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Record W4404912516 · doi:10.1016/j.wocn.2024.101376

Individual uniformity in phonetic imitation: Assessing the stability of individual variability across features and tasks

2024· article· en· W4404912516 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 · 2024
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImitationStability (learning theory)Cognitive psychologyPsychologyComputer scienceSpeech recognitionMachine learningSocial psychology

Abstract

fetched live from OpenAlex

• How consistent are individual patterns of phonetic imitation across features and tasks? • Tested explicit and implicit imitation of subphonemic VOT and F2 differences. • Cross-feature individual consistency found for explicit, but not implicit imitation. • Within-feature individual consistency found for both types of imitation. • Small but significant correlation in individual performance across tasks. Extensive individual variability has been reported in both spontaneous phonetic convergence and in explicit phonetic imitation tasks. This work tests the consistency of these individual patterns: are some individuals just globally more imitative, showing greater-than-average imitation regardless of the specific phone being imitated, and regardless of the type of imitation, or does an individual’s extent of imitation depend heavily on the phonetic content or the type of task? We examine the stability of individual variability in imitation of two types of subphonemic differences (VOT of voiceless stops and F2 of the vowels /æ/ and /u/), in two types of imitation tasks (implicit and explicit). We found that individuals' degree of imitation was significantly related across different phones within the same class (e.g., imitation of /p/ vs. /t/) in both implicit and explicit imitation tasks, but that individuals' degree of imitation of phones from different classes (e.g., imitation of stops vs. vowels) was only related in explicit, but not implicit, imitation. Findings are consistent with the idea that general cognitive or personality traits may govern individual variability in explicit imitation, but challenge the idea that they play any measurable role in predicting individual variability in implicit or spontaneous imitation. We also found a weak but significant correspondence between individual performance on the implicit and explicit imitation tasks, providing evidence that the two tasks rely on shared mechanisms, as well as a significant relationship between discrimination performance and explicit, but not implicit, imitation.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.409
Teacher spread0.347 · 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