Evolving networks of human intelligence

Why Do Bi-Factor Models Outperform Higher-Order g Factor Models? A Network Perspective

Bi-factor models of intelligence tend to outperform higher-order g factor models statistically. The literature provides the following rivalling explanations: (i) the bi-factor model represents or closely approximates the true underlying data-generating mechanism; (ii) fit indices are biased against the higher-order g factor model in favor of the bi-factor model; (iii) a network structure underlies the data. We used a Monte Carlo simulation to investigate the validity and plausibility of each of these explanations, while controlling for their rivals. To this end, we generated 1000 sample data sets according to three competing models-a bi-factor model, a (nested) higher-order factor model, and a (non-nested) network model-with 3000 data sets in total. Parameter values were based on the confirmatory analyses of the Wechsler Scale of Intelligence IV. On each simulated data set, we (1) refitted the three models, (2) obtained the fit statistics, and (3) performed a model selection procedure. We found no evidence that the fit measures themselves are biased, but conclude that biased inferences can arise when approximate or incremental fit indices are used as if they were relative fit measures. The validity of the network explanation was established while the outcomes of our network simulations were consistent with previously reported empirical findings, indicating that the network explanation is also a plausible one. The empirical findings are inconsistent with the (also validated) hypothesis that a bi-factor model is the true model. In future model selection procedures, we recommend that researchers consider network models of intelligence, especially when a higher-order g factor model is rejected in favor of a bi-factor model.

The interrelatedness of cognitive abilities in very preterm and full-term born children at 5.5 years of age: a psychometric network analysis approach

BACKGROUND

Very preterm (VP) birth is associated with a considerable risk for cognitive impairment, putting children at a disadvantage in academic and everyday life. Despite lower cognitive ability on the group level, there are large individual differences among VP born children. Contemporary theories define intelligence as a network of reciprocally connected cognitive abilities. Therefore, intelligence was studied as a network of interrelated abilities to provide insight into interindividual differences. We described and compared the network of cognitive abilities, including strength of interrelations between and the relative importance of abilities, of VP and full-term (FT) born children and VP children with below-average and average-high intelligence at 5.5 years.

METHODS

A total of 2,253 VP children from the EPIPAGE-2 cohort and 578 FT controls who participated in the 5.5-year-follow-up were eligible for inclusion. The WPPSI-IV was used to measure verbal comprehension, visuospatial abilities, fluid reasoning, working memory, and processing speed. Psychometric network analysis was applied to analyse the data.

RESULTS

Cognitive abilities were densely and positively interconnected in all networks, but the strength of connections differed between networks. The cognitive network of VP children was more strongly interconnected than that of FT children. Furthermore, VP children with below average IQ had a more strongly connected network than VP children with average-high IQ. Contrary to our expectations, working memory had the least central role in all networks.

CONCLUSIONS

In line with the ability differentiation hypothesis, children with higher levels of cognitive ability had a less interconnected and more specialised cognitive structure. Composite intelligence scores may therefore mask domain-specific deficits, particularly in children at risk for cognitive impairments (e.g., VP born children), even when general intelligence is unimpaired. In children with strongly and densely connected networks, domain-specific deficits may have a larger overall impact, resulting in lower intelligence levels.

Towards a general modeling framework of resource competition in cognitive development

The current paper presents an integrated formal model of typical and atypical development based on the mechanisms of mutualism and resource competition. The mutualistic network model is extended with the dynamics of competition for limited resources, such as time and environmental factors. The proposed model generates patterns that resemble established phenomena in cognitive development: the positive manifold, developmental phases, developmental delays and lack of early indicators in atypical development, developmental regression, and "quasi-autism" caused by extreme environmental deprivation. The presented modeling framework fits a general movement towards formal theory construction in psychology. The model is easy to replicate and develop further, and we offer several avenues for future work.

Investigating the Structure of the French WISC-V (WISC-VFR) for Five Age Groups Using Psychometric Network Modeling

Since the seminal work of Spearman, exploratory and confirmatory factor analysis represents the standard method of examining the dimensionality of psychological instruments. Recently, within the network psychometrics approach, a new procedure was proposed to estimate the dimensionality of psychological instruments: exploratory graph analysis (EGA). This study investigated the structure of the French Wechsler Intelligence Scale for Children-Fifth Edition (WISC-VFR) with five standardization sample age groups (6-7, 8-9, 10-11, 12-13, and 14-16 years) using EGA. The primary research questions include (a) how many WISC-VFR dimensions are identified in each age subgroup? (b) how are subtest scores associated within the dimensions? Because the number and the content of the dimensions identified by EGA could vary with samples, the secondary research questions include (c) is there evidence of reproducibility and generalizability of the dimensions identified by EGA? We used another procedure called bootstrap exploratory graph analysis (bootEGA). EGA and bootEGA suggested only three dimensions, which are consistent with processing speed, verbal comprehension, and the "old" perceptual reasoning factor. Results did not support the distinction between visual-spatial and fluid reasoning dimensions. EGA and bootEGA represent new tools to assess the construct validity of psychological instruments, such as the WISC-VFR.

Carroll's Three-Stratum (3S) Cognitive Ability Theory at 30 Years: Impact, 3S-CHC Theory Clarification, Structural Replication, and Cognitive-Achievement Psychometric Network Analysis Extension

Carroll's treatise on the structure of human cognitive abilities is a milestone in psychometric intelligence research. Thirty years later, Carroll's work continues to influence research on intelligence theories and the development and interpretation of intelligence tests. A historical review of the relations between the 3S and CHC theories necessitates the recommendation that the theories of Cattell, Horn, and Carroll be reframed as a family of obliquely correlated CHC theories-not a single CHC theory. Next, a previously unpublished Carroll exploratory factor analysis of 46 cognitive and achievement tests is presented. A complimentary bifactor analysis is presented that reinforces Carroll's conclusion that his 3S model more accurately represents the structure of human intelligence than two prominent alternative models. Finally, a Carroll-recommended higher-stratum psychometric network analysis (PNA) of CHC cognitive, reading, and math variables is presented. The PNA results demonstrate how PNA can complement factor analysis and serve as a framework for identifying and empirically evaluating cognitive-achievement causal relations and mechanisms (e.g., developmental cascade and investment theories), with an eye toward improved cognitive-achievement intervention research. It is believed that Carroll, given his long-standing interest in school learning, would welcome the integration of theory-driven factor and PNA research.

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores "Beyond g"

For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or g). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to g-centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9-19 standardization sample of the Woodcock-Johnson Tests of Cognitive Ability-Fourth Edition. Results support the primary broad abilities from the Cattell-Horn-Carroll (CHC) theory and suggest that the working memory-attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a g-centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, g.