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 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.

Automated summary

Contemporary emotion theories suggest that the coupling of partners' emotions during interactions can predict the quality of their relationship. This study used machine learning methods to investigate whether emotions during positive and negative interactions can predict relationship stability two years later. The results showed that the variability of individual emotions and the coupling of partners' emotions during positive interactions predicted relationship separation.