The effects of high versus low talker variability and individual aptitude on phonetic training of Mandarin lexical tones
Bibliographic record
Abstract
High variability (HV) training has been found to be more effective than low variability (LV) training when learning various non-native phonetic contrasts. However, little research has considered whether this applies to the learning of tone contrasts. The only two relevant studies suggested that the effect of HV training depends on the perceptual aptitude of participants (Perrachione et al., 2011; Sadakata & McQueen, 2014). The present study extends these findings by examining the interaction between individual aptitude and input variability using natural, meaningful second language input (both previous studies used pseudowords). A total of 60 English speakers took part in an eight session phonetic training paradigm. They were assigned to high/low/high-blocked variability training groups and learned real Mandarin tones and words. Individual aptitude was measured following previous work. Learning was measured using one discrimination task, one identification task and two production tasks. All tasks assessed generalization. All groups improved in both the production and perception of tones which transferred to untrained voices and items, demonstrating the effectiveness of training despite the increased complexity compared with previous research. Although the LV group exhibited an advantage with the training stimuli, there was no evidence for a benefit of high-variability in any of the tests of generalisation. Moreover, although aptitude significantly predicted performance in discrimination, identification and training tasks, no interaction between individual aptitude and variability was revealed. Additional Bayes Factor analyses indicated substantial evidence for the null for the hypotheses of a benefit of high-variability in generalisation, however the evidence regarding the interaction was ambiguous. We discuss these results in light of previous findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".