We discuss three varieties of affective dynamics. In each case, we suggest how these affective dynamics should be operationalized and measured in daily life using time-intensive methods, like ecological momentary assessment or ambulatory assessment, and recommend time-sensitive analyses that take into account not only the variability but also the temporal dependency of reports. Studies that explore how these affective dynamics are associated with psychological disorders and symptoms are reviewed, and we emphasize that these affective processes are within a nexus of (...) other components of emotion regulation. (shrink)

Reflecting on stressors from a detached perspective – a strategy known as distancing – can facilitate emotional recovery. Researchers have theorised that distancing works by enabling reappraisals of negative events, yet few studies have investigated specifically how distancing impacts stressor appraisals. In this experiment, we investigated how participants’ (N = 355) emotional experience and appraisals of an interpersonal conflict differed depending on whether they wrote event-reflections from a linguistically immersed (first-person) or distanced (second/third-person) perspective. Partly replicating previous findings, distanced reflection (...) predicted increases in positive affect, but not reductions in negative affect, relative to immersed reflection. Linguistic distancing also predicted increases in motivational congruence appraisals (i.e. perceived advantageousness of the event), but did not influence other appraisal dimensions. We discuss how linguistic distancing may facilitate emotional recovery by illuminating the benefits of stressful experiences, enabling people to “see the good in the bad”. (shrink)

ABSTRACTNaturalistic studies of emotional reactivity in depression have repeatedly found larger decreases in negative affect among depressed individuals in response to daily positive events. T...

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative “shrinkage” or “small variance” priors while extending prior work on the general cross-lagged panel model. Using a panel dataset of national income and subjective well-being we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each (...) other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood. This is useful because, second, these priors increase model parsimony and the stability of estimates and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern. (shrink)