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Causal Learning$
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Alison Gopnik and Laura Schulz

Print publication date: 2007

Print ISBN-13: 9780195176803

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195176803.001.0001

Beyond Covariation

Cues to Causal Structure

Chapter:
(p.154) 10 Beyond Covariation
Source:
Causal Learning
Author(s):

David A. Lagnado

Michael R. Waldmann

York Hagmaye

Steven A. Sloman

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195176803.003.0011

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning.

Keywords:   causal models, causal learning, covariation, intervention, temporal order, causal structure

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