Gaze-Contingent Auditory Displays for Improved Spatial Attention in Virtual Reality
Bibliographic record
Abstract
Virtual reality simulations of group social interactions are important for many applications, including the virtual treatment of social phobias, crowd and group simulation, collaborative virtual environments (VEs), and entertainment. In such scenarios, when compared to the real world, audio cues are often impoverished. As a result, users cannot rely on subtle spatial audio-visual cues that guide attention and enable effective social interactions in real-world situations. We explored whether gaze-contingent audio enhancement techniques driven by inferring audio-visual attention in virtual displays could be used to enable effective communication in cluttered audio VEs. In all of our experiments, we hypothesized that visual attention could be used as a tool to modulate the quality and intensity of sounds from multiple sources to efficiently and naturally select spatial sound sources. For this purpose, we built a gaze-contingent display (GCD) that allowed tracking of a user’s gaze in real-time and modifying the volume of the speakers’ voices contingent on the current region of overt attention. We compared six different techniques for sound modulation with a base condition providing no attentional modulation of sound. The techniques were compared in terms of source recognition and preference in a set of user studies. Overall, we observed that users liked the ability to control the sounds with their eyes. They felt that a rapid change in attenuation with attention but not the elimination of competing sounds (partial rather than absolute selection) was most natural. In conclusion, audio GCDs offer potential for simulating rich, natural social, and other interactions in VEs. They should be considered for improving both performance and fidelity in applications related to social behaviour scenarios or when the user needs to work with multiple audio sources of information.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".