David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with them. Preliminary experimental results suggest that 2- to 4-year-old children spontaneously construct new causal maps and that their learning is consistent with the Bayes-Net formalism.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
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||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
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.
Clark Glymour (2003). Learning, Prediction and Causal Bayes Nets. Trends in Cognitive Sciences 7 (1):43-48.
Richard Scheines, Matt Easterday & David Danks (2007). Teaching the Normative Theory of Causal Reasoning. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press 119--38.
Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik (2011). Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cognitive Science 35 (8):1407-1455.
York Hagmayer & Magda Osman (2012). From Colliding Billiard Balls to Colluding Desperate Housewives: Causal Bayes Nets as Rational Models of Everyday Causal Reasoning. Synthese 189 (S1):17-28.
Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
Caren A. Frosch, Teresa McCormack, David A. Lagnado & Patrick Burns (2012). Are Causal Structure and Intervention Judgments Inextricably Linked? A Developmental Study. Cognitive Science 36 (2):261-285.
Clark Glymour & David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
Alison Gopnik (2004). Children's Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers. Cognitive Science 28 (3):303-333.
David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
Added to index2010-12-22
Total downloads21 ( #136,179 of 1,725,949 )
Recent downloads (6 months)3 ( #210,870 of 1,725,949 )
How can I increase my downloads?