Abstract
Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5–17 years) and the Enhanced Nathan Kline Institute—Rockland Sample (NKI-RS) (N = 335, aged 6–17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7–12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.
Highlights
Fluid intelligence is a core part of human cognition and refers to the capacity to solve novel problems in the absence of task-specific knowledge
We assessed the overall fit of our models to the data using the chi-square test, root mean square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean square residual (SRMR)
Following our general analysis procedure, we investigated overall model fit, alternative models, and individual path estimates to gain a comprehensive understanding of the relationships in the watershed model and to test Hypothesis 4—that white matter contributes to working memory capacity and processing speed, which, in turn, contribute to gf
Summary
Fluid intelligence (gf) is a core part of human cognition and refers to the capacity to solve novel problems in the absence of task-specific knowledge. It has been suggested that working memory is a key determinant of gf by limiting mental information processing capacity (Fukuda et al 2010; Chuderski 2013) Proponents of this working memory account of gf cite high correlations between the two domains ranging from 0.5 to 0.9 in meta-analyses (Ackerman et al 2005; Oberauer et al 2005). More recent work has highlighted that this isomorphism only arises under conditions of high time constraints for gf tasks (Chuderski 2013) This suggests that gf and working memory are, separable constructs and underlines the importance of processing speed for gf
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