Working memory training has been a hot topic over the last decade. Although studies show benefits in trained and untrained tasks as a function of training, there is an ongoing debate on the efficacy of working memory training. There have been numerous meta-analyses put forth to the field, some finding overall broad transfer effects while others do not. However, discussion of this research typically overlooks specific qualities of the training and transfer tasks. As such, there has been next to no discussion in the literature on what training and transfer tasks features are likely to mediate training outcomes. To address this gap, here, we characterized the broad diversity of features employed in N-Back training tasks and outcome measures in published working memory training studies. Extant meta-analyses have not taken into account the diversity of methodology at this level, primarily because there are too few studies using common methods to allow for a robust meta-analysis. We suggest that these limitations preclude strong conclusions from published data. In order to advance research on working memory training, and in particular, N-Back training, more studies are needed that systematically compare training features and use common outcome measures to assess transfer effects.

IntroductionCognitive function performance decreases in older individuals compared to young adults. To curb this decline, cognitive training is applied, but it is not clear whether it improves only the trained task or also other cognitive functions. To investigate this, we considered an N‐back working memory (WM) training task and verified whether it improves both trained WM and untrained cognitive functions.MethodsAs EEG studies have noted task difficulty and age‐related changes in time‐locked EEG responses, called event‐related potentials (ERPs), we focused on the relation between the P300 ERP component, task difficulty level, and behavior response accuracy and reaction time (RT) in young and older healthy adults. We used two groups of young and older healthy participants to assess the effect of N‐back training: cognitive training group (CTG) and passive control group (PCG). Before and after training, cognitive tests were administered to both groups to evaluate transfer effects.ResultsDespite the observed age‐related differences in the P300 ERP component and in terms of RT and accuracy, our findings demonstrate a stronger improvement in the trained task for older CTGs compared to younger CTGs, larger near‐ and far‐transfer effect to WM and fluid intelligence for both younger and older CTGs, and a far‐transfer effect to attention but only for older adults. Significant differences in response accuracy were shown between young and older subjects in spatial memory and attention tests.ConclusionThe application of a WM training is a promising tool for both healthy adults, and in particular for older subjects, as it showed physiological and behavioral differences in cognitive plasticity across life span and evidence of benefits in the trained task and near‐/far‐transfer effects to other cognitive functions.

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The N-Back, a common working memory (WM) updating task, is increasingly used in basic and applied psychological research. As such, an increasing number of electroencephalogram (EEG) studies have sought to identify the electrophysiological signatures of N-Back task performance. However, stimulus type, task structure, pre-processing methods, and differences in the laboratory environment, including the EEG recording setup employed, greatly vary across studies, which in turn may introduce inconsistencies in the obtained results. Here we address this issue by conducting nine different variations of an N-Back task manipulating stimulus type and task structure. Furthermore, we explored the effect of the pre-processing method used and differences in the laboratory environment. Results reveal significant differences in behavioral and electrophysiological signatures in response to N-Back stimulus type, task structure, pre-processing method, and laboratory environment. In conclusion, we suggest that experimental factors, analysis pipeline, and laboratory differences, which are often ignored in the literature, need to be accounted for when interpreting findings and making comparisons across studies.