Oral-facial kinematics and configuration drive asymmetries in visual vowel perception
Bibliographic record
Abstract
Masapollo, Polka, and Ménard (2016) have recently reported that adults show robust directional asymmetries in unimodal visual-only vowel discrimination: a change from a relatively less to a relatively more peripheral vowel (in F1-F2 articulatory vowel space) results in significantly better performance than a change in the reverse direction. In the present experiments, we examined the nature of the information that is critical to elicit these asymmetries. Toward this end, we created schematic analogues that retained the isolated kinematics (spatial direction and motion) and/or configuration (global shape) of the lips of Masapollo et al.’s model speaker. We found that subjects showed asymmetries while discriminating dynamicpoint-light displays that specified both the kinematics and configuration of the speaker’s lips (Experiment 1). Moreover, this directional effect was not dependent on subjects’ knowledge that the point-light stimuli were based on distal articulatory movements (Experiment 2). In contrast, no asymmetries emerged during the discrimination of abstract figure-eight shapes that depicted labial kinematic cues, but not labial configuration (Experiment 3). We interpret these findings as evidence that information for both lip shape and lip motion drive asymmetries in visual vowel perception and discuss the results in relation to general and specialized processes in speech 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".