Machine Learning Models Predicting Daily Affective Dynamics Via Personality and Psychopathology Traits

To date, numerous studies have examined personality and psychopathology indexes as predictors of affective dynamics, i.e. measures of how emotions change across time. Yet, little research has examined individual differences in personality, pathology, and affective dynamics constructs comprehensively, accounted for non-linear relationships, or examined the out-of-sample generalizations of the predictions. To address these gaps, the current research utilized machine learning models to predict affective dynamics. A large variety of baseline personality and psychopathology traits (pathological personality measures, clinical anxiety, depression, anger, sleep, affective instability scales, the big five personality traits, interpersonal circumplex measures, and control beliefs) were used to predict the affective dynamics derived from person-specific modeling of affect across a 50-day daily diary study. The results showed that baseline personality traits significantly predicted the strength of day-to-day affective dynamics for emotional variability, relative emotional variability, emotional instability, emotional inertia, and emotional cyclicality for both positive and negative affect (rs 0.152-0.444). Although broadly neglected in prior research, the results suggested that interpersonal circumplex measures most strongly predicted a number of affective dynamics