PhilSci Archive

A theory of causal learning in children: Causal maps and Bayes nets

Gopnik, Alison and Glymour, Clark and Sobel, David and Schulz, Laura and Kushnir, Tamar and Danks, David (2002) A theory of causal learning in children: Causal maps and Bayes nets. UNSPECIFIED. (In Press)

[img] RTF (.rtf)

Download (663kB)


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.

Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Other
Gopnik, Alison
Glymour, Clark
Sobel, David
Schulz, Laura
Kushnir, Tamar
Danks, David
Subjects: General Issues > Causation
Depositing User: Alison Gopnik
Date Deposited: 26 Nov 2002
Last Modified: 13 Sep 2015 15:24
Item ID: 907
Public Domain: No
Subjects: General Issues > Causation
Date: November 2002

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item