A Latent Factor Analysis of Working Memory Measures Using Large-Scale Data

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Otto Waris, Anna Soveri, Miikka Ahti, Russel C. Hoffing, Daniel Ventus, Susanne M. Jaeggi, Aaron R. Seitz, Matti Laine
Publication year: 2017
Journal: Frontiers in Psychology
Journal acronym: Front. Psychol.
Volume number: 8


Abstract

Working
memory (WM) is a key cognitive system that is strongly related to other
cognitive domains and relevant for everyday life. However, the
structure of WM is yet to be determined. A number of WM models have been
put forth especially by factor analytical studies. In broad terms,
these models vary by their emphasis on WM contents (e.g., visuospatial,
verbal) vs. WM processes (e.g., maintenance, updating) as critical,
dissociable elements. Here we conducted confirmatory and exploratory
factor analyses on a broad set of WM tasks, half of them
numerical-verbal and half of them visuospatial, representing four
commonly used task paradigms: simple span, complex span, running memory,
and n-back. The tasks were selected to allow the detection of both
content-based (visuospatial, numerical-verbal) and process-based
(maintenance, updating) divisions. The data were collected online which
allowed the recruitment of a large and demographically diverse sample of
adults (n = 711). Both factor analytical methods pointed to a clear
division according to task content for all paradigms except n-back,
while there was no indication for a process-based division. Besides the
content- based division, confirmatory factor analyses supported a model
that also included a general WM factor. The n-back tasks had the highest
loadings on the general factor, suggesting that this factor reflected
high-level cognitive resources such as executive functioning and fluid
intelligence that are engaged with all WM tasks, and possibly even more
so with the n-back. Together with earlier findings that indicate high
variability of process-based WM divisions, we conclude that the most
robust division of WM is along its contents (visuospatial vs.
numerical-verbal), rather than along its hypothetical subprocesses.


Last updated on 2019-22-11 at 03:43