In a classic semantic priming study (Beeman et al., 1994), participants showed a naming advantage for strongly related targets compared to weakly related targets in the left hemisphere, whereas no difference in naming advantage was found between strongly and weakly related targets in the right hemisphere. However, it is unclear how the type of task and individual differences influence this hemispheric activation. In the current study participants completed a lexical decision task when presented with strongly, weakly, and unrelated words in each visual field-hemisphere. A left hemisphere advantage was evident for strongly and weakly related words compared to unrelated words and a right hemisphere advantage was evident for strongly related words compared to weakly related and unrelated words. Additionally, high working memory capacity participants responded more accurately to strongly related words than weakly or unrelated words in the right hemisphere, whereas low working memory capacity participants showed no difference between these conditions in the right hemisphere. Thus, the type of semantic priming task and working memory capacity seem to influence the hemispheric processing of strongly and weakly related information.
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Sci Rep
January 2025
Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
Over time, the importance of virtual power plants (VPP) has markedly risen to seamlessly incorporate the sporadic nature of renewable energy sources into the existing smart grid framework. Simultaneously, there is a growing need for advanced forecasting methods to bolster the grid's stability, flexibility, and dispatchability. This paper presents a dual-pronged, innovative approach to maximize income in the day-ahead power market through VPP.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Neurocognition and Emotion in Affective Disorders (NEAD) Centre, Psychiatric Centre Copenhagen, Mental Health Services, Capital Region of Denmark, Hovedvejen 13, 2000 Frederiksberg, Denmark; Department of Psychology, University of Copenhagen, Øster Farimagsgade 2A, 1353 Copenhagen, Denmark. Electronic address:
Cognitive impairment affects approximately 50 % of patients with mood disorders during remission, which correlates with poorer daily-life functioning. The hierarchical organization of cognitive processes may mean that some cognitive deficits, e.g.
View Article and Find Full Text PDFJ Sport Rehabil
January 2025
Exercise Science and Neuroscience Unit, Department of Exercise & Health, Paderborn University, Paderborn, Germany.
Context: Traditional assessments of high-order neurocognitive functions are conducted using pen and paper or computer-based tests; this neglects the complex motor actions athletes have to make in team ball sports. Previous research has not explored the combination of neurocognitive functions and motor demands through complex tasks for team ball sport athletes. The primary aim of the present study was to determine the construct validity of agility-based neurocognitive tests of working memory (WM) and inhibition.
View Article and Find Full Text PDFJ Commun Disord
January 2025
Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago, Chile; Centro de Justicia Educacional, Facultad de Educación, Pontificia Universidad Católica de Chile, Santiago, Chile.
Developmental language disorder (DLD) is often associated with deficits in executive functions (EFs). One common area of language difficulty in DLD is the development of vocabulary knowledge and it has been suggested that EF abilities may be linked to this difficulty. However, an explanation for this relationship remains unclear.
View Article and Find Full Text PDFInt J Med Inform
January 2025
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Golgi 39, 20131 Milan, MI, Italy; Cardio Tech-Lab, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4, 20138 Milan, Italy. Electronic address:
Background: Existing deep learning studies for the automated detection of hip prosthesis failure only consider the last available radiographic image. However, using longitudinal data is thought to improve the prediction, by combining temporal and spatial components. The aim of this study is to develop artificial intelligence models for predicting hip implant failure from multiple subsequent plain radiographs.
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