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Brain volume and intelligence: The moderating role of intelligence measurement quality

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  • Gignac, Gilles E.
  • Bates, Timothy C.

Abstract

A substantial amount of empirical research has estimated the association between brain volume and intelligence. The most recent meta-analysis (Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015) reported a correlation of .24 between brain volume and intelligence – notably lower than previous meta-analytic estimates. This headline meta-analytic result was based on a mixture of samples (healthy and clinical) and sample correlations not corrected for range restriction. Additionally, the role of IQ assessment quality was not considered. Finally, evidential value of the literature was not formally evaluated. Based on the results of our meta-analysis of the Pietschnig et al.'s sample data, the corrected correlation between brain volume and intelligence in healthy adult samples was r=.31 (k=32; N=1758). Furthermore, the quality of intelligence measurement was found to moderate the effect between brain volume and intelligence (b=.08, p=.028). Investigations that used ‘fair’, ‘good’, and ‘excellent’ measures of intelligence yielded corrected brain volume and intelligence correlations of .23 (k=9; N=547), .32 (k=10; N=646), and .39 (k=13; N=565), respectively. The Henmi/Copas adjusted confidence intervals, the p-uniform results, and the p-curve results failed to suggest evidence of publication bias and/or p-hacking. The results were interpreted to suggest that the association between in vivo brain volume and intelligence is arguably best characterised as r≈.40. Researchers are encouraged to consider intelligence measurement quality in future meta-analyses, based on the guidelines provided in this investigation.

Suggested Citation

  • Gignac, Gilles E. & Bates, Timothy C., 2017. "Brain volume and intelligence: The moderating role of intelligence measurement quality," Intelligence, Elsevier, vol. 64(C), pages 18-29.
    • Handle: RePEc:eee:intell:v:64:y:2017:i:c:p:18-29
    DOI: 10.1016/j.intell.2017.06.004
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    1. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
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    3. Walker, Dana L. & Palermo, Romina & Callis, Zoe & Gignac, Gilles E., 2023. "The association between intelligence and face processing abilities: A conceptual and meta-analytic review," Intelligence, Elsevier, vol. 96(C).
    4. Simon B Eickhoff & Robert Langner, 2019. "Neuroimaging-based prediction of mental traits: Road to utopia or Orwell?," PLOS Biology, Public Library of Science, vol. 17(11), pages 1-6, November.
    5. Gignac, Gilles E. & Stevens, Elizabeth M., 2024. "Attitude toward numbers: A better predictor of financial literacy and intelligence than need for cognition," Intelligence, Elsevier, vol. 103(C).
    6. Vieira, Bruno Hebling & Pamplona, Gustavo Santo Pedro & Fachinello, Karim & Silva, Alice Kamensek & Foss, Maria Paula & Salmon, Carlos Ernesto Garrido, 2022. "On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting," Intelligence, Elsevier, vol. 93(C).
    7. Cox, S.R. & Ritchie, S.J. & Fawns-Ritchie, C. & Tucker-Drob, E.M. & Deary, I.J., 2019. "Structural brain imaging correlates of general intelligence in UK Biobank," Intelligence, Elsevier, vol. 76(C), pages 1-1.
    8. Fernandes, Heitor B.F. & Peñaherrera-Aguirre, Mateo & Woodley of Menie, Michael A. & Figueredo, Aurelio José, 2020. "Macroevolutionary patterns and selection modes for general intelligence (G) and for commonly used neuroanatomical volume measures in primates," Intelligence, Elsevier, vol. 80(C).
    9. Lee, James J. & McGue, Matt & Iacono, William G. & Michael, Andrew M. & Chabris, Christopher F., 2019. "The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling," Intelligence, Elsevier, vol. 75(C), pages 48-58.

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