A Network Analysis of Executive Functions in Children and Adolescents With and Without Attention-Deficit/Hyperactivity Disorder.

This study applied network analysis to executive function test performances to examine differences in network parameters between demographically matched children and adolescents with and without attention-deficit/hyperactivity disorder (ADHD) (n = 141 per group; M = 12.7 ± 2.9 years-old; 72.3% boys, 66.7% White, 65.2% ≥ 12 years maternal education). All participants completed the NIH Toolbox Cognition Battery, including the Flanker, measuring inhibition, Dimensional Change Card Sort, measuring shifting, and List Sorting test, measuring working memory. Children with and without ADHD had comparable mean test performances (d range: .05-0.11) but presented with differences in network parameters. Among participants with ADHD, shifting was less central, had a weaker relationship with inhibition, and did not mediate the relationship between inhibition and working memory. These network characteristics were consistent with the executive function network structure of younger ages in prior research and may reflect an immature executive function network among children and adolescents with ADHD, aligning with the delayed maturation hypothesis.

Heterogeneous heterogeneity by default: Testing categorical moderators in mixed-effects meta-analysis.

Categorical moderators are often included in mixed-effects meta-analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between-study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between-study variances when analysing categorical moderators. To achieve this, we suggest using a mixed-effects location-scale model (MELSM) to allow group-specific estimates for the between-study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed-effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between-study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non-inflated Type 1 error rates. Together, our results support the idea that assuming unequal between-study variances is preferred as a default strategy when testing categorical moderators.

Who is and is not "average"? Random effects selection with spike-and-slab priors.

Mixed-effects models are often employed to study individual differences in psychological science. Such analyses commonly entail testing whether between-subjects variability exists and including covariates to explain that variability. We argue that researchers have much to gain by explicitly focusing on the individual in individual differences research. To this end, we propose the spike-and-slab prior distribution for random effect selection in (generalized) mixed-effects models as a means to gain a more nuanced perspective of individual differences. The prior for each random effect is a two-component mixture consisting of a point-mass "spike" centered at zero and a diffuse "slab" capturing nonzero values. Effectively, such an approach allows researchers to answer questions about particular individuals; specifically, "Who is average?", in the sense of deviating from an average effect, such as the population-averaged slope. We begin with an illustrative example, where the spike-and-slab formulation is used to select random intercepts in logistic regression. This demonstrates the utility of the proposed methodology in a simple setting while also highlighting its flexibility in fitting different kinds of models. We then extend the approach to random slopes that capture experimental effects. In two cognitive tasks, we show that despite there being little variability in the slopes, there were many individual differences in performance. In two simulation studies, we assess the ability of the proposed method to correctly identify (non)average individuals without compromising the mixed-effects estimates. We conclude with future directions for the presented methodology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Why overfitting is not (usually) a problem in partial correlation networks.

Network psychometrics is undergoing a time of methodological reflection. In part, this was spurred by the revelation that ℓ1-regularization does not reduce spurious associations in partial correlation networks. In this work, we address another motivation for the widespread use of regularized estimation: the thought that it is needed to mitigate overfitting. We first clarify important aspects of overfitting and the bias-variance tradeoff that are especially relevant for the network literature, where the number of nodes or items in a psychometric scale are not large compared to the number of observations (i.e., a low p/n ratio). This revealed that bias and especially variance are most problematic in p/n ratios rarely encountered. We then introduce a nonregularized method, based on classical hypothesis testing, that fulfills two desiderata: (a) reducing or controlling the false positives rate and (b) quelling concerns of overfitting by providing accurate predictions. These were the primary motivations for initially adopting the graphical lasso (glasso). In several simulation studies, our nonregularized method provided more than competitive predictive performance, and, in many cases, outperformed glasso. It appears to be nonregularized, as opposed to regularized estimation, that best satisfies these desiderata. We then provide insights into using our methodology. Here we discuss the multiple comparisons problem in relation to prediction: stringent alpha levels, resulting in a sparse network, can deteriorate predictive accuracy. We end by emphasizing key advantages of our approach that make it ideal for both inference and prediction in network analysis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The unity and diversity of executive functions: A network approach to life span development.

As a novel approach to conceptualizing executive functions, this study applied network analysis to a common battery of executive function tests administered to a sample covering the life span. Participants (N = 3,944; age: M = 20.8 years, SD = 19.6, range: 3-85; maternal/self education: M = 12.9 years, SD = 2.6; 53.3% girls/women, 46.7% boys/men; 61.1% White, 18.2% African American, 14.0% Latinx, 6.8% other races/ethnicities) completed tests of inhibition, shifting, and updating/working memory. Zero-order and partial correlation network models were calculated for divided age groups, with network parameters compared between groups: edge weights, corresponding to zero-order or partial correlations between two executive functions; expected influence, quantifying centrality; and global strength, quantifying differentiation. Executive functions differentiated from childhood to adolescence and dedifferentiated during young adulthood, with further dedifferentiation at older adulthood. Shifting emerged as more central than other abilities in adolescence and adulthood versus childhood, with a mediational role of shifting between inhibition and updating/working memory. A network approach can appropriately capture the unity and diversity of executive functions, by which unity reflects the reciprocal engagement between diverse abilities to produce goal-directed behavior. The engagement between abilities, and a mediational role of shifting between inhibition and updating/working memory, may be necessary for the emergence of effective goal-directed behavior. Through a network approach, the unity of executive functions represents an emergent property of the dynamics between multiple abilities (e.g., inhibition, shifting, and updating/working memory) that, when working effectively in tandem, lead to integrative processes (e.g., problem solving) that contribute to successful executive behavior and goal attainment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).