The ability to estimate proportions informs our immediate impressions of social environments (e.g., of the diversity of races or genders within a crowded room). This study examines how the distribution of attention during brief glances shapes estimates of group gender proportions. Performance-wise, subjects exhibit a canonical pattern of judgment errors: small proportions are overestimated while large values are underestimated. Subjects' eye movements at sub-second timescales reveal that these biases follow from a tendency to visually oversample members of the gender minority. Rates of oversampling dovetail with average levels of error magnitudes, response variability, and response times. Visual biases are thus associated with the inherent difficulty in estimating particular proportions. All results are replicated at a within-subjects level with non-human ensembles using natural scene stimuli; the observed attentional patterns and judgment biases are thus not exclusively guided by face-specific visual properties. Our results reveal the biased distribution of attention underlying typical judgment errors of group proportions.
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http://dx.doi.org/10.1016/j.cognition.2021.104756 | DOI Listing |
Commun Biol
January 2025
Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA.
Human behavior is strongly influenced by anticipation, but the underlying neural mechanisms are poorly understood. We obtained intracranial electrocephalography (iEEG) measurements in neurosurgical patients as they performed a simple sensory-motor task with variable (short or long) foreperiod delays that affected anticipation of the cue to respond. Participants showed two forms of anticipatory response biases, distinguished by more premature false alarms (FAs) or faster response times (RTs) on long-delay trials.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
School of Chemical & Biotechnology, SASTRA Deemed University, Thirumalaisamudram, Tamil Nadu, India.
Levan is a fructan-type homopolysaccharide that has gained increasing attention due to its unique properties and promising applications. It is a fructose-based polymer produced through microbial fermentation by diverse microorganisms, including bacteria, yeasts and archaea. The ongoing research on levan mainly focuses on optimizing production processes, elucidating its biological functions, and uncover novel applications.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
School of Chemistry and Environment, Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Water, Guangdong Ocean University, Zhanjiang 524088, China.
Microplastic pollution, a major global environmental issue, is gaining heightened attention worldwide. Marginal seas are particularly susceptible to microplastic contamination, yet data on microplastics in marine sediments remain scarce, especially in the Beibu Gulf. This study presents a large-scale investigation of microplastics in the surface sediments of the Beibu Gulf to deciphering their distribution, sources and risk to marginal seas ecosystems.
View Article and Find Full Text PDFSci Total Environ
January 2025
Center for Marine Sensors, Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl von Ossietzky University of Oldenburg, 26382 Wilhelmshaven, Germany.
Microplastics (MP) are known to be ubiquitous. The pathways and fate of these contaminants in the marine environment are receiving increasing attention, but still knowledge gaps exist. In particular, the link between mass-based MP quantification and oceanographic parameters is often lacking.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Patna, 800025, Bihar, India.
Background And Objectives: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR.
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