The Critical Role of Stereopsis in Virtual and Mixed Reality Learning Environments
Why this work is in the frame
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Bibliographic record
Abstract
Anatomy education has been revolutionized through digital media, resulting in major advances in realism, portability, scalability, and user satisfaction. However, while such approaches may well be more portable, realistic, or satisfying than traditional photographic presentations, it is less clear that they have any superiority in terms of student learning. In this study, it was hypothesized that virtual and mixed reality presentations of pelvic anatomy will have an advantage over two-dimensional (2D) presentations and perform approximately equal to physical models and that this advantage over 2D presentations will be reduced when stereopsis is decreased by covering the non-dominant eye. Groups of 20 undergraduate students learned pelvic anatomy under seven conditions: physical model with and without stereo vision, mixed reality with and without stereo vision, virtual reality with and without stereo vision, and key views on a computer monitor. All were tested with a cadaveric pelvis and a 15-item, short-answer recognition test. Compared to the key views, the physical model had a 70% increase in accuracy in structure identification; the virtual reality a 25% increase, and the mixed reality a non-significant 2.5% change. Blocking stereopsis reduced performance on the physical model by 15%, on virtual reality by 60%, but by only 2.5% on the mixed reality technology. The data show that virtual and mixed reality technologies tested are inferior to physical models and that true stereopsis is critical in learning anatomy.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it