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

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Testing the Romantic Construal Model: The Impact of Personalization, Specialness, and Value in Evaluating Romantic Actions

<p>The Romantic Construal Model proposes that people interpret actions as romantic to the extent that they perceive that those actions take the receiver’s idiosyncratic likes and dislikes into account (personalization), are out of the ordinary in terms of either frequency or the manner with which they are enacted (specialness), and convey that the person values the receiver and the relationship (conveyed value). This model was tested in two studies.</p> <p>In Study 1, 132 participants (67 men and 65 women) were instructed to modify generic behaviors to make them either more or less romantic. These modifications were then coded for personalization, specialness, and conveyed value. The results showed that higher mean levels of personalization, specialness, and value were found when participants were asked to make a behavior more rather than less romantic. Furthermore, regression analyses predicting participant ratings of romance for the modified actions were significantly predicted by the levels of specialness and conveyed value, but personalization was not related to romantic ratings.</p> <p>In Study 2, 132 participants (67 men and 65 women) read 8 vignettes describing potentially romantic behaviors that experimentally manipulated all combinations of high or low personalization, high or low specialness and high or low conveyed value. Participants rated each vignette for how romantic they thought the behavior was; the degree to which the behavior was personalized, special, and conveyed value; and how good, committed, and loved would they feel if their partner enacted that behavior in their relationship. The results of Study 2 showed that although personalization and specialness were successfully manipulated in the vignettes, value was not. Furthermore, significant effects of personalization and specialness, but not value, were obtained on romantic ratings for half of the vignettes. In contrast, participants’ subjective ratings of the romanticness of the behaviors were predicted by their ratings of value but not personalization or specialness. The implications of this study for the Romantic Construal Model are discussed and evaluated within the context of previous findings on the communication of affection</p>Dissertatio

Integrated clinical and omics approach to rare diseases : Novel genes and oligogenic inheritance in holoprosencephaly

Holoprosencephaly is a pathology of forebrain development characterized by high phenotypic heterogeneity. The disease presents with various clinical manifestations at the cerebral or facial levels. Several genes have been implicated in holoprosencephaly but its genetic basis remains unclear: different transmission patterns have been described including autosomal dominant, recessive and digenic inheritance. Conventional molecular testing approaches result in a very low diagnostic yield and most cases remain unsolved. In our study, we address the possibility that genetically unsolved cases of holoprosencephaly present an oligogenic origin and result from combined inherited mutations in several genes. Twenty-six unrelated families, for whom no genetic cause of holoprosencephaly could be identified in clinical settings [whole exome sequencing and comparative genomic hybridization (CGH)-array analyses], were reanalysed under the hypothesis of oligogenic inheritance. Standard variant analysis was improved with a gene prioritization strategy based on clinical ontologies and gene co-expression networks. Clinical phenotyping and exploration of cross-species similarities were further performed on a family-by-family basis. Statistical validation was performed on 248 ancestrally similar control trios provided by the Genome of the Netherlands project and on 574 ancestrally matched controls provided by the French Exome Project. Variants of clinical interest were identified in 180 genes significantly associated with key pathways of forebrain development including sonic hedgehog (SHH) and primary cilia. Oligogenic events were observed in 10 families and involved both known and novel holoprosencephaly genes including recurrently mutated FAT1, NDST1, COL2A1 and SCUBE2. The incidence of oligogenic combinations was significantly higher in holoprosencephaly patients compared to two control populations (P < 10 -9). We also show that depending on the affected genes, patients present with particular clinical features. This study reports novel disease genes and supports oligogenicity as clinically relevant model in holoprosencephaly. It also highlights key roles of SHH signalling and primary cilia in forebrain development. We hypothesize that distinction between different clinical manifestations of holoprosencephaly lies in the degree of overall functional impact on SHH signalling. Finally, we underline that integrating clinical phenotyping in genetic studies is a powerful tool to specify the clinical relevance of certain mutations

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