Motor Adaptation as a Process of Reoptimization

By Jun Izawa, Tushar Rane, Opher Donchin, Reza Shadmehr. Journal of Neuroscience, 2008

338 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/f1f652060b9fe3dab08af2892c3780d21b86f86d

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Citations in the corpus

Emergence of a Stable Cortical Map for Neuroprosthetic Control (2009, background): “balances the acquisition of reward states with the costs of movement [39].”
Error correction, sensory prediction, and adaptation in motor control. (2010, background): “…for this process comes from experiments that find during adaptation to a perturbation, motor commands do not return performance to a baseline trajectory, but rather a trajectory that maximizes performance while minimizing a measure of effort (Uno et al., 1989;Emken et al., 2007;Izawa et al., 2008).”
Computational mechanisms of sensorimotor control. (2011, neutral): “Optimal control can predict the trajectories learned after force field adaptation (Izawa et al., 2008).”
A computational neuroanatomy for motor control (2008, background): “(1) and in a scenario where the objective is to minimize jerk (data in b and c from Izawa et al. 2008) Exp Brain Res (2008) 185:359–381 365”
Use-Dependent and Error-Based Learning of Motor Behaviors (2010, background): “First, the solution for the redundant task may have been chosen, because it constitutes the optimal solution under the task requirements (Izawa et al., 2008).”
CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm (2008, methodology): “It departs significantly from algorithms based on optimization (Burdet and Milner, 1998; Harris and Wolpert, 1998; Stroeve, 1999; Todorov, 2000; Todorov and Jordan, 2002; Guigon et al., 2007; Trainin et al., 2007; Izawa et al., 2008) as it predicts the transients of learning, as well as from existin”
Learning from Sensory and Reward Prediction Errors during Motor Adaptation (2011, methodology): “Under this assumption, for a linear dynamical system, optimal feedback control theory suggests that two interacting mechanisms are necessary: the optimal estimator and the optimal policy [24,26].”