Differential encoding of environmental features in spatial representation
Bibliographic record
Abstract
Originally demonstrated by Wang and Spelke (2000), it has since been shown that different features of an environment may be encoded either allocentrically or egocentrically. Following this line of research, we studied subject's directional judgments of environmental features (objects and corners) from an imagined viewpoint that was either aligned or misaligned with the originally learned viewpoint. Subjects were first brought to a fixed learning position in a four-sided, irregularly shaped room and learned the locations of four corners and four different objects relative to two testing viewpoints (aligned or misaligned). They were then blindfolded, brought out of the room, and required to point in the directions of the corners and objects while imagining themselves at one of the two testing viewpoints. Three experiments were conducted: different objects placed in the middle of the room (Exp 1); different objects placed against the wall (Exp 2); and identical objects placed against the wall (Exp 3). The results showed that absolute error for both corners and objects and configurational error for corners (Exp 1, 2, and 3) and objects with the same identity (Exp 3) was higher from the misaligned viewpoint compared to the aligned viewpoint. However, the configurational error for objects with different identities was similar between viewpoints (Exp 1 and 2). The fact that configuration errors were different relative to different viewpoint under particular circumstances (e.g., environmental features) argues against a definitive allocentric representation. Thus, disregarding important variables such as viewpoint can potentially misrepresent the true nature of spatial representations.
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".