Latent Structure of Executive Functioning/Learning Tasks in the CogState Computerized Battery

Elisabeth Nordenswan,Eeva-Leena Kataja,Riikka Korja,Mira Karrasch,Matti Laine,Kirby Deater-Deckard,Linnea Karlsson,Hasse Karlsson

doi:10.1177/2158244020948846

Abstract

This study tested whether executive functioning (EF)/learning tasks from the CogState computerized test battery show a unitary latent structure. This information is important for the construction of composite measures on these tasks for applied research purposes. Based on earlier factor analytic research, we identified five CogState tasks that have been labeled as EF/learning tasks and examined their intercorrelations in a new sample of Finnish birth cohort mothers ( N = 233). Using confirmatory factor analyses, we compared two single-factor EF/learning models. The first model included the recommended summative scores for each task. The second model exchanged summative scores for first test round results for the three tasks providing these data, as initial task performance is expected to load more heavily on EF. A single-factor solution provided a good fit for the present five EF/learning tasks. The second model, which was hypothesized to tap more onto EF, had slightly better fit indices, χ2(5) = 1.37, p = .93, standardized root mean square residual (SRMR) = .02, root mean square error of approximation (RMSEA) = .00, 90% CI = [.00–.03], comparative fit index (CFI) = 1.00, and more even factor loadings (.30–.56) than the first model, χ2(5) = 4.56, p = .47, SRMR = .03, RMSEA = .00, 90% CI = [.00–.09], CFI = 1.00, factor loadings (.20–.74), which was hypothesized to tap more onto learning. We conclude that the present CogState sum scores can be used for studying EF/learning in healthy adult samples, but call for further research to validate these sum scores against other EF tests.

Highlights

Executive functions (EFs) encompass working memory, setshifting, and inhibition—abilities that are central for human functioning by coordinating and controlling cognitive processes during complex tasks
We addressed this issue by exploring with confirmatory factor analysis (CFA) whether such a composite can be formed from CogState tasks that previous factor analytic studies have linked to EFs and to the related domain of learning (Chou et al, 2015; Lees et al, 2015; Yoshida et al, 2011; Zhong et al, 2013)
We examined whether these tasks could be combined into a single composite, as previous exploratory factor analyses on CogState tasks have suggested (Chou et al, 2015; Lees et al, 2015; Yoshida et al, 2011; Zhong et al, 2013)

Summary

Introduction

Executive functions (EFs) encompass working memory, setshifting, and inhibition—abilities that are central for human functioning by coordinating and controlling cognitive processes during complex tasks. The studies that have found ISL, CPAL, GML, and TWOB to group together (Chou et al, 2015; Lees et al, 2015; Yoshida et al, 2011; Zhong et al, 2013) have included variance from other CogState tasks in the EFAs, resulting in differing factor structures and creating uncertainty regarding the robustness of the test battery’s factor structure. These EFAs do not offer a more detailed insight into the tasks’ common variance besides the researchers’ assumption that it is EF/ learning related. This study offers novel information that can help to guide clinicians and researchers who want to avoid error variance related to measurements with single tasks (Cuevas et al, 2014) using a composite CogState EF/learning measure instead

Participants

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Results

Discussion