The Effects of Grid Line Separation in Topographic Maps for Object Location Memory
Bibliographic record
Abstract
Research from the field of cognitive psychology provides evidence that cognitive representations of space based on maps or map-like sketches are subject to systematic distortion tendencies. These distortions influence the orientation capacity as they represent errors in spatial memory. Map grids are a traditional feature of map graphics that has rarely been considered in research on spatial distortions in cognitive maps. Grids traditionally assist the map reader in finding coordinates and objects, but they also provide a systematic and homogeneous structure for dividing up map information into smaller units supporting perception and spatial memory. In a previous study it was shown that grids improve object location memory. The aim of this study was to determine whether different sizes of grid cells have an effect on the quality of object location memory. Therefore, an empirical study including the test performances of 33 participants was carried out: the memory performance was measured as both the percentage of correctly recalled object locations (hit rate) and the mean distance errors of correctly recalled objects (spatial accuracy). Three different intervals of grid line spacing (Separation) were applied to topographic maps. These maps varied in their type of characteristic geographical areas, accompanied by three different levels of map complexity (Landscape). The results of this study show that both factors have an impact on object location memory in topographic maps.
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".