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Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach
Contemporary emotion theories predict that how partners’ emotions are coupled together across an interaction can inform on how well the relationship functions. However, few studies have compared how individual (i.e., mean, variability) and dyadic aspects of emotions (i.e., coupling) during interacti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10325102/ https://www.ncbi.nlm.nih.gov/pubmed/37410721 http://dx.doi.org/10.1371/journal.pone.0288048 |
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author | Hilpert, Peter Vowels, Matthew R. Mestdagh, Merijn Sels, Laura |
author_facet | Hilpert, Peter Vowels, Matthew R. Mestdagh, Merijn Sels, Laura |
author_sort | Hilpert, Peter |
collection | PubMed |
description | Contemporary emotion theories predict that how partners’ emotions are coupled together across an interaction can inform on how well the relationship functions. However, few studies have compared how individual (i.e., mean, variability) and dyadic aspects of emotions (i.e., coupling) during interactions predict future relationship separation. In this exploratory study, we utilized machine learning methods to evaluate whether emotions during a positive and a negative interaction from 101 couples (N = 202 participants) predict relationship stability two years later (17 breakups). Although the negative interaction was not predictive, the positive was: Intra-individual variability of emotions as well as the coupling between partners’ emotions predicted relationship separation. The present findings demonstrate that utilizing machine learning methods enables us to improve our theoretical understanding of complex patterns. |
format | Online Article Text |
id | pubmed-10325102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103251022023-07-07 Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach Hilpert, Peter Vowels, Matthew R. Mestdagh, Merijn Sels, Laura PLoS One Research Article Contemporary emotion theories predict that how partners’ emotions are coupled together across an interaction can inform on how well the relationship functions. However, few studies have compared how individual (i.e., mean, variability) and dyadic aspects of emotions (i.e., coupling) during interactions predict future relationship separation. In this exploratory study, we utilized machine learning methods to evaluate whether emotions during a positive and a negative interaction from 101 couples (N = 202 participants) predict relationship stability two years later (17 breakups). Although the negative interaction was not predictive, the positive was: Intra-individual variability of emotions as well as the coupling between partners’ emotions predicted relationship separation. The present findings demonstrate that utilizing machine learning methods enables us to improve our theoretical understanding of complex patterns. Public Library of Science 2023-07-06 /pmc/articles/PMC10325102/ /pubmed/37410721 http://dx.doi.org/10.1371/journal.pone.0288048 Text en © 2023 Hilpert et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hilpert, Peter Vowels, Matthew R. Mestdagh, Merijn Sels, Laura Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach |
title | Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach |
title_full | Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach |
title_fullStr | Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach |
title_full_unstemmed | Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach |
title_short | Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach |
title_sort | emotion dynamic patterns between intimate relationship partners predict their separation two years later: a machine learning approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10325102/ https://www.ncbi.nlm.nih.gov/pubmed/37410721 http://dx.doi.org/10.1371/journal.pone.0288048 |
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