Behavioral and computational perspectives on emotion regulation and subjective wellbeing: an integrative review with a focus on adolescents.
Understanding how emotional regulation (ER) mediates the relationship between self-esteem and subjective wellbeing (SWB) is central to promoting mental health among high school students. This paper provides a comprehensive integrative review of theoretical and empirical research exploring this interplay, while integrating perspectives from behavioral sciences and computational psychology. Traditional research primarily relies on self-report measures and cross-sectional designs, which limit the detection of dynamic emotional processes. On the other hand, emerging computational methods, such as machine learning-based mediation modeling and linguistic sentiment analysis enable new approaches to quantify and predict ER and wellbeing. This review develops an integrative framework that connects behavioral constructs and empirical evidence on adolescent ER and SWB with computational approaches for modeling these relationships. Rather than treating behavioral and computational methods as separate approaches, the framework considers how psychological measures, contextual and digital data, computational modeling, and adaptive feedback can be combined to support a more comprehensive understanding of adolescent wellbeing.