Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning Article Swipe
Danna Pinto
,
Anat Prior
,
Elana Zion Golumbic
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1162/nol_a_00061
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1162/nol_a_00061
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 tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2-alternative forced-choice task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1162/nol_a_00061
- https://direct.mit.edu/nol/article-pdf/3/2/214/1989316/nol_a_00061.pdf
- OA Status
- gold
- Cited By
- 16
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226212438
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226212438Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1162/nol_a_00061Digital Object Identifier
- Title
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Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-11-24Full publication date if available
- Authors
-
Danna Pinto, Anat Prior, Elana Zion GolumbicList of authors in order
- Landing page
-
https://doi.org/10.1162/nol_a_00061Publisher landing page
- PDF URL
-
https://direct.mit.edu/nol/article-pdf/3/2/214/1989316/nol_a_00061.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://direct.mit.edu/nol/article-pdf/3/2/214/1989316/nol_a_00061.pdfDirect OA link when available
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Sensitivity (control systems), Electroencephalography, Metric (unit), Computer science, Artificial intelligence, Pattern recognition (psychology), Machine learning, Statistics, Speech recognition, Psychology, Mathematics, Neuroscience, Engineering, Electronic engineering, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
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2025: 4, 2024: 3, 2023: 8, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.display_name | Neurobiology of Language |
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| publication_date | 2021-11-24 |
| publication_year | 2021 |
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