Evaluating the effects of colour blending on optical-see-through displays for ubiquitous visualizations
Why this work is in the frame
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it