Causal LearningPsychology, Philosophy, and Computation
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Zusammenfassungen
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.
Von Klappentext im Buch Causal Learning (2007) 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.
Von Klappentext im Buch Causal Learning (2007) Kapitel
- Introduction (Seite 1 - 15)
Dieses Buch erwähnt ...
Personen KB IB clear | Chaomei Chen , Daniel Kahneman , Amos Tversky | |||||||||||||||||||||||||||
Begriffe KB IB clear | Bayes-Netz , Kausalitätcausality , Kausalitätsprinzip , Leib-Seele-Problem , Lernenlearning , machine learning , Stadien der kindlichen Entwicklung nach PiagetPiaget's theory of cognitive development , Statistikstatistics | |||||||||||||||||||||||||||
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Nicht erwähnte Begriffe | Formal-operatives Denken, Konkrete Operationen, Präoperationales Stadium, Sensumotorisches Stadium |
Zitationsgraph
Zitationsgraph (Beta-Test mit vis.js)
1 Erwähnungen
- Framers - Human Advantage in an Age of Technology and Turmoil (Kenneth Cukier, Viktor Mayer-Schönberger, Francis de Véricourt) (2021)
- 4. counterfactuals
Volltext dieses Dokuments
Causal Learning: Gesamtes Buch als Volltext (: , 2982 kByte) | |
Introduction: Artikel als Volltext (: , 95 kByte) | |
Introduction: Kapitel als Volltext (: , 95 kByte) |
Bibliographisches
Beat und dieses Buch
Beat hat dieses Buch während seiner Zeit am Institut für Medien und Schule (IMS) ins Biblionetz aufgenommen. Beat besitzt kein physisches, aber ein digitales Exemplar. (das er aber aus Urheberrechtsgründen nicht einfach weitergeben darf). Es gibt bisher nur wenige Objekte im Biblionetz, die dieses Werk zitieren.