Network models of cognitive abilities in younger and older adults

Neubeck, Markus; Karbach, Julia; Könen, Tanja

doi:10.1016/j.intell.2021.101601

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…Whatever the terms, be they working memory, retrieval fluency, attentional control, cognitive control, executive functioning, top-down control processes, executive attention, processing speed, etc., the extant broad CHC abilities SEM research consistently suggests that the CHC parameters of cognitive processing or Gwm-AC efficiency are crucial to higher-level cognition typically operationally defined as psychometric g or Gf ( De Alwis et al 2014 ; Demetriou et al 2014 ; Fry and Hale 1996 ; Hunt 2011 ; Kail 2007 ; Kyllonen and Christal 1990 ; McGrew 2005 ; Neubeck et al 2022 ; Schneider and McGrew 2018 ; Tourva and Spanoudis 2020 ; Unsworth et al 2021a , 2021b ). The Gwm and AC-related constructs have also demonstrated a central role in other areas of brain network research, such as mind wandering ( Bressler and Menon 2010 ; Kane and McVay 2012 ; McVay and Kane 2012 ; Smallwood 2010 ) and focused attention meditation ( Lutz et al 2008 ; Sedlmeier et al 2012 ).…”

Section: Discussionmentioning

confidence: 99%

“…Although not focused on any specific IQ battery or comprehensive intelligence theory, studies with select sets of cognitive and or achievement tests have demonstrated how PNA descriptive models can provide support for potential causal mechanisms or theories of cognitive deficits in first episode psychosis ( Sánchez-Torres et al 2022 ), the Simple View of Reading model (SVR; Angelelli et al 2021 ), a multi-level model of learning skills ( Zoccolotti et al 2021 ), and the age-differentiated relations between cognitive efficiency abilities (i.e., inhibition, working memory, fluid intelligence, and speeded attention) ( Neubeck et al 2022 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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

McGrew¹,

Schneider

Decker

et al. 2023

J. Intell.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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

McGrew¹,

Schneider

Decker

et al. 2023

J. Intell.

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“…In addition, a recent study that used network analysis (Neubeck et al, 2022) to investigate complex relationships across eight cognitive measures has provided novel insights into cognitive ageing by: 1) identifying differences in the cognitive performance network of younger vs. older adults; 2) finding stronger connection between working memory and intelligence in older adults; and 3) identifying Speeded attention as a key variable in younger adults' cognitive performance network. Moreover, network analysis allows to identify complex interactions between SA facets in the network that eventually can be used to build a dynamic model of reciprocal causal links within SA network (see e.g.…”

Section: Discussionmentioning

confidence: 99%

This is the way: Network perspective on targets for spatial ability development programmes

Likhanov

Maslennikova

Costantini

et al. 2022

Brit J of Edu Psychol

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Background: Spatial ability (SA) was shown to be important for success in different fields, including STEM. Recent research suggested that SA is a unitary construct, rather than a set of related skills. However, it is not clear how individual differences in different facets of SA emerge, and how they relate to variance in general cognitive ability. Aims: The aim of the present study was threefold: 1) to examine the structure of SA testing nine theoretical models; 2) to explore the relation between 16 different facets of SA with general cognitive ability; and 3) to identify central facet(s) within the network of SA -with most links and/or strongest links to other facets. Sample: The study participants were 958 university students from Russia. Methods: The study used a comprehensive battery of 16 SA tests and a verbal ability measure. Results: Results supported previous research, suggesting moderate overlap between all SA facets. Factor analysis suggested several potential structures, with similar fit indices for five different theoretically driven models, including split into small-and large scale; partially independent manipulation, visualization and navigation facets. Confirmatory factor analysis, mediation and network analyses showed spatialThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…Subsequently, a number of studies have supported the accuracy of EGA methods (Christensen and Golino 2021a;Cosemans et al 2022;Golino and Demetriou 2017;Golino et al 2020) as viable alternatives to traditional methods of factor extraction estimation (e.g., Horn 1965). These results, in turn, have prompted researchers to apply EGAs to data from intelligence tests (e.g., Bulut et al 2021;McGrew et al 2023;Neubeck et al 2022;Schmank et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Bootstrap Exploratory Graph Analysis of the WISC–V with a Clinical Sample

et al. 2023

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One important aspect of construct validity is structural validity. Structural validity refers to the degree to which scores of a psychological test are a reflection of the dimensionality of the construct being measured. A factor analysis, which assumes that unobserved latent variables are responsible for the covariation among observed test scores, has traditionally been employed to provide structural validity evidence. Factor analytic studies have variously suggested either four or five dimensions for the WISC–V and it is unlikely that any new factor analytic study will resolve this dimensional dilemma. Unlike a factor analysis, an exploratory graph analysis (EGA) does not assume a common latent cause of covariances between test scores. Rather, an EGA identifies dimensions by locating strongly connected sets of scores that form coherent sub-networks within the overall network. Accordingly, the present study employed a bootstrap EGA technique to investigate the structure of the 10 WISC–V primary subtests using a large clinical sample (N = 7149) with a mean age of 10.7 years and a standard deviation of 2.8 years. The resulting structure was composed of four sub-networks that paralleled the first-order factor structure reported in many studies where the fluid reasoning and visual–spatial dimensions merged into a single dimension. These results suggest that discrepant construct and scoring structures exist for the WISC–V that potentially raise serious concerns about the test interpretations of psychologists who employ the test structure preferred by the publisher.

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Network models of cognitive abilities in younger and older adults

Cited by 4 publications

References 52 publications

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

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

This is the way: Network perspective on targets for spatial ability development programmes

Bootstrap Exploratory Graph Analysis of the WISC–V with a Clinical Sample

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