The ventriloquist in periphery: Impact of eccentricity-related reliability on audio-visual localization
Bibliographic record
Abstract
The relative reliability of separate sensory estimates influences the way they are merged into a unified percept. We investigated how eccentricity-related changes in reliability of auditory and visual stimuli influence their integration across the entire frontal space. First, we surprisingly found that despite a strong decrease in auditory and visual unisensory localization abilities in periphery, the redundancy gain resulting from the congruent presentation of audio-visual targets was not affected by stimuli eccentricity. This result therefore contrasts with the common prediction that a reduction in sensory reliability necessarily induces an enhanced integrative gain. Second, we demonstrate that the visual capture of sounds observed with spatially incongruent audio-visual targets (ventriloquist effect) steadily decreases with eccentricity, paralleling a lowering of the relative reliability of unimodal visual over unimodal auditory stimuli in periphery. Moreover, at all eccentricities, the ventriloquist effect positively correlated with a weighted combination of the spatial resolution obtained in unisensory conditions. These findings support and extend the view that the localization of audio-visual stimuli relies on an optimal combination of auditory and visual information according to their respective spatial reliability. All together, these results evidence that the external spatial coordinates of multisensory events relative to an observer's body (e.g., eyes' or head's position) influence how this information is merged, and therefore determine the perceptual outcome.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".