Immersiveness and Perceptibility of Convex and Concave Displays
Bibliographic record
Abstract
Curved displays promote three-dimensionality and may facilitate non-wearable virtual reality. Yet there is little design guidance on the optimal type of curvature that should be used. In this paper we have examined the perceived properties of convex and concave displays, at two different sizes. We conducted an experiment with 21 participants. Each participant was asked to make a series of binary choices after the participant viewed a display where one side was seen as convex and the opposite side was seen as concave. For each of 15 perceptual and aesthetic features, participants had to choose whether the convex or concave view/side of the display was stronger/more appropriate. Each participant made two binary choices (one for a small display viewed from its convex and concave sides, the other for a large display viewed from its convex and concave sides) for each perceptual and aesthetic feature, leading to a total of 30 judgments. Participants preferred the convex version of the small sized display and the concave version of the larger display. Individual differences were observed. Some participants generally preferred the convex displays, while others preferred the concave displays. The results are interpreted in terms of their implications for the future use of convex and concave displays in VR applications that do not use goggles.
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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 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".