Proba- bilistic models highlight the role of prior knowledge in accounting for how people learn as much as they do from limited ob- served data and provide a framework for ex- plaining precisely how prior knowledge in- teracts with data in guiding generalization and action. They also provide a tool for ex- ploring the kinds of knowledge that people bring to learning and reasoning tasks, allow- ing us to work forwards from rational analy- ses of tasks and environments to predictions about behavior and to work backwards from subjects’ observed behavior to viable as- sumptions about the knowledge they could bring to the task. Crucially, these models do not require that the prior knowledge be innate. Bayesian inference in hierarchi- cal probabilistic models can explain how ab- stract prior knowledge may itself be learned from data and then put to use to guide learn- ing in subsequent tasks and new environ- ments.