IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v105y2024ics0160289624000308.html
   My bibliography  Save this article

On the relationships between processing speed, intra-subject variability, working memory, and fluid intelligence – A cross-sectional study

Author

Listed:
  • Schulz-Zhecheva, Y.
  • Voelkle, M.C.
  • Biscaldi, M.
  • Beauducel, A.
  • Klein, C.

Abstract

The developmental cascade model, elaborated by Fry and Hale (2000) emphasizes the role of age-related increases in processing speed and working memory for the development of fluid intelligence. Given the intimate relationships between intra-subject variability and the aforementioned constructs, the present study set out to determine the role of intra-subject variability within the pathways outlined in the developmental cascade model, postulating a fundamental role of intra-subject variability for the development of processing speed, working memory and fluid intelligence. To that end, N = 403 participants aged 8–18 years took a testing battery including choice reaction time tasks to measure processing speed and intra-subject variability as well as span, operation span and coordination tasks to measure working memory within the empirical framework of Oberauer et al. (2003). Cattell's Culture Fair Test (CFT-20 R) was used to measure fluid intelligence. Our results confirm the well-known close relationships between processing speed, working memory, and fluid intelligence, and show that intra-subject variability is also closely related to these constructs. The results of the present study suggest the extension of the developmental cascade model by the inclusion of intra-subject variability as a fundamental construct.

Suggested Citation

  • Schulz-Zhecheva, Y. & Voelkle, M.C. & Biscaldi, M. & Beauducel, A. & Klein, C., 2024. "On the relationships between processing speed, intra-subject variability, working memory, and fluid intelligence – A cross-sectional study," Intelligence, Elsevier, vol. 105(C).
    • Handle: RePEc:eee:intell:v:105:y:2024:i:c:s0160289624000308
    DOI: 10.1016/j.intell.2024.101836
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289624000308
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intell.2024.101836?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. Tourva, Anna & Spanoudis, George, 2020. "Speed of processing, control of processing, working memory and crystallized and fluid intelligence: Evidence for a developmental cascade," Intelligence, Elsevier, vol. 83(C).
    2. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    3. Alberto Quílez-Robres & Nieves Moyano & Alejandra Cortés-Pascual, 2021. "Task Monitoring and Working Memory as Executive Components Predictive of General and Specific Academic Achievements in 6–9-Year-Old Children," IJERPH, MDPI, vol. 18(13), pages 1-15, June.
    4. Bernd Feige & Monica Biscaldi & Christopher W N Saville & Christian Kluckert & Stephan Bender & Ulrich Ebner-Priemer & Klaus Hennighausen & Reinhold Rauh & Christian Fleischhaker & Christoph Klein, 2013. "On the Temporal Characteristics of Performance Variability in Attention Deficit Hyperactivity Disorder (ADHD)," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
    5. David F. Hultsch & Stuart W. S. MacDonald & Roger A. Dixon, 2002. "Variability in Reaction Time Performance of Younger and Older Adults," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 57(2), pages 101-115.
    Full references (including those not matched with items on IDEAS)

    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. Yixuan Wang & Jianzhu Li & Ping Feng & Rong Hu, 2015. "A Time-Dependent Drought Index for Non-Stationary Precipitation Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5631-5647, December.
    2. Panayi, Efstathios & Peters, Gareth W. & Danielsson, Jon & Zigrand, Jean-Pierre, 2018. "Designating market maker behaviour in limit order book markets," Econometrics and Statistics, Elsevier, vol. 5(C), pages 20-44.
    3. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    4. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    5. Matteo Malavasi & Gareth W. Peters & Pavel V. Shevchenko & Stefan Truck & Jiwook Jang & Georgy Sofronov, 2021. "Cyber Risk Frequency, Severity and Insurance Viability," Papers 2111.03366, arXiv.org, revised Mar 2022.
    6. Lucio Masserini & Matilde Bini & Monica Pratesi, 2017. "Effectiveness of non-selective evaluation test scores for predicting first-year performance in university career: a zero-inflated beta regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 693-708, March.
    7. Sangsung Park & Sunghae Jun, 2020. "Sustainable Technology Analysis of Blockchain Using Generalized Additive Modeling," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    8. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    9. Alexander Silbersdorff & Kai Sebastian Schneider, 2019. "Distributional Regression Techniques in Socioeconomic Research on the Inequality of Health with an Application on the Relationship between Mental Health and Income," IJERPH, MDPI, vol. 16(20), pages 1-28, October.
    10. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
    11. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
    12. Elena Ioana Iconaru & Manuela Mihaela Ciucurel & Luminita Georgescu & Mariana Tudor & Constantin Ciucurel, 2021. "The Applicability of the Poincaré Plot in the Analysis of Variability of Reaction Time during Serial Testing," IJERPH, MDPI, vol. 18(7), pages 1-13, April.
    13. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    14. Pinho, Luis Gustavo B. & Nobre, Juvêncio S. & Singer, Julio M., 2015. "Cook’s distance for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 126-136.
    15. Coyle, Thomas R., 2022. "Processing speed mediates the development of tech tilt and academic tilt in adolescence," Intelligence, Elsevier, vol. 94(C).
    16. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    17. Ekaterina Abramova & Derek Bunn, 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices," Papers 2002.10566, arXiv.org.
    18. Zhang, Peng & Qiu, Zhenguo & Shi, Chengchun, 2016. "simplexreg: an R package for regression analysis of proportional data using the simplex distribution," LSE Research Online Documents on Economics 102115, London School of Economics and Political Science, LSE Library.
    19. Hofner, Benjamin & Mayr, Andreas & Schmid, Matthias, 2016. "gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i01).
    20. D. Chiru Naik & Sagar Rohidas Chavan & P. Sonali, 2023. "Incorporating the climate oscillations in the computation of meteorological drought over India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 2617-2646, July.

    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:105:y:2024:i:c:s0160289624000308. 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.