Synchronous Sounds Enhance Visual Sensitivity without Reducing Target Uncertainty
Bibliographic record
Abstract
We examined the crossmodal effect of the presentation of a simultaneous sound on visual detection and discrimination sensitivity using the equivalent noise paradigm (Dosher and Lu, 1998). In each trial, a tilted Gabor patch was presented in either the first or second of two intervals embedded in dynamic 2D white noise with one of seven possible contrast levels. The results revealed that the sensitivity of participants' visual detection and discrimination performance were both enhanced by the presentation of a simultaneous sound, though only close to the noise level at which participants' target contrast thresholds started to increase with the increasing noise contrast. A further analysis of the psychometric function at this noise level revealed that the increase in sensitivity could not be explained by the reduction of participants' uncertainty regarding the onset time of the visual target. We suggest that this crossmodal facilitatory effect may be accounted for by perceptual enhancement elicited by a simultaneously-presented sound, and that the crossmodal facilitation was easier to observe when the visual system encountered a level of noise that happened to be close to the level of internal noise embedded within the system.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".