Temporal and Amplitude Generalization in Motor Learning

A fundamental feature of human motor control is the ability to vary effortlessly over a substantial range, both the duration and amplitude of our movements. We used a three-dimensional robotic interface, which generated novel velocity dependent forces on the hand, to investigate how adaptation to these altered dynamics experienced only for movements at one temporal rate and amplitude generalizes to movements made at a different rate or amplitude. After subjects had learned to make a single point-to-point movement in a novel velocity-dependent force field, we examined the generalization of this learning to movements of both half the duration or twice the amplitude. Such movements explore a state-space not experienced during learning-any changes in behavior are due to generalization of the learning, the form of which was used to probe the intrinsic constraints on the motor control process. The generalization was assessed by determining the force field in which subjects produced kinematically normal movements. We found substantial generalization of the motor learning to the new movements supporting a nonlocal representation of the control process. Of the fields tested, the form of the generalization was best characterized by linear extrapolation in a state-space representation of the controller. Such an intrinsic constraint on the motor control process can facilitate the scaling of natural movements.