Eye Movements in Map Comparison - Preliminary Results and Lessons Learned
Bibliographic record
Abstract
Comparing maps of different geographic phenomena, or maps of the same phenomenon at different points in time, is an important task in spatial data analysis and decision-making. The process of map comparison has been studied occasionally by cartographers since the 1970s, but recent improvements in neuropsychological testing equipment and GIS technology had us review this topic in a new light. In a pilot experiment, we presented pairs of maps to volunteer participants and recorded their eye movements while judging the maps’ similarity. We analysed average values of eye movement parameters such as fixation duration and proportions of saccades between the two maps in relation to three factors: the participant’s experience in reading maps; the type of map presented; and the actual similarity between the two maps. We found, for example, that different map types engaged viewers in different comparison strategies while we did not find behavioural differences between expert and novice map readers. We will speculate about implications of experimental cartography for GIS design and report on challenges encountered with this approach.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".