Trust and biased memory of transgressions in romantic relationships.

Relative to people with low trust in their romantic partner, people with high trust tend to expect that their partner will act in accordance with their interests. Consequently, we suggest, they have the luxury of remembering the past in a way that prioritizes relationship dependence over self-protection. In particular, they tend to exhibit relationship-promoting memory biases regarding transgressions the partner had enacted in the past. In contrast, at the other end of the spectrum, people with low trust in their partner tend to be uncertain about whether their partner will act in accordance with their interests. Consequently, we suggest, they feel compelled to remember the past in a way that prioritizes self-protection over relationship dependence. In particular, they tend to exhibit self-protective memory biases regarding transgressions the partner had enacted in the past. Four longitudinal studies of participants involved in established dating relationships or fledgling romantic relationships demonstrated that the greater a person's trust in their partner, the more positively they tend to remember the number, severity, and consequentiality of their partner's past transgressions—controlling for their initial reports. Such trust-inspired memory bias was partner-specific; it was more reliably evident for recall of the partner's transgressions and forgiveness than for recall of one's own transgressions and forgiveness. Furthermore, neither trust-inspired memory bias nor its partner-specific nature was attributable to potential confounds such as relationship commitment, relationship satisfaction, self-esteem, or attachment orientations. (PsycINFO Database Record (c) 2016 APA, all rights reserved

Explaining illness with evil: pathogen prevalence fosters moral vitalism

Pathogens represent a significant threat to human health leading to the emergence of strategies designed to help manage their negative impact. We examined how spiritual beliefs developed to explain and predict the devastating effects of pathogens and spread of infectious disease. Analysis of existing data in studies 1 and 2 suggests that moral vitalism (beliefs about spiritual forces of evil) is higher in geographical regions characterized by historical higher levels of pathogens. Furthermore, drawing on a sample of 3140 participants from 28 countries in study 3, we found that historical higher levels of pathogens were associated with stronger endorsement of moral vitalis- tic beliefs. Furthermore, endorsement of moral vitalistic beliefs statistically mediated the previously reported relationship between pathogen prevalence and conser- vative ideologies, suggesting these beliefs reinforce behavioural strategies which function to prevent infection. We conclude that moral vitalism may be adaptive: by emphasizing concerns over contagion, it provided an explanatory model that enabled human groups to reduce rates of contagious disease

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

Experiences of liking versus ideas about liking.

We leverage the notion that abstraction enables prediction to generate novel insights and hypotheses for the literatures on attitudes and mate preferences. We suggest that ideas about liking (e.g., evaluations of categories or overall traits) are more abstract than experiences of liking (e.g., evaluations of particular exemplars), and that ideas about liking may facilitate mental travel beyond the here-and-now

Toward an Integrative Framework for Studying Human Evaluation: Attitudes Toward Objects and Attributes.

Evaluation is central to human experience, and multiple literatures have studied it. This article pulls from research on attitudes, human and nonhuman mating preferences, consumer behavior, and beyond to build a more comprehensive framework for studying evaluation. First, we distinguish between evaluations of objects (persons, places, things) and evaluations of attributes (dimensions, traits, characteristics). Then, we further distinguish between summarized attribute preferences (a valenced response to a direction on a dimension, such as liking sweetness in desserts) and functional attribute preferences (a valenced response to increasing levels of a dimension in a set of targets, such as the extent to which sweetness predicts liking for desserts). We situate these constructs with respect to existing distinctions in the attitude literature (e.g., specific/general, indirect/direct). Finally, new models address how people translate functional into summarized preferences, as well as how attribute preferences affect (a) subsequent evaluations of objects and (b) situation selection

Best Practices for Testing the Predictive Validity of Ideal Partner Preference-Matching.

Many psychological hypotheses require testing whether the similarity between two variables predicts important outcomes. For example, the ideal standards model posits that the match between (A) a participant's ideal partner preferences, and (B) the traits of a current/potential partner, predicts (C) evaluative outcomes (e.g., the decision to date someone, relationship satisfaction, breakup); tests of the predictive validity of ideal-matching require A × B → C analytic strategies. However, recent articles have incorrectly suggested that documenting a positive samplewide correlation between a participant's ideals and a current partner's traits (an A-B correlation) implies that participants pursued, selected, or desired partners with traits that matched their ideals. There are at least six alternative explanations for the emergence of a samplewide A-B correlation; A-B correlations do not provide evidence that ideals guide the selection/evaluation of specific partners. We review appropriately rigorous A × B → C tests that can aid scholars in identifying the circumstances in which ideal-matching exhibits predictive validity