Is emotional support the key to improving emotion regulation? A machine learning approach
Background According to the emotion regulation process, situation selection comprises actions that increase or decrease the likelihood of being in contexts that foster a certain type of emotion, positive or negative. This concept is complemented by the social basis theory, which starts with the assu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Termedia Publishing House
2023-01-01
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| Series: | Health Psychology Report |
| Subjects: | |
| Online Access: | https://hpr.termedia.pl/Is-emotional-support-the-key-to-improving-emotion-regulation-A-machine-learning-approach,156937,0,2.html |
| _version_ | 1797333286698090496 |
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| author | Christian Schetsche Luis C. Jaume Paula A. Caccia Martin Zelaya Susana Azzollini |
| author_facet | Christian Schetsche Luis C. Jaume Paula A. Caccia Martin Zelaya Susana Azzollini |
| author_sort | Christian Schetsche |
| collection | DOAJ |
| description | Background
According to the emotion regulation process, situation selection comprises actions that increase or decrease the likelihood of being in contexts that foster a certain type of emotion, positive or negative. This concept is complemented by the social basis theory, which starts with the assumption that the primary ecology of humans is characterized by its social components. Thus, reduced access to social relationships increases cognitive and physiological effort, which leads to a decrease in well-being.
Participants and procedure
In order to make a joint assessment of both concepts, the study used supervised machine learning models to analyze the associations between selected variables of social support, emotion regulation, coping, and several psychological symptoms (somatization, obsession-compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid idea-tion, and psychoticism). For this purpose, an Argentine sample (N = 812, M age = 44.35, female = 435) was collected through the Internet, nested cross-validations were performed with 8 different learning algorithms and Shapley values were computed for the predictive models that minimized the test errors.
Results
The results showed that adaptive strategies have considerable effects on maladaptive strategies, but they do not have signif-icant effects on symptoms. Contrariwise, social support variables have significant effects on symptoms, while they do not have major effects on maladaptive strategies.
Conclusions
It is concluded that the main function of regulatory flexibility does not appear to be a better adaptation to situations, but rather the maintenance of adequate levels of social support, i.e. emotional support received, perception of available emotion-al support, and perceived comprehension. Further implications are discussed, and a hypothetical model proposed. |
| first_indexed | 2024-03-08T08:01:34Z |
| format | Article |
| id | doaj.art-62d67a0f26fc46b19627b071290ab7a5 |
| institution | Directory Open Access Journal |
| issn | 2353-4184 2353-5571 |
| language | English |
| last_indexed | 2024-03-08T08:01:34Z |
| publishDate | 2023-01-01 |
| publisher | Termedia Publishing House |
| record_format | Article |
| series | Health Psychology Report |
| spelling | doaj.art-62d67a0f26fc46b19627b071290ab7a52024-02-02T12:07:14ZengTermedia Publishing HouseHealth Psychology Report2353-41842353-55712023-01-0111429530810.5114/hpr/156937156937Is emotional support the key to improving emotion regulation? A machine learning approachChristian Schetsche0Luis C. Jaume1Paula A. Caccia2Martin Zelaya3Susana Azzollini4Faculty of Psychology, University of Buenos Aires, Buenos Aires, ArgentinaFaculty of Psychology, University of Buenos Aires, Buenos Aires, ArgentinaFaculty of Psychology, University of Buenos Aires, Buenos Aires, ArgentinaInteramerican Open University, Buenos Aires, ArgentinaFaculty of Psychology, University of Buenos Aires, Buenos Aires, ArgentinaBackground According to the emotion regulation process, situation selection comprises actions that increase or decrease the likelihood of being in contexts that foster a certain type of emotion, positive or negative. This concept is complemented by the social basis theory, which starts with the assumption that the primary ecology of humans is characterized by its social components. Thus, reduced access to social relationships increases cognitive and physiological effort, which leads to a decrease in well-being. Participants and procedure In order to make a joint assessment of both concepts, the study used supervised machine learning models to analyze the associations between selected variables of social support, emotion regulation, coping, and several psychological symptoms (somatization, obsession-compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid idea-tion, and psychoticism). For this purpose, an Argentine sample (N = 812, M age = 44.35, female = 435) was collected through the Internet, nested cross-validations were performed with 8 different learning algorithms and Shapley values were computed for the predictive models that minimized the test errors. Results The results showed that adaptive strategies have considerable effects on maladaptive strategies, but they do not have signif-icant effects on symptoms. Contrariwise, social support variables have significant effects on symptoms, while they do not have major effects on maladaptive strategies. Conclusions It is concluded that the main function of regulatory flexibility does not appear to be a better adaptation to situations, but rather the maintenance of adequate levels of social support, i.e. emotional support received, perception of available emotion-al support, and perceived comprehension. Further implications are discussed, and a hypothetical model proposed.https://hpr.termedia.pl/Is-emotional-support-the-key-to-improving-emotion-regulation-A-machine-learning-approach,156937,0,2.htmlemotion regulationcopingsocial supportpsychological symptomspersonality traitssupervised machine learning |
| spellingShingle | Christian Schetsche Luis C. Jaume Paula A. Caccia Martin Zelaya Susana Azzollini Is emotional support the key to improving emotion regulation? A machine learning approach Health Psychology Report emotion regulation coping social support psychological symptoms personality traits supervised machine learning |
| title | Is emotional support the key to improving emotion regulation? A machine learning approach |
| title_full | Is emotional support the key to improving emotion regulation? A machine learning approach |
| title_fullStr | Is emotional support the key to improving emotion regulation? A machine learning approach |
| title_full_unstemmed | Is emotional support the key to improving emotion regulation? A machine learning approach |
| title_short | Is emotional support the key to improving emotion regulation? A machine learning approach |
| title_sort | is emotional support the key to improving emotion regulation a machine learning approach |
| topic | emotion regulation coping social support psychological symptoms personality traits supervised machine learning |
| url | https://hpr.termedia.pl/Is-emotional-support-the-key-to-improving-emotion-regulation-A-machine-learning-approach,156937,0,2.html |
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