A cross-linguistic study of audio-aerotactile perceptual integration using voicing continua
Bibliographic record
Abstract
Previous studies on multimodal integration in speech perception have found that not only auditory and visual cues, but also tactical sensation—such as an air-puff on skin that simulates aspiration—can be integrated in the perception of speech sounds (Gick & Derrick, 2009). However, most previous investigations have been conducted with English listeners, and it remains uncertain whether such multisensory integration can be shaped by linguistic experience. The current study investigates audio-aerotactile integration in phoneme perception for three groups: English, French monolingual and English-French bilingual listeners. Six step VOT continua of labial (/ba/—/pa/) and alveolar (/da/—/ta/) stops constructed from both English and French endpoint models were presented to listeners who performed a forced-choice identification task. Air-puffs synchronized to syllable onset and applied to the hand at random increased the number of ‘voiceless’ responses for the /da/—/ta/ continuum by both English and French listeners, which suggests that audio-aerotactile integration can occur even though some of the listeners did not have aspiration/non-aspiration contrasts in their native language. Furthermore, bilingual speakers showed larger air-puff effects for English stimuli compared to English monolinguals, which suggests a complex relationship between linguistic experience and multisensory integration in perception.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".