Previous research suggests that understanding the gist of a scene relies on global structural cues that enable rapid scene categorization. This study used a repetition blindness (RB) paradigm to interrogate the nature of the scene representations used in such rapid categorization. When stimuli are repeated in a rapid serial visual presentation (RSVP) sequence (~10 items/sec), the second occurrence of the repeated item frequently goes unnoticed, a phenomenon that is attributed to a failure to consolidate two conscious episodes (tokens) for a repeatedly activated type. We tested whether RB occurs for different exemplars of the same scene category, which share conceptual and broad structural properties, as well as for identical and mirror-reflected repetitions of the same scene, which additionally share the same local visual details. Across 2 experiments, identical and mirror-image scenes consistently produced a repetition facilitation, rather than RB. There was no convincing evidence of either RB or repetition facilitation for different members of a scene category. These findings indicate that in the first 100-150 ms of processing scenes are represented in terms of local visual features, rather than more abstract category-general features, and that, unlike other kinds of stimuli (words or objects), scenes are not susceptible to token individuation failure.
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http://dx.doi.org/10.3758/s13421-016-0640-9 | DOI Listing |
Alzheimers Dement
January 2025
Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
Introduction: The automated analysis of connected speech using natural language processing (NLP) emerges as a possible biomarker for Alzheimer's disease (AD). However, it remains unclear which types of connected speech are most sensitive and specific for the detection of AD.
Methods: We applied a language model to automatically transcribed connected speech from 114 Flemish-speaking individuals to first distinguish early AD patients from amyloid negative cognitively unimpaired (CU) and then amyloid negative from amyloid positive CU individuals using five different types of connected speech.
Front Neurorobot
January 2025
School of Business, Lingnan University, Hong Kong, China.
With the rapid development of tourism, the concentration of visitor flows poses significant challenges for public safety management, especially in low-light and highly occluded environments, where existing pedestrian detection technologies often struggle to achieve satisfactory accuracy. Although infrared images perform well under low-light conditions, they lack color and detail, making them susceptible to background noise interference, particularly in complex outdoor environments where the similarity between heat sources and pedestrian features further reduces detection accuracy. To address these issues, this paper proposes the FusionU10 model, which combines information from both infrared and visible light images.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Critical Care Medicine, Department of Emergency Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong-si, Gyeonggi-do, Republic of Korea.
The optimal duration of on-scene cardiopulmonary resuscitation (CPR) for out-of-hospital cardiac arrest (OHCA) patients remains uncertain. Determining this critical time period requires outweighing the potential risks associated with intra-arrest transport while minimizing delays in accessing definitive hospital-based treatments. This study evaluated the association between on-scene CPR duration and 30-day neurologically favorable survival based on the transport time interval (TTI) in patients with OHCA.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive learning frameworks to learn generalizable feature representations. However, these approaches often fail to mitigate shortcut learning, leading to suboptimal performance.
View Article and Find Full Text PDFBMC Emerg Med
January 2025
Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Background: Volunteers providing nursing services are among the first individuals to arrive at the scene after an incident; therefore, they must use their skills and capabilities to provide necessary care for the injured to prevent problems from worsening and complications from arising. Consequently, having structured empowerment courses for volunteers before disasters seems essential. This research aimed to determine the dimensions and components of empowering volunteer nursing service providers in disasters.
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