Forming intimate relationships is a fundamental human motive. Emotions play a critical role in intimate relationships—they are central to the development and maintenance of these bonds, and these very bonds can influence both individual and interpersonal emotional dynamics across time. Investigating emotional dynamics in an interpersonal context provides unique insight into the functioning of intimate relationships and, at the same time, provides a window into the interdependence of partners’ daily experiences. Reviewing a selection of the literature involving emotional dynamics in (...) intimate relationships, we explore how intimate relationships shape partner’s emotional experiences and the implications this may have for their relationship across time. (shrink)

The study of affect dynamics aims to discover the patterns and regularities with which emotions and affective experiences and components change across time, the underlying mechanisms involved, and their potential relevance for healthy psychological functioning. The intention of this special section is to serve as a mini handbook covering the contemporary state of research into affect dynamics. Contributions address theoretical viewpoints on the origins and functions of emotional change, methodological and modeling approaches, biological and social perspectives on affect dynamics, and (...) the downstream consequences for well-being and psychopathology. (shrink)

More instability (MSSD) and variability (SD) of negative affect (NA) have been related to current and future depressive symptoms. We investigated whether NA instability and variability were predictive of the rate of symptom improvement during treatment and of reaching remission status. Forty-six individuals with major depressive disorder completed six days of ecological momentary assessments (10 beeps/day) before starting a combination of pharmacotherapy and supportive therapy. During and after treatment, the Hamilton Depression Rating Scale (HDRS) diagnostic interview was performed monthly for (...) 18 months. The rate of change in HDRS scores over five months (during treatment) and remission status (HDRS ≤ 7) over eighteen months were predicted by the SD, MSSD and Mean of NA, and relevant baseline predictors. Higher Mean NA and baseline symptom severity, but not NA variability or instability, predicted stronger depressive symptom reduction during treatment. Higher odds of remitting were only associated with lower Mean NA. Higher mean NA may indicate more room for improvement and thus for a steeper rate of symptom change, while lower mean NA levels may imply that remission status is more readily reached. To resolve the inconclusive findings for instability and variability as predictors of symptom improvement replication in larger samples is required. (shrink)

Affective dynamics are discussed from the perspective of a view of origin and functions of affective valence based in control processes. This view posits that affect reflects the error signal of a feedback loop managing rate of progress at goal attainment or threat avoidance. Negative feelings signal doing poorly, demanding more effort. Positive feelings signal doing better than necessary, allowing coasting, which yields goal attainment without unnecessary resource expenditure. Given multiple simultaneous goals, these functions assist in moment-to-moment priority management, facilitating (...) attainment of all. (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)