Enhanced Audiovisual Processing in People with one Eye: Unaltered by Increased Temporal Load
Bibliographic record
Abstract
We investigate whether the loss of one eye leads to enhanced multisensory processing. Previously, we measured speed detection and discrimination of auditory, visual, and audiovisual targets presented as a stream of paired objects and familiar sounds in people with one eye compared to controls viewing binocularly or with one eye patched. All participants were equally able to detect the presence of auditory, visual, or bimodal targets; however, when discriminating between the unimodal and bimodal targets, both control groups demonstrated the Colavita visual dominance effect, preferential processing of visual over auditory information for the bimodal stimuli. People with one eye, however, showed no Colavita effect but rather equal preference for processing visual and auditory stimuli. In the current experiment we increased the temporal processing load in an attempt to favour auditory processing and thereby reverse Colavita visual dominance with a one-back stimulus repetition detection and discrimination paradigm. The Colavita effect was reduced in both control groups, but people with one eye continued to show no Colavita effect. People with one eye display equal auditory and visual processing, suggesting better multisensory processing, likely as a form of cross-modal adaptation and compensation for their loss of binocularity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| 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".