Switching component models for capturing emotional response patterning and synchronization processes.

Functionalist definitions of emotions state that they consist of synchronized or patterned changes in multiple experiential, physiological, and behavioral response channels which enable the organism to quickly and efficiently cope with environmental threats or opportunities. Yet, detecting response patterning and synchronicity in empirical data represents a formidable challenge (Gross, 2010). Indeed, while technological advances have enabled the collection of intensive time-series data on multiple response channels, the development of suited statistical tools is lagging behind, as some key challenges that come with the complex nature of the research questions and associated data, are not easy to address. Specifically, methods are needed that can determine (1) when and what exactly changes (e.g., mean level, covariation), (2) which subsystems (e.g., experiential, behavioral, physiological, neurological) are involved and in what way, and (3) how this differs across individuals. The aim of this project is to tackle these challenges by developing a new modeling framework for capturing time-varying response patterning and synchronicity that combines the key principles of regime-switching models and principal component analysis. We will apply the framework to empirical data, and disseminate it to substantive researchers by, amongst others, building easy-to-use software.