Although it has been consistently shown that readers generate bridging inferences during story comprehension, little is currently known about the neural substrates involved when people generate inferences and how these substrates shift with factors that facilitate or impede inferences, such as whether inferences are highly predictable or unpredictable. In the current study, functional magnetic resonance imaging (fMRI) signal increased for highly predictable inferences (relative to events that were previously explicitly stated) bilaterally in both the superior temporal gyri and the inferior frontal gyri. Interestingly, high working memory capacity comprehenders, who are most likely to generate inferences during story comprehension, showed greater signal increases than did low working memory capacity comprehenders in the right superior temporal gyrus and right inferior frontal gyrus. When comprehenders needed to draw unpredictable inferences in a story, fMRI signal increased relative to explicitly stated events in the left inferior gyrus and in the middle frontal gyrus, irrespective of working memory capacity. These results suggest that the predictability of a text (i.e., the causal constraint) and the working memory capacity of the comprehender influence the different neural substrates involved during the generation of bridging inferences.
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http://dx.doi.org/10.1162/jocn.2008.20160 | DOI Listing |
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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