A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence

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.

Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences

“…These properties positively affect processing speed and functional connectivity within and between brain regions (Ferrer et al, 2013; Kievit et al, 2016; Penke et al, 2012; Wendelken et al, 2017). Moreover, greater white-matter-tract integrity has been repeatedly associated with greater mental abilities in different age groups (Booth et al, 2013; Ferrer et al, 2013; Fuhrmann, Simpson-Kent, Bathelt, the CALM Team, & Kievit, 2020; Kievit et al, 2016; Wendelken et al, 2017).…”

Cognitive dimensions of learning in children with problems in attention, learning and memory

“…For example, machine learning methods have distinguished subgroups of the children in the cohort with distinct individual profiles of cognition, learning and behaviour (Astle, Bathelt, CALM Team, & Holmes, 2019;Bathelt et al, 2018), and network analysis has identified inter-related behavioural and cognitive symptoms that are not captured by traditional diagnostic criteria (Mareva & Holmes, 2019). Using structural equation modelling, work has examined a priori distinctions between fluid and crystallized abilities (Simpson-Kent et al, 2020) and evaluated conceptually distinct models of hierarchical, cognitive-only factors (Fuhrmann, Simpson-Kent, Bathelt, & Kievit, 2019). These findings have yet to be integrated in a single, multi-level model of learning difficulties.…”

Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database

“…It also may lead to missing many meaningful relationships latent in the experimental data which have become big data nowadays. Another effort is to compare cognitive concepts (or psychological constructs) with each other by trying to identify relationships in idiosyncratic features or performances in several cognitive tasks (Beaty et al, 2014;Chuderski and Jastrzêbski, 2018;Eisenberg et al, 2019;Fuhrmann et al, 2019;Rey-Mermet et al, 2019) as well as by investigating overlaps in neural substrates using neuroimaging and neuropsychological methods (Hassabis et al, 2007;Mullally and Maguire, 2014;Woolgar et al, 2018;Brandl et al, 2019;Jonikaitis and Moore, 2019). While these approaches provide insights based on empirical facts, completing such low-profile tasks exhaustively is challenging.…”