Depth of Feelings: Alternatives for Modeling Affect in User Models and Cognitive Architectures – Eva Hudlicka (Psychometrix Associates)
Neuroscience and psychology research over the past two decades has demonstrated a close connection between cognition and affect. Affective factors emotions and personality traits can profoundly influence perception, decision-making and behavior, contributing to a variety of biases and heuristics. These effects may enhance or degrade cognitive processes and performance, depending on the context. The ability to explicitly represent affective factors in user models and cognitive architectures has a number of benefits, including more accurate user models, increased realism and believability of synthetic agents, and improved effectiveness of decision-aiding systems. Consideration of affective factors can also provide disambiguating information for speech recognition and natural language understanding. This talk will first provide an overview of emotion research in psychology relevant for the construction of computational models of emotion. The talk will then discuss the motivation and alternatives for incorporating emotions and personality traits within user models and cognitive architectures. The representational and reasoning requirements for several alternative modeling approaches will be discussed, along with examples from existing cognitive-affective architectures. These include shallow and deep models of emotions, and a generic methodology for modeling multiple, interacting affective factors. The talk will conclude with a discussion of the challenges involved in building and validating computational emotion models.
Eva Hudlicka is a Principal Scientist and President of Psychometrix Associates, in Blacksburg, VA. Her primary research focus is the development of computational models of emotion; both the cognitive processes involved in appraisal, and the effects of emotions on cognition. This research is conducted within the context of a computational cognitive-affective architecture, the MAMID architecture, which implements a generic methodology for modeling the interacting effects of multiple affective factors on decision-making. Her prior research includes affect-adaptive user interfaces, visualization and UI design, decision-support system design, and knowledge elicitation. Dr. Hudlicka received her BS in Biochemistry from Virginia Tech, her MS from The Ohio State University in Computer Science, and her PhD in Computer Science from the University of Massachusetts-Amherst. Prior to founding Psychometrix Associates in 1995, she was a Senior Scientist at Bolt Beranek & Newman in Cambridge, MA.