Intuitive Theories as Grammars for Causal Inference

Joshua B. Tenenbaum, Thomas L. Griffiths and Sourabh Niyogi

in Causal Learning

Published in print April 2007 | ISBN: 9780195176803
Published online April 2010 | e-ISBN: 9780199958511 | DOI:

Series: Oxford Series in Cognitive Development

Intuitive Theories as Grammars for Causal Inference

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This chapter presents a framework for understanding the structure, function, and acquisition of causal theories from a rational computational perspective. Using a “reverse engineering” approach, it considers the computational problems that intuitive theories help to solve, focusing on their role in learning and reasoning about causal systems, and then using Bayesian statistics to describe the ideal solutions to these problems. The resulting framework highlights an analogy between causal theories and linguistic grammars: just as grammars generate sentences and guide inferences about their interpretation, causal theories specify a generative process for events, and guide causal inference.

Keywords: causal learning; causal reasoning; intuitive theories; Bayesian inference; probabilistic models; generative grammar

Chapter.  14382 words.  Illustrated.

Subjects: Developmental Psychology

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