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Prediction of Social Anxiety Based on Mindfulness and Bias in Facial Emotion Processing
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Bahram Peymannia   , Fatemeh Tabesh |
| Department of Psychology, faculty of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran. |
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Abstract: (638 Views) |
Social anxiety is a fundamental problem in interpersonal relationships that causes many problems for people with this disorder. The present study aimed to predict social anxiety based on mindfulness, and bias in facial emotion processing. The statistical population of research included all the female students of Shahid Chamran University of Ahvaz in 2023, and a sample of 200 people was selected from among them, using a random sampling method. The research method in this research is a predictive correlation, and due to the goal, it is applied research. To collect data, two social phobia questionnaires were used by Kanwar (2000), and Bauer's Mindfulness Questionnaire (2006), and a computerized facial emotion recognition task was designed by Nazari et al. (2015). The stepwise regression method was used to analyze the data. The results indicated the correlation of the components of description and non-judgment (p<.001) and bias in processing anger and fear emotions (p<.001) with social anxiety. The study revealed a statistically significant negative association between the variables of description and non-judgment with social anxiety. Additionally, a statistically significant positive link was seen among the variables of bias in anger and fear processing and social anxiety. Consequently, based on the findings of the current research, it seems that the lack of mindfulness in the components of description, and judgment plays a role in the biased processing of negative emotions, such as anger and fear, and such a process causes the continuation of social anxiety. |
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| Keywords: social anxiety, mindfulness, facial emotion processing |
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Full-Text [PDF 278 kb]
(19 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2023/03/24 | Revised: 2024/04/13 | Accepted: 2023/12/11 | Published: 2024/03/17
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