Motor adaptation as a process of reoptimization.
TL;DR: Motor control in a novel environment is not a process of perturbation cancellation: rather, the process resembles reoptimization: through practice in the novel environment, the authors learn internal models that predict sensory consequences of motor commands, and use the internal model to search for a better movement plan to minimize implicit motor costs and maximize rewards.
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Abstract: 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
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Citations
Error Correction, Sensory Prediction, and Adaptation in Motor Control
TL;DR: Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors, and is used to produce a lifetime of calibrated movements.
A computational neuroanatomy for motor control
Reza Shadmehr,John W. Krakauer +1 more
TL;DR: It is argued that the lesion approach and theoretical motor control can mutually inform each other and one may identify distinct motor control processes from computational models and map them onto specific deficits in patients.
Computational mechanisms of sensorimotor control.
TL;DR: Five computational mechanisms that the brain may use to limit their deleterious effects: optimal feedback control, impedance control, predictive control, Bayesian decision theory, and sensorimotor learning are reviewed.
618
Emergence of a Stable Cortical Map for Neuroprosthetic Control
TL;DR: The authors show that the neural representation for control of a neuroprosthetic device undergoes a process of consolidation, after which it is stable, readily recalled, and resistant to interference.
613
The coordination of movement: optimal feedback control and beyond
TL;DR: Two crucial areas of research, hierarchical control and the problem of movement initiation, are highlighted that need to be developed for an optimal feedback control theory framework to characterise movement coordination more fully and to serve as a basis for studying the neural mechanisms involved in voluntary motor control.
537
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TL;DR: The motor system in the present case is defined as including the visual and proprioceptive feedback loops that permit S to monitor his own activity, and the information capacity of the motor system is specified by its ability to produce consistently one class of movement from among several alternative movement classes.
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TL;DR: This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.
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TL;DR: The investigation of how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented, suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.
Signal-dependent noise determines motor planning
Chris Harris,Daniel M. Wolpert +1 more
TL;DR: This theory provides a simple and powerful unifying perspective for both eye and arm movement control and accurately predicts the trajectories of both saccades and arm movements and the speed–accuracy trade-off described by Fitt's law.
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