Defining a boundary in goal localization: Infinite number of points or extended surfaces.
Bibliographic record
Abstract
Four experiments examined the roles of extended surfaces and the number of points in the boundary superiority effect in goal localization. Participants learned the locations of 4 objects in the presence of a boundary, landmarks, or both in an immersive virtual environment by reproducing the locations with feedback. Participants then localized the objects in the presence of either the boundary or the landmarks during testing without feedback. The results showed that when both 1 landmark and a circular boundary were presented during learning, localization error during testing increased significantly when only the landmark was presented during testing, whereas localization error did not increase when only the boundary was presented during testing, thus demonstrating a boundary superiority effect. This boundary superiority effect was not observed when 36 landmarks forming a circle and a circular boundary were presented during learning. The landmark superiority effect was observed when 36 landmarks, forming a circular shape, and 1/36th part of the circular boundary were presented during learning. Furthermore, when a varied number of landmarks were presented with a circular boundary during learning, the localization error when the boundary was removed during testing was negatively correlated with the number of the landmarks. These results indicate that the superiority of a circular boundary to a landmark might be due to the larger number of points in the circular boundary but not due to the extended surface of the circular boundary.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".