Working memory training in healthy young adults: Support for the null from a randomized comparison to active and passive control groups

Abstract

Training of working memory as a method of increasing working memory capacity and fluid intelligence has received much attention in recent years. This burgeoning field remains highly controversial with empirically-backed disagreements at all levels of evidence, including individual studies, systematic reviews, and even meta-analyses. The current study investigated the effect of a randomized six week online working memory intervention on untrained cognitive abilities in a community-recruited sample of healthy young adults, in relation to both a processing speed training active control condition, as well as a no-contact control condition. Results of traditional null hypothesis significance testing, as well as Bayesian factor analyses, revealed support for the null hypothesis across all cognitive tests administered before and after training. Importantly, all three groups were similar at pre-training for a variety of individual variables purported to moderate transfer of training to fluid intelligence, including personality traits, motivation to train, and expectations of cognitive improvement from training. Because these results are consistent with experimental trials of equal or greater methodological rigor, we suggest that future research re-focus on: 1) other promising interventions known to increase memory performance in healthy young adults, and; 2) examining sub-populations or alternative populations in which working memory training may be efficacious.

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

“…Hence, BFs allow for evaluating the strength of evidence not only for the presence of an association, but explicitly also for the absence of a proposed association. Accordingly, using BFs has become increasingly popular in the area of cognitive enhancement (e.g., Antón et al 2014;Clark et al 2017;De Simoni and von Bastian 2017;Guye and von Bastian 2017;Kirk et al 2014;Paap et al 2014;Sprenger et al 2013;von Bastian et al 2017;von Bastian and Oberauer 2013).Based on previous findings, we expected positive associations of motivation (Brose et al 2012), a growth mindset (Jaeggi et al 2014), and conscientiousness (Studer-Luethi et al 2012) with change in training performance. Regarding neuroticism, our expectations were less specific, given that previous literature reported evidence for a negative association of neuroticism with mean training performance and transfer effects, but not with training gains (e.g., Studer-Luethi et al 2012, 2016.…”

“…Hence, BFs allow for evaluating the strength of evidence not only for the presence of an association, but explicitly also for the absence of a proposed association. Accordingly, using BFs has become increasingly popular in the area of cognitive enhancement (e.g., Antón et al 2014;Clark et al 2017;De Simoni and von Bastian 2017;Guye and von Bastian 2017;Kirk et al 2014;Paap et al 2014;Sprenger et al 2013;von Bastian et al 2017;von Bastian and Oberauer 2013).…”

“…Latent growth curve modeling Over the past decade, there has been an exploding interest in computer-based commercial Bbrain training^programs and in scientific evidence relating to the effectiveness of such interventions, triggered by promising results of working memory (WM) training gains generalizing to previously untrained cognitive abilities such as intelligence in both younger (e.g., Jaeggi et al 2008) and older adults (e.g., Borella et al 2010). Although the idea of improving general cognitive functioning within a few weeks is enticing, there is also accumulating evidence against a generalized effect of WM training (e.g., Clark et al 2017;De Simoni and von Bastian 2017;Guye and von Bastian 2017;Sprenger et al 2013). Even on the meta-analytic level, evidence is mixed regarding the effectiveness of cognitive training in both younger and older adults (e.g., Au et al 2015;Dougherty et al 2016;Karbach and Verhaeghen 2014;Kelly et al 2014;Lampit et al 2014;Melby-Lervåg and Hulme 2013;Melby-Lervåg et al 2016;Schwaighofer et al 2015;Soveri et al 2017).…”

Locating neural transfer effects of n-back training on the central executive: a longitudinal fMRI study

“…As mentioned above, there is no agreement because, whereas some studies have reported near and far transfer effects [8][9][10] , others have only found near transfer effects [11][12][13][14] , and still others did not find any kind of transfer [15][16][17][18] . At least five meta-analyses 5,7,[19][20][21] have been conducted to clarify the possible transfer effects in the working memory domain, but the overall results have been inconclusive.…”

Validation of a matrix reasoning task for mobile devices

“…Eight outliers (3.8%) were removed (z NoResp ≥ 4 and z Hits ≥ 2.5) from the data set, in which participants completed two sessions (N = 213). Descriptive statistics for the performance on different versions of UCMRT at two time points are presented in & Perrig, 2017), and some even reporting worse performance in the second session than in the first (Clark, Lawlor-Savage, & Goghari, 2017;Colom et al, 2013;Redick et al, 2013;Stough et al, 2011). On the other hand, significant changes in performance in the two sessions were observed as a function of group (Table 2).…”