Picture naming requires early visual analysis, accessing stored structural knowledge, semantic activation, and lexical retrieval. We tested the effect of perceptual, lexical, and semantic variables on the performance of aphasics in picture naming and assessed prevalence of natural categories vs artifact dissociations. Forty-nine aphasics were asked to name 60 pictures, from three natural (animals, fruits, and vegetables) and three artificial categories (tools, furniture, and vehicles). For each item visual (drawing complexity, image agreement), semantic (prototypicality, concept familiarity) and lexical variables (word frequency, name agreement) were available. The effect of these variables showed individual differences; altogether, visual complexity had little influence, whereas lexical and semantic variables were more influential. Name agreement was most important, followed by word frequency. On a multiple single case analysis 10 patients (20%) showed a natural/artificial category dissociation. Five of the six subjects faring better with artifacts were males, and all of four patients faring better with natural categories were females. Interpretations of this finding are discussed.
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Cortex
December 2024
Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany; University Hospital and Faculty of Medicine Leipzig, Clinic for Cognitive Neurology, Leipzig, Germany.
Retrieving words quickly and correctly is an important language competence. Semantic contexts, such as prior naming of categorically related objects, can induce conceptual priming but also lexical-semantic interference, the latter likely due to enhanced competition during lexical selection. In the continuous naming (CN) paradigm, such semantic interference is evident in a linear increase in naming latency with each additional member of a category out of a seemingly random sequence of pictures being named (cumulative semantic interference/CSI effect).
View Article and Find Full Text PDFSci Rep
January 2025
Nanfang College Guangzhou, Guangzhou, 510970, China.
Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. However, despite their advancements, these models fail to take full advantage of the multi-level features (e.
View Article and Find Full Text PDFBehav Sci (Basel)
January 2025
School of Psychology, Central China Normal University, Wuhan 430079, China.
Words are the basic units of language and vital for comprehending the language system. Lexical processing research has always focused on either conceptual or affective word meaning. Previous studies have indirectly compared the conceptual and affective meanings of words.
View Article and Find Full Text PDFBrain Sci
January 2025
Department of Exercise Science and Pre-Health Professions, Creighton University, Omaha, NE 68178, USA.
Background/objectives: We examined the effects of cardiovascular exercise on verbal fluency using a between-groups design.
Methods: Within our experimental (i.e.
Neuropsychologia
January 2025
Faculty of Psychology and Educational Sciences, University of Geneva, 40 Boulevard du Pont d'Arve, 1205 Geneva, Switzerland. Electronic address:
Background: Word production difficulty is one of the most common and persisting symptoms in people suffering from aphasia (i.e., anomia).
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