Ignoring salient distracting information is paramount to efficiently guiding attention during visual search. Learning to reject or suppress these strong sources of distraction leads to more effective visual search for targets. Participants can learn to overcome salient distractors if given reliable search regularities. If salient distractors appear in 1 location more frequently than any other, the visual system can use this environmental regularity to reduce attentional capture at the more frequent location (Wang & Theeuwes, 2018). We asked if reduced attentional capture is limited to location-based regularities, or, if the visual attentional system is configured to use feature-based regularities in reducing attentional capture as well. In 4 experiments examining attentional capture by task-irrelevant color singletons, participants searched for a shape singleton target among homogenously colored distractors. Critically, on a proportion of trials, a salient, color singleton distractor was presented. Color singleton distractors that appeared at a frequent location captured attention less than color singleton distractors that appeared at infrequent locations, replicating previous findings. In subsequent experiments we manipulated the frequency of the colors of the color singleton distractors and observed robust increases in capture based on color feature regularities. Despite strong location information, we observed reliable attentional capture attenuation by frequently presented distractor colors. Our results suggest that attentional capture is attenuated by both location and feature information. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1037/xhp0000613 | DOI Listing |
Comput Biol Med
January 2025
College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China; Zhejiang Institute of Optoelectronics, Jinhua, 321004, China. Electronic address:
Accurate segmentation of brain tumors from MRI scans is a critical task in medical image analysis, yet it remains challenging due to the complex and variable nature of tumor shapes and sizes. Traditional convolutional neural networks (CNNs), while effective for local feature extraction, struggle to capture long-range dependencies crucial for 3D medical image analysis. To address these limitations, this paper presents VcaNet, a novel architecture that integrates a Vision Transformer (ViT) with a fusion channel and spatial attention module (CBAM), aimed at enhancing 3D brain tumor segmentation.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Division of Metabolomics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
Lipidomics has attracted attention in the discovery of unknown biomolecules and for capturing the changes in metabolism caused by genetic and environmental factors in an unbiased manner. However, obtaining reliable lipidomics data, including structural diversity and quantification data, is still challenging. Supercritical fluid chromatography (SFC) is a suitable technique for separating lipid molecules with high throughput and separation efficiency.
View Article and Find Full Text PDFChaos
January 2025
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina.
The diffusion of information plays a crucial role in a society, affecting its economy and the well-being of the population. Characterizing the diffusion process is challenging because it is highly non-stationary and varies with the media type. To understand the spreading of newspaper news in Argentina, we collected data from more than 27 000 articles published in six main provinces during 4 months.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
School of Computer Science and Technology, Soochow University, Jiangsu 215006, China.
Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.
View Article and Find Full Text PDFJ Pharm Policy Pract
January 2025
Clinical Pharmacy Department, King Fahad Medical City, Riyadh, Saudi Arabia.
Background: Cancer cases in the Kingdom of Saudi Arabia (KSA) have tripled in recent years. Quality of Life (QoL) measurements are crucial for healthcare professionals because they reveal important information about how patients respond to drugs and their general health. This study aimed to collect and summarise articles exploring the QoL of patients undergoing oncology treatments in KSA.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!