The core focus of my research is to help elucidate the mechanisms of emotion-cognition interactions and emotion generation through the development of computational models. This approach involves the development of computational models of emotions within the broader context of cognitive-affective architectures. The broader aim is an improved understanding of the role of affective factors in decision-making and performance, and the application of these findings to more effective human-machine system design, and to the development of principled design guidelines for cognitive-affective engineering.  

I have explored these issues within the context of a specific computational cognitive-affective architecture: the MAMID architecture (Hudlicka, 2002; 2003), which uses a generic methodology for modeling the effects of multiple, interacting emotions and personality traits on the cognitive processes mediating decision-making (Hudlicka, 1997; 1998; 2002).

MAMID Cognitive-Affective Architecture: Modules & Mental Constructs

Generic Methodology for Modeling Effects of Emotions and Traits on Cognition

Example: Modeling Effects of Anxiety-Linked Threat Bias on Cognitive Processes

I am interested in both the theoretical and the practical implications of computational emotion research. From a theoretical perspective, I am interested in identifying the processes and structures mediating the effects of emotions on decision-making and performance, and emotion generation. I am also interested in contributing to the development of more precise, architecture-based definitions of emotion, and the specification of architectural requirements necessary to model emotions.

From a practical perspective, I am interested in applying these research findings, as well as the actual cognitive-affective architectures, within the broader context of human-computer interaction. Specifically:

•  Developing affect-adaptive user interfaces and decision-aiding systems (e.g., Hudlicka, 1999; Hudlicka & McNeese, 2002).

•  Developing more realistic virtual agents for a variety of purposes, including education and training, decision-aiding, and entertainment (e.g., interactive gaming).

•  Translating the theoretical findings and practical experience into guidelines for developing affective user models, within the broader context of human-machine systems.