Syllables in Sync Form a Link: Neural Phase-locking Reflects Word Knowledge during Language Learning
Bibliographic record
Abstract
Language is composed of small building blocks, which combine to form larger meaningful structures. To understand language, we must process, track, and concatenate these building blocks into larger linguistic units as speech unfolds over time. An influential idea is that phase-locking of neural oscillations across different levels of linguistic structure provides a mechanism for this process. Building on this framework, the goal of the current study was to determine whether neural phase-locking occurs more robustly to novel linguistic items that are successfully learned and encoded into memory, compared to items that are not learned. Participants listened to a continuous speech stream composed of repeating nonsense words while their EEG was recorded and then performed a recognition test on the component words. Neural phase-locking to individual words during the learning period strongly predicted the strength of subsequent word knowledge, suggesting that neural phase-locking indexes the subjective perception of specific linguistic items during real-time language learning. These findings support neural oscillatory models of language, demonstrating that words that are successfully perceived as functional units are tracked by oscillatory activity at the matching word rate. In contrast, words that are not learned are processed merely as a sequence of unrelated syllables and thus not tracked by corresponding word-rate oscillations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".