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      Unlearning versus savings in visuomotor adaptation: comparing effects of washout, passage of time, and removal of errors on motor memory

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          Abstract

          Humans are able to rapidly adapt their movements when a visuomotor or other systematic perturbation is imposed. However, the adaptation is forgotten or unlearned equally rapidly once the perturbation is removed. The ultimate cause of this unlearning remains poorly understood. Unlearning is often considered to be a passive process due to inability to retain an internal model. However, we have recently suggested that it may instead be a process of reversion to habit, without necessarily any forgetting per se. We compared the timecourse and nature of unlearning across a variety of protocols where unlearning is known to occur: error-clamp trials, removal of visual feedback, removal of the perturbation, or simply a period of inactivity. We found that, in agreement with mathematical models, there was no significant difference in the rate of decay between subject who experienced zero-error clamp trials, and subjects who made movements with no visual feedback. Time alone did lead to partial unlearning (over the duration we tested), but the amount of unlearning was inconsistent across subjects. Upon re-exposure to the same perturbation, subjects who unlearned through time or by reverting to veridical feedback exhibited savings. By contrast, no savings was observed in subjects who unlearned by having visual feedback removed or by being placed in a series of error-clamp trials. Thus although these various forms of unlearning can all revert subjects back to baseline behavior, they have markedly different effects on whether long-term memory for the adaptation is spared or is also unlearned. On the basis of these and previous findings, we suggest that unlearning is not due to passive forgetting of an internal model, but is instead an active process whereby adapted behavior gradually reverts to baseline habits.

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          Most cited references15

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          A computational neuroanatomy for motor control.

          The study of patients to infer normal brain function has a long tradition in neurology and psychology. More recently, the motor system has been subject to quantitative and computational characterization. The purpose of this review is to argue that the lesion approach and theoretical motor control can mutually inform each other. Specifically, one may identify distinct motor control processes from computational models and map them onto specific deficits in patients. Here we review some of the impairments in motor control, motor learning and higher-order motor control in patients with lesions of the corticospinal tract, the cerebellum, parietal cortex, the basal ganglia, and the medial temporal lobe. We attempt to explain some of these impairments in terms of computational ideas such as state estimation, optimization, prediction, cost, and reward. We suggest that a function of the cerebellum is system identification: to build internal models that predict sensory outcome of motor commands and correct motor commands through internal feedback. A function of the parietal cortex is state estimation: to integrate the predicted proprioceptive and visual outcomes with sensory feedback to form a belief about how the commands affected the states of the body and the environment. A function of basal ganglia is related to optimal control: learning costs and rewards associated with sensory states and estimating the "cost-to-go" during execution of a motor task. Finally, functions of the primary and the premotor cortices are related to implementing the optimal control policy by transforming beliefs about proprioceptive and visual states, respectively, into motor commands.
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            Learning of action through adaptive combination of motor primitives.

            Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects' adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.
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              Interlimb coordination during locomotion: what can be adapted and stored?

              Interlimb coordination is critically important during bipedal locomotion and often must be adapted to account for varying environmental circumstances. Here we studied adaptation of human interlimb coordination using a split-belt treadmill, where the legs can be made to move at different speeds. Human adults, infants, and spinal cats can alter walking patterns on a split-belt treadmill by prolonging stance and shortening swing on the slower limb and vice versa on the faster limb. It is not known whether other locomotor parameters change or if there is a capacity for storage of a new motor pattern after training. We asked whether adults adapt both intra- and interlimb gait parameters during split-belt walking and show aftereffects from training. Healthy subjects were tested walking with belts tied (baseline), then belts split (adaptation), and again tied (postadaptation). Walking parameters that directly relate to the interlimb relationship changed slowly during adaptation and showed robust aftereffects during postadaptation. These changes paralleled subjective impressions of limping versus no limping. In contrast, parameters calculated from an individual leg changed rapidly to accommodate split-belts and showed no aftereffects. These results suggest some independence of neural control of intra- versus interlimb parameters during walking. They also show that the adult nervous system can adapt and store new interlimb patterns after short bouts of training. The differences in intra- versus interlimb control may be related to the varying complexity of the parameters, task demands, and/or the level of neural control necessary for their adaptation.
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                Author and article information

                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                28 June 2013
                2013
                : 7
                : 307
                Affiliations
                [1] 1Motor Performance Laboratory, Department of Neurology, Columbia University College of Physicians and Surgeons New York, NY, USA
                [2] 2Department of Neurology, Johns Hopkins University Baltimore, MD, USA
                [3] 3Department of Neuroscience, Johns Hopkins University Baltimore, MD, USA
                Author notes

                Edited by: Rachael D. Seidler, University of Michigan, USA

                Reviewed by: K. Thoroughman, Washington University, USA; Dezso Nemeth, University of Szeged, Hungary

                *Correspondence: Adrian M. Haith, Department of Neurology, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD 21287, USA e-mail: adrian.haith@ 123456jhu.edu
                Article
                10.3389/fnhum.2013.00307
                3711055
                23874277
                e998f51b-38bc-4252-b10f-d04e32ff090a
                Copyright © 2013 Kitago, Ryan, Mazzoni, Krakauer and Haith.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 01 January 2013
                : 08 June 2013
                Page count
                Figures: 3, Tables: 0, Equations: 2, References: 27, Pages: 7, Words: 5450
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                adaptation,visuomotor rotation,unlearning,decay,savings
                Neurosciences
                adaptation, visuomotor rotation, unlearning, decay, savings

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