Semantic richness refers to the amount of semantic information associated with a concept. Reaction-time (RT) studies have shown that words referring to rich concepts elicit faster responses than those referring to impoverished ones, suggesting that richer concepts are activated more quickly. In a recent functional neuroimaging study, richer concepts evoked less neural activity, which was interpreted as faster activation. The interpretations of these findings appear to conflict with event-related potential (ERP) studies showing no evidence that speed of concept activation is influenced by typical semantic variables. Resolution of this apparent contradiction is important because the interpretation of 40 years of semantic-memory RT studies depends on whether factors such as semantic richness influence the duration of initial concept activation or later decision and response processes. Consistent with previous studies of the effects of semantic factors on ERP, the present study shows that richness influences the magnitude, but not the latency, of the P2 and N400 ERP components (which are early relative to behavioral responses), suggesting that effects of richness on RT reflect temporal effects on downstream decision or response mechanisms rather than on upstream concept activation.
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http://dx.doi.org/10.1016/j.brainres.2009.05.092 | DOI Listing |
Q J Exp Psychol (Hove)
January 2025
Department of Psychology, National University of Singapore.
Lexico-semantic effects in lexical decision and semantic categorization tasks have been investigated using the megastudy approach, but not with other traditional spoken word recognition tasks. To address this gap, the present megastudy, using words from the McRae et al. (2005) norms, examined the single-word shadowing task, where 96 native English speakers repeated aloud each word they heard as quickly and as accurately as possible.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China.
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.
Cross-view geo-localization (CVGL) aims to determine the capture location of street-view images by matching them with corresponding 2D maps, such as satellite imagery. While recent bird's eye view (BEV)-based methods have advanced this task by addressing viewpoint and appearance differences, the existing approaches typically rely solely on either OpenStreetMap (OSM) data or satellite imagery, limiting localization robustness due to single-modality constraints. This paper presents a novel CVGL method that fuses OSM data with satellite imagery, leveraging their complementary strengths to enhance localization robustness.
View Article and Find Full Text PDFBehav Res Methods
December 2024
Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
Word associations are among the most direct ways to measure word meaning in human minds, capturing various relationships, even those formed by non-linguistic experiences. Although large-scale word associations exist for Dutch, English, and Spanish, there is a lack of data for Mandarin Chinese, the most widely spoken language from a distinct language family. Here we present the Small World of Words-Zhongwen (Chinese) (SWOW-ZH), a word association dataset of Mandarin Chinese derived from a three-response word association task.
View Article and Find Full Text PDFJ Exp Psychol Learn Mem Cogn
December 2024
Department of Psychology, Mississippi State University.
Past research has shown that semantically richer (i.e., modified) words are retrieved more easily at a subsequent point during language comprehension relative to less rich (i.
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