Exploring Multivariate Dynamics of Emotions Through Time-Varying Self-Assessed Arousal and Valence Ratings

Emotions arise from a complex interplay of various factors, including conscious experience, physiological processes, and contextual elements. Although emotions are inherently dynamic processes, this aspect is oftentimes neglected in experimental protocols. In this study, we employed dynamical systems theory to investigate the time-varying self-assessed emotion ratings. We used the continuous ratings of the publicly available CASE dataset, in which thirty individuals rated their level of arousal and valence while watching videos designed to evoke four different emotions. First, we analyzed the univariate dynamics by reconstructing the phase space from the arousal and valence series separately, and quantified their regularity and spatial complexity by using three metrics: Fuzzy, Sample, and Distribution Entropy. Then, we combined the arousal and valence series and proposed a novel index, the Multichannel Distribution Entropy (MDistEn), to estimate the complexity of the bivariate phase space. By coupling the two dimensions, we found that MDistEn resulted as an effective marker of fear, showing patterns statistically different from all of the other stimuli (p-value $\leq$ 0.001). These findings support the investigation of the time-varying dynamics of annotated emotion ratings as a promising pathway to discriminate the onset of fear-related pathological states.