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      Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes

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          Abstract

          The motor system demonstrates an exquisite ability to adapt to changes in the environment and to quickly reset when these changes prove transient. If similar environmental changes are encountered in the future, learning may be faster, a phenomenon known as savings. In studies of sensorimotor learning, a central component of savings is attributed to the explicit recall of the task structure and appropriate compensatory strategies. Whether implicit adaptation also contributes to savings remains subject to debate. We tackled this question by measuring, in parallel, explicit and implicit adaptive responses in a visuomotor rotation task, employing a protocol that typically elicits savings. While the initial rate of learning was faster in the second exposure to the perturbation, an analysis decomposing the 2 processes showed the benefit to be solely associated with explicit re-aiming. Surprisingly, we found a significant decrease after relearning in aftereffect magnitudes during no-feedback trials, a direct measure of implicit adaptation. In a second experiment, we isolated implicit adaptation using clamped visual feedback, a method known to eliminate the contribution of explicit learning processes. Consistent with the results of the first experiment, participants exhibited a marked reduction in the adaptation function, as well as an attenuated aftereffect when relearning from the clamped feedback. Motivated by these results, we reanalyzed data from prior studies and observed a consistent, yet unappreciated pattern of attenuation of implicit adaptation during relearning. These results indicate that explicit and implicit sensorimotor processes exhibit opposite effects upon relearning: Explicit learning shows savings, while implicit adaptation becomes attenuated

          Abstract

          Humans learning a new motor task typically improve with repeated practice due to the faster expression of more effective explicit strategies; this study reveals that when motor learning occurs without awareness, performance deteriorates upon relearning.

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          The assessment and analysis of handedness: The Edinburgh inventory

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            Statistical Power Analysis for the Behavioral Sciences

            <i>Statistical Power Analysis</i> is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: <br> * a chapter covering power analysis in set correlation and multivariate methods;<br> * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;<br> * expanded power and sample size tables for multiple regression/correlation.<br>
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              Nonparametric statistical testing of EEG- and MEG-data.

              In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                5 March 2021
                March 2021
                5 March 2021
                : 19
                : 3
                : e3001147
                Affiliations
                [1 ] Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America
                [2 ] Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
                [3 ] School of Psychology, University of Leeds, Leeds, United Kingdom
                [4 ] John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
                [5 ] Department of Physical Therapy, University of Delaware, Newark, Delaware, United States of America
                [6 ] Department of Psychological and Brain Sciences, University of Delaware, Newark, Delaware, United States of America
                German Primate Center, GERMANY
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-6170-1041
                https://orcid.org/0000-0001-5724-3028
                https://orcid.org/0000-0003-0109-593X
                Article
                PBIOLOGY-D-20-02151
                10.1371/journal.pbio.3001147
                7968744
                33667219
                99e8e92f-d304-4c66-870f-65e18e56f221
                © 2021 Avraham et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 July 2020
                : 15 February 2021
                Page count
                Figures: 3, Tables: 0, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: NS105839
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: NS116883
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: K12 HD055931
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: M3X 1934650
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: DC017091
                Award Recipient :
                This work was supported by grants NS116883, NS105839 and DC017091 from the National Institutes of Health ( https://www.nih.gov/) awarded to RBI. HEK was funded by grants K12 HD055931 from the National Institutes of Health and M3X 1934650 from the National Science Foundation ( https://www.nsf.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                All relevant individual data and summary statistics are within the paper and its Supporting Information. All raw data files are available from the GitHub repository: https://git.io/Jtiip.

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