People hold strong beliefs about the role of emotional cues in detecting deception. While research on the diagnostic value of such cues has been mixed, their influence on human veracity judgements is yet to be fully explored. Here, we address the relationship between emotional information and veracity judgements. In Study 1, the role of emotion recognition in the process of detecting naturalistic lies was investigated. Decoders' veracity judgements were compared based on differences in trait empathy and their ability to recognise microexpressions and subtle expressions. Accuracy was found to be unrelated to facial cue recognition and negatively related to empathy. In Study 2, we manipulated decoders' emotion recognition ability and the type of lies they saw: experiential or affective (emotional and unemotional). Decoders received either emotion recognition training, bogus training, or no training. In all scenarios, training did not affect veracity judgements. Experiential lies were easier to detect than affective lies; however, affective unemotional lies were overall the hardest to judge. The findings illustrate the complex relationship between emotion recognition and veracity judgements, with abilities for facial cue detection being high yet unrelated to deception accuracy.
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http://dx.doi.org/10.1177/1747021820978851 | 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.
View Article and Find Full Text PDFBMJ Ment Health
January 2025
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.
View Article and Find Full Text PDFNephrol Nurs J
January 2025
Director, the Marian K. Shaughnessy Nurse Leadership Academy.
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.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
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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