Bayesian methods have revolutionized major areas of artificial intelligence, machine learning, natural language processing and computer vision. Recently Bayesian approaches have also begun to take hold in cognitive science, as a principled framework for explaining how humans might learn, reason, perceive and communicate about their world. This tutorial will sketch some of the challenges and prospects for Bayesian models in cognitive science, and also draw some lessons for bringing probabilistic approaches to artificial intelligence closer to human-level abilities.

The focus will be on learning and reasoning tasks where people routinely make successful generalizations from very sparse evidence. These tasks include word learning and semantic interpretation, inference about unobserved properties of objects and relations between objects, reasoning about the goals of other agents, and causal learning and inference. These inferences can be modeled as Bayesian computations operating over constrained representations of world structure – what cognitive scientists have called “intuitive theories” or “schemas”. For each task, we will consider how the appropriate knowledge representations are structured, how these representations guide Bayesian learning and reasoning, and how these representations could themselves be learned via Bayesian methods. Models will be evaluated both in terms of how well they capture quantitative or qualitative patterns of human behavior, and their ability to solve analogous real-world problems of learning and inference. The models we discuss will draw on – and hopefully, offer new insights for – several directions in contemporary machine learning, including semi-supervised learning, modeling relational data, structure learning in graphical models, hierarchical Bayesian modeling, and Bayesian nonparametrics.