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 probabilistic programs (using the WebPPL language).


This book is an open source project. We welcome content contributions (via GitHub)!

The ProbMods Contibutors are:
Noah D. Goodman (editor)
Joshua B. Tenenbaum
Daphna Buchsbaum
Joshua Hartshorne
Robert Hawkins
Timothy J. O’Donnell
Michael Henry Tessler


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


We are grateful for crucial technical assitance from: Andreas Stuhlmüller, Tomer Ullman, John McCoy, Long Ouyang, Julius Cheng.

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).

Previous edition

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