Objectives: The objective of this study was to develop a framework for investigating the roles of neural coding and cognition in speech perception.
Design: N1 and P3 auditory evoked potentials, QuickSIN speech understanding scores, and the Digit Symbol Coding cognitive test results were used to test the accuracy of either a compensatory processing model or serial processing model.
Results: The current dataset demonstrated that neither the compensatory nor the serial processing model were well supported. An additive processing model may best represent the relationships in these data.
Conclusions: With the outcome measures used in this study, it is apparent that an additive processing model, where exogenous neural coding and higher order cognition contribute independently, best describes the effects of neural coding and cognition on speech perception. Further testing with additional outcome measures and a larger number of subjects is needed to confirm and further clarify the relationships between these processing domains.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6579703 | PMC |
http://dx.doi.org/10.1097/AUD.0000000000000674 | DOI Listing |
J Clin Exp Neuropsychol
January 2025
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Introduction: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process required to detect these noncredible symptoms. The present study investigated whether unsupervised machine learning (ML) could serve as one such method, and detect noncredible symptom reporting in adults undergoing ADHD evaluations.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China.
Land use changes profoundly affect hydrological processes and water quality at various scales, necessitating a comprehensive understanding of sustainable water resource management. This paper investigates the implications of land use alterations in the Gap-Cheon watershed, analyzing data from 2012 and 2022 and predicting changes up to 2052 using the Future Land Use Simulation (FLUS) model. The study employs the Hydrological Simulation Program-FORTRAN (HSPF) model to assess water quantity and quality dynamics.
View Article and Find Full Text PDFSci China Life Sci
January 2025
Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China.
Genomic sources from China are underrepresented in the population-specific reference database. We performed whole-genome sequencing or genome-wide genotyping on 1,207 individuals from four linguistically diverse groups (1,081 Sinitic, 56 Mongolic, 40 Turkic, and 30 Tibeto-Burman people) living in North China included in the 10K Chinese People Genomic Diversity Project (10K_CPGDP) to characterize the genetic architecture and adaptative history of ethnic groups in the Silk Road Region of China. We observed a population split between Northwest Chinese minorities (NWCMs) and Han Chinese since the Upper Paleolithic and later Neolithic genetic differentiation within NWCMs.
View Article and Find Full Text PDFJ Mol Model
January 2025
Processes, Materials and Environment Laboratory (LPME), Faculty of Sciences and Technology of Fez, Sidi Mohamed Ben Abdellah University, B.P. 2202, Fez, Morocco.
Context: Natural fluorapatite (FAP) has been investigated as an adsorbent for the removal of dyes such as methylene blue (MB) and crystal violet (CV) from aqueous solutions. Effective dye removal is crucial for water treatment, particularly for industrial wastewater containing toxic dyes. FAP, a naturally abundant material, was characterized using XRD, FTIR, and SEM analysis.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
School of Automation and Information Engineering, Sichuan University of Science & Engineering, Key Laboratory of Artificial Intelligence, Yibin, 644000, Sichuan, China.
Accurately classifying optical coherence tomography (OCT) images is essential for diagnosing and treating ophthalmic diseases. This paper introduces a novel generative adversarial network framework called MGR-GAN. The masked image modeling (MIM) method is integrated into the GAN model's generator, enhancing its ability to synthesize more realistic images by reconstructing them based on unmasked patches.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!