Optimizing steady-state responses to index statistical learning: Response to Benjamin and colleagues

Neural entrainment refers to the tendency of neural activity to align with an ongoing rhythmic stimulus. Measures of neural entrainment have been increasingly leveraged as a tool to understand how the brain tracks different types of regularities in sensory input. However, the methods used to quantify neural entrainment are varied, with numerous analytic decision points whose consequences have not been well-characterized. In a valuable contribution to this field, Benjamin, Dehaene-Lambertz and Flo (submitted) systematically compare various methodological approaches for studying neural entrainment. They demonstrate that the use of overlapping epochs, in which sliding time windows are extracted and analyzed, results in an artifactual inflation of entrainment estimates at the frequency of overlap. Here, in response to this updated best practice recommendation, we reanalyzed three previously published datasets that had been previously analyzed with overlapping epochs. Although our main results and conclusions are unaltered from those originally reported, we agree with Benjamin and colleagues that overlapping epochs should generally be avoided in classic analyses of steady-state experiments, which aim to quantify overall peaks in phase or power across an entire experimental duration. However, we present a case that overlapping epochs may be beneficial in fine-grained analyses of neural entrainment over time. The use of overlapping epochs in such analyses could improve temporal resolution without complicating interpretability of the results in cases where the question of interest relates to relative changes in neural entrainment over time within a given frequency. (C) 2021 Elsevier Ltd. All rights reserved.