Bridging the big (data) gap: levels of control in small- and large-scale cognitive neuroscience research

Recently, cognitive neuroscience has experienced unprecedented growth in the availability of large-scale datasets. These developments hold great methodological and theoretical promise: they allow increased statistical power, the use of nonparametric and generative models, the examination of individual differences, and more. Nevertheless, unlike most 'traditional' cognitive neuroscience research, which uses controlled experimental designs, large-scale projects often collect neuroimaging data not directly related to a particular task (e.g., resting state). This creates a gap between small- and large-scale studies that is not solely due to differences in sample size. Measures obtained with large-scale studies might tap into different neurocognitive mechanisms and thus show little overlap with the mechanisms probed by small-scale studies. In this opinion article, we aim to address this gap and its potential implications for the interpretation of research findings in cognitive neuroscience.

Automated summary

In recent years, there has been an unprecedented growth in the availability of large-scale datasets in cognitive neuroscience. However, unlike traditional cognitive neuroscience research, large-scale projects often collect data that is not directly related to a specific task. This creates a gap between small- and large-scale studies that may have different neurocognitive mechanisms.