Causal Learning: Psychology, Philosophy, and Computation

by Alison Gopnik (Editor), Laura Schulz (Editor)

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:	9780198039280
Publisher:	Oxford University Press
Publication date:	03/22/2007
Series:	Oxford Series in Cognitive Development
Sold by:	Barnes & Noble
Format:	eBook
File size:	4 MB

About the Author

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.

Table of Contents

Contributors ix

Introduction Alison Gopnik Laura Schulz 1

Part I Causation and Intervention

1 Interventionist Theories of Causation in Psychological Perspective Jim Woodward 19

2 Infants' Causal Learning: Intervention, Observation, Imitation Andrew N. Meltzoff 37

3 Detecting Causal Structure: The Role of Interventions in Infants' Understanding of Psychological and Physical Causal Relations Jessica A. Sommerville 48

4 An Interventionist Approach to Causation in Psychology John Campbell 58

5 Learning From Doing: Intervention and Causal Inference Laura Schulz Tamar Kushnir Alison Gopnik 67

6 Causal Reasoning Through Intervention York Hagmayer Steven Sloman David Lagnado Michael R. Waldmann 86

7 On the Importance of Causal Taxonomy Christopher Hitchcock 101

Part II Causation and Probability

Introduction to Part II Causation and Probability Alison Gopnik Laura Schulz 117

8 Teaching the Normative Theory of Causal Reasoning Richard Scheines Matt Easterday David Danks 119

9 Interactions Between Causal and Statistical Learning David M. Sobel Natasha Z. Kirkham 139

10 Beyond Covariation: Cues to Causal Structure David A. Lagnado Michael R. Waldmann York Hagmayer Steven A. Sloman 154

11 Theory Unification and Graphical Models in Human Categorization David Danks 173

12 Essentialism as a Generative Theory of Classification Bob Rehder 190

13 Data-Mining Probabilists or Experimental Determinists? A Dialogue on the Principles Underlying Causal Learning in Children Thomas Richardson Laura Schulz Alison Gopnik 208

14 Learning the Structure of Deterministic Systems Clark Glymour 231

Part III Causation, Theories, and Mechanisms

Introduction to Part IIICausation, Theories, and Mechanisms Alison Gopnik Laura Schulz 243

15 Why Represent Causal Relations? Michael Strevens 245

16 Causal Reasoning as Informed by the Early Development of Explanations Henry M. Wellman David Liu 261

17 Dynamic Interpretations of Covariation Data Woo-kyoung Ahn Jessecae K. Marsh Christian C. Luhmann 280

18 Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations Clark Glymour 294

19 Intuitive Theories as Grammars for Causal Inference Joshua B. Tenenbaum Thomas L. Griffiths Sourabh Niyogi 301

20 Two Proposals for Causal Grammars Thomas L. Griffiths Joshua B. Tenenbaum 323

Notes 347

Index 353

Editorial 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 ICausal Learning/I, 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

From the Publisher