Maps and Biased Familiarity: Cognitive Distance Error and Reference Points
Bibliographic record
Abstract
When map readers process information on cartographic maps, there is a competition for visual attention controlled by both top-down and bottom-up mechanisms. We hypothesize that when learning, map readers are predisposed to allocate attention asymmetrically and to initially favour some locations over others. This asymmetrical allocation of attention facilitates learning for certain locations as a result of familiarity bias. In this study, participants were asked to learn city locations on one of three cartographic maps. Maps displayed distributions of cities with true or novel locations and names. Results indicate that cognitive distance error was significantly less for “home” reference points, visually central reference points, and reference points within a visual cluster. Female participants outperformed male participants when learning novel maps; male participants performed significantly better with maps with true locations and city names. Both female and male learners performed better when processing maps with familiar locations and names. The results support the idea that a biased allocation of attention would cause learners to consider favoured relationships more frequently and to improve their accuracy relative to less favoured relationships and those that receive less attention. Results also support the notion that multiple factors on a map cause attention bias and that bias should disappear with sufficient experience.
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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 | 0.002 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".