Four experiments examined attentional capture by colour as assessed by two different investigative methods. Subjects performed a visual search task for a vertical-target line embedded among tilted-distractor lines, presented inside 4, 8, or 12 coloured discs. Interestingly, when the colour singleton was task irrelevant, and data were analysed by means of the display-size method combined with the zero-slope criterion, no evidence for attentional capture by colour was found. However, when data were analysed by means of the distance method, which consists of monitoring the spatial relationship between the target and the singleton, results showed that the target was found faster and/or more accurately when it was inside the singleton than when it was in a nonsingleton location. This provided evidence for a stimulus-driven attentional capture. In addition, the application of signal detection methodology showed that attentional capture, as revealed by the distance method, resulted from a perceptual modulation at the singleton location, rather than from a criterion shift. We conclude that, at least with the kind of stimuli used here, the display-size method combined with the zero-slope criterion is less than ideal for investigating how static discontinuities can affect the automatic deployment of visual attention.
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http://dx.doi.org/10.1080/02724980343000242 | DOI Listing |
J Infect Dev Ctries
December 2024
Department of Veterinary Medicine, Faculty of Agriculture and Veterinary Medicine, An-Najah National University, Nablus, Palestine.
Introduction: Peste des petits ruminants (PPR) is a highly contagious and fatal disease affecting small ruminants, particularly goats and sheep, and is caused by Morbillivirus caprinae, a virus in the genus Morbillivirus, family Paramyxoviridae. PPR has significant economic and social impacts, especially in Africa, Asia, and the Middle East, where small ruminants are vital to rural livelihoods and food security. This disease is a priority for global eradication due to its disproportionate impact on low-income farmers and wildlife conservation.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Software, Shandong University, No. 1500, Shunhua Road, Hi-Tech Industrial Development Zone, Jinan 250100, Shandong, China.
Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellular level. However, to effectively investigate changes in chromosomal structure across different cell types, there is a requisite for methods that can identify cell types utilizing single-cell Hi-C data. Current frameworks for cell type prediction based on single-cell Hi-C data are limited, often struggling with features interpretability and biological significance, and lacking convincing and robust classification performance validation.
View Article and Find Full Text PDFThe development of diffusion models, such as Glide, DALLE 2, Imagen, and Stable Diffusion, marks a significant advancement in generative AI for image synthesis. In this paper, we introduce a novel framework for synthesizing intrinsic connectivity networks (ICNs) by utilizing the nonlinear capabilities of denoising diffusion probabilistic models (DDPMs). This approach builds upon and extends traditional linear methods, such as independent component analysis (ICA), which are commonly used in neuroimaging studies.
View Article and Find Full Text PDFEpidemiol Methods
January 2023
Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Atlanta, GA, USA.
In epidemiological studies, the capture-recapture (CRC) method is a powerful tool that can be used to estimate the number of diseased cases or potentially disease prevalence based on data from overlapping surveillance systems. Estimators derived from log-linear models are widely applied by epidemiologists when analyzing CRC data. The popularity of the log-linear model framework is largely associated with its accessibility and the fact that interaction terms can allow for certain types of dependency among data streams.
View Article and Find Full Text PDFFront Neuroinform
January 2025
Hefei University, Hefei, China.
Introduction: Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios.
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