Two experiments are reported that examined qualitative differences in how semantic information is represented in the two hemispheres. In the first experiment, items that were associatively related but did not share semantic features or membership in semantic categories produced priming when delivered to the LH (RVF) but not to the RH (LVF). In the second experiment items that shared semantic features but were neither associates nor in the same category produced priming in the RH (LVF), but not in the LH (RVF). Together, the two experiments support the theory that, in the right hemisphere, semantic memories are represented within a distributed system, on the basis of semantic features, whereas, in the left hemisphere representations are, as in local models, relatively more holistic, and are connected via associative links.
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http://dx.doi.org/10.1016/s0010-9452(08)70140-0 | DOI Listing |
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June 2025
Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, India.
Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. Recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection.
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December 2024
Medical Research Council (MRC) Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK.
We investigated semantic cognition in the logopenic variant of primary progressive aphasia, including (i) the status of verbal and non-verbal semantic performance; and (ii) whether the semantic deficit reflects impaired semantic control. Our hypothesis that individuals with logopenic variant of primary progressive aphasia would exhibit semantic control impairments was motivated by the anatomical overlap between the temporoparietal atrophy typically associated with logopenic variant of primary progressive aphasia and lesions associated with post-stroke semantic aphasia and Wernicke's aphasia, which cause heteromodal semantic control impairments. We addressed the presence, type (semantic representation and semantic control; verbal and non-verbal), and progression of semantic deficits in logopenic variant of primary progressive aphasia.
View Article and Find Full Text PDFAm J Geriatr Psychiatry
December 2024
Department of Clinical and Experimental Sciences (DA, BB), University of Brescia, Brescia, Italy; Molecular Markers Laboratory (BB), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy. Electronic address:
Objectives: The present study aims to assess the prevalence, associated clinical symptoms, longitudinal changes, and imaging correlates of Loss of Insight (LOI), which is still unexplored in syndromes associated with Frontotemporal Lobar Degeneration (FTLD).
Design: Retrospective longitudinal cohort study, from Oct 2009 to Feb 2023.
Setting: Tertiary Frontotemporal Dementia research clinic.
Neural Netw
January 2025
State Key Laboratory of Public Big Data, Guizhou University, 550025, China; Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Guizhou University, 550025, China; College of Computer Science and Technology, Guizhou University, 550025, China. Electronic address:
Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations.
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January 2025
Medical Big Data Lab, Shenzhen Research Institute of Big Data, Shenzhen, 518172, China. Electronic address:
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal performance due to the intricate pathophysiology of the ICH.
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