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ERIC Number: EJ780865
Record Type: Journal
Publication Date: 2007-Sep
Pages: 50
Abstractor: Author
Reference Count: 36
ISSN: ISSN-0364-0213
Adaptive Non-Interventional Heuristics for Covariation Detection in Causal Induction: Model Comparison and Rational Analysis
Hattori, Masasi; Oaksford, Mike
Cognitive Science, v31 n5 p765-814 Sep 2007
In this article, 41 models of covariation detection from 2 x 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. (Contains 6 tables, 5 figures and 13 notes. Appended are the following: Mathematical supplements on the indices; and statistical tables.) [Additional fundings for this article were provided by the Institute of Human Sciences, Ritsumeikan University and The Daiwa Anglo-Japanese Foundation.]
Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: Japan Society for the Promotion of Science.
Authoring Institution: N/A
Identifiers - Location: Japan