Phonetic Imitation from an Individual-Difference Perspective: Subjective Attitude, Personality and “Autistic” Traits
Bibliographic record
Abstract
Numerous studies have documented the phenomenon of phonetic imitation: the process by which the production patterns of an individual become more similar on some phonetic or acoustic dimension to those of her interlocutor. Though social factors have been suggested as a motivator for imitation, few studies has established a tight connection between language-external factors and a speaker's likelihood to imitate. The present study investigated the phenomenon of phonetic imitation using a within-subject design embedded in an individual-differences framework. Participants were administered a phonetic imitation task, which included two speech production tasks separated by a perceptual learning task, and a battery of measures assessing traits associated with Autism-Spectrum Condition, working memory, and personality. To examine the effects of subjective attitude on phonetic imitation, participants were randomly assigned to four experimental conditions, where the perceived sexual orientation of the narrator (homosexual vs. heterosexual) and the outcome (positive vs. negative) of the story depicted in the exposure materials differed. The extent of phonetic imitation by an individual is significantly modulated by the story outcome, as well as by the participant's subjective attitude toward the model talker, the participant's personality trait of openness and the autistic-like trait associated with attention switching.
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.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".