A Computational Unification of Cognitive Control, Emotion, and Learning

Existing models that integrate emotion and cognition generally do not fully specify why cognition needs emotion and conversely why emotion needs cognition. In this thesis, we present a unified computational model that combines an abstract cognitive theory of behavior control (PEACTIDM) and a detailed theory of emotion (based on an appraisal theory), integrated in a theory of cognitive architecture (Soar). The theory of cognitive control specifies a set of required computational functions and their abstract inputs and outputs, while the appraisal theory specifies in more detail the nature of these inputs and outputs and an ontology for their representation. We argue that there is a surprising functional symbiosis between these two independently motivated theories that leads to a deeper theoretical integration than has been previously obtained in other computational treatments of cognition and emotion. We use an implemented model in Soar to test the feasibility of the resulting integrated theory, and explore its implications and predictive power in several task domains. With this integration, we then explore a possible functional benefit of emotion; namely, as an intrinsic motivator of reinforcement learning. This integration leads to other reinforcement learning extensions, such as automatic setting of the learning and exploration rate parameters.