Two experiments investigated the impact of the relationship between processing and storage stimuli on the working memory span task performance of children aged 7 and 9 years of age. In Experiment 1, two types of span task were administered (sentence span and operation span), and participants were required to recall either the products of the processing task (sentence-final word, arithmetic total) or a word or digit unrelated to the processing task. Experiment 2 contrasted sentence span and operation span combined with storage of either words or digits, in tasks in which the item to be remembered was not a direct product of the processing task in either condition. In both experiments, memory span was significantly greater when the items to be recalled belonged to a different stimulus category from the material that was processed, so that in sentence span tasks, number recall was superior to word recall, and in operation span tasks, word recall was superior to number recall. Explanations of these findings in terms of similarity-based interference and response competition in working memory are discussed.
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http://dx.doi.org/10.1080/02724980443000683 | DOI Listing |
Netw Neurosci
December 2024
Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA.
Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (>1 Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting state ( = 926, 473 females).
View Article and Find Full Text PDFNetw Neurosci
December 2024
Computer and Information Sciences, University of Strathclyde, Glasgow, UK.
Measuring transient functional connectivity is an important challenge in electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high-temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a methodology to overcome these problems called filter average short-term (FAST) functional connectivity.
View Article and Find Full Text PDFHealth Care Sci
December 2024
School of Computer Science and Engineering, Vellore Institute of Technology Vellore India.
Background: The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.
Methods: The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data.
Cureus
November 2024
Neurology, Multiple Sclerosis Unit, University Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, ESP.
Cladribine is an immune reconstitution therapy for multiple sclerosis (MS) that selectively produces long-term reductions in highly pathological memory B cells, with temporary reductions in other B- and T-cell subsets, thereby restoring immune function close to baseline levels in the short term. Here, we describe two cases of relapsing MS (RMS) treated with a second course of cladribine. Both patients were initially diagnosed with clinically isolated syndrome and later enrolled in the ORACLE-MS and CLASSIC-MS studies.
View Article and Find Full Text PDFBMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
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