Individual differences in the distributional learning and overnight consolidation of the Mandarin level-falling tone contrast
Bibliographic record
Abstract
In perceptual studies, musicality and pitch aptitude have been implicated in tone learning, while vocabulary size has been implicated in distributional (segment) learning. Moreover, working memory plays a role in the overnight consolidation of explicit-declarative L2 learning. This study examines how these factors uniquely account for individual differences in the distributional learning and consolidation of an L2 tone contrast, where learners are tonal language speakers, and the training is implicit. Following a previous study investigating distributional tone learning, 66 L1-Cantonese participants who learned and consolidated a Mandarin level-falling tone contrast through distributional exposure were measured in a pitch threshold task, Montreal Battery of Evaluation of Amusia, Mandarin Peabody Picture Vocabulary Test, and an Operation Span task. Pitch threshold predicted immediate learning improvement while working memory predicted overnight consolidation by a bimodal group (not a unimodal group). The findings imply that pitch aptitude may be important in encoding stepwise tonal tokens, and the predictive power of working memory in overnight consolidation extends to implicit tone learning. Meanwhile, musical aptitude may not confer an additional advantage for speakers with native-tone experiences, and learners with a larger L2 vocabulary size might have resisted adaptation to distributional exposure because of robust L2 tonal representations.
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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".