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Beyond Covariation: Cues to Causal Structure

Lagnado, DA; Waldmann, MR; Hagmaye, Y; Sloman, SA; (2010) Beyond Covariation: Cues to Causal Structure. In: Causal Learning: Psychology, Philosophy, and Computation. Green open access

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Abstract

© 2007 by Alison Gopnik and Laura Schulz. All rights reserved. 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.

Type: Book chapter
Title: Beyond Covariation: Cues to Causal Structure
ISBN-13: 9780195176803
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/acprof:oso/9780195176803.003.0011
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology
URI: https://discovery.ucl.ac.uk/id/eprint/14531
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