Depth of Feelings: Alternatives for Modeling Affect in User Models

Abstract

Neuroscience and psychology research has demonstrated a close connection between cognition and affect, and a number of emotion-induced effects on perception, cognition, and behavior. The integration of emotions within user models would therefore enhance their realism and fidelity. Emotions can also provide disambiguating information for speech recognition and natural language understanding, and enhance the effectiveness of dialogue systems. This paper discusses the motivation and alternatives for incorporating emotions within user models. The paper first identifies key model characteristics that define an analytical framework. The framework provides a basis for identifying the functional and architectural requirements on one hand, and alternative modeling approaches on the other, thereby laying the groundwork for a set of model development guidelines. The paper then describes examples of existing models for two core affective processes, cognitive appraisal and emotion-induced effects on cognition, within the context of the analytical framework.