An influential theoretical perspective describes an implicit category-learning system that associates regions of perceptual space with response outputs by integrating information preattentionally and predecisionally across multiple stimulus dimensions. In this study, we tested whether this kind of implicit, information-integration category learning is possible across stimulus dimensions lying in different sensory modalities. Humans learned categories composed of conjoint visual-auditory category exemplars comprising a visual component (rectangles varying in the density of contained lit pixels) and an auditory component (in Exp. 1, auditory sequences varying in duration; in Exp. 2, pure tones varying in pitch). The categories had either a one-dimensional, rule-based solution or a two-dimensional, information-integration solution. Humans could solve the information-integration category tasks by integrating information across two stimulus modalities. The results demonstrated an important cross-modal form of sensory integration in the service of category learning, and they advance the field's knowledge about the sensory organization of systems for categorization.
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http://dx.doi.org/10.3758/s13414-014-0659-6 | DOI Listing |
Emergencias
December 2024
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seúl, República de Corea. Department of Digital Health, SAIHST, Sungkyunkwan University, Seúl, República de Corea.
Objective: To develop a Metabolic Derangement Score (MDS) based on parameters available after initial testing and assess the score's ability to predict survival after out-of hospital cardiac arrest (OHCA) and the likely usefulness of extracorporeal life support (ECLS).
Methods: A total of 5100 cases in the Korean Cardiac Arrest Research Consortium registry were included. Patients' mean age was 67 years, and 69% were men.
Heliyon
January 2025
Cancer Early Detection Advanced Research Center (CEDAR), Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach.
View Article and Find Full Text PDFBMC Res Notes
January 2025
Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.
Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.
View Article and Find Full Text PDFEur J Hum Genet
January 2025
Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI.
View Article and Find Full Text PDFEndocrinol Metab (Seoul)
January 2025
Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules.
Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer.
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