We propose a causal Bayesian model of false belief reasoning in children. This model realizes theory of mind as the rational use of intuitive theories and supports causal prediction, explanation, and theory revision. The model undergoes an experience-driven false belief transition. We investigate the relationship between prediction, explanation, and surprise; this is used to interpret an empirical study of children’s explanations in an extension of the false belief task. Our study includes the standard outcome, surprising to younger children, and a novel “Psychic Sally ” condition that challenges older children with an unexpected outcome. In everyday life, humans constantly attribute unobservable mental states to one another, and use them to

Recent evidence suggests that the emotions play a crucial role in perception, learning and rational decision making. Despite arguments to the contrary, all artificial intelligent systems are, to some extent, autonomous. This research investigates how emotion can be used as the basis for autonomy. We propose the use of an emotion-based control language that maps over all layers of a computational architecture. We report on how theoretical work and both design and computational experiments with this concept are being used to direct perception, behavior selection and reasoning in cognitive agents. 1

Cognition involves the control of behaviour both within external environments and internal to an autonomous system. The control of behaviour is vital to an autonomous system as it responds to its external environment and acts to further its goals. Excepting the most spartan of environments, the potential available information and the associated combinatorics in a perception, cognition and action sequence can tax even the most powerful of symbol processing agents. This research into cognitive architectures is described within a theoretical framework spanning major issues in artificial intelligence and cognitive science. This extends earlier work on motivation with a cognitive model of reasoning that, together with an affective mechanism, enable consistent decision making across a variety of different types of cognitive and reactive processes. We argue that there is no need for emotion in the type of systems we are developing. The affect magnitude concept solves some problems with BDI models, and allows for adaptive decision making over a number of tasks in different domains. The cognitive and affective components are brought together using motivational constructs. We show that the generic cognitive model can adapt to different environments and tasks as it makes use of the motivational models to direct reactive and other situated processes