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.

The role of information-processing capacity and goals in attitude-congruent selective exposure effects.

Two experiments explored the role of information-processing capacity and strategies in regulating attitude-congruent selective exposure. In Experiment 1, participants were placed under time pressure and randomly assigned to conditions in which either an attitude-expressive or no-information processing goal was made salient. Analyses revealed an attitude-congruent selective exposure effect and indicated that this effect was stronger when an attitude-expressive goal was made salient than when no goal was made salient. In Experiment 2, information-processing goals and time pressure were factorially manipulated. Analyses revealed an attitude-congruent selective exposure effect and indicated that this effect was especially strong when time pressure was high and an attitude-expressive goal was made salient. In both experiments, bias at exposure was found to predict bias at later stages of information processing (attention and memory). Supplementary analyses and data confirmed that the attitude-expressive goal manipulation activated its intended motivational processing strategy.