IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v82y2020ics0160289620300672.html
   My bibliography  Save this article

Fluid reasoning is equivalent to relation processing

Author

Listed:
  • Jastrzębski, Jan
  • Ociepka, Michał
  • Chuderski, Adam

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.

Suggested Citation

  • Jastrzębski, Jan & Ociepka, Michał & Chuderski, Adam, 2020. "Fluid reasoning is equivalent to relation processing," Intelligence, Elsevier, vol. 82(C).
  • Handle: RePEc:eee:intell:v:82:y:2020:i:c:s0160289620300672
    DOI: 10.1016/j.intell.2020.101489
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289620300672
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.intell.2020.101489?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shokri-Kojori, Ehsan & Krawczyk, Daniel C., 2018. "Signatures of multiple processes contributing to fluid reasoning performance," Intelligence, Elsevier, vol. 68(C), pages 87-99.
    2. Chuderski, Adam, 2019. "Even a single trivial binding of information is critical for fluid intelligence," Intelligence, Elsevier, vol. 77(C).
    3. Jarosz, Andrew F. & Raden, Megan J. & Wiley, Jennifer, 2019. "Working memory capacity and strategy use on the RAPM," Intelligence, Elsevier, vol. 77(C).
    4. Ren, Xuezhu & Wang, Tengfei & Sun, Sumin & Deng, Mi & Schweizer, Karl, 2018. "Speeded testing in the assessment of intelligence gives rise to a speed factor," Intelligence, Elsevier, vol. 66(C), pages 64-71.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Demetriou, Andreas & Golino, Hudson & Spanoudis, George & Makris, Nikolaos & Greiff, Samuel, 2021. "The future of intelligence: The central meaning-making unit of intelligence in the mind, the brain, and artificial intelligence," Intelligence, Elsevier, vol. 87(C).
    2. Lange-Küttner, Christiane & Averbeck, Bruno B. & Hentschel, Maren & Baumbach, Jan, 2021. "Intelligence matters for stochastic feedback processing during sequence learning in adolescents and young adults," Intelligence, Elsevier, vol. 86(C).
    3. Demetriou, Andreas & Mougi, Antigoni & Spanoudis, George & Makris, Nicolaos, 2022. "Changing developmental priorities between executive functions, working memory, and reasoning in the formation of g from 6 to 12 years," Intelligence, Elsevier, vol. 90(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Yaohui & Zhan, Peida & Fu, Yanbin & Chen, Qipeng & Man, Kaiwen & Luo, Yikun, 2023. "Using a multi-strategy eye-tracking psychometric model to measure intelligence and identify cognitive strategy in Raven's advanced progressive matrices," Intelligence, Elsevier, vol. 100(C).
    2. Must, Olev & Must, Aasa, 2018. "Speed and the Flynn Effect," Intelligence, Elsevier, vol. 68(C), pages 37-47.
    3. Gignac, Gilles E. & Bartulovich, Asher & Salleo, Emilee, 2019. "Maximum effort may not be required for valid intelligence test score interpretations," Intelligence, Elsevier, vol. 75(C), pages 73-84.
    4. Raden, Megan J. & Jarosz, Andrew F., 2022. "Strategy Transfer on Fluid Reasoning Tasks," Intelligence, Elsevier, vol. 91(C).
    5. Wilhelm, Oliver & Kyllonen, Patrick, 2021. "To predict the future, consider the past: Revisiting Carroll (1993) as a guide to the future of intelligence research," Intelligence, Elsevier, vol. 89(C).
    6. Laborda, Leopoldo & Elosúa, M. Rosa & Gómez-Veiga, Isabel, 2019. "Ethnicity and intelligence in children exposed to poverty environments: An analysis using the Oaxaca-Blinder decomposition," Intelligence, Elsevier, vol. 72(C), pages 49-58.
    7. Maran, Thomas & Ravet-Brown, Theo & Angerer, Martin & Furtner, Marco & Huber, Stefan E., 2020. "Intelligence predicts choice in decision-making strategies," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 84(C).
    8. Borter, Natalie & Troche, Stefan J. & Rammsayer, Thomas H., 2018. "Speed- and accuracy-related measures of an intelligence test are differentially predicted by the speed and accuracy measures of a cognitive task," Intelligence, Elsevier, vol. 71(C), pages 1-7.
    9. Protzko, John & Colom, Roberto, 2021. "A new beginning of intelligence research. Designing the playground," Intelligence, Elsevier, vol. 87(C).
    10. Li, Chenyu & Ren, Xuezhu & Schweizer, Karl & Wang, Tengfei, 2022. "Strategy use moderates the relation between working memory capacity and fluid intelligence: A combined approach," Intelligence, Elsevier, vol. 91(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intell:v:82:y:2020:i:c:s0160289620300672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.