Despite the recent surge in research on unsupervised category learning, the majority of studies have focused on unconstrained tasks in which no instructions are provided about the underlying category structure. Relatively little research has focused on constrained tasks in which the goal is to learn predefined stimulus clusters in the absence of feedback. The few studies that have addressed this issue have focused almost exclusively on stimuli for which it is relatively easy to attend selectively to the component dimensions (i.e., separable dimensions). In the present study, we investigated the ability of participants to learn categories constructed from stimuli for which it is difficult, if not impossible, to attend selectively to the component dimensions (i.e., integral dimensions). The experiments demonstrate that individuals are capable of learning categories constructed from the integral dimensions of brightness and saturation, but this ability is generally limited to category structures requiring selective attention to brightness. As might be expected with integral dimensions, participants were often able to integrate brightness and saturation information in the absence of feedback--an ability not observed in previous studies with separable dimensions. Even so, there was a bias to weight brightness more heavily than saturation in the categorization process, suggesting a weak form of selective attention to brightness. These data present an important challenge for the development of models of unsupervised category learning.
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http://dx.doi.org/10.1080/17470218.2012.658821 | DOI Listing |
NPJ Digit Med
January 2025
Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results.
View Article and Find Full Text PDFNat Commun
January 2025
Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.
Understanding vibrissal transduction has advanced by serial sectioning and identified afferent recordings, but afferent mapping onto the complex, encapsulated follicle remains unclear. Here, we reveal male rat C2 vibrissa follicle innervation through synchrotron X-ray phase contrast tomograms. Morphological analysis identified 5% superficial, ~32 % unmyelinated and 63% myelinated deep vibrissal nerve axons.
View Article and Find Full Text PDFPrev Vet Med
December 2024
School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD, United Kingdom.
Paratuberculosis (Johne's disease), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a common, economically-important and potentially zoonotic contagious disease of cattle, with worldwide distribution. Disease management relies on identification of animals which are at high-risk of being infected or infectious.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, China.
Cervical cancer is one of the deadliest cancers that pose a significant threat to women's health. Early detection and treatment are commonly used methods to prevent cervical cancer. The use of pathological image analysis techniques for the automatic interpretation of cervical cells in pathological slides is a prominent area of research in the field of digital medicine.
View Article and Find Full Text PDFNPJ Digit Med
December 2024
Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Formative verbal feedback during live surgery is essential for adjusting trainee behavior and accelerating skill acquisition. Despite its importance, understanding optimal feedback is challenging due to the difficulty of capturing and categorizing feedback at scale. We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts.
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