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Working memory training in older adults: Bayesian evidence supporting the absence of transfer.

Guye, S. and Von Bastian, C. C., 2017. Working memory training in older adults: Bayesian evidence supporting the absence of transfer. Psychology and aging, 32 (8), 732-746.

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DOI: 10.1037/pag0000206


The question of whether working memory training leads to generalized improvements in untrained cognitive abilities is a longstanding and heatedly debated one. Previous research provides mostly ambiguous evidence regarding the presence or absence of transfer effects in older adults. Thus, to draw decisive conclusions regarding the effectiveness of working memory training interventions, methodologically sound studies with larger sample sizes are needed. In this study, we investigated whether or not a computer-based working memory training intervention induced near and far transfer in a large sample of 142 healthy older adults (65-80 years). Therefore, we randomly assigned participants to either the experimental group, which completed 25 sessions of adaptive, process-based working memory training, or to the active, adaptive visual search control group. Bayesian linear mixed-effects models were used to estimate performance improvements on the level of abilities, using multiple indicator tasks for near (working memory) and far transfer (fluid intelligence, shifting, and inhibition). Our data provided consistent evidence supporting the absence of near transfer to untrained working memory tasks and the absence of far transfer effects to all of the assessed abilities. Our results suggest that working memory training is not an effective way to improve general cognitive functioning in old age.

Item Type:Article
Additional Information:©American Psychological Association, [2017]. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: [10.1037/pag0000206].
Group:Faculty of Science & Technology
ID Code:29788
Deposited By: Symplectic RT2
Deposited On:27 Sep 2017 11:35
Last Modified:14 Mar 2022 14:07


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