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Face Recognition Ability (FRA) varies widely throughout the population. Previous research highlights a positive relationship between self-perceived and objectively measured FRA in the healthy population, suggesting that people do have insight into their FRA. Given that this relationship has not been investigated in Italian samples yet, the main aim of the present work was to develop an Italian translation of the Prosopagnosia Index-20 (PI-20), a self-report measure of FRA, to investigate the relationship between PI-20 performances and an objective assessment given by the Cambridge Face Memory Test Long Form (CFMT+) in the Italian population. A sample of 553 participants filled in the PI-20 Italian version 1 or 2 (PI-20_GE or PI-20_BA) and completed the CFMT+. Results showed a negative correlation between both versions of the Italian PI-20 and CFMT+ scores, meaning that the more self-evaluations were negative, the worse they objectively performed. The same results applied to the extreme limits of the distribution (i.e., 10% of the highest and lowest PI-20 scores). Furthermore, both age and administration order of the tests were predictor variables of CFMT+ scores. Overall, our results suggest that people possess insight, although relatively limited, into their FRA.
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http://dx.doi.org/10.1016/j.heliyon.2023.e14125 | DOI Listing |
Psychon Bull Rev
September 2025
Department of Experimental Psychology, University of Oxford, Oxford, UK.
Individuals who are superior at face recognition are described as 'super recognisers' (SRs). On standard face recognition tasks SRs outperform individuals who have typical face recognition ability. However, high accuracy on face recognition tasks may be driven by superior ability in one or more of several component processes including face perception, face matching, and face memory.
View Article and Find Full Text PDFAnal Bioanal Chem
September 2025
School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China.
The prompt and accurate identification of pathogenic bacteria is crucial for mitigating the transmission of infections. Conventional detection methods face limitations, including lengthy processing, complex sample pretreatment, high instrumentation costs, and insufficient sensitivity for rapid on-site screening. To address these challenges, an aptamer (Apt)-sensor based on functionalized magnetic nanoparticles (MNPs) was developed for detecting Escherichia coli.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Cyberspace Security (School of Cryptology), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, Hainan, China. Electronic address:
The primary challenge of large-margin learning lies in designing classifiers with strong discriminative power. Although existing large margin methods have achieved success in various classification tasks, they often suffer from weak task generalization and imbalanced handling of easy and hard samples. In this paper, we propose a margin adaptive synthetic virtual Softmax loss (SV-Softmax), which dynamically generates virtual prototypes by synthesizing embedded features and their corresponding prototypes.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Psychology & Sociology, Texas A&M University - Corpus Christi, Corpus Christi, Texas, United States of America.
While the use of personal protective equipment protects healthcare workers against transmissible disease, it also obscures the lower facial regions that are vital for transmitting emotion signals. Previous studies have found that face coverings can impair recognition of emotional expressions, particularly those that rely on signals from the lower regions of the face, such as disgust. Recent research on the individual differences that may influence expression recognition, such as emotional intelligence, has shown mixed results.
View Article and Find Full Text PDFJ Vis
September 2025
Neuroscience Program, Western University, London, ON, Canada.
Studies of visual face processing often use flat images as proxies for real faces due to their ease of manipulation and experimental control. Although flat images capture many features of a face, they lack the rich three-dimensional (3D) structural information available when binocularly viewing real faces (e.g.
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