Adoption of the Cattell–Horn–Carroll Model of Cognitive Abilities by Australian Psychologists

James, Laura; Jacobs, Kate Erin; Roodenburg, John

doi:10.1111/ap.12110

Cited by 2 publications

(1 citation statement)

References 41 publications

(46 reference statements)

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“…The best model for the development of psychological instruments in intelligence evaluation is the Cattell-Horn-Carroll (CHC), seen as the best psychometric evidence for human aptitudes (Abu-Hamour et al 2016;Hurks et al 2016;James et al 2015;Lecerf et al 2010;Wechsler et al 2016). CHC theory consists of a hierarchical multidimensional model with ten factors of cognitive functioning: Fluid intelligence (Gf), Quantitative knowledge (Gq), Crystallized intelligence (Gc), Reading and writing (Grw), Short-term memory (Gsm), Visual processing (Gv), Auditory Processing (Ga), Long-term memory storage and retrieval ability (Glr), Processing Speed (Gs) and Decision speed (Gt).…”

Section: Limitations Across Studiesmentioning

confidence: 99%

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

Vieira¹,

Pamplona

Fachinello

et al. 2021

Preprint

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Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI95% = [0.35, 0.50]) while for studies predicting fluid intelligence obtained from a single test, expected performance was estimated as r = 0.15 (CI95% = [0.13, 0.17]). We further enumerate some virtues and pitfalls we identified in the methods for the assessment of intelligence and machine learning. The lack of treatment of confounder variables, including kinship, and small sample sizes were two common occurrences in the literature which increased risk of bias. Reporting quality was fair across studies, although reporting of results and discussion could be vastly improved. We conclude that the current literature on the prediction of intelligence from neuroimaging data is reaching maturity. Performance has been reliably demonstrated, although extending findings to new populations is imperative. Current results could be used by future works to foment new theories on the biological basis of intelligence differences.

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