A cognitive architecture for modeling emotion dynamics: Intensity estimation from physiological signals

Current approaches to emotion recognition do not address the fact that emotions are dynamic processes. This work concerns itself with the development of a cognitive architecture for modeling the dynamics of emotions with specific focus on a gray-box model for dynamic emotion intensity estimation that can incorporate findings from appraisal models, specifically Scherer's Component Process Model. It is based on Dynamic Field Theory which allows the combination of theoretical knowledge with data-driven experimental approaches. A user study is conducted applying the proposed model to estimate intensity of negative emotions from physiological signals. Results show significant improvements of the proposed model to common methodology and baselines. The flexible cognitive architecture opens a wide field of experiments and directions to deepen the understanding of emotion processes as a whole.