Emotion theorists have long held that a fundamental characteristic of an emotion is how its constituent processes change and interact over time. Assessing these temporal dynamics of emotion in the brain is critical for understanding the neural representation of emotions as well as advancing theories of emotional processing. We review the neuroimaging research on three temporal dynamic features of emotion: time of onset, duration, and resurgence and show how assessing these temporal dynamics in the brain have led to improved understanding (...) of the structure and function of emotional processes such as revealing which appraisals come first, how emotional processing endures both explicitly and implicitly, and that the resurgence of emotional processing may consist of either single or multiple processes. (shrink)

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 (a) emotion-eliciting event (event duration and event (...) appraisal), (b) emotion itself (nature of the emotion component, nature of the emotion, and emotion intensity), and (c) emotion-experiencing person (dispositions and emotion regulatory actions). Initial evidence on the psychological and neural mechanisms that underlie their effects is discussed. (shrink)

The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data (ILD). 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: (1) single- versus multiple-person data; (2) univariate versus multivariate models; (3) stationary versus nonstationary models; (4) linear versus nonlinear models; (5) discrete time versus continuous time models; (6) discrete (...) versus continuous variables; (7) time versus frequency domain; and (8) 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)