Literature DB >> 21635317

Adaptive non-interventional heuristics for covariation detection in causal induction: model comparison and rational analysis.

Masasi Hattori1, Mike Oaksford.   

Abstract

In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model. 2007 Cognitive Science Society, Inc.

Year:  2007        PMID: 21635317     DOI: 10.1080/03640210701530755

Source DB:  PubMed          Journal:  Cogn Sci        ISSN: 0364-0213


  11 in total

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3.  Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

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5.  Effect of grouping of evidence types on learning about interactions between observed and unobserved causes.

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6.  Reasoning strategies and prior knowledge effects in contingency learning.

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Journal:  Mem Cognit       Date:  2022-04-28

Review 7.  Causal reasoning with mental models.

Authors:  Sangeet S Khemlani; Aron K Barbey; Philip N Johnson-Laird
Journal:  Front Hum Neurosci       Date:  2014-10-28       Impact factor: 3.169

8.  A new method of Bayesian causal inference in non-stationary environments.

Authors:  Shuji Shinohara; Nobuhito Manome; Kouta Suzuki; Ung-Il Chung; Tatsuji Takahashi; Hiroshi Okamoto; Yukio Pegio Gunji; Yoshihiro Nakajima; Shunji Mitsuyoshi
Journal:  PLoS One       Date:  2020-05-22       Impact factor: 3.240

9.  Effects of question formats on causal judgments and model evaluation.

Authors:  Yiyun Shou; Michael Smithson
Journal:  Front Psychol       Date:  2015-04-21

10.  A machine learning model with human cognitive biases capable of learning from small and biased datasets.

Authors:  Hidetaka Taniguchi; Hiroshi Sato; Tomohiro Shirakawa
Journal:  Sci Rep       Date:  2018-05-09       Impact factor: 4.379

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