Measuring Visual Fatigue and Cognitive Load via Eye Tracking while Learning with Virtual Reality Head-Mounted Displays: A Review
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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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".