Emotions are traditionally considered to be brief states that last for seconds or a few minutes at most. However, due to pioneering theoretical work of Frijda and recent empirical studies, it has become clear that the duration of emotions is actually highly variable with durations ranging from a few seconds to several hours, or even longer. We review research on determinants of emotion duration. Three classes of determinants are identified: features related to the emotion-eliciting event, emotion itself, and emotion-experiencing person. (...) Initial evidence on the psychological and neural mechanisms that underlie their effects is discussed. (shrink)

Intensity profiles of emotional experience over time have been found to differ primarily in explosiveness and accumulation. However, the determinants of these temporal features remain poorly understood. In two studies, we examined whether emotion regulation strategies are predictive of the degree of explosiveness and accumulation of negative emotional episodes. Participants were asked to draw profiles reflecting changes in the intensity of emotions elicited either by negative social feedback in the lab or by negative events in daily life. In addition, trait, (...) and state usage of a set of emotion regulation strategies was assessed. Multilevel analyses revealed that trait rumination was positively associated with emotion accumulation. State rumination was also positively associated with emotion accumulation and, to a lesser extent, with emotion explosiveness. These results provide support for emotion regulation theories, which hypothesise that rumination is a central mechanism underlying the maintenance of negative emotions. (shrink)

The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data. We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: single- versus multiple-person data; univariate versus multivariate models; stationary versus nonstationary models; linear versus nonlinear models; discrete time versus continuous time models; discrete versus continuous variables; time versus frequency domain; (...) and modeling the process versus computing descriptives. In addition, we discuss what we believe to be the most urging future challenges regarding the modeling of affect dynamics. (shrink)