Gaze Behaviour in Audiovisual Speech Perception: Asymmetrical Distribution of Face-Directed Fixations
Bibliographic record
Abstract
Speech perception under natural conditions entails integration of auditory and visual information. Understanding how visual and auditory speech information are integrated requires detailed descriptions of the nature and processing of visual speech information. To understand better the process of gathering visual information, we studied the distribution of face-directed fixations of humans performing an audiovisual speech perception task to characterise the degree of asymmetrical viewing and its relationship to speech intelligibility. Participants showed stronger gaze fixation asymmetries while viewing dynamic faces, compared to static faces or face-like objects, especially when gaze was directed to the talkers' eyes. Although speech perception accuracy was significantly enhanced by the viewing of congruent, dynamic faces, we found no correlation between task performance and gaze fixation asymmetry. Most participants preferentially fixated the right side of the faces and their preferences persisted while viewing horizontally mirrored stimuli, different talkers, or static faces. These results suggest that the asymmetrical distributions of gaze fixations reflect the participants' viewing preferences, rather than being a product of asymmetrical faces, but that this behavioural bias does not predict correct audiovisual 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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".