Causal Learning: Psychology, Philosophy, and Computation

Causal Learning: Psychology, Philosophy, and Computation

by Alison Gopnik, Laura Schulz
     
 

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ISBN-10: 0195176804

ISBN-13: 9780195176803

Pub. Date: 03/22/2007

Publisher: Oxford University Press

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

Overview

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.

Product Details

ISBN-13:
9780195176803
Publisher:
Oxford University Press
Publication date:
03/22/2007
Series:
Oxford Series in Cognitive Development
Pages:
384
Product dimensions:
10.00(w) x 7.20(h) x 1.40(d)

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.

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