Reliable gains? Evidence for substantially underpowered designs in studies of working memory training transfer to fluid intelligence

• Main Authors: Tim eBogg, Leanne eLasecki
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2015-01-01
• Series: Frontiers in Psychology
• Subjects: fluid intelligence; Meta-analysis; transfer effects; statistical power; Cognitive training intervention; small-study effects
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.01589/full

In recent years, cognitive scientists and commercial interests (e.g., Fit Brains, Lumosity) have focused research attention and financial resources on cognitive tasks, especially working memory tasks, to explore and exploit possible transfer effects to general cognitive abilities, such as fluid intelligence. The increased research attention has produced mixed findings, as well as contention about the disposition of the evidence base. To address this contention, J. Au and colleagues (2014; doi:10.3758/s13423-014-0699-x) recently conducted a meta-analysis of extant controlled experimental studies of n-back task training transfer effects on measures of fluid intelligence in healthy adults; the results of which showed a small training transfer effect. Using several approaches, the current review evaluated and re-analyzed the meta-analytic data for the presence of two different forms of small-study effects: 1) publication bias in the presence of low power and; 2) low power in the absence of publication bias. The results of these approaches showed no evidence of selection bias in the working memory training literature, but did show evidence of small-study effects related to low power in the absence of publication bias. While the effect size estimate identified by Au and colleagues provided the most precise estimate to date, it should be interpreted in the context of a uniformly low-powered base of evidence. The present work concludes with a brief set of considerations for assessing the adequacy of a body of research findings for the application of meta-analytic techniques.