Sleep and Second-Language Acquisition Revisited: The Role of Sleep Spindles and Rapid Eye Movements
Bibliographic record
Abstract
INTRODUCTION: Second-language learning (SLL) depends on distinct functional-neuroanatomical systems including procedural and declarative long-term memory. Characteristic features of rapid eye movement (REM) and non-REM sleep such as rapid eye movements and sleep spindles are electrophysiological markers of cognitively complex procedural and declarative memory consolidation, respectively. In adults, grammatical learning depends at first on declarative memory ("early SLL") then shifts to procedural memory with experience ("late SLL"). However, it is unknown if the shift from declarative to procedural memory in early vs late SLL is supported by sleep. Here, we hypothesized that increases in sleep spindle characteristics would be associated with early SLL, whereas increases in REM activity (eg, density and EEG theta-band activity time-locked to rapid eye movements) would be associated with late SLL. METHODS: Eight Anglophone (English first language) participants completed four polysomnographic recordings throughout an intensive 6-week French immersion course. Sleep spindle data and electroencephalographic spectral power time-locked to rapid eye movements were extracted from parietal temporal electrodes. RESULTS: As predicted, improvements in French proficiency were associated with changes in spindles during early SLL. Furthermore, we observed increased event-related theta power time-locked to rapid eye movements during late SLL compared with early SLL. The increases in theta power were significantly correlated with improvements in French proficiency. DISCUSSION: This supports the notion that sleep spindles are involved in early SLL when grammar depends on declarative memory, whereas cortical theta activity time-locked to rapid eye movements is involved in late SLL when grammar depends on procedural memory.
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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 | 0.001 |
| 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) | 0.001 | 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".