Bibliographic record
Abstract
The McGurk effect denotes a phenomenon of speech perception where a listener attends to mismatched audio and visual stimuli and perceives an illusory third sound, typically a conflation of the audio-visual stimulus. This multimodal interaction has been exploited in various English-language experiments. The article explores the manifestations of this effect in other languages, such as Japanese and Chinese, as well as considerations for age and keenness (hearing acuity) through a literary review of existing research. The literature confirms the McGurk effect is present in other languages, albeit to differing degrees. The differences in the McGurk effect across languages may be attributed to linguistic and cultural differences. Age differences demonstrate a greater lip-reading reliance as age increases in participants; a similar reliance on visual information is seen in participants as hearing impairment increases. Experimental designs should refine audiovisual stimuli by using immersive technology such as three-dimensional models in virtual reality or ambisonic playback that offers multi-directional sound signals. Future research should also address the influence of audiovisual integration in marketing, foreign language education, and developing better accommodations for the hearing impaired.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".