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.
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http://dx.doi.org/10.1007/s41465-019-00134-7 | DOI Listing |
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The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges.
View Article and Find Full Text PDFSensors (Basel)
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