Assessing Temporal Emotion Dynamics Using Networks

Abstract

Multivariate psychological processes have recently been studied, visualized, and analyzed as networks. In this network approach, psychological constructs are represented as complex systems of interacting components. In addition to insightful visualization of dynamics, a network perspective leads to a new way of thinking about the nature of psychological phenomena by offering new tools for studying dynamical processes in psychology. In this article, we explain the rationale of the network approach, the associated methods and visualization, and illustrate it using an empirical example focusing on the relation between the daily fluctuations of emotions and neuroticism. The results suggest that individuals with high levels of neuroticism had a denser emotion network compared with their less neurotic peers. This effect is especially pronounced for the negative emotion network, which is in line with previous studies that found a denser network in depressed subjects than in healthy subjects. In sum, we show how the network approach may offer new tools for studying dynamical processes in psychology.

Mixed Emotions: Network Analyses of Intra-Individual Co-occurrences Within and Across Situations

“…This is important, given that correlations are sometimes misleadingly labelled "cooccurrences" in the emotion literature (e.g., Vansteenlandt et al, 2005). Revealing the overlooked co-occurrences is one of the advantages of the here employed network analysis over previous network studies which have examined (partial) correlations among situational emotion measures (e.g., Bringmann et al, 2016;Lee Pe et al, 2015).…”

“…Some studies have used network analysis to visualize the strength of correlations between a set of variables, using networks where lines between two variables are proportional to the size of their correlations or regression weights. These approaches are receiving increasing attention and have been applied to the study of emotions (Bringmann et al, 2016;Constantini et al, 2015), clinical symptoms (McNally et al, 2014); and brain activity networks (Supekar, Menon, Rubin, Musen, & Greicius 2008). These networks examine (partial) correlations among variables and therefore give interesting overviews of the co-variance structure among emotions.…”

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

“…There was initially no effect of the female's PA on the male's PA, but this cross-lagged effect appeared halfway through the study (see the thin red arrow) and got stronger over time (see the thick red arrow). (Borsboom & Cramer, 2013;Bringmann et al, 2016;Fried et al, 2017;McNally, 2016;Pe et al, 2015;Wigman et al, 2015). However, many hypotheses underlying the network perspective involve change in the temporal dynamics.…”

PTSD symptomics: network analyses in the field of psychotraumatology

“…Fried, Eidhof et al, 2017). Second, it is crucial that researchers start focusing on the assessment and analysis of dynamic, temporal data (Bringmann et al, 2016 ; Epskamp et al, 2017; Hamaker & Wichers, 2017). This allows the field to move from modelling cross-sectional group-level data to modelling the temporal dynamics of causal systems across time, and might bring us closer to developing novel recommendations for intervention or prevention strategies (Bos et al, 2017).…”