We investigated the possibility of whether impressions of semantic words showing complex concepts could be stably expressed by hues. Using a paired comparison method, we asked ten subjects to select from a pair of hues the one that more suitably matched a word impression. We employed nine Japanese semantic words and used twelve hues from vivid tones in the practical color coordinate system. As examples of the results, for the word "vigorous" the most frequently selected color was yellow and the least selected was blue to purple; for "tranquil" the most selected was yellow to green and the least selected was red. Principal component analysis of the selection data indicated that the cumulative contribution rate of the first two components was 94.6%, and in the two-dimensional space of the components, all hues were distributed as a hue-circle shape. In addition, comparison with additional data of color impressions measured by a semantic differential method suggested that most semantic word impressions can be stably expressed by hue, but the impression of some words, such as "magnificent" cannot. These results suggest that semantic word impression can be expressed reasonably well by color, and that hues are treated as impressions from the hue circle, not from color categories.
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http://dx.doi.org/10.1364/JOSAA.35.000B55 | DOI Listing |
Sensors (Basel)
December 2024
Psychology Department, Middle Tennessee State University, Murfreesboro, TN 37132, USA.
Consumer-grade EEG devices, such as the InteraXon Muse 2 headband, present a promising opportunity to enhance the accessibility and inclusivity of neuroscience research. However, their effectiveness in capturing language-related ERP components, such as the N400, remains underexplored. This study thus aimed to investigate the feasibility of using the Muse 2 to measure the N400 effect in a semantic relatedness judgment task.
View Article and Find Full Text PDFBehav Sci (Basel)
December 2024
College of Chinese Language and Literature, Qufu Normal University, No. 57, Jingxuan Road, Qufu 273165, China.
Two experiments were conducted to examine native and non-native speakers' recognition of Chinese two-character words (2C-words) in the context of audio sentence comprehension. The recording was played of a sentence, in which a collocation composed of a number word, a sortal classifier, and a noun (NCN) was embedded. When the participants were about to hear the noun of the NCN (Noun), the playing stopped, and a target was visually presented, which was the Noun, the character-transposed word of the Noun (NounT), or a control word (NounC), or was a homophone nonword for Noun, NounT, or NounC.
View Article and Find Full Text PDFSci Rep
January 2025
College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161000, China.
Neuropsychologia
January 2025
Center for Aphasia Research and Rehabilitation, Georgetown University Medical Center, USA.
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 PDFJ Biomed Inform
December 2024
Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510060, China. Electronic address:
Generative methods are currently popular for medical report generation, as they automatically generate professional reports from input images, assisting physicians in making faster and more accurate decisions. However, current methods face significant challenges: 1) Lesion areas in medical images are often difficult for models to capture accurately, and 2) even when captured, these areas are frequently not described using precise clinical diagnostic terms. To address these problems, we propose a Visual-Linguistic Diagnostic Semantic Enhancement model (VLDSE) to generate high-quality reports.
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