Towards an Integrated Cognitive Architecture for Modeling and Recognizing User Affect

Qiang Ji, Wayne D. Gray, Markus Guhe, and Michael J. Schoelles

We outline the cognitive model CASS (Cognitive CAffective State System). As the name suggests it is a cognitive model that also takes human affect into account. CASS combines Dynamic Bayesian Networks (DBNs) and an ACT-R model. The DBN model (R-BARS, the Rensselaer Bayesian Affect Recognition System) determines the user’s most likely affective states using both current and stored sensory data. The affective cognitive model integrates R-BARS with ACT-R to play two roles: (1) the use of model tracing to de-termine the impact of affective state on cognitive process-ing, and (2) linking changes in affective state to changes in the value of ACT-R’s parameters so as to directly generate (i.e., predict) the influence of affect on cognition. The cog-nitive implications of the user’s affective state are determined by analyzing the deviation of user behavior from the optimal path determined by the model.

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