Where Is It (in the Map)? Recall and Recognition of Spatial Information
Bibliographic record
Abstract
Findings of empirical studies of spatial memory using maps are direct responses to the successful transfer and processing of map information. The memory performance of map users is an important indication of the quality of a map design. Studies of spatial memory have mainly relied on recall performances, but maps can be used in various ways depending on the map user's task and applied strategy. Therefore, one memory paradigm does not cover the entire spectrum of options for examining the retrieval of map information. Three different experiments were designed to analyze and compare memory performances using different map information in recall and recognition (combining episodic and semantic memory) paradigms. The results demonstrate that map complexity, as varied by the amount of displayed map detail, contributes significantly to memory performance. Moreover, memory enhancement affected by map-structuring elements (grids) depends on the respective paradigm and also on the visual appearance of the structuring elements. Both paradigms for examining the influence of map information on cognitive processing can be applied specifically to analyze the efficiency of map designs. On the basis of the different effects of map information, a reasoned application of these paradigms to test map designs is indispensable.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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".