Vowel and Tone Identification for Mandarin Congenital Amusics: Effects of Vowel Type and Semantic Content
Bibliographic record
Abstract
Purpose This study aimed to explore the effects of Mandarin congenital amusia with or without lexical tone deficit (i.e., tone agnosia and pure amusia) on Mandarin vowel and tone identification in different types of vowels (e.g., monophthong, diphthongs, and triphthongs) embedded in consonant-vowel contexts with and without semantic content. Method Thirteen pure amusics (i.e., amusics with normal lexical processing), 5 tone agnosics (i.e., with lexical tone deficit), and 12 controls were screened with Montreal Battery of Evaluation of Amusia and lexical tone tests (Nan et al., 2010; Peretz et al., 2003). Vowel-plus-tone identification tasks with the factors of vowel type and syllables with and without semantic content (e.g., real and nonsense words) were examined among the 3 groups, and identification scores were calculated in 3 formats: vowel-plus-tone identification, vowel identification, and tone identification. Results Tone agnosics showed significantly poorer performances on identifications of vowel, tone, and vowel plus tone across monophthongs, diphthongs, and triphthongs in both real and nonsense words compared to pure amusics and controls. Their deficits were similar across the 3 types of vowels, while the deficit on vowel-plus-tone identification was more severe in nonsense words than in real words. On the other hand, pure amusics performed similarly with controls across all these conditions. Conclusions Tone agnosia might affect both musical pitch and phonological processing, resulting in deficits in lexical tone and vowel perception. On the contrary, pure amusics's effect is primarily on musical pitch perception but not on lexical tone or phonemic deficit. Vowel type did not affect speech deficits for tone agnosics, while they relied more on semantic content as a compensation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".