Causal Learning: Psychology, Philosophy, and Computation
Causal Learning: Psychology, Philosophy, and Computation
Professor of Psychology
Assistant Professor
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Abstract
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 research in all of these areas of cognitive science, with chapters by researchers in all these disciplines. 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. This new work uses the framework of probabilistic models and interventionist accounts of causation in philosophy in order to provide a rigorous formal basis for “theory theories” of concepts and cognitive development. Moreover, the causal learning mechanisms this interdisciplinary research program has uncovered go dramatically beyond both the traditional mechanisms of nativist theories such as modularity theories, and empiricist ones such as association or connectionism. The chapters cover three topics: the role of intervention and action in causal understanding, the role of causation in categories and concepts, and the relationship between causal learning and intuitive theory formation. Though coming from different disciplines, the chapters converge on showing how we can use our own actions and the evidence we observe in order to accurately learn about the world.
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Front Matter
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Introduction
Alison Gopni andLaura Schulz
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Part I Causation and Intervention
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1
Interventionist Theories of Causation in Psychological Perspective
Jim Woodward
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2
Infants’ Causal Learning: Intervention, Observation, Imitation
Andrew N. Meltzoff
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3
Detecting Causal Structure: The Role of Interventions in Infants’ Understanding of Psychological and Physical Causal Relations
Jessica A. Sommerville
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4
An Interventionist Approach to Causation in Psychology
John Campbell
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5
Learning From Doing: Intervention and Causal Inference
Laura Schulz and others
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6
Causal Reasoning Through Intervention
York Hagmayer and others
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7
On the Importance of Causal Taxonomy
Christopher Hitchcock
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1
Interventionist Theories of Causation in Psychological Perspective
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Part II Causation and Probability
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8
Teaching the Normative Theory of Causal Reasoning
Richard Seheines and others
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9
Interactions Between Causal and Statistical Learning
David M. Sobel andNatasha Z. Kirkham
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10
Beyond Covariation: Cues to Causal Structure
David A. Lagnado and others
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11
Theory Unification and Graphical Models in Human Categorization
David Danks
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12
Essentialism as a Generative Theory of Classification
Bob Rehder
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13
Data-Mining Probabilists or Experimental Determinists? A Dialogue on the Principles Underlying Causal Learning in Children
Thomas Richardson and others
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14
Learning the Structure of Deterministic Systems
Clark Glymour
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8
Teaching the Normative Theory of Causal Reasoning
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Part III Causation, Theories, and Mechanisms
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15
Why Represent Causal Relations?
Michael Strevens
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16
Causal Reasoning as Informed by the Early Development of Explanations
Henry M. Wellman andDavid Liu
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17
Dynamic Interpretations of Covariation Data
Woo kyoung Ahn and others
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18
Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations
Clark Glymour
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19
Intuitive Theories as Grammars for Causal Inference
Joshua B. Tenenbaum and others
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20
Two Proposals for Causal Grammars
Thomas L. Griffiths andJoshua B. Tenenbaum
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15
Why Represent Causal Relations?
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End Matter
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