Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning.

ABSTRACT: Statistical learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive electroencephalograph (EEG) recordings of neural activity in humans. Importantly, we used carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral

SUBMITTER: Pinto D 

PROVIDER: S-EPMC10158570 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature