ArticlePloS one2023
Emotion dynamic patterns between intimate relationship partners predict their separation two years later: A machine learning approach.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Disrupted physiological coregulation in youth at clinical high risk for psychosis: Insights from a dyadic interaction study.Emotion (Washington, D.C.) · 2026Article
- Attachment Insecurity Mediates the Associations Between Childhood Trauma and Duration of Emotions During a Laboratory-Based Sexual Conflict Discussion Among Couples.Archives of sexual behavior · 2025Article
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Authors and funding
4 authors.
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Abstract
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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