Modeling interactions between metacognition and emotion in a cognitive architecture

While research in metacognition has grown significantly in the past 10 years, there has been a relative lack of research devoted to the focused study of the interactions between metacognition and affective processes. Computational models represent a useful tool which can help remedy this situation by constructing causal models of demonstrated correlational relationships, and by generating empirical hypotheses which can be verified experimentally. In this paper we describe enhancements to an existing cognitive–affective architecture that will enable it to perform a subset of metacognitive functions. We focus on modeling the role of a specific metacognitive factor, the feeling of confidence, and the anxiety-linked metacognitive strategy of emotion-focused coping. Introduction and Objectives Metacognition, defined variously as ‘thoughts about thoughts’ or ‘awareness and control’ of one’s thoughts, is considered by many to be an essential component of skilled performance, influencing memory functions, learning and skill acquisition, and problem-solving. While research in metacognition has grown significantly in the past 10 years, there has been a relative paucity of research devoted to the focused study of the interactions between metacognition and affective processes. A notable exception is the work of Wells (2000), and Matthews and Wells (2004). However, this work focuses on metacognition and emotion in the context of emotional disorders (e.g., depression, generalized anxiety, obsessive compulsive disorders), rather than on the role of emotion in normal subjects, or the interaction between emotion and metacognition in transient affective states (e.g., high stress or frustration). Computational models represent a useful tool for elucidating the mechanisms of cognitive processing. Ideally, these models are based on existing empirical data and used to confirm hypothesized mechanisms. However, in situations where such data are lacking, these models can be helpful in generating empirical hypotheses for further experimental testing and data gathering. In this paper we describe enhancements to an existing cognitive–affective architecture (MAMID) (Hudlicka 2002; 2003), which support the exploration of affectivemetacognitive interactions. The enhancements will enable MAMID to perform a subset of the metacognitive functions involved in monitoring and control of cognition, and the associated metacognitive knowledge and beliefs. The initial focus will be on modeling the role of a specific metacognitive factor, the feeling of confidence (FOC), and the anxiety-linked metacognitive strategy of emotionfocused coping. The intended benefits of the model are in both the theoretical and the applied realms. In the theoretical realm, the exercise of building a model requires an operationalization of concepts and relationships which help refine existing psychological theories, and generate empirical hypotheses for further testing. In the applied realm, the explicit model of metacognition, and its interactions with affective factors, promises to provide a more realistic model of human behavior, both adaptive and maladaptive (e.g., models where metacognition diminishes performance (e.g., Wilson and Schooler 1991)), and generate more effective agent behavior (e.g., improved performance under stress). This paper is organized as follows. First, we briefly summarize key findings about metacognition and about known interactions between metacognition and emotion. Next, we describe the existing MAMID cognitiveaffective architecture, within which the proposed metacognitive component will be implemented. We then describe the proposed design for modeling the role of FOC in metacognitive monitoring and control, and the metacognitive knowledge required. We then outline two models where emotion influences metacognitive processing. We conclude with a brief discussion of related work, a summary and future work. Metacognition and Its Interaction with