Causal Learning: Psychology, Philosophy, and Computation

Causal Learning

Psychology, Philosophy, and Computation

Edited by Alison Gopnik and Laura Schulz

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ISBN13: 9780195176803ISBN10: 0195176804 Hardback, 384 pages

Mar 2007, In Stock

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$49.95 (05)

• Description
• Reviews
• Product Details
• Author Information
• Table of Contents

Table of Contents

Introduction. Allison Gopnik and Laura Schulz

Part I. Causation and Intervention

1. Interventionist Theories of Causation in Psychological Perspective , Jim Woodward

2. Infants' Causal Learning: Intervention, Observation, Imitation , Andrew N. Meltzoff

3. Detecting Causal Structure: The Role of Intervention in Infants' Understanding of Psychological and Physical Causal Relations , Jessica A. Sommerville

4. An Interventionist Approach to Causation in Psychology , John Campbell

5. Learning From Doing: Intervention and Causal Inference , Laura Schulz, Tamar Kushnir, and Alison Gopnik

6. Casual Reasoning Through Intervention , York Hagmayer, Steven Sloman, David Lagnado, and Michael R. Waldmann

7. On the Importance of Causal Taxonomy , Christopher Hitchcock

Part II: Causation and Probability.

Introduction to Part II. Alison Gopnik and Laura Schulz

8. Teaching the Normative Theory of Casual Reasoning , Richard Scheines, Matt Easterday, and David Danks

9. Interactions Between Causal and Statistical Learning , David M. Sobel and Natasha Z. Kirkham

10. Beyond Covariation: Cues to Causal Structure , David A. Lagnado, Michael R. Waldmann, York Hagmayer, and Steven A. Sloman

11. Theory Unification and Graphical Models in Human Categorization , David Danks

12. Essential as a Generative Theory of Classification , Bob Rehder

13. Data-mining Probabilists or Experimental Determinists?: A Dialogue on the Principles Underlying Causal Learning in Children , Thomas Richardson, Laura Schultz, and Alison Gopnik

14. Learning the Structure of Deterministic Systems , Clark Glymour

Part III: Causation, Theories and Mechanisms.

Introduction to Part III. Alison Gopnik and Laura Schulz

15. Why Represent Causal Relations? , Michael Strevens

16. Causal Reasoning as Informed by the Early Development of Explanations , Henry M. Wellman and David Liu

17. Dynamic Interpretations of Covariation Data , Woo-kyoung Ahn, Jessecae K. Marsh, and Christian C. Luhmann

18. Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations , Clark Glymour

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

Notes.

Index.

Description

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.

Reviews

"New formal methods, especially those based on Bayesian networks, have revolutionized the cognitive science of causality, opening up exciting theoretical directions and new empirical challenges. This book integrates and distills the state-of-the-art in the field, with contributions from leading researchers in developmental and cognitive psychology, philosophy, and machine learning." --Nick Chater, Professor of Cognitive and Decision Sciences, University College London

"The question of how humans learn about the world is in large part the question of how humans reason about causality. It's hard to imagine a more fundamental aspect of human cognition than this. In Causal Learning, an impressive array of leading scholars takes a good, hard, thoughtful look at causality, yielding many new and surprising insights. This edited volume accomplishes what is often desired but rarely achieved: an exciting and truly interdisciplinary venture that successfully combines psychology, philosophy, statistics, and computational modeling. A must-read for anyone interested in human learning." --Susan Gelman, Frederick G. L. Huetwell Professor of Psychology, University of Michigan

"An exemplar of inter-disciplinary work: adventurous, coherent, readable, and even witty. A striking intervention into some often-encrusted literatures." --Peter Godfrey-Smith, Professor of Philosophy, Harvard University

"This well-edited text has clear prose and insightful thinking...highly enjoyable...well worth the effort of reading. Gopnik and Schulz have put together a well-developed overview of the current state of research in the field of causal learning...Overall, I believe that this is a well-written book on an important field of behavioral science. It would serve any research psychologist well to read it through at least once."--PsycCritiques

Product Details

384 pages; 10 halftones, 116 line illus.; 7 x 10; ISBN13: 978-0-19-517680-3ISBN10: 0-19-517680-4

About the Author(s)

Alison Gopnik is Professor of Psychology at the University of California at Berkeley. She is the coauthor of Words, Thoughts and Theories (1997), and The Scientist mn the Crib (1999). She has written over a hundred scientific articles as well as articles for The New York Times, The New York Review of Books and Slate.com.

Laura Schulz is Assistant Professor of Brain and Cognitive Sciences at the Massachussets Institute of Technology. She has been the recipient of National Science Foundation and American Association of University Women fellowships. She has published in Developmental Psychology, Child Development, Psychological Review and Trends in Cognitive Sciences.