David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.
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Citations of this work BETA
David Danks, David Rose & Edouard Machery (2013). Demoralizing Causation. Philosophical Studies:1-27.
David Rose & David Danks (2013). In Defense of a Broad Conception of Experimental Philosophy. Metaphilosophy 44 (4):512-532.
David Rose & David Danks (2012). Causation: Empirical Trends and Future Directions. Philosophy Compass 7 (9):643-653.
Christoph Hoerl (2011). Causal Reasoning. Philosophical Studies 152 (2):167-179.
Edouard Machery (2010). Précis of Doing Without Concepts. Philosophical Studies 149 (3):602-611.
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