Emotion regulation and disordered eating behaviour in youths: Two daily‐diary studies

ObjectiveDisordered eating cognitions and behaviours in childhood and adolescence have been identified as precursors for the development of eating disorders. Another important contributor to eating disorder risk is maladaptive emotion regulation. However, while the regulation of negative affect has been the focus of much research, the literature on the role of positive emotion regulation in eating pathology is extremely limited. The present study extends previous research by examining the regulation of both positive and negative affect in disordered eating using two waves of a daily diary design.MethodEvery evening for 21 days, 139 youths (8–15 years) reported their use of rumination, dampening, and disordered eating cognitions and behaviours. 1 year later, during the onset of the COVID‐19 pandemic, 115 of these youths were followed‐up.ResultsAs predicted, higher levels of rumination and dampening were found to be associated with a higher frequency of weight concerns and restrictive eating on person‐level (both Waves) and day‐level (Wave 2). Further, a higher frequency of rumination at Wave 1 predicted increases in the frequency of restrictive eating 1 year later.ConclusionsOur findings underline the importance of examining regulation of both positive and negative emotion in order to understand eating disorder risk

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

Relations among emotional clarity, emotion differentiation, and depressive symptoms in children and adolescents

We conduct a secondary analysis of data from an extensive ecological momentary assessment (EMA) study adressing the relations among emotional clarity, emotion differentiation, and depressive symptoms in children and adolescents

Neural responses to reward valence and magnitude from pre- to early adolescence

Background: Neural activation during reward processing is thought to underlie critical behavioral changes that take place during the transition to adolescence (e.g., learning, risk-taking). Though literature on the neural basis of reward processing in adolescence is booming, important gaps remain. First, more information is needed regarding changes in functional neuroanatomy in early adolescence. Another gap is understanding whether sensitivity to different aspects of the incentive (e.g., magnitude and valence) changes during the transition into adolescence. We used fMRI from a large sample of preadolescent children to characterize neural responses to incentive valence vs. magnitude during anticipation and feedback, and their change over a period of two years. Methods: Data were taken from the Adolescent Cognitive and Brain DevelopmentSM (ABCD®) study release 3.0. Children completed the Monetary Incentive Delay task at baseline (ages 9–10) and year 2 follow-up (ages 11–12). Based on data from two sites (N = 491), we identified activation-based Regions of Interest (ROIs; e.g., striatum, prefrontal regions, etc.) that were sensitive to trial type (win $5, win$0.20, neutral, lose $0.20, lose$5) during anticipation and feedback phases. Then, in an independent subsample (N = 1470), we examined whether these ROIs were sensitive to valence and magnitude and whether that sensitivity changed over two years. Results: Our results show that most ROIs involved in reward processing (including the striatum, prefrontal cortex, and insula) are specialized, i.e., mainly sensitive to either incentive valence or magnitude, and this sensitivity was consistent over a 2-year period. The effect sizes of time and its interactions were significantly smaller (0.002≤η2≤0.02) than the effect size of trial type (0.06≤η2≤0.30). Interestingly, specialization was moderated by reward processing phase but was stable across development. Biological sex and pubertal status differences were few and inconsistent. Developmental changes were mostly evident during success feedback, where neural reactivity increased over time. Conclusions: Our results suggest sub-specialization to valence vs. magnitude within many ROIs of the reward circuitry. Additionally, in line with theoretical models of adolescent development, our results suggest that the ability to benefit from success increases from pre- to early adolescence. These findings can inform educators and clinicians and facilitate empirical research of typical and atypical motivational behaviors during a critical time of development

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