Variability in anger intensity profiles: Structure and predictive basis

Abstract

The aim of this study is to describe variability in the shape and amplitude of intensity profiles of anger episodes and how it relates to duration, and to investigate whether this variability can be predicted on the basis of appraisals and emotion regulation strategies used. Participants were asked to report on a wide range of recollected anger episodes. By means of K-spectral centroid clustering, two prototypical shapes of anger intensity profiles were identified: early- and late-blooming episodes. Early-blooming episodes are relatively short and reach their peak immediately. These profiles are associated with low-importance events and adaptive regulation. Late-blooming episodes last longer and reach their peak (relatively) late in the episode. These profiles are related to high-importance events and maladaptive regulation. For both early- and late-blooming profiles, overall amplitude is positively associated with event importance and the use of maladaptive regulation strategies and negatively with the use of adaptive ones.

Looking at Emotion Regulation Through the Window of Emotion Dynamics

“…Heylen and colleagues (Heylen et al, 2014) found that reappraisal typically occurs during early-blooming anger profiles, which is consistent with the notion that reappraisal softens the discrepancy between the desired and actual state of the world by weakening the mismatch at the first-level valuation stage. As such, strategies that down-regulate (up-regulate) the emotional response can be expected to occur relatively often during early-blooming (late-blooming) profiles.…”

“…Verduyn and colleagues Verduyn et al, 2012) inferred that these profiles mainly differ in degree of explosiveness, skewness, and number of peaks. Focusing on the emotion of anger, Heylen and colleagues further identified two frequently occurring intensity profiles: early-and late-blooming profiles (Heylen, Verduyn, Van Mechelen, & Ceulemans, 2014). Early-blooming profiles reflect anger episodes that peak at the start as a result of which recovery sets in rather fast.…”

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

“…In contrast, in a clustering approach, for every individual or dyad a separate model is estimated, making this approach very flexible. Clustering could then be conducted on the shape of the autoregressive and cross-lagged effects within the dyads (for a similar approach, see Heylen, Verduyn, Van Mechelen, & Ceulemans, 2015). Such clusters could be then meaningfully related to other dyad characteristics, such as relationship satisfaction, relationship duration, or personality traits of the partners.…”

The Impact of Non-normative Displays of Emotion in the Workplace: How Inappropriateness Shapes the Interpersonal Outcomes of Emotional Displays

“…Emotions are experienced and expressed at various strengths and magnitudes (Frijda et al, 1992;Sonnemans and Frijda, 1994). Differences in the intensity of felt emotions reflect the importance of the trigger (i.e., the event giving rise to the emotion) for the individual (Ortony et al, 1988;Clore, 1994;Heylen et al, 2015). For instance, people become angrier when an important goal is frustrated compared to a less essential goal.…”