Vertical Binocular Misalignment in AR Impairs Reading Performance
Bibliographic record
Abstract
Vertical binocular misalignment (VBM) can degrade image quality and contribute to visual discomfort in stereoscopic head-mounted displays, particularly for see-through AR. In this project, we investigate whether VBM impairs visual performance — namely, users’ ability to process briefly-presented AR content, like text notifications. We also quantify how the impacts of VBM vary with an AR system’s virtual image distance (VID). Across three experiments, participants were asked to (a) detect and (b) resolve, fuse and process AR content presented with constant and time-varying VBM. Short text stimuli (words or sentences) were briefly presented on a multi-display haploscope, using additive and transmissive displays to emulate see-through AR. Experiments were repeated at three VIDs: 57, 100, 139 cm (1.75, 1, 0.72 D). The magnitude and frequency of VBM was adaptively sampled on each trial. Visual performance (as measured by participants’ time to fuse and read text) was steadily impaired with increasing VBM. For high VBM magnitudes, time to fuse did not meaningfully differ between VIDs; for low VBM, time to fuse was fastest in the furthest VID. Participants’ ability to detect VBM also improved at further VIDs. Correlations were observed between all three user outcome measures: detection, visual performance, and comfort. Overall, we find that visual performance metrics provide a useful framework to complement detection and visual comfort approaches, consistent with recent work on VBM and related artifacts in AR. The results of this study can be used to inform VBM tolerance guidelines and VID placement tradeoffs in future AR devices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".