For years the Wisconsin card sorting test (WCST) has been used as a test of frontal lobe function. Recent event-related potential (ERP) research has shown large differences in the amplitude of P3b responses evoked by early and late trials within each WCST series ([8]: Barceló F., Sanz M., Molina V., Rubia FJ. The Wisconsin Card Sorting Test and the assessment of frontal function: A validation study with event-related potentials. Neuropsychologia 1997;35:399-408). In this study, 16 normal subjects performed a WCST adaptation to investigate the role of attentional set shifting in these WCST P3b effects. Two control tasks were designed to examine whether early-late WCST P3b changes reflect category selection (attention) or category storage (memory) operations. Results suggest both a sharp P3b attenuation during shift WCST trials, followed by a gradual P3b build-up during post-shift trials. This P3b modulation could not be attributed to selection or storage of simple sensory stimulus dimensions, nor was it observed when the new rule was externally prompted by the first card in the WCST series. Instead, WCST P3b changes seem related to the endogenously generated shift in the perceptual rule used to sort the cards (i.e., the shift in set). The gradual build-up in P3b amplitude paralleled a progressive improvement in sorting efficiency over several post-shift WCST trials. A model based on formal theories of visual attention and attentional set shifting is proposed to account for these effects. The model offers firm grounds for prediction and bridges the gap between related clinical and experimental evidence.
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http://dx.doi.org/10.1016/s0028-3932(00)00046-4 | DOI Listing |
Heliyon
December 2024
Foundation University, Islamabad, Pakistan.
Globally, the effects of climate change are becoming more pronounced. Simultaneously, concerns associated with climate change effects have garnered widespread attention. The motive of this study is to know about the prominent antecedents of climate abnormalities in Pakistan, which may lead to economic abnormality and instability.
View Article and Find Full Text PDFBioinform Adv
November 2024
School of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000, China.
Motivation: Much evidence suggests that the subcellular localization of long-stranded noncoding RNAs (LncRNAs) provides key insights for the study of their biological function.
Results: This study proposes a novel deep learning framework, LncLSTA, designed for predicting the subcellular localization of LncRNAs. It firstly exploits LncRNA sequence, electron-ion interaction pseudopotentials, and nucleotide chemical property as feature inputs.
Womens Health Rep (New Rochelle)
December 2024
Department of Family Health Care Nursing, School of Nursing, University of California, San Francisco, California, USA.
Purpose: Women in the decade before menopause are at risk for depression. This study describes dietary factors associated with depression risk in late premenopausal women that could be modifiable with targeted interventions.
Methods: Descriptive cross-sectional study comparing a community-based sample of 342 healthy premenopausal women categorized as low-risk and high-risk for depression in a secondary analysis of dietary variables.
J Biomed Inform
January 2025
Department of Information Management and Business Analytics, Montclair State University, Feliciano School of Business, NJ, USA. Electronic address:
Background And Objective: Subjective cognitive decline (SCD) refers to self-reported difficulties in concentration, memory, and decision-making. Since SCD is based on subjective experiences, no specific medical test can definitively confirm its presence, making early detection challenging. Thus, it is advantageous to develop an AI model to capitalize on self-reported health complications, personality traits, and sociodemographic factors for early detection of SCD.
View Article and Find Full Text PDFSci Rep
January 2025
College of Computer and Data Science, Minjiang University, Fuzhou, 350018, China.
This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections, and a modified Convolutional Block Attention Module (CBAM) to detect gauge panels and pointer keypoints in images.
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