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.

In this three‐wave study (n = 121 couples), we tested whether one couple‐member's relational transgressions (high and low severity) at Wave 1 predicted less idealization on warmth and competence traits and greater disillusionment by the partner at the next two waves. It was hypothesized that (a) greater frequency of the target partner's severe transgressions in 1 month would be needed to reduce how much the other partner idealized the target in the competence domain, (b) higher frequency of even relatively less severe transgressions would lower the partner's idealization of the target in the warmth domain, and (c) any transgressions would raise perceivers' disillusionment. Longitudinal analyses (controlling for earlier idealization and disillusionment) substantially supported predictions.

Some people send or receive sexually explicit images or messages when using dating apps. Seeing unwanted content may produce adverse effects, consistent with expectancy violations theory (EVT), and disillusion some users. To test links between encountering sexually explicit materials and dating app disillusionment (with oneself, with others, and regret over app usage), we surveyed two samples of dating app users. Study 1 (n = 531 college students) focused on Tinder, whereas Study 2 (n = 209 Mechanical Turk workers) examined dating apps broadly. In each study, a latent class analysis sorted users into four groups, based on their dating app engagement with sexual content. Participants who rarely exchanged and did not enjoy sexual content were most regretful, as even one bad experience might have violated their expectations. Contrary to EVT, participants with high enjoyment of explicit materials felt disillusioned with themselves. Participants citing relationship-seeking purposes for app usage were highly disillusioned when heavily involved with explicit content.

To differentiate romantic disillusionment from similar constructs of dissatisfaction and regret, functional Magnetic Resonance Imaging (fMRI) data obtained when romantically involved individuals (N = 39) were reminded of relationship events representing these emotions were analyzed. Whole‐brain activations suggested disillusionment‐linked processes not observed for dissatisfaction or regret. Compared to dissatisfying events, disillusioning ones showed greater activity in regions pertaining to evaluation, reflection, and reconciling conflicting information (e.g., anterior cingulate cortex). No regions showed significantly more activation for dissatisfying than disillusioning events. Compared to regret‐inducing events, disillusioning events showed greater activation in areas thought pertinent to detail processing and decision making (occipital fusiform and lingual gyrus). Regret‐inducing events activated regions suggesting the planning and thoughts of how one could have acted differently (e.g., prefrontal cortex).