In terms of this later distinction, it is clear that many of the tools animators, actors, and believable agent practitioner use are outside the realm of naturalistic human behavior. Manipulations that impact the concept “believability” include notions such as “hyper reality”, the effects of lighting and music. These are clearly relevant topics in cognitive and social psychology, and their use may be an important channel into understanding aspects of human cognitive and social behavior, but they should not be equated with human cognitive and social behavior (although this tension is rarely explicitly acknowledge in agent work, and often conceptually muddled).

In terms of the former distinction, we should also be skeptical of simply mapping cognitive models to physical behavior as current AI models are poor proxies for human cognition. If the goal is to build (generative) models of human behavior, then simply binding models to physical behavior will certainly fail to produce realistic behavior, but these failures can be interesting and inform the construction of better models.

My sense is most venues about “computational emotion” have emphasized expression believability, and so this is my attempt to emphasize the cognitive role of emotion and build connections to cognitive emotion psychology. Thus, the particular focus of the ACE workshop is on functionalism and realism, in particular with respect to cognition, and not believability. Expression of emotion is quite naturally buried in the call, and in this setting, the concern is to use computational methods to induce emotion in human subjects as a methodological tool towards furthering this goal.

Finally, turning to the topic of emotion modeling, I agree that most models emphasize a vector approach, which seems a superficial and sterile approach for any application that emphasizes cognitive realism in an interactive setting. Rather, what is interesting in emotion research is the machinery that underlies emotional reactions and the way these mechanisms change in response to processing that observers refer to as emotional. Discrete emotion labels such as “Joy” and “Fear” are likely linguistic terms that poorly relate to underlying architectural mechanisms and overemphasizing them can lead to overly simplistic architectural commitments. As Michael mentions, Sloman and this group have emphasized this view for years. A few more recent approaches have this flavor, including the work of Stacy Marsella and myself. I would argue that such “deep” models will also be essential for richly interactive believable agents as well, but that is still an empirical question.

I suppose that if we do not consider natural, but clear ways of explaining emotional states and behaviours, it hardly matters how complex or simplistic an emotional model is. Our model will always be an abstraction, and for it to be useful, we must be able to clearly explain how our somewhat arbitrary model works, hopefully in elegant ways, which are remotely believable (even if they’re total characatures of emotion).

I know I’m stating the obvious here, but I guess I think that there might be a third track to our studies of emotional models: not just Cog Sci and Expressive AI, but the desires which Ludic systems (sorry, GAMES) have of Expressive AI, too… unless that’s already contained under the banner of Expressive AI?