Exploration of joint redundancy but not task space variability facilitates supervised motor learning

Significance Theories of reinforcement learning claim that motor variability helps in motor learning and are supported by recent experimental work. In contrast, theories of motor control propose that variability is noise that needs to be suppressed. We attempt to reconcile these apparent contradictory positions. Using the formulation of the unconstrained manifold hypothesis, we show that motor variability has two components—a part arising out of the redundancy that does not affect task-space and another component related to task-space variability. We show that the motor variability component resulting from the redundancy determines both dynamic and kinematic learning ability across subjects without affecting task-space variability. The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.