Speech and non-speech measures of audiovisual integration are not correlated
Bibliographic record
Abstract
Many natural events generate both visual and auditory signals, and humans are remarkably adept at integrating information from those sources. However, individuals appear to differ markedly in their ability or propensity to combine what they hear with what they see. Individual differences in audiovisual integration have been established using a range of materials including speech stimuli (seeing and hearing a talker) and simpler audiovisual stimuli (seeing flashes of light combined with tones). Although there are multiple tasks in the literature that are referred to as “measures of audiovisual integration,” the tasks themselves differ widely with respect to both the type of stimuli used (speech versus non-speech) and the nature of the tasks themselves (e.g., some tasks use conflicting auditory and visual stimuli whereas others use congruent stimuli). It is not clear whether these varied tasks are actually measuring the same underlying construct: audiovisual integration. This study tested the convergent validity of four commonly-used measures of audiovisual integration, two of which use speech stimuli (susceptibility to the McGurk effect and a measure of audiovisual benefit), and two of which use non-speech stimuli (the sound-induced flash illusion and audiovisual integration capacity). We replicated previous work showing large individual differences in each measure, but found no significant correlations between any of the measures. These results suggest that tasks that are commonly referred to as measures of audiovisual integration may not be tapping into the same underlying construct.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".