Connecting spatial memories of two nested spaces.
Bibliographic record
Abstract
Four experiments investigated the manner in which people use spatial reference directions to organize spatial memories of 2 conceptually nested layouts. Participants learned directions of 8 remote cities centered to Beijing or Edmonton, where the experiments occurred, using a map or using direct pointing. The map and the environment were aligned, and participants faced north (0°). Participants also learned locations of 7 objects on a table. Participants faced north (0°) during learning but were instructed to learn the layout along the northwest-southeast (45°-225°) axis. Judgments of relative direction (imagine you are standing at X, facing Y, point to Z) were used to determine spatial reference directions in retrieval of bearings between 2 objects or 2 cities. The results showed that when the tested bearing and the imagined heading were within an array, participants used 0° as the reference direction in retrieving bearings between cities but used 45°-225° to retrieve bearings between objects. When the tested bearing and the imagined heading were across 2 arrays, participants used the reference direction of the array from which the tested bearing was. These results indicated that bearings between items within an array were represented only with respect to the reference directions of this array and the relationship between spatial reference directions in these 2 arrays was also represented.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".