Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies.

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

Trust and biased memory of transgressions in romantic relationships.

Relative to people with low trust in their romantic partner, people with high trust tend to expect that their partner will act in accordance with their interests. Consequently, we suggest, they have the luxury of remembering the past in a way that prioritizes relationship dependence over self-protection. In particular, they tend to exhibit relationship-promoting memory biases regarding transgressions the partner had enacted in the past. In contrast, at the other end of the spectrum, people with low trust in their partner tend to be uncertain about whether their partner will act in accordance with their interests. Consequently, we suggest, they feel compelled to remember the past in a way that prioritizes self-protection over relationship dependence. In particular, they tend to exhibit self-protective memory biases regarding transgressions the partner had enacted in the past. Four longitudinal studies of participants involved in established dating relationships or fledgling romantic relationships demonstrated that the greater a person's trust in their partner, the more positively they tend to remember the number, severity, and consequentiality of their partner's past transgressions-controlling for their initial reports. Such trust-inspired memory bias was partner-specific; it was more reliably evident for recall of the partner's transgressions and forgiveness than for recall of one's own transgressions and forgiveness. Furthermore, neither trust-inspired memory bias nor its partner-specific nature was attributable to potential confounds such as relationship commitment, relationship satisfaction, self-esteem, or attachment orientations.