Intuitive Theories as Grammars for Causal Inference
Name
tgn-grammar.pdf
Description
Submitted version
Size
253 KB
Format
Adobe PDF
Checksum (MD5)
2cd59105bf472eb7f03bee824e8f6a16
Author(s)
Tenenbaum, Joshua B.
Griffiths, Thomas L.
Niyogi, Sourabh
Date Issued
April 2010
Publisher
Oxford University Press
Citation
Tenenbaum, Joshua B., Griffiths, Thomas L. and Niyogi, Sourabh. 2010. "Intuitive Theories as Grammars for Causal Inference."
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1093/acprof:oso/9780195176803.003.0020
