Evaluating the effects of colour blending on optical-see-through displays for ubiquitous visualizations
Bibliographic record
Abstract
Optical-see-through (OST) augmented reality headsets offer users the flexibility to access relevant data visualizations anytime and anywhere. However, the appearance of content displayed on OST displays varies in colour and transparency depending on the environment they are viewed in, potentially leading to interpretation challenges. We present the findings of a psychophysical study (N = 24), aimed at assessing the impact of two environmental factors – lighting intensity and background colour – on user performance and colour perception accuracy in a visualization and colour-matching task using an OST headset. Our results suggest the effect of background colour on visualization interpretation is notable only under bright lighting conditions. Interestingly, participants perceived low-colour-contrast scenarios as more challenging, although their performance did not decline. Additionally, visualization colours were perceptibly and distinctly mismatched, but did not blend with the background colours. Finally, we discuss visual comfort and colour coding in the context of designing ubiquitous visualizations on OST displays, highlighting open challenges.
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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 | 0.002 | 0.019 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".