Virtual Unreality – Promise vs. Performance of Technology in Anatomy Education
Bibliographic record
Abstract
A number of studies have shown that computer generated, interactive, 3D projections (a type of virtual reality or VR) of human anatomy have serious handicaps when compared to ordinary physical models. The mechanism underlying the deficiency is not clear. In the present study we replicated the finding of a large benefit for physical models over equivalent VR; amounting to an effect size of 1.38. We then showed, with a series of experiments, that the advantage is not a consequence of haptic (touch) feedback, or “transfer appropriate processing” (Learning in 3D is more effective when tested in 3D). Instead, it appears that the advantage of the physical model is entirely a result of binocular vision; restricting viewing to one eye extinguishes the advantage. Additional testing with the Microsoft HoloLens, using an exact duplicate of the physical model rendered as a mixed reality (MR) object, showed performance equal to the 3D projection and equal to the physical model when learned and tested with monocular vision and much worse than the physical model. The 10–20 fold cost of using VR and MR learning objects over the physical models does not appear to be justified given the cost and inferior performance in increasing anatomic knowledge. Support or Funding Information Paul R. MacPherson Institute for Leadership, Innovation and Excellence in Teaching and the Faculty of Health Sciences, McMaster University. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".