How Tall is that Bar Chart? Virtual Reality, Distance Compression and Visualizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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