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.

For students to thrive in the U.S. educational system, they must successfully cope with omnipresent demands of exams. Nearly all students experience testing situations as stressful, and signs of stress (e.g., racing heart) are typically perceived negatively. This research tested the efficacy of a psychosituational intervention targeting cognitive appraisals of stress to improve classroom exam performance. Ninety-three students (across five semesters) enrolled in a community college developmental mathematics course were randomly assigned to stress reappraisal or placebo control conditions. Reappraisal instructions educated students about the adaptive benefits of stress arousal, whereas placebo materials instructed students to ignore stress. Reappraisal students reported less math evaluation anxiety and exhibited improved math exam performance relative to controls. Mediation analysis indicated reappraisal improved performance by increasing students’ perceptions of their ability to cope with the stressful testing situation (resource appraisals). Implications for theory development and policy are discussed.

This study examined the effects of reappraising stress arousal on affective displays, physiological responses, and social performance during an evaluative situation. Participants were sampled from across the social anxiety spectrum and instructed to reappraise arousal as beneficial or received no instructions. Independent raters coded affective displays, nonverbal signaling, and speech performance. Saliva samples collected at baseline and after evaluation were assayed for salivary alpha-amylase (sAA), a protein that indexes sympathetic activation. Arousal reappraisal participants exhibited less shame and anxiety, less avoidant nonverbal signaling, and performed marginally better than no instruction controls. Reappraisal participants also exhibited increased levels of sAA and increased appraisals of coping resources compared with controls. Furthermore, stress appraisals mediated relationships between reappraisal and affective displays. This research indicates that reframing stress arousal can improve behavioral displays of affect during evaluative situations via altering cognitive appraisals.

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When good things happen, individuals will often retell this good news to others, a process termed capitalization. In so doing, individuals sharing their good news (i.e., capitalizers)boost their mood and relationships with the person(s) to whom they retell their news (i.e., responders). Most extant research has focused on the benefits for the capitalizers.Capitalization, however, is a social process that affects both capitalizers and responders, and research has only begun to explore the benefits of capitalization for responders. In this article, we provide a fresh perspective on the state of this literature by proposing the interpersonal model of capitalization (InterCAP). We illustrate how InterCAP (a) integrates and organizes existing research and theory, (b) formally emphasizes the interpersonal and iterative nature of the capitalization process, and (c) identifies gaps in current knowledge. We conclude by offering recommendations for integrating InterCAP with other theoretical models and suggestions for future research.