Asymmetric discrimination of phonetically incongruent audio-visual vowels
Bibliographic record
Abstract
Masapollo, Polka, and Ménard (2017) recently reported a robust directional asymmetry in visual vowel perception: perceivers discriminated a change from an English /u/ viseme to a French /u/ viseme significantly better than a change in the reverse direction. This asymmetry parallels a frequent pattern in auditory vowel perception that points to a universal bias favoring “focal” vowels, whose distal articulatory gestures result in the convergence of adjacent formant frequencies. In the present study, we investigated how the integration of acoustic and visual speech cues influences these directional effects in bimodal vowel perception. In AX discrimination tests in which acoustic and visual cues were phonetically-incongruent, subjects showed an asymmetry, comparable to that found with unimodal stimuli, when there was an acoustic change in focalization, with visual focalization held constant. These findings indicate that acoustic cues are capable of inducing an asymmetry even when there is no cross-modal correspondence between the acoustic and visual channels. We are currently examining whether a similar asymmetry also emerges when there is a visual change in focalization, with acoustic focalization held constant. By comparing different types of audio-visual incongruence, we will be able to examine whether one sensory channel has more perceptual potency than the other.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".