On the Nature and Origin of Intuitive Theories: Learning, Physics and Psychology

This thesis develops formal computational models of intuitive theories, in particular intuitive physics and intuitive psychology, which form the basis of commonsense reasoning. The overarching formal framework is that of hierarchical Bayesian models, which see the mind as having domain-specific hypotheses about how the world works. The work first extends models of intuitive psychology to include higher-level social utilities, arguing against a pure 'classifier ' view. Second, the work extends models of intuitive physics by introducing a ontological hierarchy of physics concepts, and examining how well people can reason about novel dynamic displays. I then examine the question of learning intuitive theories in general, arguing that an algorithmic approach based on stochastic search can address several puzzles of learning, including the 'chicken and egg ' problem of concept learning. Finally, I argue the need for a joint theory-space for reasoning about intuitive physics and intuitive psychology, and provide such a simplified space in the form of a generative model for a novel domain called Lineland. Taken together, these results forge links between formal modeling