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Autor/inn/enHattori, Masasi; Oaksford, Mike
TitelAdaptive Non-Interventional Heuristics for Covariation Detection in Causal Induction: Model Comparison and Rational Analysis
QuelleIn: Cognitive Science, 31 (2007) 5, S.765-814 (50 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0364-0213
SchlagwörterStimuli; Responses; Computer Simulation; Heuristics; Causal Models; Logical Thinking; Cognitive Processes; Tables (Data); Meta Analysis; Plants (Botany); Task Analysis; Undergraduate Students; Foreign Countries; Japan
AbstractIn 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.] (Author).
AnmerkungenLawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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