Individual Differences in Multisensory Interactions: The Influence of Temporal Phase Coherence and Auditory Salience on Visual Contrast Sensitivity
Bibliographic record
Abstract
While previous research has investigated key factors contributing to multisensory integration in isolation, relatively little is known regarding how these factors interact, especially when considering the enhancement of visual contrast sensitivity by a task-irrelevant sound. Here we explored how auditory stimulus properties, namely salience and temporal phase coherence in relation to the visual target, jointly affect the extent to which a sound can enhance visual contrast sensitivity. Visual contrast sensitivity was measured by a psychophysical task, where human adult participants reported the location of a visual Gabor pattern presented at various contrast levels. We expected the most enhanced contrast sensitivity, the lowest contrast threshold, when the visual stimulus was accompanied by a task-irrelevant sound, weak in auditory salience, modulated in-phase with the visual stimulus (strong temporal phase coherence). Our expectations were confirmed, but only if we accounted for individual differences in optimal auditory salience level to induce maximal multisensory enhancement effects. Our findings highlight the importance of interactions between temporal phase coherence and stimulus effectiveness in determining the strength of multisensory enhancement of visual contrast as well as highlighting the importance of accounting for individual differences.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".