The Effect of Visual and Interactive Representations on Human Performance and Preference with Scalar Data Fields
Bibliographic record
Abstract
2D scalar data fields are often represented as heatmaps because color can help viewers perceive structure without having to interpret individual digits. Although heatmaps and color mapping have received much research attention, there are alternative representations that have been generally overlooked and might overcome heatmap problems. For example, color perception is subject to context-based perceptual bias and high error, which can be addressed through representations that use digits to enable more accurate value reading. We designed a series of three experiments that compare five techniques: a regular table of digits (Digits), a state-of-the-art heatmap (Color), a heatmap with an interactive tooltip showing the value under the cursor (Tooltip), a heatmap with the digits overlapped over it (DigitsColor), and FatFonts. Data analysis from the three experiments, which test locating values, finding extrema, and clustering tasks, show that overlapping digits on color (DigitsColor) offers a substantial increase in accuracy (between 10 and 60 percent points of improvement over the plain heatmap (Color), depending on the task) at the cost of extra time when locating extrema or forming clusters, but none when locating values. The interactive tooltip offered a poor speed-accuracy tradeoff, but participants preferred it to the plain heatmap (color) or digits-only (Digits) representations. We conclude that hybrid color-digit representations of scalar data fields could be highly beneficial for uses where spatial resolution and speed are not the main concern.
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.
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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".