The unity and diversity of executive functions: A systematic review and re-analysis of latent variable studies.

Abstract

Confirmatory factor analysis (CFA) has been frequently applied to executive function measurement since first used to identify a three-factor model of inhibition, updating, and shifting; however, subsequent CFAs have supported inconsistent models across the life span, ranging from unidimensional to nested-factor models (i.e., bifactor without inhibition). This systematic review summarized CFAs on performance-based tests of executive functions and reanalyzed summary data to identify best-fitting models. Eligible CFAs involved 46 samples ( = 9,756). The most frequently accepted models varied by age (i.e., preschool = one/two-factor; school-age = three-factor; adolescent/adult = three/nested-factor; older adult = two/three-factor), and most often included updating/working memory, inhibition, and shifting factors. A bootstrap reanalysis simulated 5,000 samples from 21 correlation matrices (11 child/adolescent; 10 adult) from studies including the three most common factors, fitting seven competing models. Model results were summarized as the mean percent accepted (i.e., average rate at which models converged and met fit thresholds: CFI ≥ .90/RMSEA ≤ .08) and mean percent selected (i.e., average rate at which a model showed superior fit to other models: ΔCFI ≥ .005/.010/ΔRMSEA ≤ -.010/-.015). No model consistently converged and met fit criteria in all samples. Among adult samples, the nested-factor was accepted (41-42%) and selected (8-30%) most often. Among child/adolescent samples, the unidimensional model was accepted (32-36%) and selected (21-53%) most often, with some support for two-factor models without a differentiated shifting factor. Results show some evidence for greater unidimensionality of executive function among child/adolescent samples and both unity and diversity among adult samples. However, low rates of model acceptance/selection suggest possible bias toward the publication of well-fitting but potentially nonreplicable models with underpowered samples. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Processing speed, working memory, and executive functions: Independent or inter-related predictors of general intelligence

“…the average of reaction times across conditions) could be merged into one factor. With respect to the discussion on the unity versus diversity of executive functions (Friedman & Miyake, 2017;Karr et al, 2018), these results suggest that the unity of executive functions might largely be due to similar processes being involved across different EF tasks and their respective conditions. In contrast, diversity of EF might be reflected in the manipulation specific variance within EF tasks, as this manipulation specific variance did not show any consistent correlations between the different EF tasks.…”

“…However, in current research there is considerable heterogeneity and discussion with respect to the conception of executive functions 1 (Karr et al, 2018). 1 The term executive functions has been used to summarize either a very broad range of higher-order cognitive processes (Barbey et al, 2012;Diamond, 2013) or to describe a specific set of attention control mechanisms supposedly within the central executive of working memory (Jurado & Rosselli, 2007).…”

“…shifting). While it is still under debate in how far these different EFs have to be separated or share common variance (Friedman & Miyake, 2017;Miyake & Friedman, 2012), the majority of results suggests that there is considerable overlap between the three EFs (Karr et al, 2018). Moreover, EFs have recently been subsumed within the hierarchical structure of intelligence (Jewsbury et al, 2016).…”

Executive functions and memory in bipolar disorders I and II: new insights from meta‐analytic results

“…Another possible limitation is the correlation between test scores, which may have raised the issue of statistical dependence when results were combined across studies for the calculation of composite effect sizes. However, in this regard, it is important to note that, though cognitive constructs may be related to one another (73), the scores on cognitive tests themselves are not reliably correlated (74,75). Also, all effect sizes per cognitive component per study were collapsed into a single value, so that each study only contributed one effect size to each calculation.…”

Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood

“…Although these studies suggest that individual differences in neurocognition predict problematic substance use at critical developmental periods, they have exclusively focused on neurocognitive constructs derived from earlier factor analytic work (e.g., working memory, inhibition 16 ). Such a focus is problematic because these cognitive ontologies have recently been found to display questionable replicability 17,18 , and because constructs defined mainly based on behavioral test score covariation, rather than on an understanding of the mechanistic processes involved in cognition, are likely to display ambiguous links to neural circuitry 19 . Computational psychiatry, an emerging field in which biologically plausible formal models are used to describe performance on clinically relevant tasks 19 , offers a promising alternative framework.…”