Influence of Mandarin tone exposure on the processing of intonation by 14-year-old American adolescents: An fMRI study
Bibliographic record
Abstract
This study investigated, for American adolescents, whether the learning of non-native speech contrasts in one prosodic domain (Mandarin Chinese tones) would influence the processing of non-native contrasts in another prosodic domain (Mandarin Chinese intonation). Two groups of 14-year-old American teenagers were tested using the functional magnetic resonance imaging (fMRI) technique, including eight who had received a two-week Mandarin tone training program and eight with no exposure to Mandarin. Subjects were scanned during identification tasks. Despite their similar behavioral performance on identification of Mandarin intonation, preliminary results showed different cortical activation patterns for the two groups. Teenagers exposed to Mandarin showed similar activation patterns for the Mandarin intonation and Mandarin tone task, with more right-hemisphere activation for intonation, which differed from activation for English intonation. Teenagers without exposure activated similar areas for Mandarin and English intonation. Familiarity with Mandarin tonal contrasts affects brain activation, not only for Mandarin tones but also for Mandarin intonation, suggesting that training effects may transcend levels. [Work supported by NIH (HD 37954) and the Talaris Research Institute.]
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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".