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Learning Causal Structure from Reasoning

Barbey, Aron and Wolff, Phillip (2007) Learning Causal Structure from Reasoning. In: UNSPECIFIED.

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Abstract

According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants for both types of causal chains, and, when compared to the model theory, provided a better fit to the data. The results suggest that certain kinds of causal reasoning may depend on force dynamic—rather than on logical or purely statistical—representations.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Barbey, Aron
Wolff, Phillip
Subjects: General Issues > Causation
Depositing User: Justin Sytsma
Date Deposited: 13 Feb 2007
Last Modified: 07 Oct 2010 15:14
Item ID: 3176
URI: http://philsci-archive.pitt.edu/id/eprint/3176

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