Behavioral and subcortical signatures of musical expertise in Mandarin Chinese speakers
Bibliographic record
Abstract
Both musical training and native language have been shown to have experience-based plastic effects on auditory processing. However, the combined effects within individuals are unclear. Recent research suggests that musical training and tone language speaking are not clearly additive in their effects on processing of auditory features and that there may be a disconnect between perceptual and neural signatures of auditory feature processing. The literature has only recently begun to investigate the effects of musical expertise on basic auditory processing for different linguistic groups. This work provides a profile of primary auditory feature discrimination for Mandarin speaking musicians and nonmusicians. The musicians showed enhanced perceptual discrimination for both frequency and duration as well as enhanced duration discrimination in a multifeature discrimination task, compared to nonmusicians. However, there were no differences between the groups in duration processing of nonspeech sounds at a subcortical level or in subcortical frequency representation of a nonnative tone contour, for fo or for the first or second formant region. The results indicate that musical expertise provides a cognitive, but not subcortical, advantage in a population of Mandarin speakers.
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 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) | 0.003 | 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".