Keywords: Bayesian hierarchical modeling Cardiovascular processes Emotions

Linear dynamical system State space modeling are especially of interest in the study of emotion dynamics, with the system representing the evolving emotion components of an individual. However, for simultaneous modeling of individual and population differences, a hierarchical extension of the basic state space model is necessary. Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters. Further, we apply our model to data that were collected using the Oregon adolescent interaction task: 66 normal and 67 depressed adolescents engaged in a conflict-oriented interactionwith their parents and second-to-second physiological and behavioral measures were obtained. System parameters in normal and depressed adolescentswere compared,which led to interesting discussions in the light of findings in recent literature on the links between cardiovascular processes, emotion dynamics and depression. We illustrate that our approach is flexible and general: Themodel canbe applied to any time series formultiple systems (where a systemcan represent any entity) andmoreover, one is free to focus on various components of this versatile model. © 2010 Elsevier Inc. All rights reserved. 1