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Project

Predicting psychological well-being with affective patterns

Dysregulation of affect is the core feature of mood disorders and, more generally, is associated with many mental disorders. That is, affective patterns, which describe the waxing and waning of our affect over time, are undeniably related to concurrent psychological well-being. Recently, researchers have theorized that affective patterns do also have a predictive value. Moreover, preliminary evidence shows that affective patterns may be able to predict not only the deterioration of psychological well-being but also the recovery from mood disorders. In this project we will first conduct a meta-analysis to replicate, differentiate and fine-tune the current perspectives on how affective patterns may predict future psychological well-being. Second, we will develop new methods to capture affective patterns with state-of-the art time-series models and subsequently link these models to future psychological well-being with interpretable machine learning techniques. Finally, we will develop a user-friendly online tool to make these predictions easily available for researchers as well as practitioners. 

Date:1 Oct 2020 →  Today
Keywords:Prediction, Affect and emotions, Psychological well-being
Disciplines:Statistics and data analysis, Psychometrics, Psychopathology, Machine learning and decision making