Motor learning in novel tasks requires exploration to find the appropriate coordination patterns to perform the task. Prior work has shown that compared to adults, children show limited exploration when learning a task that required using upper body movements to control a 2D cursor on a screen. Here, by changing the task dimensionality to 1D, we examined two competing hypotheses: whether children show limited exploration as a general strategy, or whether children are suboptimal in adapting their exploration to task dimensionality. Two groups of children (9- and 12-year olds), and one group of adults learned a virtual task that involved learning to control a cursor on the screen using movements of the upper body. Participants practiced the task for a single session with a total of 232 reaching movements. Results showed that 9-year olds show worse task performance relative to adults, as indicated by higher movement times and path lengths. Analysis of the coordination strategies indicated that both groups of children showed lower variance along the first principal component, suggesting that they had greater exploration than adults which was suboptimal for the 1D task. These results suggest that motor learning in children is characterized not by limited exploration per se, but by a limited adaptability in matching motor exploration to task dimensionality.
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http://dx.doi.org/10.1016/j.neulet.2021.136355 | DOI Listing |
Biophys Chem
January 2025
Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700106, India. Electronic address:
Quantitative characterization of protein conformational landscapes is a computationally challenging task due to their high dimensionality and inherent complexity. In this study, we systematically benchmark several widely used dimensionality reduction and clustering methods to analyze the conformational states of the Trp-Cage mini-protein, a model system with well-documented folding dynamics. Dimensionality reduction techniques, including Principal Component Analysis (PCA), Time-lagged Independent Component Analysis (TICA), and Variational Autoencoders (VAE), were employed to project the high-dimensional free energy landscape onto 2D spaces for visualization.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Center of Digital Dentistry, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No.22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.
Background: Establishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammals. However, no mature, non-destructive method currently exists in clinical dentistry to quickly, accurately, and comprehensively assess the integrity and thickness of enamel chair-side. This study aims to develop a deep learning work, 2.
View Article and Find Full Text PDFMem Cognit
January 2025
École de Psychologie, Université de Moncton, Moncton, NB, E1A 3E9, Canada.
In short-term ordered recall tasks, phonological similarity impedes item and order recall, while semantic similarity benefits item recall with a weak or null effect on order recall. Ishiguro and Saito recently suggested that these contradictory findings were due to an inadequate assessment of semantic similarity. They proposed a novel measure of semantic similarity based on the distance between items in a three-dimensional space composed of the semantic dimensions of valence, arousal, and dominance.
View Article and Find Full Text PDFJ Imaging
January 2025
Department of Ophthalmology, General University Hospital of Alexandroupolis, 68131 Alexandroupolis, Greece.
Blink detection is considered a useful indicator both for clinical conditions and drowsiness state. In this work, we propose and compare deep learning architectures for the task of detecting blinks in video frame sequences. The first step is the training and application of an eye detector that extracts the eye regions from each video frame.
View Article and Find Full Text PDFJ Imaging
January 2025
Istituto di Scienze Applicate e Sistemi Intelligenti (ISASI), Consiglio Nazionale delle Ricerche (CNR), DHITECH, Campus Università del Salento, Via Monteroni s.n., 73100 Lecce, Italy.
Despite significant advancements in the automatic classification of skin lesions using artificial intelligence (AI) algorithms, skepticism among physicians persists. This reluctance is primarily due to the lack of transparency and explainability inherent in these models, which hinders their widespread acceptance in clinical settings. The primary objective of this study is to develop a highly accurate AI-based algorithm for skin lesion classification that also provides visual explanations to foster trust and confidence in these novel diagnostic tools.
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