Audiovisual integration capacity modulates as a function of illusory visual contours, visual display circumference, and sound type
Bibliographic record
Abstract
Research into the capacity of audiovisual integration has previously assessed whether capacity is strictly limited to a single item, or whether it can exceed one item under certain environmental conditions. More recently, investigations have turned to examining the effects of various stimulus factors on capacity. Across two experiments, we looked at a number of factors that were expected to play a modulatory role on capacity. Experiment 1 deployed a manipulation of illusory polygons, revealing an increase in audiovisual capacity, even in an absence of visual connections. This demonstrates that exceeding the capacity of 1 does not only represent a functional increase in the binding of a singular, complex visual object, but that it can also represent binding of multiple simpler objects. Findings also support the hypothesis that capacity modulates quantitatively, but not qualitatively, with respect to speed of presentation. Experiment 2 examined the effects of different sound types (sine tones or white noise) and of different spatial visual field sizes on the capacity of audiovisual integration. The results indicated that capacity is maximized when stimuli are presented in a smaller circle (7.5°) if alongside a sine tone, and when presented in a larger circle (18.5°) alongside a white noise. These results suggest that audiovisual integration capacity is dependent on the combination of sound type and visual spatial field size. The combination of these results reveal additional phenomenological features of the capacity of audiovisual integration, and provides impetus for further research into applications of the findings.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".