Although categorization is fundamental to speech processing, little is known about the learning systems that mediate auditory categorization and even less is known about changes across the life span. Vision research supports dual-learning systems that are grounded in neuroscience and are partially dissociable. The reflective, rule-based system is prefrontally mediated and uses working memory and executive attention to develop and test rules for classifying in an explicit fashion. The reflexive, information-integration system is striatally mediated and operates by implicitly associating perception with actions that lead to reinforcement. We examine the extent to which dual-learning systems mediate auditory and speech learning in younger and older adults. We examined auditory category learning when a rule-based strategy (Experiment 1) or information-integration strategy (Experiment 2) was optimal, and found an age-related rule-based deficit, but intact information-integration learning. Experiment 3 examined natural auditory category learning, and found an age-related performance deficit. Computational modeling suggested that this was attributable to older adults' persistent reliance on suboptimal, unidimensional strategies when 2-dimensional strategies were optimal. Working memory capacity was also found to be associated with improved rule-based and natural auditory category learning, but not information-integration category learning. These results suggest that dual-learning systems are operative in speech category learning across the life span, and that performance deficits, when present, are attributable to deficiencies in frontally mediated, rule-based processes.
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http://dx.doi.org/10.1037/a0034969 | DOI Listing |
J Am Med Inform Assoc
January 2025
Department of Computer Science, Duke University, Durham, NC 27708, United States.
Objective: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.
Material And Methods: We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them.
J Dent Sci
January 2025
Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
Background/purpose: In this study, we utilized magnetic resonance imaging data of the temporomandibular joint, collected from the Division of Oral and Maxillofacial Surgery at Taipei Veterans General Hospital. Our research focuses on the classification and severity analysis of temporomandibular joint disease using convolutional neural networks.
Materials And Methods: In gray-scale image series, the most critical features often lie within the articular disc cartilage, situated at the junction of the temporal bone and the condyles.
Int J Ment Health Nurs
February 2025
Service 5, Fiona Stanley Fremantle Hospitals Group, Australia.
Graduate nurses are now comprehensively prepared in Australia and have limited mental health knowledge and experiences to work in acute mental health settings. As such, graduate nurses would need the support that they can get from their respective mental health services to progress from novice to advanced beginner. Nursing education is an important support for graduate nurses to develop the knowledge and skills required for them to transition successfully into acute mental health settings.
View Article and Find Full Text PDFBiomed Eng Online
January 2025
Department of Pulmonary and Critical Care Medicine, National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
Background: Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.
Methods: All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers.
BMC Med Educ
January 2025
Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Background: The National Commission for Academic Accreditation and Assessment (NCAAA) in Saudi Arabia underscores the importance of assessing student satisfaction to ensure program quality. No previous studies have explored the satisfaction levels of dental students enrolled in clinical Periodontics courses at King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS). This study aims to assess dental students' satisfaction with clinical Periodontics courses and to explore potential differences in satisfaction based on gender and academic level.
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