Neural evidence for linguistic statistical learning is independent of rhythmic and cognitive abilities in neurotypical adults
Bibliographic record
Abstract
Statistical Learning (SL) is an essential mechanism for speech segmentation. Individual differences in SL ability are associated with language acquisition. For instance, better SL correlated with a larger vocabulary size and impaired SL was found in populations with language impairments. The aim of the current study was to contribute to uncovering the underpinnings of individual differences in auditory SL for word segmentation. We hypothesized that individuals with better musical - specifically rhythmic - abilities would show better SL. Participants (N = 106) were exposed to an artificial language consisting of trisyllabic nonsense words. Electroencephalography (EEG) measures of neural entrainment to the auditory signal allow online assessment of SL. The current study used this method to measure individual SL performance during exposure. To assess individual differences, we linked the neural measure of SL to a battery of tests measuring rhythmic, musical, and cognitive abilities, as well as vocabulary size. We replicated earlier work, finding both online (neural) and offline (behavioral) evidence of SL in our sample. In contrast to our expectations regarding individual differences, we found evidence for the null hypothesis regarding correlations between the tests of rhythmic ability and the neural measurement of SL. Exploratory analyses concerning working memory remained inconclusive, while exploratory analyses regarding vocabulary size yielded moderate evidence for a small correlation with the neural measure of SL. Overall, our results suggest that linguistic SL is largely independent from abilities in other cognitive domains, including rhythmic processing and musical abilities, as measured within a sample of healthy, typically developed adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".