Visual influences on the natural referent vowel bias
Bibliographic record
Abstract
Research indicates that perceivers (both adult and infant) are universally biased to attend to vowels with extreme articulatory/acoustic properties (peripheral in F1/F2 vowel space). Yet, the nature of this perceptual phenomenon (i.e., the natural referent vowel [NRV] bias) is not fully understood. The present research investigates whether this bias is attributable to general auditory processes or to phonetic processes that track articulatory information available across modalities. In experiment 1, we examined whether adult perceivers are biased to attend to visual information that specifies extreme vocalic articulations. As predicted by the phonetic account, we found a bias favoring relatively more peripheral vowels when only acoustic or only visual speech information was present. In experiment 2, we investigated how the integration of acoustic and visual speech cues influence the effects documented in experiment 1. When acoustic and visual cues were phonetically congruent, a peripheral vowel bias was observed. In contrast, when acoustic and visual cues were phonetically incongruent, this bias was disrupted. Collectively, these results are compatible with the view that the NRV bias is phonetic in nature—the speech processing system appears to be biased toward extreme vocalic gestures, which may be specified in the optic, as well as in the acoustic, signal.
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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 | 0.007 |
| 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 | 0.001 |
| 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".