The impact of multisensory integration deficits on speech perception in children with autism spectrum disorders
Bibliographic record
Abstract
Speech perception is an inherently multisensory process. When having a face-to-face conversation, a listener not only hears what a speaker is saying, but also sees the articulatory gestures that accompany those sounds. Speech signals in visual and auditory modalities provide complementary information to the listener (Kavanagh and Mattingly, 1974), and when both are perceived in unison, behavioral gains in in speech perception are observed (Sumby and Pollack, 1954). Notably, this benefit is accentuated when speech is perceived in a noisy environment (Sumby and Pollack, 1954). To achieve a behavioral gain from multisensory processing of speech, however, the auditory and visual signals must be perceptually bound into a single, unified percept. The most commonly cited effect that demonstrates perceptual binding in audiovisual speech perception is the McGurk effect (McGurk and MacDonald, 1976), where a listener hears a speaker utter the syllable “ba,” and sees the speaker utter the syllable “ga.” When these two speech signals are perceptually bound, the listener perceives the speaker as having said “da” or “tha,” syllables that are not contained in either of the unisensory signals, resulting in a perceptual binding, or integration, of the speech signals (Calvert and Thesen, 2004).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".