A biologically-inspired affective model based on cognitive situational appraisal

• Main Authors: Shu, Feng, Tan, Ah-Hwee
• Other Authors: School of Computer Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/98288; http://hdl.handle.net/10220/12423
• Institution: Nanyang Technological University

Although various emotion models have been proposed based on appraisal theories, most of them focus on designing specific appraisal rules and there is no unified framework for emotional appraisal. Moreover, few existing emotion models are biologically-inspired and are inadequate in imitating emotion process of human brain. This paper proposes a bio-inspired computational model called Cognitive Regulated Affective Architecture (CRAA), inspired by the cognitive regulated emotion theory and the network theory of emotion. This architecture is proposed by taking the following positions: (1) Cognition and emotion are not separated but interacted systems; (2) The appraisal of emotion depends on and should be regulated through cognitive system; and (3) Emotion is generated though numerous neural computations and networks of brain regions. This model contributes to an integrated system which combines emotional appraisal with the cognitive decision making in a multi-layered structure. Specifically, a self-organizing neural model called Emotional Appraisal Network (EAN) is proposed based on the Adaptive Resonance Theory (ART), to learn the associations from appraisal components involving expectation, reward, power, and match to emotion. An appraisal module is positioned within EAN contributing to translate cognitive information to emotion appraisal. The above model has been evaluated in a first person shooting game known as Unreal Tournament. Comparing with non-emotional NPC, emotional NPC obtains a higher evaluation in improving game playability and interest. Moreover, comparing with existing emotion models, our CRAA model obtains a higher accuracy in determining emotion expressions.