Working memory is thought to be strongly related to cognitive control. Recent studies have sought to understand this relationship under the prism of the dual mechanisms of control (DMC) framework, in which cognitive control is thought to operate in two distinct modes: proactive and reactive. Several authors have concluded that a high working memory capacity is associated with a tendency to engage the more effective mechanism of proactive control. However, the predicted pattern of proactive control use has never been observed; correlational evidence is made difficult to interpret by the overall superiority of participants with a high working memory capacity: they tend to perform better even when proactive control should be detrimental. In two experiments, we used an experimental-correlational approach to experimentally induce the use of reactive or proactive control in the AX-CPT. The relation between working memory capacity and performance was unaffected, incompatible with the hypothesis that the better performance of participants with a high working memory capacity in the task is due to their use of proactive control. It remains unclear how individual differences in working memory capacity relate to cognitive control under the DMC framework.
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http://dx.doi.org/10.1177/17470218211066410 | DOI Listing |
Sleep
January 2025
UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).
View Article and Find Full Text PDFJ Gerontol B Psychol Sci Soc Sci
January 2025
Linguistics and English as a Second Language, Faculty of Arts, University of Groningen, Groningen, the Netherlands.
Objectives: The complex life experience of speaking two or more languages has been suggested to preserve cognition in older adulthood. This study aimed to investigate this further by examining the relationship between multilingual experience variables and cognitive functioning in a large cohort of older adults in the diversely multilingual north of the Netherlands.
Method: 11,332 older individuals participating in the Lifelines Cohort Study completed a language experience questionnaire.
Sensors (Basel)
January 2025
Development Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, France.
Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
Monitoring existing cracks is a critical component of structural health monitoring in bridges, as temperature fluctuations significantly influence crack development. The study of the Huai'an Bridge indicated that concrete cracks predominantly occur near the central tower, primarily due to temperature variations between the inner and outer surfaces. This research aims to develop a deep learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict crack depth based on the thermal variations experienced by the main tower.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.
Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of ASD detection. However, with the deepening of clinical research, more and more evidence suggests that dynamic functional connectivity analysis can more comprehensively reveal the complex and variable characteristics of brain networks and their underlying mechanisms, thus providing more solid scientific support for computer-aided diagnosis of ASD.
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