Advances in Marital Quality, Intimate Relationships and Psychosocial Well-being: From Cognitive Mechanisms, Computational Modeling to Intervention Implications

Marital quality and intimate relationships are among the most powerful social contexts shaping psychosocial well-being across the lifespan. A growing body of research links relationship quality to subjective well-being and to mental and physical health outcomes, yet important questions remain about how these associations emerge and persist over time. Recent advances in social cognition, cognitive neuroscience, and affective computing provide new opportunities to examine the cognitive and neurocognitive processes involved in intimate relationships, such as intention understanding, attribution, emotion recognition, attention to threat/support cues, learning from interaction outcomes, and emotion regulation, using experimental paradigms and multimodal data. In parallel, computational modeling and machine-learning methods increasingly enable scalable, interpretable assessments based on questionnaires, behavioral markers, neurophysiological signals, and digital traces. However, evidence is still fragmented across subfields, and mechanistic accounts are not yet consistently connected to assessment and intervention implications.This Research Topic aims to integrate empirical, theoretical, and methodological advances on marital quality and intimate relationships by combining (i) psychosocial and health-oriented research questions with (ii) cognition-centered mechanisms and (iii) computational modeling approaches. While marital/relationship quality robustly predicts well-being, we still lack integrative, testable accounts of how cognitive and neurocognitive processes within dyads, such as mentalizing, attribution and expectation formation, social prediction, learning and belief updating after conflict, and cognitive control/emotion regulation, translate interaction patterns into downstream outcomes (e.g., depression, anxiety, stress-related health). We therefore encourage submissions that (a) refine mechanistic explanations using cognition-relevant constructs and measurements, (b) develop computationally explicit and interpretable models for assessment and prediction (including multimodal and explainable AI approaches), and © clarify intervention-relevant implications by identifying measurable cognitive targets and risk/protective profiles. Ultimately, the collection seeks to move from descriptive associations toward mechanism-based models that can inform prevention and psychosocial interventions.This Research Topic welcomes multidisciplinary work on marital quality and intimate relationships, retaining the following core themes, while explicitly encouraging cognition-relevant mechanisms, measures, and models:- Associations between marital quality/satisfaction and well-being, mental and physical health (e.g., depression, anxiety), including social-cognitive and emotion-regulation pathways- Computational models assessing marital quality using multimodal data (questionnaires, neurophysiological signals, behavioral data), including interpretable/“explainable” models- Marital quality and health in older adults using mediation, network analysis, and APIM, plus cognitive aging and neurocognitive markers where relevant- Psychosocial determinants of fertility intention using big-data analytics, including decision-making and cognitive evaluation of costs/benefits under uncertainty- Mechanisms linking upward social comparison, cyberbullying, and mental health in the digital age, including attention bias, social evaluation, and emotion regulation- Psychosocial and cognitive determinants of fertility intention, decision-making mechanisms, and big-data modelingManuscript types: Original Research, Brief Research Report, Methods, Review, Perspective, Hypothesis & Theory. Topic editors: Guangjie Yuan, Cassandra Chaney, Mathew Nicho Source: https://www.frontiersin.org/research-topics/83312/advances-in-marital-quality-intimate-relationships-and-psychosocial-well-being-from-cognitive-mechanisms-computational-modeling-to-intervention-implications