High variability phonetic training facilitates categorical perception of Mandarin lexical tones in L2 older adults: A link to auditory processing
Bibliographic record
Abstract
The current study investigated the facilitatory effects of High Variability Phonetic Training (HVPT) in second language (L2) categorical perception (CP) of Mandarin lexical tones. It also explored whether and how individual differences in auditory processing predicted gains from such training. The participants were 32 native English-speaking adults aged over 60 years who were learning Mandarin Chinese as their L2. They were randomly divided into the HVPT group (HG) ( n = 16) and the control group (CG) ( n = 16). Their L2 CP performance was assessed through an identification task and discrimination task before training, immediately after training, and two months later. Auditory processing tests were also conducted to measure the participants’ ability to encode spectral and temporal details of sounds. Linear mixed-effects (LME) models showed that, compared to the CG, the HG exhibited a more pronounced improvement in tonal categorization. Furthermore, regression analysis confirmed that individual differences in perceptual acuity significantly predicted gains from training in L2 CP of Mandarin lexical tones.
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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".