Crowding and the word superiority effect are two perceptual phenomena that influence reading. The identification of the inner letters of a word can be hindered by crowding from adjacent letters, but it can be facilitated by the word context itself (the word superiority effect). In the present study, strings of four-letters (words and non-words) with different inter-letter spacings (ranging from an optimal spacing to produce crowding to a spacing too large to produce crowding) were presented briefly in the periphery and participants were asked to identify the third letter of the string. Each word had a partner word that was identical except for its third letter (e.g., COLD, CORD) so that guessing as the source of the improved performance for words could be ruled out. Unsurprisingly, letter identification accuracy for words was better than non-words. For non-words, it was lowest at closer spacings, confirming crowding. However, for words, accuracy remained high at all inter-letter spacings showing that crowding did not prevent identification of the inner letters. This result supports models of "holistic" word recognition where partial cues can lead to recognition without first identifying individual letters. Once the word is recognized, its inner letters can be recovered, despite their feature loss produced by crowding.
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http://dx.doi.org/10.1016/j.visres.2024.108436 | DOI Listing |
Neuropsychologia
January 2025
Center for Aphasia Research and Rehabilitation, Georgetown University Medical Center.
The underlying causes of reading impairment in neurodegenerative disease are not well understood. The current study seeks to determine the causes of surface alexia and phonological alexia in primary progressive aphasia (PPA) and typical (amnestic) Alzheimer's disease (AD). Participants included 24 with the logopenic variant (lvPPA), 17 with the nonfluent/agrammatic variant (nfvPPA), 12 with the semantic variant (svPPA), 19 with unclassifiable PPA (uPPA), and 16 with AD.
View Article and Find Full Text PDFSci Rep
January 2025
School of Economics and Management, Beijing jiaotong University, Shandong, 264401, China.
Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues.
View Article and Find Full Text PDFFront Psychiatry
December 2024
Departamento de Personalidad, Evaluación y Tratamiento Psicológicos, Universidad de Salamanca, Salamanca, Spain.
Introduction: It is crucial to provide a quality educational response to the needs of autistic children across various mathematical domains. However, there is no consensus on which of the early skills have the greatest predictive effect in the short and long term within these domains. Therefore, this research aimed to a) compare early numerical skills and mathematics domains, and 2) analyze the predictive value of early numerical skills into mathematics domains.
View Article and Find Full Text PDFTher Adv Musculoskelet Dis
December 2024
Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos, Madrid, Spain.
Background: Rheumatology has experienced notable changes in the last decades. New drugs, including biologic agents and Janus kinase (JAK) inhibitors, have blossomed. Concepts such as window of opportunity, arthralgia suspicious for progression, or difficult-to-treat rheumatoid arthritis (RA) have appeared; and new management approaches and strategies such as treat-to-target have become popular.
View Article and Find Full Text PDFJ Colloid Interface Sci
December 2024
School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300400 PR China. Electronic address:
The formation and growth of lithium dendrites is an ever-present and urgent problem in lithium-ion batteries (LIBs). At the same time, the low melting point of commercial polyolefin separators may lead to safety issues during application. On this basis, in this work, poly (m-phenylene isophthalamide) (PMIA)/Zr-based metal-organic framework (NH-UiO-66) composite separator was prepared by non-solvent induced phase separation (NIPS).
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