Rather than working memory capacity acting as a distinct subordinate function of fluid intelligence, there is an emerging consensus that their relationship can be understood as an outcome of common functions dictated by the strength and flexibility of bindings which integrate representations relationally. The current study considers the Arithmetic Chain Task (Oberauer, Demmrich, Mayr, & Kliegl, 2001) which contrasts access (integrating previously stored information for use in the arithmetic processing) against mere retention (holding previously stored information for recall after the arithmetic processing). Participants (n = 122) completed the Arithmetic Chain Task (ACT) with a novel manipulation that split the access condition into fixed-order vs. random-order access. Both forms of access require integration of previously stored information into the arithmetic, but random-order access restricts systematic chunking, necessitating multiple flexible bindings that can be updated in light of new information. Participants also completed a measure of working memory and a measure of fluid intelligence. Results replicated Oberauer et al.'s findings on a demarcation between retention and access, though the current data indicate that random-order presentation is necessary to distinguish access from retention. Crucially, this random-order access is also required to link the ACT to a factor representing the commonality in WM and Gf. These results suggest that what is common to WM and Gf is the capacity to maintain multiple durable and flexible bindings.
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http://dx.doi.org/10.1016/j.actpsy.2019.102893 | DOI Listing |
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFSeizure
December 2024
University College Hospital, London, UK; UCL Queen Square Institute of Neurology: Department of Clinical and Experimental Epilepsy, London WC1N 3BG, UK. Electronic address:
Objective: Professional bodies recommend the use of performance validity tests (PVTs) to aid the interpretation of scores obtained in neuropsychological assessments, but base rates of failure differ according to neurological diagnosis and the associated impairments. This review summarises the PVT literature in people with epilepsy with the aim of establishing base rates of PVT failure and the factors associated with PVT performance in this population.
Methods: Ovid and PubMed databases were searched for studies reporting PVT test performance in people with epilepsy.
Comput Biol Med
January 2025
LMA Laboratory, University of Bejaia, Bejaia 06000, Algeria. Electronic address:
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.
View Article and Find Full Text PDFInt J Eat Disord
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Objective: Difficulty updating information in working memory has been proposed to underlie ruminative thinking in individuals with anorexia nervosa (AN). However, evidence regarding updating difficulties in AN remains inconclusive, particularly among adolescents. It has been proposed that exposure to negative emotion and disorder-salient stimuli may uniquely influence updating in AN.
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