Imitation of F0 tone contours by Mandarin and English speakers is both categorical and continuous
Bibliographic record
Abstract
• Mandarin speakers imitated the F0 contours more categorically than English speakers. • A combination of model selection and model averaging is used to qualify and quantify the categoricality of the imitation. • Imitation reflects both pre-existing categories and within-category phonetic variations. • F0 imitation is shaped not only by lexical tone contrasts but also by broader categorical influences. Native speakers imitate F0 contours that vary between two lexical tones non-linearly–they do not precisely reproduce the presented F0 features but instead cluster them toward tonal categories, the so-called contrast mediation effect. However, less is known whether non-native speakers who lack the lexical tone phonology will show linear imitation of F0 contours. Addressing this question will deepen our understanding of whether F0 imitation is solely influenced by lexical tone contrasts or also shaped by other sources of non-linearity beyond phonological contrasts. To investigate this, the current study examined the categorization and imitation of a Mandarin flat-falling tonal continuum by both Mandarin speakers and English speakers who were naïve to tonal languages. Imitation distributions were analyzed by comparing two models: a linear regression model, which assumes participants linearly track phonetic cues, and a mixture regression model, which assumes imitation reflects underlying categories. The mixture regression model fit the data better for the Mandarin speakers while the reverse was true for the English speakers, suggesting that Mandarin speakers imitated the F0 contours more categorically than English speakers. However, for both groups, the data was best fit using a weighted combination of both models. For the Mandarin group this result along with additional analyses of duration, F1 and intensity suggest that tone categories involve both phonological and phonetic information and imitation taps both, possibly via hyper- and hypo-articulation. For English participants, the evidence for categorical mediation suggests that imitation is mediated by factors other than lexically contrastive linguistic categories, although the exact nature of the factors is unclear.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".