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Are There Two Kinds of Reasoners?

Authors :
Henry Markovits
Source :
Journal of Intelligence, Vol 12, Iss 3, p 25 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

There is little consensus about the underlying parameters of human reasoning. Two major theories have been proposed that suppose very different mechanisms. The mental model theory proposes that people use working memory intensive processes in order to construct limited models of problem parameters. Probabilistic theories propose that reasoning is a process by which people use the sum of their existing knowledge in order to generate an estimate of the probability of a conclusion given problem parameters. Following an initial proposition by Verschueren et al., the dual-strategy model supposes that these different approaches to reasoning are in fact an important individual difference. Specifically, a recently developed diagnostic questionnaire has identified two major categories of reasoners: Counterexample reasoners use a mental model form of processing, while Statistical reasoners use a probabilistic form of processing. In the following, I describe results that show that the Counterexample/Statistical distinction affects information processing across a variety of reasoning and judgment tasks. In addition, strategy use correlates with performance on very different kinds of thinking, such as contingency judgments, processing of negative emotions, or susceptibility to social biases. Although this distinction is related to differences in cognitive ability, it has been found to predict performance over and above these differences. More recent results have shown that it is possible to experimentally modify strategy use. These results suggest that strategy use is an important individual difference that can affect performance in a wide variety of contexts.

Details

Language :
English
ISSN :
20793200
Volume :
12
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Journal of Intelligence
Publication Type :
Academic Journal
Accession number :
edsdoj.46a13e6b898d43fe9cedf6b8ff25be53
Document Type :
article
Full Text :
https://doi.org/10.3390/jintelligence12030025