Popular frameworks of attention propose that visual orienting occurs through a combination of bottom-up (stimulus-driven) and top-down (goal-directed) processes. Much of the basic research on these processes adheres paradigmatically to experimental methods that introduce salient but task-irrelevant stimuli (objects or transients) to the visual environment to determine whether attention is captured to their locations. This common practice of changing or adding a stimulus to a location to determine whether it captures attention reflects a notion that locations at which new features or stimuli spontaneously appear are prioritized above all else. In this article, we challenge this notion with results from a modified additional singleton paradigm. In the critical condition, following a preview array of placeholder stimuli, 1 placeholder stimulus transforms into a target diamond and changes luminance at the same time that all other placeholders, except 1 (the truly "static singleton") change in luminance. This static singleton location, which involves neither a new stimulus nor any sensory transient, produces a clear pattern of attentional capture originating near its location. These findings violate multiple bottom-up and top-down perspectives while encouraging a new approach to studying attentional capture. (PsycINFO Database Record
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http://dx.doi.org/10.1037/xhp0000326 | DOI Listing |
Curr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
Atten Percept Psychophys
January 2025
Department of Psychology, Chungnam National University, Daejeon, Republic of Korea.
The issue of whether a salient stimulus in the visual field captures attention in a stimulus-driven manner has been debated for several decades. The attentional window account proposed to resolve this issue by claiming that a salient stimulus captures attention and interferes with target processing only when an attentional window is set wide enough to encompass both the target and the salient distractor. By contrast, when a small attentional window is serially shifted among individual stimuli to find a target, no capture is found.
View Article and Find Full Text PDFPLoS One
January 2025
Harvard extension school, Harvard University, Boston, Massachusetts, United States of America.
To address the limitations of existing stock price prediction models in handling real-time data streams-such as poor scalability, declining predictive performance due to dynamic changes in data distribution, and difficulties in accurately forecasting non-stationary stock prices-this paper proposes an incremental learning-based enhanced Transformer framework (IL-ETransformer) for online stock price prediction. This method leverages a multi-head self-attention mechanism to deeply explore the complex temporal dependencies between stock prices and feature factors. Additionally, a continual normalization mechanism is employed to stabilize the data stream, enhancing the model's adaptability to dynamic changes.
View Article and Find Full Text PDFCult Health Sex
January 2025
Centre for Gender Research, University of Uppsala, Sweden.
Temporal constructs are central to reproduction and kinship, as epitomised by the pervasive concept of the biological clock within public imaginaries. While queer scholarship has problematised linear models of kinship and reproductive temporality, the specific temporalities associated with donor-conceived families have received less scholarly attention, despite the increasing prevalence of these family structures. In this article, we explore the question: how does donor conception reconfigure temporal logics.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, No.727 Jingming South Road, Kunming, 650504 Yunnan China.
For diagnosing mental health conditions and assessing sleep quality, the classification of sleep stages is essential. Although deep learning-based methods are effective in this field, they often fail to capture sufficient features or adequately synthesize information from various sources. For the purpose of improving the accuracy of sleep stage classification, our methodology includes extracting a diverse array of features from polysomnography signals, along with their transformed graph and time-frequency representations.
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