How Tall is that Bar Chart? Virtual Reality, Distance Compression and Visualizations
Bibliographic record
Abstract
As VR technology becomes more available, VR applications will be increasingly used to present information visualizations. While data visualization in VR is an interesting topic, there remain questions about how effective or accurate such visualization can be. One known phenomenon with VR environments is that people tend to unconsciously compress or underestimate distances. However, it is unknown if or how this effect will alter the perception of data visualizations in VR. To this end, we replicate portions of Cleveland and McGill's foundational perceptual visualization studies, in VR. Through a series of three studies we find that distance compression does negatively affect estimations of actual lengths (heights of bars), but does not appear to impact relative comparisons. Additionally, by replicating the position-angle experiments, we find that (as with traditional 2D visualizations) people are better at relative length evaluations than relative angles. Finally, by looking at these open questions, we develop a series of best practices for performing data visualization in a VR environment.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".