Audio-Visual Integration in 3D Space Near the Body
Bibliographic record
Abstract
Previous research has investigated variations in the effectiveness of audio-visual (AV) integration dependent on location relative to the observer, with inconsistent results. Here, we examine AV interactions in the 3D space around an observer and address six factors that may contribute to these inconsistencies. Using a redundant-targets-effect paradigm in virtual reality, we conducted speeded detection and localization tasks to randomly intermixed auditory, visual and audio-visual stimuli presented in near or far, left or right regions of space around an observer. We varied stimulus characteristics to control for distance-related magnitude variations, examined static and looming stimuli, and analysed response times, multisensory benefits, and race model violations across conditions. Our findings reveal location-related effects on AV integration for looming but not stationary stimuli. Specifically, we observed near-space enhancement for AV looming stimuli for participants' sensory-motor responses and a left/near space enhancement for the multisensory benefit. Our method of intermixing stimulus locations and magnitude adjustments to control for inverse effectiveness was critical for demonstrating these effects. Task goals modified outcomes in complex ways. These results provide new insights into AV integration in 3D space, extend previous findings and highlight the importance and limitations of methodological factors.
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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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".