force control, motor learning, and disorders.
When lifting objects with one hand, the brain builds internal models predicting the force required. This study shows these predictive grip force models transfer across hands independently of how heavy the object feels — dissociating motor prediction from perceptual weight estimates.