Semantic context effects in picture naming and categorization tasks are central to the development and evaluation of current models of word production. When pictures are named in a semantically blocked context, response latencies are delayed. Belke (2013) found that when the naming task was replaced with a semantic categorization task (natural vs. man-made), response latencies were facilitated. From this pattern, she concluded that semantic interference in blocked picture naming has its locus at the lexical level but its origin at the preceding semantic level. However, other studies using the blocking procedure have failed to find facilitation in semantic categorization tasks (Damian et al., 2001; Riley et al., 2015), calling this conclusion into question. In three blocked picture naming and categorization experiments, we investigated different variables that might account for the discrepant results in semantic categorization. We used different semantic categorization tasks, different response modalities, different response set sizes, and different blocking procedures. Semantic facilitation was reliably found in naturalness categorization, but there was no semantic effect in natural size categorization. We discuss the implications of these findings for appropriate task selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1037/xhp0001203 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hierarchical graph-based integration network for propaganda detection in textual news articles on social media.
Sci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFGlo-net: A dual task branch based neural network for multi-class glomeruli segmentation.
Comput Biol Med
January 2025
Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Future Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China. Electronic address:
Accurate segmentation and classification of glomeruli are fundamental to histopathology slide analysis in renal pathology, which helps to characterize individual kidney disease. Accurate segmentation of glomeruli of different types faces two main challenges compared to traditional primitives segmentation in computational image analysis. Limited by small kernel size, traditional convolutional neural networks could hardly understand the complete context information of different glomeruli.
View Article and Find Full Text PDFMultiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar.
Sensors (Basel)
January 2025
School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China.
Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks.
View Article and Find Full Text PDFData-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer.
Sensors (Basel)
December 2024
Master's Program in Information and Computer Science, Doshisha University, Kyoto 610-0394, Japan.
The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex shapes due to their focus on texture features and limited ability to incorporate positional information. As orthopedic surgery increasingly requires precise automatic diagnosis, we explored SegFormer, an enhanced Vision Transformer model that better handles spatial awareness in segmentation tasks.
View Article and Find Full Text PDFIntratumoral and peritumoral radiomics for forecasting microsatellite status in gastric cancer: a multicenter study.
BMC Cancer
January 2025
Department of Radiology, The People's Hospital of PingYang, Wenzhou Medical University, Wenzhou, 325400, China.
Objective: This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients.
Methods: A retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!