RESUMO
Statistical language-learning, the capacity to extract regularities from a continuous speech stream, arguably involves the ability to segment the stream before the discrete constituents can be stored in memory. According to recent accounts, the segmentation process is reflected in the alignment of neural activity to the statistical structure embedded in the input. However, the degree to which it can predict the subsequent leaning outcome is currently unclear. As this is a relatively new avenue of research on statistical learning, a scoping review approach was adopted to identify and explore the current body of evidence on the use of neural phase entrainment as a measure of online neural statistical language-learning and its relation to the learning outcome, as well as the design characteristics of these studies. All included studies (11) observed entrainment to the underlying statistical pattern with exposure to the structured speech stream. A significant association between entrainment and learning outcome was observed in six of the studies. We discuss these findings in light of what neural entrainment in statistical word-learning experiments might represent, and speculate that it might reflect a general auditory processing mechanism, rather than segmentation of the speech stream per se. Lastly, as we find the current selection of studies to provide inconclusive evidence for neural entrainment's role in statistical learning, future research avenues are proposed.