Spatial sound and its effect on visual quality perception and task performance within a virtual environment
Bibliographic record
Abstract
Immersive 3D virtual environments such as simulations and serious games for education and training are typically multimodal, incorporating at the very least both visual and auditory cues, each of which may require considerable computational resources, particularly if high fidelity environments are sought. It is widely accepted that sound can influence the other modalities. Our own previous work has shown that sound cues (both contextual and non-contextual with respect to the visual scene) can either increase or decrease (depending on the sound) visual fidelity (quality) perception in addition to the time required to complete a simple task (task completion time) within a virtual environment. However, despite the importance and benefits of spatial sound (sound that goes far beyond traditional stereo and surround sound techniques, allowing users to perceive the position of a sound source at an arbitrary position in three-dimensional space), our previous work did not consider spatial sound cues. Here we will build upon our previous work by describing the results of an experiment that will be conducted to examine visual fidelity (quality) perception and task performance in the presence of various spatial sound cues including acoustical reverberation and occlusion/diffraction effects, while completing a simple task within a virtual environment.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".