Causal Learning
Psychology, Philosophy, and Computation
1st Edition
Published on 22. March 2007
371 pages
978-0-19-803928-0 (ISBN)
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.
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04/2007
Oxford University Press Inc
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Persons
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.
Content
- Intro
- Contents
- Contributors
- Introduction
- PART I: CAUSATION AND INTERVENTION
- 1 Interventionist Theories of Causation in Psychological Perspective
- 2 Infants' Causal Learning: Intervention, Observation, Imitation
- 3 Detecting Causal Structure: The Role of Interventions in Infants' Understanding of Psychological and Physical Causal Relations
- 4 An Interventionist Approach to Causation in Psychology
- 5 Learning From Doing: Intervention and Causal Inference
- 6 Causal Reasoning Through Intervention
- 7 On the Importance of Causal Taxonomy
- PART II: CAUSATION AND PROBABILITY
- Introduction to Part II: Causation and Probability
- 8 Teaching the Normative Theory of Causal Reasoning
- 9 Interactions Between Causal and Statistical Learning
- 10 Beyond Covariation: Cues to Causal Structure
- 11 Theory Unification and Graphical Models in Human Categorization
- 12 Essentialism as a Generative Theory of Classification
- 13 Data-Mining Probabilists or Experimental Determinists? A Dialogue on the Principles Underlying Causal Learning in Children
- 14 Learning the Structure of Deterministic Systems
- PART III: CAUSATION, THEORIES, AND MECHANISMS
- Introduction to Part III: Causation, Theories, and Mechanisms
- 15 Why Represent Causal Relations?
- 16 Causal Reasoning as Informed by the Early Development of Explanations
- 17 Dynamic Interpretations of Covariation Data
- 18 Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations
- 19 Intuitive Theories as Grammars for Causal Inference
- 20 Two Proposals for Causal Grammars
- Notes
- Index
- A
- B
- C
- D
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- I
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