The ability to spontaneously access knowledge of relational concepts acquired in one domain and apply it to a novel domain has traditionally been explored in the analogy literature via the problem-solving paradigm. In the present work, we propose a novel procedure based on categorisation as a complementary approach to assess spontaneous analogical transfer-using one category learning task to enhance learning of the same underlying category structures in another domain. In Experiment 1, we demonstrate larger improvements in classification performance across blocks of training in a target category learning task among participants that underwent a base category learning task relative to a separate group of participants learning the target category structures for the first time, thus providing evidence for spontaneous transfer of the category structures. In Experiment 2, we demonstrate similar evidence of spontaneous transfer for participants that underwent a comparison-based base category learning task under a more rigorous context shift between the base and target category learning tasks. Additional exploratory analyses across both experiments showcase ways in which this paradigm can be used to answer questions regarding the analogical transfer of relational category structures and generate promising paths for future work.
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Med Educ Online
December 2025
Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.
Background: Medical Humanities (MH) curricula integrate humanities disciplines into medical education to nurture essential qualities in future physicians. However, the impact of MH on clinical competencies during formative training phases remains underexplored. This study aimed to determine the influence of MH curricula on internship performance.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Shollinganallur, Chennai, India.
Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy.
View Article and Find Full Text PDFNutrients
January 2025
School of Health and Medical Sciences, University of Southern Queensland, Ipswich 4305, Australia.
: Proper nutrition and hydration are essential for the health, growth, and athletic performance of student-athletes. Adequate energy availability and sufficient intake of macro- and micronutrients support adolescent development, prevent nutrient deficiencies, and reduce the risk of disordered eating. These challenges are particularly relevant to student-athletes, who are vulnerable to nutrition misinformation and often exhibit limited nutrition knowledge.
View Article and Find Full Text PDFSensors (Basel)
January 2025
The 54th Research Institute, China Electronics Technology Group Corporation, College of Signal and Information Processing, Shijiazhuang 050081, China.
The multi-sensor fusion, such as LiDAR and camera-based 3D object detection, is a key technology in autonomous driving and robotics. However, traditional 3D detection models are limited to recognizing predefined categories and struggle with unknown or novel objects. Given the complexity of real-world environments, research into open-vocabulary 3D object detection is essential.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Behavioral computing based on visual cues has become increasingly important, as it can capture and annotate teachers' and students' classroom states on a large scale and in real time. However, there is a lack of consensus on the research status and future trends of computer vision-based classroom behavior recognition. The present study conducted a systematic literature review of 80 peer-reviewed journal articles following the Preferred Reporting Items for Systematic Assessment and Meta-Analysis (PRISMA) guidelines.
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