A motor learning strategy reflects neural circuitry for limb control

Abstract

During motor skill acquisition, the brain learns a mapping between intended limb motion and requisite muscular forces. We propose that regions where sensory and motor representations overlap are crucial for motor learning. In primary motor cortex, for example, cells that modulate their activity for motor actions at a joint tend to receive input from that same portion of the periphery. We predict that this correspondence reflects a default strategy--a Bayesian prior--in which subjects tend to associate loads at a joint with motion at that joint (local sensorimotor association) when there is ambiguity regarding the nature of the load. As predicted, we found that in the presence of uncertainty, humans inappropriately generalized elbow loads as though they were based on elbow velocity. Generalization improved when we reduced uncertainty by decreasing coupling between elbow velocity and load during training. These results illustrate a key link between motor learning and the underlying neural circuitry.