Representing Spatial Layout According to Intrinsic Frames of Reference
Bibliographic record
Abstract
Mou and McNamara have suggested that object locations are represented according to intrinsic reference frames. In three experiments, we investigated the limitations of intrinsic reference frames as a mean to represent object locations in spatial memory. Participants learned the locations of seven or eight common objects in a rectangular room and then made judgments of relative direction based on their memory of the layout. The results of all experiments showed that when all objects were positioned regularly, judgments of relative direction were faster or more accurate for novel headings that were aligned with the primary intrinsic structure than for other novel headings; however, when one irregularly positioned object was added to the layout, this advantage was eliminated. The experiments further indicated that with a single view at study, participants could represent the layout from either an egocentric orientation or a different orientation, according to experimental instructions. Together, these results suggest that environmental reference frames and intrinsic axes can influence performance for novel headings, but their role in spatial memory depends on egocentric experience, layout regularity, and instructions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".