Use of geometric properties of landmark arrays for reorientation relative to remote cities and local objects.
Bibliographic record
Abstract
Five experiments investigated how human adults use landmark arrays in the immediate environment to reorient relative to the local environment and relative to remote cities. Participants learned targets' directions with the presence of a proximal 4 poles forming a rectangular shape and an array of more distal poles forming a rectangular shape. Then participants were disoriented and pointed to targets with the presence of the proximal poles or the distal poles. Participants' orientation was estimated by the mean of their pointing error across targets. The targets could be 7 objects in the immediate local environment in which the poles were located or 7 cities around Edmonton (Alberta, Canada) where the experiments occurred. The directions of the 7 cities could be learned from reading a map first and then from pointing to the cities when the poles were presented. The directions of the 7 cities could also be learned from viewing labels of cities moving back and forth in the specific direction in the immediate local environment in which the poles were located. The shape of the array of the distal poles varied in salience by changing the number of poles on each edge of the rectangle (2 vs. 34). The results showed that participants regained their orientation relative to local objects using the distal poles with 2 poles on each edge; participants could not reorient relative to cities using the distal pole array with 2 poles on each edge but could reorient relative to cities using the distal pole array with 34 poles on each edge. These results indicate that use of cues in reorientation depends not only on the cue salience but also on which environment people need to reorient to.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".