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 evolution and function of adult attachment: a comparative and phylogenetic analysis.

Although the evolutionary functions of attachment in infant-caregiver relationships are undisputed, it is unclear what functions--if any--attachment serves in adult romantic relationships. The objective of this research was to examine the evolution and function of adult attachment (i.e., pair bonding) by applying comparative and phylogenetic methods to archival data collected on 2 diverse samples of mammalian species. The authors found that species exhibiting adult attachment were more likely than others to be characterized by paternal care, developmental immaturity or neoteny, small social groups, and small body sizes. The authors also used phylogenetic techniques to reconstruct the evolution of adult attachment and test alternative evolutionary models of the comparative correlates of pair bonding. Phylogenetic analyses suggested that the relationship between paternal care and adult attachment may be a functional one (i.e., due to convergent evolution) but that the relationship between neoteny and adult attachment may be due to homology (i.e., shared ancestry). Discussion focuses on the potential of comparative and phylogenetic methods for advancing the science of social and personality psychology.