Evaluating Typing Performance in Different Mixed Reality Manifestations using Physiological Features
Bibliographic record
Abstract
Mixed reality enables users to immerse themselves in high-workload interaction spaces like office work scenarios. We envision physiologically adaptive systems that can move users into different mixed reality manifestations, to improve their focus on the primary task. However, it is unclear which manifestation is most conducive for high productivity and engagement. In this work, we evaluate whether physiological indicators for engagement can be discriminated for different manifestations. For this, we engaged participants in a typing task in three different mixed reality manifestations (augmented reality, augmented virtuality, virtual reality) and monitored physiological correlates (EEG, ECG, and eye tracking) of users' engagement and workload. We found that users achieved best typing performances in augmented reality and augmented virtuality. At the same time, physiological engagement peaked in augmented virtuality, while workload decreased. We conclude that augmented virtuality strikes a good balance between the different manifestations, as it facilitates displaying the physical keyboard for improved typing performance and, at the same time, allows one to block out the real world, removing many real-world distractors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".