The Effects of Map Reading Expertise and Map Type on Eye Movements in Map Comparison Tasks
Bibliographic record
Abstract
Comparing maps of different geographical phenomena, or maps of the same geographical phenomenon at different points in time, is a frequent task in many disciplines. The process of map comparison has been studied occasionally by cartographers since the 1970s, but recent improvements in neuropsychological testing equipment and in geographical information system (GIS) technology had us review this topic in a new light. We propose a cognitive approach using eye movement recording to understand the process of comparing two static maps displayed simultaneously on a screen. Two groups of subjects with different levels of expertise with map reading were shown pairs of maps and asked to judge their similarity or difference. We used three types of maps that differed in their spatial granularity: (A) randomly generated, 64-by-64 pixel, black-and-white images, (B) grayscale choropleth maps representing socio-economic variables for counties in lower Michigan, and (C) land-use maps of the surroundings of selected Canadian cities in different years resulting from classified satellite imagery. Subjects were asked whether two maps presented on the screen were similar (tests A and B) or different (test C). Response times, fixation durations and fixation counts differed significantly for the three map types. Land-use maps required the longest response times indicating that they were most difficult to compare. At the same time, land-use maps required more fixations than the other two types of maps, while the duration of these fixations was not different from the other map types. When comparing two maps of the same type, saccades between the two maps provide information on the subject’s decision-making process. We found that for the land-use maps, the number of these cross-saccades was significantly smaller than for the two other map types. Pairs of land-use maps were characterized by a fine raster grid and fewer pixel-by-pixel differences between the two maps, while both, random grids in test A and county maps in test B consist of clear-cut spatial units. We conclude that whenever spatial units can be distinguished on a map and corresponding units on a second map can be found easily, subjects will tend to compare the two maps in a unit-by-unit approach. In contrast, if maps consist of smoother spatial patterns, subjects will try to memorize patterns on one map (usually the one on the right-hand side), and make fewer saccades to compare these patterns with those on the other map. The results from this experiment could be used to provide context-adaptive tools for map comparison in GIS. The behavioral differences between groups (experts vs. novices) in this experiment were mostly not significant. This supports the notion of developing standard GIS tools that are offered to users with a wide range of expertise.
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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".