DHB Collaborative Research: Developing Non-Stationary and Network-based Methods for Modeling the Perception and Physiology of Emotion

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DHB Collaborative Research: Developing Non-Stationary and Network-based Methods for Modeling the Perception and Physiology of EmotionPI: Sy-Miin Chow, University of North Carolina (lead)Collaborating institution: UC DavisRecent developments in dynamic systems modeling have led to new conceptualizations of emotions as dynamic processes. This new paradigm has created exciting research venues to extend our understanding of the perception and physiology of emotions. It is clear that, given the complexity of both the experiential and measurement aspects of emotions, such venues can only be pursued through interdisciplinary collaborations. This project brings together researchers in the fields of psychometrics, emotion/psychophysiology, statistics, bioinformatics/biostatistics and financial econometrics to develop techniques for studying the dynamics of emotions and affective processes. Multiple measures of emotions will be collected over different time scales, with the aims of using these data to (1) develop methods for estimating and diagnosing differential equation models with time-varying parameters and random effects, (2) develop methods for analyzing the dynamics of emotions as indicated by facial electromyography (EMG) data, (3) use network-based methods to represent within-individual transitions among discrete affective states and (4) develop and organize tools for studying complex, non-stationary processes and disseminate these tools to audiences across a variety of disciplines. In addition to introducing novel methodologies for testing existing theories of emotions, this project also provides new opportunities for methodologists to refine and develop new techniques for studying dynamic systems. Beyond emotions, the tools developed in this project can be used to examine other dynamic processes such as lifespan development, disease propagation, and the emergence and disaggregation of social networks.