Modelling causal reasoning under ambiguity

Yiyun Shou; Michael Smithson

doi:10.13140/RG.2.1.2523.3769

Modelling causal reasoning under ambiguity

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Causal reasoning with ambiguous observations requires subjects to estimate and evaluate ambiguous observations.This paper proposes a hierarchical model that accounts for the uncertainty of both the distribution of the functional form selection and distribution of the ambiguity treatment selection. The posterior distribution of the causal estimates is determined by both the functional form and the ambiguity processing strategy adopted by the reasoner. A model is tested in a simulation study for its ability to recover the strategy and functional form adopted by subjects across a range of hypothetical conditions. In addition, the model is applied to the results of an experimental study.

Original language	English
Title of host publication	Proceedings of the 37th Annual Conference of the Cognitive Science Society
Editors	DC Noelle, R Dale, AS Warlaumont, J Yoshimi, T Matlock, & C Jennings
Place of Publication	Austin, USA
Publisher	Cognitive Science Society
Pages	2170ï¼2175
Edition	Peer Reviewed
ISBN (Print)	9780991196722
DOIs	https://doi.org/10.13140/RG.2.1.2523.3769
Publication status	Published - 2015
Event	37th Annual Conference of the Cognitive Science Society - Pasadena, USA Duration: 1 Jan 2015 → … https://mindmodeling.org/cogsci2015/index.html

Conference

Conference	37th Annual Conference of the Cognitive Science Society
Period	1/01/15 → …
Other	July 22-25 2015
Internet address	https://mindmodeling.org/cogsci2015/index.html

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10.13140/RG.2.1.2523.3769

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title = "Modelling causal reasoning under ambiguity",

abstract = "Causal reasoning with ambiguous observations requires subjects to estimate and evaluate ambiguous observations.This paper proposes a hierarchical model that accounts for the uncertainty of both the distribution of the functional form selection and distribution of the ambiguity treatment selection. The posterior distribution of the causal estimates is determined by both the functional form and the ambiguity processing strategy adopted by the reasoner. A model is tested in a simulation study for its ability to recover the strategy and functional form adopted by subjects across a range of hypothetical conditions. In addition, the model is applied to the results of an experimental study.",

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Modelling causal reasoning under ambiguity. / Shou, Yiyun; Smithson, Michael.
Proceedings of the 37th Annual Conference of the Cognitive Science Society. ed. / DC Noelle, R Dale, AS Warlaumont, J Yoshimi, T Matlock, & C Jennings. Peer Reviewed. ed. Austin, USA: Cognitive Science Society, 2015. p. 2170ï¼2175.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Causal reasoning with ambiguous observations requires subjects to estimate and evaluate ambiguous observations.This paper proposes a hierarchical model that accounts for the uncertainty of both the distribution of the functional form selection and distribution of the ambiguity treatment selection. The posterior distribution of the causal estimates is determined by both the functional form and the ambiguity processing strategy adopted by the reasoner. A model is tested in a simulation study for its ability to recover the strategy and functional form adopted by subjects across a range of hypothetical conditions. In addition, the model is applied to the results of an experimental study.

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DO - 10.13140/RG.2.1.2523.3769

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BT - Proceedings of the 37th Annual Conference of the Cognitive Science Society

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Modelling causal reasoning under ambiguity

Abstract

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