The ability to accurately repeat meaningless nonwords or lists of spoken digits in correct order have been associated with vocabulary acquisition in both first and second language. Individual differences in these tasks are thought to depend on the phonological loop component of working memory. However, phonological working memory may itself depend on more elementary processes. We asked whether auditory non-verbal short-term memory (STM) for patterns in time supports immediate recall of speech-based sequences. Participants tapped temporal sequences consisting of short and long beeps and repeated nonsense sentences sounding like their native language or an unfamiliar language. As a language learning task, they also memorized familiar-word-foreign-word pairs. Word learning was directly predicted by nonsense sentence repetition accuracy. It was also predicted by temporal pattern STM. However, this association was mediated by performance on the repetition measure. We propose that STM for temporal patterns may reflect a component skill that provides the context signal necessary to encode order in phonological STM. It would be needed to support representation of the prosodic profile of language material, which allows syllables in words and words in sentences to be ordered and temporally grouped for short-term representation and long-term learning.
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http://dx.doi.org/10.3390/brainsci12050549 | DOI Listing |
JMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
View Article and Find Full Text PDFPLoS Biol
January 2025
Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Canada.
The role of epigenetics and chromatin in the maintenance of postmitotic neuronal cell identities is not well understood. Here, we show that the histone methyltransferase Trithorax (Trx) is required in postmitotic memory neurons of the Drosophila mushroom body (MB) to enable their capacity for long-term memory (LTM), but not short-term memory (STM). Using MB-specific RNA-, ChIP-, and ATAC-sequencing, we find that Trx maintains homeostatic expression of several non-canonical MB-enriched transcripts, including the orphan nuclear receptor Hr51, and the metabolic enzyme lactate dehydrogenase (Ldh).
View Article and Find Full Text PDFPLoS One
January 2025
Departamento de Psicología Evolutiva y Comunicación, Campus Universitario de Vigo, University of Vigo, Vigo, Spain.
The main purpose of this study was to examine the age-related changes in inhibitory control of 450 children at the ages of 7-8, 11-12, and 14-16 when controlling for working memory capacity (WMC) and processing speed to determine whether inhibition is an independent factor far beyond its possible reliance on the other two factors. This examination is important for several reasons. First, empirical evidence about age-related changes of inhibitory control is controversial.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures.
View Article and Find Full Text PDFWater Res X
May 2025
Institute for Artificial Intelligence R&D of Serbia, Fruškogorska 1, Novi Sad 21000, Serbia.
This study evaluates three Machine Learning (ML) models-Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. We adopt a data-centric approach, utilizing large, validated datasets to train the models, and apply SHapley Additive exPlanations (SHAP) to enhance the interpretability and reliability of the ML models. The results show that TKAN outperforms LSTM but slightly lags behind TCN in streamflow forecasting.
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