Background: The Rey Auditory Verbal Learning Test (RAVLT) is the third most popular verbal memory test and the tenth most frequently used neuropsychological test. The original scoring system of RAVLT does not differentiate stages of memory processing, but a recently developed composite scoring system has this potential. The objectives were to compare the two systems in terms of their capacity to differentiate the stages of memory processing and to study the effect of demographic variables on the learning trials (T) of the Turkish form of RAVLT (T-RAVLT).
Method: The sample consisted of 600 Caucasian Turkic adults, who were categorized into three levels of age, three levels of education, and two levels of gender. Individual administration of T-RAVLT was performed using the standard procedures of RAVLT.
Results: The components in the exploratory factor analysis (EFA) and latent variables in the confirmatory factor analysis (CFA) of the original scores were consistent with sequentially ordered T-RAVLT stages. Demographic variables (age, education, and gender) affected performances in all of the learning trials. The composite scores revealed retrieval and retention as separate components, but these scores could not be predicted from the relevant T-RAVLT scores.
Conclusions: Findings recommend a combined utilization of the two scoring systems: The original system to provide scores on the performance at each stage of T-RAVLT and the combined system to provide separate scores on learning, retention, and retrieval, the three stages of memory processing. A selective effect of demographic variables on T1 was not observed, indicating a need for cross-cultural studies that are meticulously controlled for age and education.
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http://dx.doi.org/10.1080/13803395.2022.2080186 | DOI Listing |
J Child Lang
January 2025
Cognitive Science, Johns Hopkins University, Baltimore, MDUSA.
English-speaking children sometimes make errors in production and comprehension of biclausal questions, known as "Scope-Marking Errors". In production, these errors surface as medial wh questions (e.g.
View Article and Find Full Text PDFNeurocomputing (Amst)
January 2025
Department of Electrical and Computer Engineering, University of Maryland at College Park, 8223 Paint Branch Dr, College Park, MD, 20740, USA.
Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited computational capacity of the devices, low power budget, varied memory access methods, and I/O bus bandwidth governed by the underlying processor's architecture. Furthermore, integrating an inference framework with time-sensitive applications - such as games and video-based software to perform tasks like ray tracing denoising and video processing - introduces the need to minimize data movement between processors and increase data locality in the target processor.
View Article and Find Full Text PDFDev Rev
March 2025
Child Study Center, Yale School of Medicine, 230 S Frontage Rd, New Haven, CT 06519, USA.
Parent-child interactions shape children's cognitive outcomes such that caregivers can guide attention and facilitate learning opportunities. These interactions provide infants and toddlers with rich, naturalistic experiences that engage complex cognitive functions and lay the groundwork for the development of mature executive functions. Although most caregivers seek to engage children optimally, they can unintentionally impede this developmental process by being under-engaged or intrusive.
View Article and Find Full Text PDFHeliyon
January 2025
John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary.
Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018 China.
Psychological studies have demonstrated that the music can affect memory by triggering different emotions. Building on the relationships among music, emotion, and memory, a memristor-based emotion associative learning circuit is designed by utilizing the nonlinear and non-volatile characteristics of memristors, which includes a music judgment module, three emotion generation modules, three emotional homeostasis modules, and a memory module to implement functions such as learning, second learning, forgetting, emotion generation, and emotional homeostasis. The experimental results indicate that the proposed circuit can simulate the learning and forgetting processes of human under different music circumstances, demonstrate the feasibility of memristors in biomimetic circuits, verify the impact of music on memory, and provide a foundation for in-depth research and application development of the interaction mechanism between emotion and memory.
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