Optimal Audiovisual Integration in People with One Eye
Bibliographic record
Abstract
People with one eye show altered sensory processing. Such changes might reflect a central reweighting of sensory information that might impact on how multisensory cues are integrated. We assessed whether people who lost an eye early in life differ from controls with respect to audiovisual integration. In order to quantify the relative weightings assigned to each sensory system, participants were asked to spatially localize audiovisual events that have been previously shown to be optimally combined and perceptually fused from the point of view of location in a normal population, where the auditory and visual components were spatially disparate. There was no difference in the variability of localizing unimodal visual and auditory targets by people with one eye compared to controls. People with one eye did however, demonstrate slower reaction times to localize visual stimuli compared to auditory stimuli and were slower than binocular and eye-patched control groups. When localizing bimodal targets, the weightings assigned to each sensory modality in both people with one eye and controls were predictable from their unimodal performance, in accordance with Maximum Likelihood Estimation and the time it took all three groups to localize the bimodal targets was faster than for vision alone. Regardless of demonstrating a longer response time to visual stimuli, people with one eye appear to integrate the auditory and visual components of multisensory events optimally when determining spatial location.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".