On Valence: A Self-Predictive Processing Model of Emotion Regulation

Emotion regulation is a fundamental process that shapescognitive, affective, and behavioral responses to emotionalstimuli. Traditional emotion regulation models conceptualizeregulation as a sequential modulation of emotional responses.However, they do not fully explain how emotions areconstructed in a way that allows such regulation to occur.Predictive processing (PP) provides a mechanistic frameworkfor understanding emotion generation by proposing that thebrain minimizes prediction errors (PEs) to optimize perceptionand behavior. Yet, standard PP accounts reduce valence to PEminimization, failing to explain how PEs can generate differentsubjective feelings. To address these limitations, we propose avalence-focused model of emotion regulation that integratespredictive processing with self-referential cognition. Weincorporate emotional valence as interpretative processes ofthe self-model, which assigns emotional significance based ongoals, values, and autobiographical context. This modelbridges the gap between emotion generation and regulation,highlighting the dynamic interplay between prediction errors,subjective valuation, and self-referential processes. Thisapproach not only advances theoretical understanding but alsoopens new avenues for computational modeling and empiricalresearch into the adaptive and maladaptive aspects ofemotional experience.