From models to rules: mechanization of reasoning as a way to cope with cognitive overloading in combinatorial problems

Acta Psychologica
Paolo Cherubini, Alberto Mazzocco

Abstract

It is hypothesized that the transition from unfamiliar problems to familiar, well-learned problems coincides with the transition from a model-based style of reasoning to a rule-based style of reasoning; model-based style of reasoning helps understanding the problem structure, but can overload working memory when the number of models required increases; rule-based style of reasoning avoids cognitive overloading, at the cost of making individuals liable to mechanization errors. In Experiment 1, the number of models required to respond to a verification task affected response latencies with unfamiliar problems, but not with familiar problems, supporting the initial hypothesis. In Experiment 2, participants were prone to mechanization errors when confronted with slightly modified problems in the late stages of the experiment, supporting the hypothesis that they had developed a reasoning rule in the early stages and were blindly applying it. The findings suggest that model-based reasoning and rule-based reasoning serve different purposes and have different costs and benefits, are both available to human reasoners, and familiarization with a problem may induce the transition from the former to the latter. The findings also suggest th...Continue Reading

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Citations

Jul 9, 2008·The Quarterly Journal of Experimental Psychology : QJEP·Carlo ReverberiPaolo Cherubini
Jun 4, 2014·Advances in Health Sciences Education : Theory and Practice·Kevin McLaughlinGeoff R Norman
Apr 7, 2010·Journal of Evaluation in Clinical Practice·Peter J VeaziePatrick J O'Connor
Oct 2, 2007·NeuroImage·Carlo ReverberiEraldo Paulesu
Jun 17, 2008·Medical Decision Making : an International Journal of the Society for Medical Decision Making·Olga KostopoulouRoger Holder

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