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Bayesian state space models for affective dynamics

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  • Title:
    Bayesian state space models for affective dynamics
  • Author: Lodewyckx, Tom ; Tuerlinckx, Francis ; Kuppens, Peter ; Allen, Nicholas B ; Sheeber, Lisa
  • Subjects: Affect ; hierarchical modeling ; State space modeling
  • Description: In the last years, emotion research has been focusing on the conceptualization of emotions as multicomponential, dynamical systems. This development created a new set of 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). In a first part, we want to introduce a 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 (e.g. external covariates, regime-switching). 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: published
  • Creation Date: 2009
  • Language: English
  • Source: Lirias (KU Leuven Association)
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