Phonetic imitation in contexts of stimulus-directed and non-stimulus-directed attention
Bibliographic record
Abstract
Research suggests that phonetic imitation is an automatic and subconscious process, but it is clearly a behavior that is variable across participants and conditions. This experiment explores how a participant's amount and type of attention to the speech signal moderates their amount and type of imitation. Six paired conditions varied the activities of participants (all native speakers of English) while listening to the model talker (a female speaker of North American English): blocked exposure (no instructions for listening block) vs immediate shadowing; math task vs picture-drawing task; and word-memorization task vs talker-description task. In all conditions, participants' baseline productions of the stimuli list were recorded prior to exposure to the model talker. Early analyses of whole word duration suggest participants are more likely to imitate the model when their attention is not directed toward the stimuli (i.e., no particular redirection or redirection through a picture-drawing task) than when their attention is stimulus-directed (i.e., being instructed to memorize the words or describe the talker). Further analyses of other acoustic parameters which may reveal imitative behavior are currently underway and include measures of vowel duration, vowel spectra, and f0.
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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 | 0.001 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".