Asymmetries in vowel perception arise from phonetic encoding strategies
Bibliographic record
Abstract
Directional asymmetries in vowel discrimination studies reveal that speech perceivers (both adult and infant) are biased toward extreme vocalic articulations, which lead to acoustic vowel signals with well-defined spectral prominences due to formant convergence. These directional effects occur with vowel stimuli presented in either the acoustic or the visual modality and are independent of specific linguistic experience. Current research is focused on elucidating the perceptual processes underlying this universal vowel bias. In the present investigation, the inter-stimulus interval (ISI) in AX discrimination tasks for unimodal acoustic and visual vowels was manipulated (500 ms vs. 1000 ms) in order to examine whether asymmetries are present under experimental conditions that reduce demands on attention and working memory. Subjects discriminated either video-only or audio-only tokens of naturally-spoken English [u] and French [u] which differ in their degree of visible lip-rounding and proximity between F1 and F2. We observed robust asymmetries with these stimuli with English- and French-speaking adults in earlier studies using a relatively long ISI (1500 ms). The present results demonstrated that asymmetries in both auditory and visual vowel discrimination are diminished in the short ISI conditions, suggesting that this vowel bias derives from phonetic encoding processes, rather than general psychophysical processes.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".