Accessibility Experiences and Perceived Relationship Superiority

Similar to the positive illusion people demonstrate for themselves, people also have exaggeratedly positive views of their relationship. This perceived superiority encompasses the belief that one's relationship has more good features and fewer bad features than other people's relationships, and it plays a functional role in reducing doubt and sustaining conviction in relationships. This dissertation tests the role of accessibility experiences in influencing perceived superiority in close relationships. People can make judgments on the basis of two distinct factors: (a) accessible content (what information is brought to mind); and (b) accessibility experience (how easily information is brought to mind). Prior research on perceived superiority has focused only on the accessible content dimension, while completely neglecting the possibly critical influence of people's accessibility experiences. Three studies were designed to test the role of accessibility experience in perceived superiority. Study 1 (n = 154) provided evidence that people find listing positive or negative thoughts about their own or others' relationships differently easy or difficult. Study 2 (n = 118) further examined this issue by returning to the method of prior research by manipulating, within-subjects, the relationship target and valence variables. Study 3 (n = 198) provided evidence that directly manipulating accessibility experience through a thoughts-listing task affects a variety of relationship variables

To think or to do: the impact of assessment and locomotion orientation on the Michelangelo phenomenon

This work examines how individual differences in assessment and locomotion shape goal pursuits in ongoing relationships. The Michelangelo phenomenon describes the role that close partners play in affirming versus disaffirming one another's pursuit of the ideal self. Using data from a longitudinal study of ideal goal pursuits among newly committed couples, we examined whether the action orientation that characterizes locomotion creates an optimal environment in which to give and receive affirmation, whereas the evaluative orientation that characterizes assessment creates a suboptimal environment for giving and receiving affirmation. Consistent with hypotheses, locomotion is positively associated with partner affirmation, movement toward the ideal self, and couple wellbeing, whereas parallel associations with assessment are negative. We also explore the behavioral mechanisms that may account for such associations

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

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