People Separate Allocentric and Egocentric Cues to Judge Orientation of their Surroundings and the Self
Bibliographic record
Abstract
Studies on spatial perception demonstrated that humans use both allocentric and egocentric reference frames to make judgements about the positions of the landmarks in their surrounding (e.g., objects in a room). However, how these reference frames are used to perceive the orientation of these landmarks is not well studied. In this study, participants aligned a luminous rod with either the room (perpendicular to the ceiling; allocentric task) or with their body (parallel to the torso; egocentric task) in the dark. Using York University’s Tumbling Room, the chair and the room were rotated independently around the pitch axis (0°, 22.5°, 67.5°). The angle between the chair and the room was always 45°, except in the baseline (both upright). Half the participants were tilted forward during the experiment and the other half tilted backward. Comparing the average alignment errors showed that participants were accurate in aligning the rod to the room (average error=-3.4°; negative error indicates rod was tilted forward). However, participants made significantly larger errors when aligning the rod to their body compared to aligning it to the room where they tilted the rod about 18° more backward than their actual body tilt. There was significant effect of tilt direction, forward vs. backward, on the errors made where they tilted the rod more in the tilt directions. When participants were tilted backward, they made significantly larger errors during the egocentric task when their body was tilted compared to upright, but not when they were tilted forward. Our results suggest that people can make allocentric judgements accurately regardless of body or room orientation. Egocentric judgements depend solely on the body orientation and show a backwards bias. This shows that people are very good at separating allocentric and egocentric cues. They thereby focus on the task-relevant reference frame while disregarding the other.
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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 | 0.001 |
| 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".