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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...

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Detalles Bibliográficos
Autores principales: Hilpert, Peter, Vowels, Matthew R., Mestdagh, Merijn, Sels, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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