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      Self-controlled practice and nudging during structural learning of a novel control interface

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          Abstract

          Self-controlled practice schedules have been shown to enhance motor learning in several contexts, but their effectiveness in structural learning tasks, where the goal is to eventually learn an underlying structure or rule, is not well known. Here we examined the use of self-controlled practice in a novel control interface requiring structural learning. In addition, we examined the effect of ‘nudging’–i.e., whether altering task difficulty could influence self-selected strategies, and hence facilitate learning. Participants wore four inertial measurement units (IMUs) on their upper body and the goal was to use motions of the upper body to move a screen cursor to different targets presented on the screen. The structure in this task that had to be learned was based on the fact that the signals from the IMUs were linearly mapped to the x- and y- position of the cursor. Participants (N = 62) were split into 3 groups (random, self-selected, nudge) based on whether they had control over the sequence in which they could practice the targets. To test whether participants learned the underlying structure, participants were tested both on the trained targets, as well as novel targets that were not practiced during training. Results showed that during training, the self-selected group showed shorter movement times relative to the random group, and both self-selected and nudge groups adopted a strategy of tending to repeat targets. However, in the test phase, we found no significant differences in task performance between groups, indicating that structural learning was not reliably affected by the type of practice. In addition, nudging participants by adjusting task difficulty did not show any significant benefits to overall learning. These results suggest that although self-controlled practice influenced practice structure and facilitated learning, it did not provide any additional benefits relative to practicing on a random schedule in this task.

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          Bayesian inference for psychology. Part II: Example applications with JASP

          Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.
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            Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning.

            Effective motor performance is important for surviving and thriving, and skilled movement is critical in many activities. Much theorizing over the past few decades has focused on how certain practice conditions affect the processing of task-related information to affect learning. Yet, existing theoretical perspectives do not accommodate significant recent lines of evidence demonstrating motivational and attentional effects on performance and learning. These include research on (a) conditions that enhance expectancies for future performance, (b) variables that influence learners' autonomy, and (c) an external focus of attention on the intended movement effect. We propose the OPTIMAL (Optimizing Performance through Intrinsic Motivation and Attention for Learning) theory of motor learning. We suggest that motivational and attentional factors contribute to performance and learning by strengthening the coupling of goals to actions. We provide explanations for the performance and learning advantages of these variables on psychological and neuroscientific grounds. We describe a plausible mechanism for expectancy effects rooted in responses of dopamine to the anticipation of positive experience and temporally associated with skill practice. Learner autonomy acts perhaps largely through an enhanced expectancy pathway. Furthermore, we consider the influence of an external focus for the establishment of efficient functional connections across brain networks that subserve skilled movement. We speculate that enhanced expectancies and an external focus propel performers' cognitive and motor systems in productive "forward" directions and prevent "backsliding" into self- and non-task focused states. Expected success presumably breeds further success and helps consolidate memories. We discuss practical implications and future research directions.
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              Learning-performance distinction and memory processes for motor skills: a focused review and perspective.

              Behavioral research in cognitive psychology provides evidence for an important distinction between immediate performance that accompanies practice and long-term performance that reflects the relative permanence in the capability for the practiced skill (i.e. learning). This learning-performance distinction is strikingly evident when challenging practice conditions may impair practice performance, but enhance long-term retention of motor skills. A review of motor learning studies with a specific focus on comparing differences in performance between that at the end of practice and at delayed retention suggests that the delayed retention or transfer performance is a better indicator of motor learning than the performance at (or end of) practice. This provides objective evidence for the learning-performance distinction. This behavioral evidence coupled with an understanding of the motor memory processes of encoding, consolidation and retrieval may provide insight into the putative mechanism that implements the learning-performance distinction. Here, we propose a simplistic empirically-based framework--motor behavior-memory framework--that integrates the temporal evolution of motor memory processes with the time course of practice and delayed retention frequently used in behavioral motor learning paradigms. In the context of the proposed framework, recent research has used noninvasive brain stimulation to decipher the role of each motor memory process, and specific cortical brain regions engaged in motor performance and learning. Such findings provide beginning insights into the relationship between the time course of practice-induced performance changes and motor memory processes. This in turn has promising implications for future research and practical applications. Copyright © 2011 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                14 April 2020
                2020
                : 15
                : 4
                : e0223810
                Affiliations
                [001]Department of Kinesiology, Michigan State University, East Lansing, MI, United States of America
                Curtin University, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [¤]

                Current address: Department of Neurology, Pennsylvania State University, Hershey, PA, United States of America

                Author information
                http://orcid.org/0000-0002-6622-0706
                http://orcid.org/0000-0003-4618-5170
                Article
                PONE-D-19-26775
                10.1371/journal.pone.0223810
                7156047
                32287279
                dbf9e29e-fdf1-4665-a561-5e2fa1ab033f
                © 2020 Lee, Jayasinghe

                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
                : 23 September 2019
                : 23 March 2020
                Page count
                Figures: 4, Tables: 0, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: 1654929
                Award Recipient :
                Funded by: National Science Foundation
                Award ID: 1703735
                Award Recipient :
                This material is based upon work supported by the National Science Foundation ( http://www.nsf.gov) under Grant nos. 1654929 and 1703735 awarded to ML. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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