The Role of Neural Entrainment in Statistical Learning
Abstract
Statistical learning (SL) refers to our ability to extract patterns from the environment. Research has long acknowledged the importance of SL in language; however, the neural mechanisms underlying SL remain largely unknown. One potential mechanism is neural entrainment, which refers to the tendency of endogenous neural oscillations to align with an ongoing rhythmic stimulus. In the context of SL, neural entrainment may align neural excitability to the ongoing structure of the speech stream, increasing sensitivity to underlying patterns and supporting the learning of word boundaries. This thesis tested the hypothesis that neural entrainment plays a causal role in SL by directly manipulating entrainment at specific frequencies cross-modally using a visual stimulus during learning. We found that boosting neural entrainment to match the frequency of the most informative moments of continuous speech (word onsets) resulted in better performance in an implicit SL task. These results support that neural entrainment plays a causal role in SL, as opposed to simply reflecting downstream effects of the learning process.