Dissecting appraisal and multicomponential features of emotion: Evidence from multilevel annotation during naturalistic stimulation.

This study described the relationship between discrete emotions and their underlying components from a detailed data set of continuous annotations of more than 50 emotion variables during short films. Theoretical accounts such as appraisal models predict that emotions arise through a combination of distinctive components engaged by the evaluation of different situational dimensions. Here we build on the component process model that highlights a prime role of appraisals which determine motivation, expression, physiology, and feeling features associated with emotion experience. We obtained continuous annotations from all these domains during movie watching and observed a hierarchical organization of discrete emotions by appraisal of valence and self-relevance. Furthermore, we applied predictive models to understand the contribution of different emotion components to discrete emotion categories. We found that all 13 discrete emotions in our data set were reliably predicted as a function of particular emotion components. Our study contributed key insights using rich descriptors and machine learning to dissect the nature of emotion and supports the notion that appraisal processes are a key component in the differentiation of emotion experience. These findings also have implications on the complexity and function of emotion as an adaptive process. (PsycInfo Database Record (c) 2026 APA, all rights reserved).