Fluid reasoning is equivalent to relation processing

Jan Jastrzębski, Michał Ociepka and Adam Chuderski

Intelligence, 2020, vol. 82, issue C

Abstract: Fluid reasoning (Gf)—the ability to reason abstractly—is typically measured using nonverbal inductive reasoning tests involving the discovery and application of complex rules. We tested whether Gf, as measured by such traditional assessments, can be equivalent to relation processing (a much simpler process of validating whether perceptually available stimuli satisfy the arguments of a single predefined relation—or not). Confirmatory factor analysis showed that the factor capturing variance shared by three relation processing tasks was statistically equivalent to the Gf factor loaded by three hallmark fluid reasoning tests. Moreover, the two factors shared most of their residual variance that could not be explained by working memory. The results imply that many complex operations typically associated with the Gf construct, such as rule discovery, rule integration, and drawing conclusions, may not be essential for Gf. Instead, fluid reasoning ability may be fully reflected in a much simpler ability to effectively validate single, predefined relations.

Keywords: Fluid reasoning; Relation processing; Working memory (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160289620300672
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intell:v:82:y:2020:i:c:s0160289620300672

DOI: 10.1016/j.intell.2020.101489

Access Statistics for this article

Intelligence is currently edited by R.J. Haier

More articles in Intelligence from Elsevier
Bibliographic data for series maintained by Catherine Liu ().