Intelligence and the brain: A model-based approach

Abstract

Various biological correlates of general intelligence (g) have been reported. Despite this, however, the relationship between neurological measurements and g is not fully clear. We use structural equation modeling to model the relationship between behavioral Wechsler Adult Intelligence Scale (WAIS) estimates of g and neurological measurements (voxel-based morphometry and diffusion tensor imaging of eight regions of interest). We discuss psychometric models that explicate the relationship between g and the brain in a manner in line with the scientific study of g. Fitting the proposed models to the data, we find that a MIMIC model (for multiple indicators, multiple causes), where the contributions of different brain regions to a unidimensional g are estimated separately, provides the best fit against the data.

A watershed model of individual differences in fluid intelligence

“…For example, recent evidence suggests that differences in emotional states are better seen as a broad network of regions showing a different activation profile, rather than activity in individual regions in isolation mapping onto individual emotional states (Lindquist et al, 2012). Similarly, many concurrent and partially independent neural properties determine individual differences in broad cognitive skills such as general intelligence (Kievit et al, 2012, Ritchie et al, 2015b). In a SEM framework, this prediction means that variability in each endophenotype will make partially independent contributions to variability in the phenotype, in line with a so-called MIMIC model (Multiple Indicators, Multiple Causes; see Jöreskog and Goldberger, 1975, Kievit et al, 2012).…”

Simpson's paradox in psychological science: a practical guide

“…Similar results—where the direction and strength of the correlation between properties of the brain and intelligence change over developmental time—have been found by Tamnes et al (2011). This implies that an individual, cross-sectional, study could have found a correlation between cortical thickness and intelligence anywhere in the range from negative to positive, leading to incomplete or incorrect (if such a finding would be uncritically generalized to other age-groups) inferences at the level of subgroups or individuals (see also Kievit et al, 2012a). …”

Crystallized and fluid intelligence are predicted by microstructure of specific white‐matter tracts

“…This graph fulfills a Markov property that can be explained as follows: If variable A is connected to B and B to C, and there is no other direct or indirect path from A to C, B "totally mediates" the influence of A on C. In other words, B "screens off" A from C. Under appropriate conditions (Pearl, 2000), the resulting graphs allow some inference about mechanistic causal relations. This framework was leveraged by Kievit et al (2012Kievit et al ( , 2016 and Kievit, Fuhrmann, Borgeest, Simpson-Kent, and Henson (2018) to provide an SEM specification for the study of the relation of FA with intelligence, work that is worth summarizing in the next paragraph. A particularly useful type of SEM is the Multiple Indicator, Multiple Causes (MIMIC) introduced by Jöreskog and Goldberger (2006) in which latent variables are introduced as mediators between two sets of observed variables.…”

“…In Kievit et al (2012), a MIMIC model was estimated with the following three levels to the data of 80 subjects: (a) Voxel-based region of interest (ROI) measures for four FA (VBFA) tracts and 4 Gy matter, (b) a single g (WAIS) latent variable, (c) the four WAIS indices (WM, VC, PO, and PS). This model was applied and showed a good fit but considered only a single cognitive domain based on a sample with a limited age range (18-29 years) and a limited small number of FA measures that were not actually tract based.…”

“…Exploring a wider range of cognitive domains namely, the four WAIS indices for intelligence as in Kievit et al (2012). Exploring a wider range of cognitive domains namely, the four WAIS indices for intelligence as in Kievit et al (2012).…”