Intuitive Theories as Grammars for Causal Inference

Abstract

© 2007 by Alison Gopnik and Laura Schulz. All rights reserved. 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.