The past 15 years have witnessed a rapid growth in computational models of emotion and affective architectures. Researchers in cognitive science, AI, HCI, robotics, and gaming are developing'models of emotion' for theoretical research regarding the nature of emotion, as well as a range of applied purposes: to create more believable and effective synthetic characters and robots, and to enhance human-computer interaction. Yet in spite of the many stand-alone emotion models, and the numerous affective agent and robot architectures developed to date, there is a lack of consistency, and lack of clarity, regarding what exactly it means to'model emotions'. 'Emotion modeling' can mean the dynamic generation of emotion via black-box models that map specific stimuli onto associated emotions. It can mean generating facial expressions, gestures, or movements depicting specific emotions in synthetic agents or robots.
Appraisal theory, a functional approach to understanding emotion elicitation is described. Three distinct classes of appraisal models are reviewed: structural - which describe the cognitive contents of appraisal and how those contents map onto the elicitation of various distinct emotions; procedural - which describe the cognitive processes underlying appraisal; and relational - which describe how both person and situation information is combined in producing specific appraisal outcomes. A theoretical example of each class of model is described, and the state of the empirical literature addressing such models is reviewed. The relevance of the general theoretical approach, and of the three types of appraisal models, to developing architectures for modeling emotion are discussed.
In this paper we present a novel approach to a grounded synthesis of emotional appraisal, based on a multicausal model of the appraisal process. We investigate the functional nature of emotion by implementing a robotic model in a predator/prey scenario which is able to discriminate and anticipate outcomes through emotional appraisal. The robots evolve to react in apparently emotional ways, showing how the functionality of emotion can emerge naturally. We demonstrate through this implementation the value of emotion appraisal as a form of anticipation. This supports the view that emotional behavior can often be seen as an effective alternative to rational cognition. Our effort here is to build a model that can be simultaneously seen as belonging to both NCS and more classical theorizing based on cognitions and representations, understandable both mechanically and subjectively from a human standpoint.