Learning verbs is an important part of learning one's native language. Prior studies have shown that children younger than 5 years can have difficulty in learning and extending new verbs. The current study extended these studies by showing children multiple events that can be compared during learning, including Japanese- and English-speaking children. In the study, 2-, 3-, and 4-year-olds saw two similar events and then one varied (progressive alignment) or three varied (low alignable) events in a learning phase before test, and this was repeated for four sets. Children were asked to extend these novel verbs in easy (non-cross-mapping) and difficult (cross-mapping) test trials. A repeated-measures analysis of variance showed a significant Age by Condition interaction. In contrast to prior results, the 4-year-olds in both languages did well in both conditions and across test trial types. The 3-year-olds, especially in Japanese, performed best in the progressive alignment condition, showing that experience in seeing similar events was useful for verb learning. The 2-year-olds mostly struggled in this task, showing success only in the low-alignment condition, non-cross-mapping (easy) test trial. These are new findings given that no previous study has examined the role of different levels of variability during learning in a cross-language sample, and no prior study has examined the impact of objects at test in this way. This study shows that an important mechanism for verb learning-the comparison of events-could be useful across languages.
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http://dx.doi.org/10.1016/j.jecp.2024.106129 | DOI Listing |
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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