Izawa J, Rane T, Donchin O, and Shadmehr R (2008) Motor adaptation as a process of re-optimization.
Journal of Neuroscience.
Abstract Adaptation is sometimes viewed as a process where the nervous system learns to predict and cancel effects of a novel environment, returning movements to near baseline (unperturbed) conditions. An alternate view is that cancellation is not the goal of adaptation. Rather, the goal is to maximize performance in that environment. If performance criteria are well defined, theory allows one to predict the re-optimized trajectory. For example, if velocity dependent forces perturb the hand perpendicular to the direction of a reaching movement, the best reach plan is not a straight line but a curved path that appears to over-compensate for the forces. If this environment is stochastic (changing from trial to trial), the re-optimized plan should take into account this uncertainty, removing the over-compensation. If the stochastic environment is zero-mean, peak velocities should increase to allow for more time to approach the target. Finally, if one is reaching through a via-point, the optimum plan in a zero-mean deterministic environment is a smooth movement, but in a zero-mean stochastic environment is a segmented movement. We observed all of these tendencies in how people adapt to novel environments. Therefore, motor control in a novel environment is not a process of perturbation cancellation. Rather, the process resembles re-optimization: through practice in the novel environment, we learn internal models that predict sensory consequences of motor commands. Through reward based optimization, we use the internal model to search for a better movement plan to minimize implicit motor costs and maximize rewards.