David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Oxford University Press (2007)
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
|Keywords||Learning, Psychology of Causation|
|Categories||categorize this paper)|
|Buy the book||$29.95 used (44% off) $41.66 new (22% off) $49.84 direct from Amazon (6% off) Amazon page|
|Call number||BF318.C38 2007|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
York Hagmayer, Steven A. Sloman, David A. Lagnado & Michael R. Waldmann, Causal Reasoning Through Intervention.
Thomas Richardson, Laura Schulz & Alison Gopnik, Data-Mining Probabilists or Experimental Determinists.
Laura Schulz, Tamar Kushnir & Alison Gopnik, Learning From Doing: Intervention and Causal Inference.
Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi, Intuitive Theories as Grammars for Causal Inference.
Henry M. Wellman & David Liu, Causal Reasoning as Informed by the Early Development of Explanations.
References found in this work BETA
No references found.
Citations of this work BETA
Alison Gopnik (2009). Rational Constructivism: A New Way to Bridge Rationalism and Empiricism. Behavioral and Brain Sciences 32 (2):208-209.
Phyllis McKay Illari (2011). Mechanistic Evidence: Disambiguating the Russo–Williamson Thesis. International Studies in the Philosophy of Science 25 (2):139 - 157.
Pierre Poirier & Guillaume Beaulac (2011). Le véritable retour des définitions. Dialogue 50 (1):153-164.
Michael Heidelberger (2011). Causal and Symbolic Understanding in Historical Epistemology. Erkenntnis 75 (3):467-482.
Jon Williamson (2011). Mechanistic Theories of Causality Part II. Philosophy Compass 6 (6):433-444.
Similar books and articles
York Hagmayer, Björn Meder, Momme von Sydow & Michael R. Waldmann (2011). Category Transfer in Sequential Causal Learning: The Unbroken Mechanism Hypothesis. Cognitive Science 35 (5):842-873.
Robert Gerlai (1997). A Causal Relationship Between LTP and Learning? Has the Question Been Answered by Genetic Approaches? Behavioral and Brain Sciences 20 (4):617-618.
Michael Strevens (2007). Why Represent Causal Relations? In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, Computation. Oxford University Press. 245--260.
Knud Illeris (ed.) (2009). Contemporary Theories of Learning: Learning Theorists -- In Their Own Words. Routledge.
Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
John Campbell (2006). An Interventionist Approach to Causation in Psychology. In Alison Gopnik & Larry J. Schulz (eds.), Causal Learning: Psychology, Philosophy and Computation. Oup. 58--66.
Added to index2009-01-28
Total downloads39 ( #51,485 of 1,410,046 )
Recent downloads (6 months)5 ( #46,199 of 1,410,046 )
How can I increase my downloads?