Audio-visual integration in noise: Influence of auditory and visual stimulus degradation on eye movements and perception of the McGurk effect
Bibliographic record
Abstract
Seeing a talker's face can aid audiovisual (AV) integration when speech is presented in noise. However, few studies have simultaneously manipulated auditory and visual degradation. We aimed to establish how degrading the auditory and visual signal affected AV integration. Where people look on the face in this context is also of interest; Buchan, Paré and Munhall (Brain Research, 1242, 162-171, 2008) found fixations on the mouth increased in the presence of auditory noise whilst Wilson, Alsius, Paré and Munhall (Journal of Speech, Language, and Hearing Research, 59(4), 601-615, 2016) found mouth fixations decreased with decreasing visual resolution. In Condition 1, participants listened to clear speech, and in Condition 2, participants listened to vocoded speech designed to simulate the information provided by a cochlear implant. Speech was presented in three levels of auditory noise and three levels of visual blurring. Adding noise to the auditory signal increased McGurk responses, while blurring the visual signal decreased McGurk responses. Participants fixated the mouth more on trials when the McGurk effect was perceived. Adding auditory noise led to people fixating the mouth more, while visual degradation led to people fixating the mouth less. Combined, the results suggest that modality preference and where people look during AV integration of incongruent syllables varies according to the quality of information available.
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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 | 0.006 |
| 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 | 0.001 |
| 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".