Evaluating Cybersickness in Virtual 3D Models for Anatomy Learning
Bibliographic record
Abstract
Introduction Cybersickness is an array of symptoms associated with exposure to three‐dimensional visualization technology (3DVT) environments, such as virtual reality (VR). It is thought that cybersickness is a type of motion sickness caused by a mismatch in sensory and vestibular input when using these modalities. Symptoms of cybersickness are often akin to those of traditional motion sickness, such as headache and nausea. In the literature, as many as 40‐60% of VR users report symptoms of cybersickness, though in our laboratory approximately 20% of users report symptoms of cybersickness. Our past research has shown that the physical environment is often preferred by students, and more effective for learning anatomy, compared to 3DVT environments. We hypothesize that the preference for, and effectiveness of, the physical environment over 3DVT may be a result of cybersickness due to the isolation of the learner from the physical environment. However, no direct measurements of cybersickness or comparisons between 3DVT environments in anatomy are available. Methods Our study involves comparisons of cybersickness during anatomy learning between three 3D modalities: a VR Oculus Quest 2 TM headset, an autostereoscopic screen (Alioscopy TM ), and a 3D‐printed physical model. Undergraduate students, with no formal anatomy training, will be randomized via a Latin square design to view one of three skeletal models (human hemipelvis, zygomatic bone, or calcaneus) in each of the modalities. Participants may not touch the model but may rotate it along the horizontal plane using an Xbox TM controller. Participants will have four minutes to learn ten bony landmarks on the viewed model and will then take an untimed, recognition‐based test for landmarks learned before progressing onto the next modality. Cybersickness will be assessed following testing in each modality, via self‐reports on the simulation sickness questionnaire (SSQ). Results We hypothesise that reported cybersickness will be highest for the VR displayed on the head‐mounted display of the Oculus Quest 2 TM which entirely covers the visual field, compared to learning through the Alioscopy TM screen or the physical model due to the relative lack of isolation of the learner from the physical environment in the last two treatment groups. Conclusion With the rapid development of 3DVT for use in anatomical education, cybersickness is an important issue in evaluating the quality of 3D modalities. The results of this study will allow educators and students to make informed decisions about the use of 3DVT in anatomy thus preventing uncomfortable physical symptoms associated with learning using 3DVT and improving learning as a result.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".