Measuring Visual Fatigue and Cognitive Load via Eye Tracking while Learning with Virtual Reality Head-Mounted Displays: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Virtual Reality Head-Mounted Displays (HMDs) reached the consumer market and are used for learning purposes. Risks regarding visual fatigue and high cognitive load arise while using HMDs. These risks could impact learning efficiency. Visual fatigue and cognitive load can be measured with eye tracking, a technique that is progressively implemented in HMDs. Thus, we investigate how to assess visual fatigue and cognitive load via eye tracking. We conducted this review based on five research questions. We first described visual fatigue and possible cognitive overload while learning with HMDs. The review indicates that visual fatigue can be measured with blinks and cognitive load with pupil diameter based on thirty-seven included papers. Yet, distinguishing visual fatigue from cognitive load with such measures is challenging due to possible links between them. Despite measure interpretation issues, eye tracking is promising for live assessment. More researches are needed to make data interpretation more robust and document human factor risks when learning with HMDs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it