Learning From Doing

Laura Schulz, Tamar Kushnir and Alison Gopnik

in Causal Learning

Published in print April 2007 | ISBN: 9780195176803
Published online April 2010 | e-ISBN: 9780199958511 | DOI:

Series: Oxford Series in Cognitive Development

Learning From Doing

Show Summary Details


This chapter starts from the premise that much of children's knowledge takes the form of abstract, coherent, causal claims that are learned from, and defeasible by, evidence. This view is consistent with an interventionist view of causal knowledge, formalized in computational models using causal Bayes net representations. The chapter reviews empirical studies suggesting that, consistent with this account, preschoolers use patterns of evidence to: a) create novel, effective interventions; b) infer the structure of causal relationships, including relationships involving unobserved causes; c) accurately predict distinct outcomes from observed evidence and evidence generated by interventions; d) integrate novel evidence with prior beliefs; and e) distinguish informative interventions from confounded ones.

Keywords: cognitive development; preschoolers; causal knowledge; causal models; action; probabilistic models; statistical learning; Bayesian inference; Bayes nets

Chapter.  13563 words.  Illustrated.

Subjects: Developmental Psychology

Full text: subscription required

How to subscribe Recommend to my Librarian

Buy this work at Oxford University Press »

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.