Motivation is commonly recognized by researchers and practitioners as a key factor for motor learning. The OPTIMAL theory of motor learning (Wulf & Lewthwaite, 2016) claims that practice conditions that enhance learners' expectancies for future successful outcomes or that are autonomy supportive are motivating, thus leading to better learning. To examine the current evidence of the association between motivation and motor learning, we searched the literature for studies that manipulated expectancies and/or autonomy support. Specifically, our goals were to assess whether these manipulations resulted in group differences in motivation and, if so, whether increased motivation was associated with learning advantages. Results showed that out of 166 experiments, only 21% (n = 35) included at least one measure of motivation, even though this is the main factor proposed by OPTIMAL theory to explain the learning benefits of these manipulations. Among those, only 23% (n = 8) found group-level effects on motivation, suggesting that these manipulations might not be as motivating as expected. Of the eight experiments that found a group-level effect on motivation, five also observed learning benefits, offering limited evidence that when practice conditions increase motivation, learning is more likely to occur. Overall, the small number of studies assessing motivation precludes any reliable conclusions on the association between motivation and motor learning from being drawn. Together, our results question whether manipulations implemented in the research lines supporting OPTIMAL theory are indeed motivating and highlight the lack of sufficient evidence in these literatures to support that increased motivation benefits motor learning.
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http://dx.doi.org/10.1016/j.psychsport.2024.102690 | DOI Listing |
Sleep
January 2025
UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).
View Article and Find Full Text PDFJ Neural Transm (Vienna)
January 2025
Postgraduate Program in Physical Therapy (PPGFT), Department of Physical Therapy (DFisio), University of São Carlos (UFSCar), Washington Luis Road, Km 235, São Carlos, São Paulo, 13565-905, Brazil.
The cerebellum is a structure in the suprasegmental nervous system classically known for its involvement in motor functions such as motor planning, coordination, and motor learning. However, with scientific advances, other functions of the cerebellum, such as cognitive, emotional, and autonomic processing, have been discovered. Currently, there is a body of evidence demonstrating the involvement of the cerebellum in nociception and pain processing.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, Italy.
Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual's safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Information Science and Technology, Donghua University, Shanghai 201620, China.
Modern city construction focuses on developing smart transportation, but the recognition of the large number of non-motorized vehicles in the city is still not sufficient. Compared to fixed recognition equipment, drones have advantages in image acquisition due to their flexibility and maneuverability. With the dataset collected from aerial images taken by drones, this study proposed a novel lightweight architecture for small objection detection based on YOLO framework, named EBR-YOLO.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time.
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