With the outburst of the COVID-19 pandemic, disposable surgical face-masks (DSFMs) have been widely adopted as a preventive measure. DSFMs hide the bottom half of the face, thus making identity and emotion recognition very challenging, both in typical and atypical populations. Individuals with autism spectrum disorder (ASD) are often characterized by face processing deficits; thus, DSFMs could pose even a greater challenge for this population compared to typically development (TD) individuals. In this study, 48 ASDs of level 1 and 110 TDs underwent two tasks: (i) the Old-new face memory task, which assesses whether DSFMs affect face learning and recognition, and (ii) the Facial affect task, which explores DSFMs' effect on emotion recognition. Results from the former show that, when faces were learned without DSFMs, identity recognition of masked faces decreased for both ASDs and TDs. In contrast, when faces were first learned with DSFMs, TDs but not ASDs benefited from a "context congruence" effect, that is, faces wearing DSFMs were better recognized if learned wearing DSFMs. In addition, results from the Facial affect task show that DSFMs negatively impacted specific emotion recognition in both TDs and ASDs, although differentially between the two groups. DSFMs negatively affected disgust, happiness and sadness recognition in TDs; in contrast, ASDs performance decreased for every emotion except anger. Overall, our study demonstrates a general, although different, disruptive effect on identity and emotion recognition both in ASD and TD population.
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http://dx.doi.org/10.1002/aur.2922 | DOI Listing |
Atten Percept Psychophys
January 2025
Department of Psychology, Rutgers University - New Brunswick, 152 Frelinghuysen Rd, Piscataway, NJ, 08854, USA.
Human observers can often judge emotional or affective states from bodily motion, even in the absence of facial information, but the mechanisms underlying this inference are not completely understood. Important clues come from the literature on "biological motion" using point-light displays (PLDs), which convey human action, and possibly emotion, apparently on the basis of body movements alone. However, most studies have used simplified and often exaggerated displays chosen to convey emotions as clearly as possible.
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National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia
Climate change poses enormous, rapidly increasing risks to human well-being that remain poorly appreciated. The growing understanding of this threat has generated a phenomenon often called 'eco-anxiety'. Eco-anxiety (and its synonyms) is best documented in the Global North, mostly among people who are better educated and whose reasons for concern are both altruistic and self-interested.
View Article and Find Full Text PDFBMC Psychol
January 2025
Department of Basic and Clinical Psychology, and Psychobiology, Universitat Jaume I, Castellon, Spain.
Background: Improving mental health within correctional facilities, specifically to address self-harm behaviors, is a crucial endeavor. However, significant challenges arise when implementing evidence-based programs within this complex setting. Despite these hurdles, the Systems Training for Emotional Predictability and Problem Solving (STEPPS) program has garnered recognition, notably in the United States, for its efficacy in tackling such issues.
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Nephrology nurses working in hemodialysis units face unique challenges managing multiple patients - an experience often contributing to higher levels of burnout and stress, and potentially lower job satisfaction and retention rates, exacerbating the existing nursing shortage in dialysis settings. Targeted strategies are essential to improve job satisfaction. In this study, we explored the relationship between emotional intelligence and job satisfaction among nephrology nurses working in acute and chronic hemodialysis settings.
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Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu 641032 India.
Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people.
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