This book explores the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models. We examine how a broad range of empirical phenomena, including intuitive physics, concept learning, causal reasoning, social cognition, and language understanding, can be modeled using a functional probabilistic programming language called WebPPL.


N. D. Goodman and J. B. Tenenbaum (2016). Probabilistic Models of Cognition (2nd ed.). Retrieved YYYY-MM-DD from
  title = {{Probabilistic Models of Cognition}},
  edition = {Second},
  author = {Goodman, Noah D and Joshua B. Tenenbaum},
  year = {2016},
  howpublished = {\url{}},
  note = {Accessed: }

Open source

Previous edition

The first edition of this book used the probabilistic programming language Church and can be found here.


We are grateful to the following people, who contributed content or technical expertise: Timothy J. O’Donnell, Andreas Stuhlmüller, Tomer Ullman, John McCoy, Long Ouyang, Julius Cheng, and Robert Hawkins.

The construction and ongoing support of this tutorial are made possible by grants from the Office of Naval Research, the James S. McDonnell Foundation, the Stanford VPOL, and the Center for Brains, Minds, and Machines (funded by NSF STC award CCF-1231216).