Network Models for Cognitive Development and Intelligence

Abstract

Cronbach's (1957) famous division of scientific psychology into two disciplines is still actual for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores -the empirical basis for the well-known g-factor -as well as more complex factorial structures. Links between network modeling, factor modeling and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.

Distinct regionalization patterns of cortical morphology are associated with cognitive performance across different domains

“…The sampling model posits that because cognitive tasks do not measure a single cognitive process but instead require multiple processes, the overlap in the processes required across similar tasks produces a positive correlation between them 34,35 . An alternative mutualism model simulates the positive manifold via mutually beneficial interactions between initially uncorrelated systems that support cognitive behaviours 17,18 . For example, having good shortterm memory may aid the development of cognitive strategies, which can in turn lead to better short-term memory 36 .…”

“…However, the notion that the shared variance among test scores represents a causal psychological construct affecting general intelligence is heavily debated 16,17 . An alternative theory argues that the positive manifold develops through mutually beneficial interactions of independent cognitive processes during development 17,18 . In line with the latter theory, individual differences in cognitive performance would be associated with heterogeneous, distributed neural correlates.…”

A conceptual introduction to Bayesian model averaging

“…Network analysis is an increasingly important tool in psychometrics (Epskamp, Rhemtulla, & Borsboom, 2017;Marsman et al, 2018;van der Maas, Kan, Marsman, & Stevenson, 2017) as well as in cognitive neuroscience (Petersen & Sporns, 2015;Smith et al, 2011). In these analyses, the interactions between a large number of variables are modeled as a network, with the weights of the connections typically given by the partial correlation between their respective variables (and set to zero if there is no interaction according to the network).…”

What are psychological constructs? On the nature and statistical modeling of emotions, intelligence, personality traits and mental disorders.

“…In this aspect, HPCs are different from natural kinds (which have necessary and sufficient features), but like natural kinds, they are non-arbitrary. Recent work suggests that a wide range of psychological constructs may best be understood as HPCs: mental disorders (Borsboom, 2017;Kendler et al, 2011), personality characteristics (Mõttus & Allerhand, 2017), attitudes (Dalege et al, 2015), and intelligence (van der Maas et al, 2006;van der Maas, Kan, Marsman, & Stevenson, 2017).…”