Motor learning involves plasticity in a network of brain areas across the cortex and cerebellum. Such traces of learning have the potential to affect subsequent learning of other tasks. In some cases, prior learning can interfere with subsequent learning, but it may be possible to potentiate learning of one task with a prior task if they are sufficiently different. Because prism adaptation involves extensive neuroplasticity, we reasoned that the elevated excitability of neurons could increase their readiness to undergo structural changes, and in turn, create an optimal state for learning a subsequent task. We tested this idea, selecting two different forms of learning tasks, asking whether exposure to a sensorimotor adaptation task can improve subsequent de novo motor skill learning. Participants first learned a new visuomotor mapping induced by prism glasses in which prism strength varied trial-to-trial. Immediately after and the next day, we tested participants on a mirror tracing task, a form of de novo skill learning. Prism-trained and control participants both learned the mirror tracing task, with similar reductions in error and increases in distance traced. Both groups also showed evidence of offline performance gains between the end of day 1 and the start of day 2. However, we did not detect differences between groups. Overall, our results do not support the idea that prism adaptation learning can potentiate subsequent de novo learning. We discuss factors that may have contributed to this result.
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Genet Med
December 2024
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN; Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN. Electronic address:
Purpose: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) with genetic data to understand which decisions may affect performance.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
Akşehir Kadir Yallagöz Health School, Selcuk University, Konya, Türkiye.
Aim: The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation.
Methods: Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction.
Nurs Educ Perspect
October 2024
About the Authors Judith Bacchus Cornelius, PhD, RN, FAAN, ANEF, is a professor, College of Health and Human Services, University of North Carolina at Charlotte, Charlotte, North Carolina. Charlene Downing, PhD, RN, is a professor, Department of Nursing, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa. Adesola A. Ogunfowokan, PhD, RN, FWACN, is a professor, Community Health Nursing, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria. Nompumelelo Ntshingila, DCur(UJ), is an associate professor, Department of Nursing, Faculty of Health Sciences, University of Johannesburg. Florence Okoro, PhD, RN, is an associate professor, College of Health and Human Services, University of North Carolina at Charlotte. Ijeoma Enweana, DNP, RN, CVN, is adjunct nursing faculty, Presbyterian School of Nursing, Queens University of Charlotte, Charlotte, North Carolina. Oluwayemisi Olagunju, PhD, is senior lecturer, Department of Nursing Science, Obafemi Awolowo University. Funding was received from the University of North Carolina at Charlotte Global Learning and Internationalization Institute. For more information, contact Dr. Cornelius at
The COVID-19 pandemic presented opportunities for educational innovations and the development of intercultural learning experiences. A global health assignment guided by a collaborative online international learning pedagogy was assigned to doctoral nursing students from three different countries. Icebreaker activities, along with the Culturally You diagram, commenced the team-building process.
View Article and Find Full Text PDFBrain Struct Funct
December 2024
School of Medicine, Department of Neuropharmacology, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia.
This editorial celebrates the 80th birthday of Distinguished Professor Laszlo Zaborszky, co-founder of Brain Structure and Function, and reflects on his monumental contributions to neuroscience, particularly his pioneering work on the cholinergic basal forebrain. Professor Zaborszky's research has reshaped our understanding of this brain region's organization and function, uncovering its critical role in cognitive processes such as learning, memory, and attention. His findings have challenged longstanding assumptions, demonstrating that the cholinergic projections to the cortex are highly organized, with implications for neurodegenerative diseases like Alzheimer's.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks.
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