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Causal Learning – Psychology, Philosophy, and Computation - Oxford Scholarship Online
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Causal Learning: Psychology, Philosophy, and Computation

Alison Gopnik and Laura Schulz


This book outlines the recent revolutionary work in cognitive science formulating a “probabilistic model” theory of learning and development. It provides an accessible and clear introduction to the probabilistic modeling in psychology, including causal model, Bayes net, and Bayesian approaches. It also outlines new cognitive and developmental psychological studies of statistical and causal learning, imitation and theory-formation, new philosophical approaches to causation, and new computational approaches to the representation of intuitive concepts and theories. This book brings together resea ... More

Keywords: concepts, folk theories, cognitive development, Bayesian inference, causal models, causal knowledge, causal learning, probabilistic models, statistical learning, Bayes nets

Bibliographic Information

Print publication date: 2007 Print ISBN-13: 9780195176803
Published to Oxford Scholarship Online: April 2010 DOI:10.1093/acprof:oso/9780195176803.001.0001


Affiliations are at time of print publication.

Alison Gopnik, editor
University of California at Berkeley

Laura Schulz, editor
Brain and Cognitive Sciences, Massachusetts Institute of Technology

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Alison Gopni, and Laura Schulz

Part I Causation and Intervention

2 Infants’ Causal Learning

Andrew N. Meltzoff

3 Detecting Causal Structure

Jessica A. Sommerville

5 Learning From Doing

Laura Schulz, Tamar Kushnir, and Alison Gopnik

6 Causal Reasoning Through Intervention

York Hagmayer, Steven Sloman, David Lagnado, and Michael R. Waldmann

8 Teaching the Normative Theory of Causal Reasoning

Richard Seheines, Matt Easterday, and David Danks

9 Interactions Between Causal and Statistical Learning

David M. Sobel, and Natasha Z. Kirkham

10 Beyond Covariation

David A. Lagnado, Michael R. Waldmann, York Hagmaye, and Steven A. Sloman

13 Data-Mining Probabilists or Experimental Determinists?

Thomas Richardson, Laura Schulz, and Alison Gopnik

17 Dynamic Interpretations of Covariation Data

Woo kyoung Ahn, Jessecae K. Marsh, and Christian C. Luhmann

19 Intuitive Theories as Grammars for Causal Inference

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

20 Two Proposals for Causal Grammars

Thomas L. Griffiths, and Joshua B. Tenenbaum

End Matter