Enhanced Audiovisual Processing in People with One Eye
Bibliographic record
Abstract
Previous research has shown that people with only one eye have enhanced spatial vision implying sensory compensation for their loss of binocularity. We investigated whether the loss of one eye may lead to enhanced multisensory processing as a result of cross–modal sensory compensation. In Experiment 1, we measured speeded detection and discrimination of auditory, visual and audiovisual targets presented as a stream of paired familiar objects and sounds in people with one eye and controls viewing binocularly or with one eye patched. We found that all participants were equally able to detect the presence of auditory, visual or bimodal targets. However, when asked to discriminate between the unimodal and bimodal targets both control groups demonstrated preferential processing of visual over auditory information with the bimodal stimuli – the Colavita visual dominance effect. Moreover, participants with one eye, showed no Colavita effect and demonstrated equal preference of processing visual and auditory stimuli, suggesting better multisensory integration. In Experiment 2, we increased the temporal processing load by asking participants to detect and discriminate back–to–back stimulus repetitions in a stream of paired familiar objects and sounds expecting that auditory performance will dominate due to the tasks' temporal nature. Preliminary results indicate that all participants are equally able to detect the presence of auditory, visual or bimodal repetitions, however, when asked to discriminate between the unimodal and bimodal repetitions, the Colavita effect persists in both control groups. However, participants with one eye show no Colavita effect, and again demonstrate equal preference of processing visual and auditory stimuli. These results indicate that binocular viewing controls consistently demonstrate visual dominance, even when auditory dominance is expected but participants with one eye display equal auditory and visual processing, likely as a form of crossmodal 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".