Quantifying User Behaviour in Multisensory Immersive Experiences
Bibliographic record
Abstract
With the advent of standalone virtual reality (VR) headsets, multisensory VR experiences are emerging with the hopes of better simulating real-world experiences. For example, innovations in haptic suits and scent diffusion devices are burgeoning. While stimulating multiple senses re-orientates the perceived quality of experience (QoE) by increasing factors such as realism, presence and immersion, the impact it has on user behaviour has yet to be fully quantified. Advances in wearables and instrumented VR headsets, however, have allowed for such user behaviours to be easily measured in real-time. In this paper, we describe a pilot experiment in which participants play a custom-developed VR game under two conditions: (1) conventional audio-visual and (2) multisensory, where haptic feedback is provided via a haptic sleeve and olfaction is enabled via a scent diffusion add-on for the VR headset. We describe a developed instrumented VR headset capable of measuring electroencephalography (EEG), electrocardiography (ECG), and electro-oculography (EOG) signals in real-time. From these signals, metrics of mental workload, engagement, and attention are extracted. We report changes seen in these behavioural measures between the two conditions, thus providing insights on factors driving the improved QoE seen with multisensory experiences.
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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) | 0.001 | 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 | 0.001 |
| 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".