Effects of Long-Term Exposure on Sensitivity and Comfort with Stereoscopic Displays
Bibliographic record
Abstract
Stereoscopic 3D media has recently increased in appreciation and availability. This popularity has led to concerns over the health effects of habitual viewing of stereoscopic 3D content; concerns that are largely hypothetical. Here we examine the effects of repeated, long-term exposure to stereoscopic 3D in the workplace on several measures of stereoscopic sensitivity (discrimination, depth matching, and fusion limits) along with reported negative symptoms associated with viewing stereoscopic 3D. We recruited a group of adult stereoscopic 3D industry experts and compared their performance with observers who were (i) inexperienced with stereoscopic 3D, (ii) researchers who study stereopsis, and (iii) vision researchers with little or no experimental stereoscopic experience. Unexpectedly, we found very little difference between the four groups on all but the depth discrimination task, and the differences that did occur appear to reflect task-specific training or experience. Thus, we found no positive or negative consequences of repeated and extended exposure to stereoscopic 3D in these populations.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".