Background: The ability to facial emotion recognition (FER), a key component of socioemotional competence, is often impaired in schizophrenic disorders. The purpose of the present study was to examine the relationship between emotion recognition performance and symptoms in a group of patients with schizophrenia spectrum disorders.
Sampling And Methods: Seventy-nine patients meeting DSM-IV-TR criteria for schizophrenia, schizophreniform disorder and schizoaffective disorder were assessed by the Positive and Negative Syndrome Scale and a FER task. In schizophrenia patients and healthy control subjects, FER performance was compared. In order to avoid a possible confounding role of cognitive impairment, we carried out partial correlations corrected for an index of global cognition.
Results: Patients performed worse than a healthy control group on all negative emotions. Partial correlations showed that cognitive/disorganized symptoms correlated with a worse performance in the FER task, whereas no correlations were found with positive, negative, excitement and depressive symptoms.
Conclusions: Our findings support that in schizophrenia FER impairment is specific for negative emotions and that there is a relationship between this deficit and cognitive/disorganized symptoms, regardless of the general cognitive level.
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http://dx.doi.org/10.1159/000350453 | 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
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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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