Accidental Landmarks: How Showing (and Removing) Emphasis in a 2D Visualization Affected Retrieval and Revisitation
Bibliographic record
Abstract
Many visualizations display large datasets in which it can be difficult for users to find (and re-find) specific items. In systems that provide highlighting tools (e.g., filtering or brushing), emphasized points can become "accidental landmarks" - visual anchors that help users remember locations that are near the emphasized points. Accidental landmarks could be useful (by aiding revisitation), but if users become dependent on them, removing or changing the highlighting could cause problems. We provide designers with information about these issues through two crowdsourced studies in which people learned a set of item locations (in visualizations with or without emphasized points); we then removed or changed the highlighting to see if performance suffered. In the first study, which used a simple grid of points, results showed that changing or removing emphasized points significantly impeded users' ability to re-find targets, but the highlighting did not improve performance during training. In the second study, which used a more complex scatterplot, we found that highlighting significantly improved performance during training, but that removing or changing the emphasis points only reduced refinding performance for a few target types. Our work demonstrates that visualization designers need to consider how transient visual effects such as emphasis can affect spatial learning and revisitation, and provides new knowledge about how visual features can affect performance.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".