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The Journal of Neuroscience, March 12, 2008, 28(11):2883-2891; doi:10.1523/JNEUROSCI.5359-07.2008

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Behavioral/Systems/Cognitive
Motor Adaptation as a Process of Reoptimization

Jun Izawa,¹ Tushar Rane,¹ Opher Donchin,² and Reza Shadmehr¹

¹Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, and ²Motor Learning Laboratory, Department of Biomedical Engineering, Ben Gurion University of the Negev, Be'er Sheva 84105, Israel

Correspondence should be addressed to Dr. Jun Izawa, Department of Biomedical Engineering, Johns Hopkins University, 416 Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205-2195. Email: jizawa{at}jhu.edu

Adaptation is sometimes viewed as a process in which 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 reoptimized 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 overcompensate for the forces. If this environment is stochastic (changing from trial to trial), the reoptimized plan should take into account this uncertainty, removing the overcompensation. 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 reoptimization: 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.

Key words: motor learning; motor adaptation; cerebellar damage; ataxia; optimal control; internal model

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Received July 13, 2007; revised Jan. 14, 2008; accepted Jan. 15, 2008.

Correspondence should be addressed to Dr. Jun Izawa, Department of Biomedical Engineering, Johns Hopkins University, 416 Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205-2195. Email: jizawa{at}jhu.edu

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