Depressive symptomatology is associated with impaired recognition of emotion. Previous investigations have predominantly focused on emotion recognition of static facial expressions neglecting the influence of social interaction and critical contextual factors. In the current study, we investigated how youth and maternal symptoms of depression may be associated with emotion recognition biases during familial interactions across distinct contextual settings. Further, we explored if an individual's current emotional state may account for youth and maternal emotion recognition biases. Mother-adolescent dyads (N = 128) completed measures of depressive symptomatology and participated in three family interactions, each designed to elicit distinct emotions. Mothers and youth completed state affect ratings pertaining to self and other at the conclusion of each interaction task. Using multiple regression, depressive symptoms in both mothers and adolescents were associated with biased recognition of both positive affect (i.e., happy, excited) and negative affect (i.e., sadness, anger, frustration); however, this bias emerged primarily in contexts with a less strong emotional signal. Using actor-partner interdependence models, results suggested that youth's own state affect accounted for depression-related biases in their recognition of maternal affect. State affect did not function similarly in explaining depression-related biases for maternal recognition of adolescent emotion. Together these findings suggest a similar negative bias in emotion recognition associated with depressive symptoms in both adolescents and mothers in real-life situations, albeit potentially driven by different mechanisms.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1037/a0033923 | DOI Listing |
Front Psychol
December 2024
Departent of Learning, Data-Analytics and Technology, Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, Netherlands.
Learning experiences are intertwined with emotions, which in turn have a significant effect on learning outcomes. Therefore, digital learning environments can benefit from taking the emotional state of the learner into account. To do so, the first step is real-time emotion detection which is made possible by sensors that can continuously collect physiological and eye-tracking data.
View Article and Find Full Text PDFActa Psychol (Amst)
December 2024
Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Affective Theory of Mind (ToM) is the ability to understand other peoples' emotional states and feelings. Several studies showed impaired affective ToM abilities in people with Parkinson's disease (PD). However, most studies tested this ability by using single-stimulus modality tasks (visual cues).
View Article and Find Full Text PDFSci Rep
December 2024
The Department of Mechanical Engineering and Mechatronics, Ariel University, Ariel, Israel.
Autism spectrum disorder (ASD) involves challenges in communication and social interaction, including challenges in recognizing emotions. Existing technological solutions aim to improve social behaviors in individuals with ASD by providing learning aids. This paper presents a real-time environmental translator designed to enhance social behaviors in individuals with ASD using sensory substitution.
View Article and Find Full Text PDFSci Rep
December 2024
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, 434100, Hubei, China.
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer.
View Article and Find Full Text PDFAnn Gen Psychiatry
December 2024
University of Campania "Luigi Vanvitelli", Piazza Miraglia 2, 80138, Naples, Italy.
This randomized-controlled study evaluates the effectiveness of a newly developed social cognition rehabilitation intervention, the modified Social Cognition Individualized Activity Lab (mSoCIAL), in improving social cognition and clinical and functional outcomes of persons with schizophrenia recruited in two Italian sites: University of Campania "Luigi Vanvitelli" in Naples and ASST Fatebenefratelli-Sacco in Milan. mSoCIAL consists of a social cognitive training module focusing on different domains of social cognition and of a narrative enhancement module. We assessed changes in social cognition, clinical characteristics and functional variables in patients with schizophrenia who participated in 10 weekly sessions of mSoCIAL or received treatment as usual (TAU).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!