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Elemental Causal Learning From Transitions

Soo, Kevin (2016) Elemental Causal Learning From Transitions. Master's Thesis, University of Pittsburgh. (Unpublished)

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Much research on elemental causal learning has focused on how causal strength is learned from the states of variables. In longitudinal contexts, the way a cause and effect change over time can be informative of the underlying causal relationship. We propose a framework for inferring the causal strength from different observed transitions, and compare the predictions to existing models of causal induction. According to this framework, transitions where the cause and effect change simultaneously are the most informative about the underlying causal strength. The predictions of this framework are tested in an experiment where subjects observe a cause and effect over time, updating their judgments of causal strength after observing different transitions. The results are largely consistent with the proposed framework, showing that causal learning in longitudinal contexts relies on patterns of transitions – a previously overlooked source of information from which causal strength can be learned.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Soo, Kevinkevin.soo@pitt.eduKWS10
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairRottman,
Committee MemberNokes-Malach,
Committee MemberWoodward,
Date: 20 June 2016
Date Type: Publication
Defense Date: 2 December 2015
Approval Date: 20 June 2016
Submission Date: 11 April 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 63
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Psychology
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Causal learning, causal reasoning, time
Date Deposited: 20 Jun 2016 20:00
Last Modified: 15 Nov 2016 14:32


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