Estimation of state space models for affective dynamics using Markov chain Monte Carlo methods

Lately, emotion research has been focusing on the conceptualization of emotions as multicomponential, dynamical systems. This evolution led to new challenging research questions, concerning for instance autoregressive dependencies (related to concepts of emotional homeostasis) or cross-lagged relations (related to the mutual influence of emotion components). We want to discuss a basic linear Gaussian state-space approach for the dynamical modeling of emotion components. It will be shown how Markov chain Monte Carlo methods are used to estimate the model parameters. Various model extensions are discussed, such as estimating the influence of external covariates, regime-switching of parameters, etc. In a second part, we apply this framework to high resolution psychophysiological and behavioral data obtained during emotionally evocative adolescent-parent interactions and illustrate how it can be used to obtain new insights in the dynamical nature of emotions.status: publishe