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Zusatztext ...well worth the effort of reading...a well-developed overview of the current state of research in the field of causal learning. Klappentext The world has a causal structure, in the sense that some events make other events happen. Although understanding causal structure is essential for predicting and controlling the environment, causal structure is, at least usually, not obvious from superficial, perceptual cues. How then do our minds infer this structure? In the last few years, questions about causal inference and learning have become an important focus of investigation in many different disciplines - developmental psychology, cognitive psychology, ethology, philosophy, and computer science. As is common in scientific research, there has been relatively little interaction on the topic between these disciplines. However, in spite of the minimal interaction, a general review of the research shows the beginning of a formal way of determining how, in principle, the problem of causal inference and learning can be solved, and a wealth of methods for determining how it is, in fact, solved by children, adults, and animals. This volume brings together this research and provides a more sophisticated understanding of causal inference and learning. Zusammenfassung Causal Learning provides a compendium of research determining how, in principle, the problem of causal inference and learning can be solved, and a wealth of methods for determining how it is, in fact, solved by children, adults, and animals. Inhaltsverzeichnis 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 Probalists 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 ...
