Human observers can allocate their attention to locations likely to contain a target and can also learn to avoid locations likely to contain a salient distractor during visual search. However, it is unclear which spatial frame of reference such learning is applied to. As such, our aim was to systematically disentangle the contributions of spatiotopic, retinotopic, and configural frames of reference to provide a comprehensive account of how the probabilistic distractor filtering effect comes about. We first demonstrate that the filtering effect is better determined by the probability of a salient distractor appearing at a relative location (i.e., in relation to one's eye position or an item's position in relation to other items within a display) rather than a fixed (spatiotopic) location, by varying the position of visual search arrays (along with fixation) across spatial contexts. We then separate retinotopic and configural reference frames by varying the configural but not retinotopic properties of biased (i.e., displays containing a probable distractor location) and unbiased visual search arrays and vice versa. In doing so, we find the filtering effect to be restricted to biased contexts when retinotopic positions are maintained, but configural properties are varied. In contrast, when the configural properties of visual search arrays are maintained, we show the transfer of the filtering effect across retinotopic positions. Thus, we demonstrate that probabilistic distractor filtering primarily emerges via a configural representation that codes the relative positions of items within search displays independent of spatiotopic and retinotopic coordinates. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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Ophthalmol Retina
March 2025
Department of Ophthalmology & Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada. Electronic address:
Topic: To determine the pooled relative risk (RR) of Pentosan Phosphate Sodium Maculopathy (PPSM) in patients using PPS and model the dose-response relationship of this association from existing literature.
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Med Image Anal
March 2025
Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
This study addresses the challenges of confounding effects and interpretability in artificial-intelligence-based medical image analysis. Whereas existing literature often resolves confounding by removing confounder-related information from latent representations, this strategy risks affecting image reconstruction quality in generative models, thus limiting their applicability in feature visualization. To tackle this, we propose a different strategy that retains confounder-related information in latent representations while finding an alternative confounder-free representation of the image data.
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March 2025
Burapha University Hospital, Burapha University, Saen Suk, Chonburi, Thailand.
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Geroscience
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Dept. Of Bioinformatics, Semmelweis University, 1094, Budapest, Hungary.
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Objective: Coronavirus disease-2019 (COVID-19) can cause not only respiratory symptoms but also facial paralysis. Lianhua Qingwen (LHQW) has been reported to have therapeutic effects on COVID-19 and facial neuritis (FN). We explored the potential mechanism of LHQW in the treatment of COVID-19 and FN through a network-pharmacology approach.
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