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

By Gilles E. Gignac, Timothy C. Bates. 2017

96 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/5a0924f229e6e9ca1acf85af83c2ef58926ce5ca

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

In Pediatrics, Perinatology and Child Health

Citations in the corpus

A distributed brain network predicts general intelligence from resting-state human neuroimaging data (2018, methodology): “While some of these subscale factors account for a substantive proportion of the variance across their respective tasks, their measurement quality is at most fair [44] due to the limited number of constituent tasks; thus we choose to focus on the general factor g only.”