Differential learning (DL) is a motor learning method characterized by high amounts of variability during practice and is claimed to provide the learner with a higher learning rate than other methods. However, some controversy surrounds DL theory, and to date, no overview exists that compares the effects of DL to other motor learning methods. To evaluate the effectiveness of DL in comparison to other motor learning methods in the acquisition and retention phase. Systematic review and exploratory meta-analysis. PubMed (MEDLINE), Web of Science, and Google Scholar were searched until February 3, 2020. To be included, (1) studies had to be experiments where the DL group was compared to a control group engaged in a different motor learning method (lack of practice was not eligible), (2) studies had to describe the effects on one or more measures of performance in a skill or movement task, and (3) the study report had to be published as a full paper in a journal or as a book chapter. Twenty-seven studies encompassing 31 experiments were included. Overall heterogeneity for the acquisition phase (post-pre; = 77%) as well as for the retention phase (retention-pre; = 79%) was large, and risk of bias was high. The meta-analysis showed an overall small effect size of 0.26 [0.10, 0.42] in the acquisition phase for participants in the DL group compared to other motor learning methods. In the retention phase, an overall medium effect size of 0.61 [0.30, 0.91] was observed for participants in the DL group compared to other motor learning methods. Given the large amount of heterogeneity, limited number of studies, low sample sizes, low statistical power, possible publication bias, and high risk of bias in general, inferences about the effectiveness of DL would be premature. Even though DL shows potential to result in greater average improvements between pre- and post/retention test compared to non-variability-based motor learning methods, more high-quality research is needed before issuing such a statement. For robust comparisons on the relative effectiveness of DL to different variability-based motor learning methods, scarce and inconclusive evidence was found.
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http://dx.doi.org/10.3389/fpsyg.2021.533033 | DOI Listing |
Psychon Bull Rev
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Experimental Psychology, University College London, London, UK.
Hand movements frequently occur with speech. The extent to which the memories that guide co-speech hand movements are tied to the speech they occur with is unclear. Here, we paired the acquisition of a new hand movement with speech.
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January 2025
Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Humans exploit motor synergies for motor control; however, how they emerge during motor learning is not clearly understood. Few studies have dealt with the computational mechanism for generating synergies. Previously, optimal control generated synergistic motion for the upper limb; however, it has not yet been applied to the high-dimensional whole-body system.
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January 2025
Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers.
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January 2025
Arizona State University, Department of Psychology, Tempe, AZ, 85287 USA.
The cerebellum, identified to be active during cognitive and social behavior, has multisynaptic connections through the cerebellar nuclei (CN) and thalamus to cortical regions, yet formation and modulation of these pathways are not fully understood. Perineuronal nets (PNNs) respond to changes in local cellular activity and emerge during development. PNNs are implicated in learning and neurodevelopmental disorders, but their role in the CN during development is unknown.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical Power and Machines Engineering, Higher Institute of Engineering (HIE), El-Shorouk Academy, El-Shorouk City, Egypt.
Enhancing the performance of 5ph-IPMSM control plays a crucial role in advancing various innovative applications such as electric vehicles. This paper proposes a new reinforcement learning (RL) control algorithm based twin-delayed deep deterministic policy gradient (TD3) algorithm to tune two cascaded PI controllers in a five-phase interior permanent magnet synchronous motor (5ph-IPMSM) drive system based model predictive control (MPC). The main purpose of the control methodology is to optimize the 5ph-IPMSM speed response either in constant torque region or constant power region.
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