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Recent electrophysiological research highlights the significance of global scene properties (GSPs) for scene perception. However, since real-world scenes span a range of low-level stimulus properties and high-level contextual semantics, GSP effects may also reflect additional processing of such non-global factors. We examined this question by asking whether Event-Related Potentials (ERPs) to GSPs will still be observed when specific low- and high-level scene properties are absent from the scene. We presented participants with computer-based artificially-manipulated scenes varying in two GSPs (spatial expanse and naturalness) which minimized other sources of scene information (color and semantic object detail). We found that the peak amplitude of the P2 component was sensitive to the spatial expanse and naturalness of the artificially-generated scenes: P2 amplitude was higher to closed than open scenes, and in response to manmade than natural scenes. A control experiment showed that the effect of Naturalness on the P2 is not driven by local texture information, while earlier effects of naturalness, expressed as a modulation of the P1 and N1 amplitudes, are sensitive to texture information. Our results demonstrate that GSPs are processed robustly around 220 ms and that P2 can be used as an index of global scene perception.
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http://dx.doi.org/10.1016/j.neuropsychologia.2020.107434 | DOI Listing |
Prehosp Disaster Med
September 2025
CACI, Inc, Falls Church, VirginiaUSA.
Introduction: Targeted identification, effective triage, and rapid hemorrhage control are essential for optimal outcomes of mass-casualty incidents (MCIs). An important aspect of Emergency Medical Service (EMS) care is field triage, but this skill is difficult to teach, assess, and research.
Study Objective: This study assessed triage efficacy and hemorrhage control of emergency responders from different professions who used the Sort, Assess, Life-Saving Treatment (SALT) triage algorithm in a virtual reality (VR) simulation of a terrorist subway bombing.
IET Syst Biol
September 2025
School of Computer and Information Techonology, Xinyang Normal University, Xinyang, China.
Accurate polyp segmentation is crucial for computer-aided diagnosis and early detection of colorectal cancer. Whereas feature pyramid network (FPN) and its variants are widely used in polyp segmentation, inherent limitations existing in FPN include: (1) repeated upsampling degrades fine details, reducing small polyp segmentation accuracy and (2) naive feature fusion (e.g.
View Article and Find Full Text PDFBiol Psychol
September 2025
Department of Psychology, Wright State University, Dayton OH. Electronic address:
Category-selectivity is a ubiquitous property of high-level visual cortex manifested in distinct cortical responses to faces, objects, and scenes. These signatures emerge early during visual processing, with each category sensitive to specific types of visual information at different time points. However, it is still not clear what information is extracted during early scene-selective processing, as scenes are rich, complex, and multidimensional stimuli.
View Article and Find Full Text PDFAm J Forensic Med Pathol
September 2025
Research Center for Health Sciences, Institute of Health, Shiraz University of Medical Sciences.
Forensic entomology is a crucial discipline in forensic investigations, primarily used for postmortem interval (PMI) estimation, species identification, and crime scene reconstruction. Recent advancements in molecular techniques, computational models, and climate-adaptive forensic entomology have enhanced the field's forensic applications. However, challenges related to methodological standardization, environmental variability, and legal admissibility persist.
View Article and Find Full Text PDFVision Res
August 2025
Sharif University of Technology, Cognitive Neuroscience, Azadi Ave, P.O. Box 11365-9466, Tehran, 11365-9466, Tehran, Iran. Electronic address:
Scene context is known to significantly influence visual perception, enhancing object recognition particularly under challenging viewing conditions. Behavioral and neuroimaging studies suggest that high-level scene information modulates activity in object-selective brain areas through top-down mechanisms, yet the underlying mechanism of this process remains unclear. Here, we introduce a biologically inspired context-based computational model (CBM) that integrates scene context into object recognition via an explicit feedback mechanism.
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