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Record W4415824154 · doi:10.1016/j.wocn.2025.101457

Imitation of F0 tone contours by Mandarin and English speakers is both categorical and continuous

2025· article· en· W4415824154 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Phonetics · 2025
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsCentre for Research on Brain Language and MusicMcGill University
FundersSocial Sciences and Humanities Research CouncilNational Social Science Fund Youth ProjectSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureNational Office for Philosophy and Social SciencesChina Scholarship CouncilChina Sponsorship Council
KeywordsMandarin ChineseImitationCategorical variableTone (literature)Contrast (vision)Categorization

Abstract

fetched live from OpenAlex

• 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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.342
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it