The Influence of Selective Attention to Auditory and Visual Speech on the Integration of Audiovisual Speech Information
Bibliographic record
Abstract
Conflicting visual speech information can influence the perception of acoustic speech, causing an illusory percept of a sound not present in the actual acoustic speech (the McGurk effect). We examined whether participants can voluntarily selectively attend to either the auditory or visual modality by instructing participants to pay attention to the information in one modality and to ignore competing information from the other modality. We also examined how performance under these instructions was affected by weakening the influence of the visual information by manipulating the temporal offset between the audio and video channels (experiment 1), and the spatial frequency information present in the video (experiment 2). Gaze behaviour was also monitored to examine whether attentional instructions influenced the gathering of visual information. While task instructions did have an influence on the observed integration of auditory and visual speech information, participants were unable to completely ignore conflicting information, particularly information from the visual stream. Manipulating temporal offset had a more pronounced interaction with task instructions than manipulating the amount of visual information. Participants' gaze behaviour suggests that the attended modality influences the gathering of visual information in 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 | 0.001 | 0.005 |
| 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".