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      Competition between parallel sensorimotor learning systems

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          Abstract

          Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge and an implicit process that adapts subconsciously. How do these systems interact? Does one system’s contributions suppress the other, or do they operate independently? Here, we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system’s response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.

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          Error correction, sensory prediction, and adaptation in motor control.

          Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.
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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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              Learning of visuomotor transformations for vectorial planning of reaching trajectories.

              The planning of visually guided reaches is accomplished by independent specification of extent and direction. We investigated whether this separation of extent and direction planning for well practiced movements could be explained by differences in the adaptation to extent and directional errors during motor learning. We compared the time course and generalization of adaptation with two types of screen cursor transformation that altered the relationship between hand space and screen space. The first was a gain change that induced extent errors and required subjects to learn a new scaling factor. The second was a screen cursor rotation that induced directional errors and required subjects to learn new reference axes. Subjects learned a new scaling factor at the same rate when training with one or multiple target distances, whereas learning new reference axes took longer and was less complete when training with multiple compared with one target direction. After training to a single target, subjects were able to transfer learning of a new scaling factor to previously unvisited distances and directions. In contrast, generalization of rotation adaptation was incomplete; there was transfer across distances and arm configurations but not across directions. Learning a rotated reference frame only occurred after multiple target directions were sampled during training. These results suggest the separate processing of extent and directional errors by the brain and support the idea that reaching movements are planned as a hand-centered vector whose extent and direction are established via learning a scaling factor and reference axes.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                28 February 2022
                2022
                : 11
                : e65361
                Affiliations
                [1 ] Department of Biomedical Engineering, Johns Hopkins School of Medicine ( https://ror.org/037zgn354) Baltimore United States
                [2 ] Neuroscience Center, University of North Carolina ( https://ror.org/0130frc33) Chapel Hill United States
                [3 ] Vanderbilt University School of Medicine ( https://ror.org/02vm5rt34) Nashville United States
                [4 ] Department of Kinesiology and Health Science, York University ( https://ror.org/05fq50484) Toronto Canada
                [5 ] IFIBIO Houssay, Deparamento de Fisiología y Biofísia, Facultad de Medicina, Universidad de Buenos Aires ( https://ror.org/0081fs513) Buenos Aires Argentina
                [6 ] Department of Neurology, Johns Hopkins School of Medicine ( https://ror.org/037zgn354) Baltimore United States
                [7 ] Department of Neuroscience, Johns Hopkins School of Medicine ( https://ror.org/037zgn354) Baltimore United States
                [8 ] The Santa Fe Institute ( https://ror.org/01arysc35) Santa Fe United States
                Peking University ( https://ror.org/02v51f717) China
                Brown University ( https://ror.org/05gq02987) United States
                Peking University ( https://ror.org/02v51f717) China
                Peking University ( https://ror.org/02v51f717) China
                Peking University ( https://ror.org/02v51f717) China
                The University of Queensland ( https://ror.org/00rqy9422) Australia
                Author information
                https://orcid.org/0000-0001-9140-1077
                https://orcid.org/0000-0002-7791-9408
                https://orcid.org/0000-0002-5658-8654
                https://orcid.org/0000-0002-4316-1846
                https://orcid.org/0000-0002-7686-2569
                Article
                65361
                10.7554/eLife.65361
                9068222
                35225229
                b30da110-3d8b-40af-8c74-13ed07911365
                © 2022, Albert et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 01 December 2020
                : 11 February 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: F32NS095706
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: CNS-1714623
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: R01NS078311
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                When a common error drives parallel implicit and explicit learning systems, increases in the explicit response will suppress implicit adaptation.

                Life sciences
                motor learning,implicit learning,explicit learning,savings,interference,human
                Life sciences
                motor learning, implicit learning, explicit learning, savings, interference, human

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