Naïve Cartography: How Intuitions about Display Configuration Can Hurt Performance
Bibliographic record
Abstract
Map-making has traditionally been the domain of professional cartographers, but with the advent of interactive display systems, users now have the flexibility to create and configure their own digital maps and other visual displays. This flexibility can be beneficial only if users have good intuitions about which display configurations are effective or ineffective for different tasks. Here we examine people's intuitions about display effectiveness and whether these intuitions match the actual effectiveness of different displays. Surveys of undergraduate students and post-graduate meteorology students reveal that they consistently prefer enhanced displays, especially those that add animation and realism. These naïve intuitions contrast with the principles of cartography, which emphasize the importance of abstracting from the real world to create simple displays that make task-relevant information salient. Both a review of objective studies and a new study presented here support traditional principles of cartography and are inconsistent with naïve intuitions. We interpret these studies in relation to new theoretical notions of users’ folk fallacies about how perception works, and derive implications for the design of interactive display systems and education.
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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 | 0.028 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".