Encoding of different environmental features with or without spatial updating
Bibliographic record
Abstract
Wang and Spelke (2000) have shown that certain features of an environment are encoded egocentrically, whereas others are encoded allocentrically. We studied directional judgments of environmental features (objects and corners) from viewpoints that were either aligned or misaligned with the originally learned viewpoint. Further, we explored the role of spatial updating in determining the type of spatial processing. Subjects were brought to a learning position in a four-sided, irregularly shaped room and learned the locations of four corners and four different objects. They were then blindfolded and led to the centre of the room either along a direct path (providing spatial updating) or along a disorienting path. They were then required to point in the directions of the corners and objects while imagining themselves at one of two testing viewpoints (aligned or misaligned with the learning viewpoint). The results showed that absolute error for both corners and objects was higher from the misaligned viewpoint compared to the aligned viewpoint regardless of whether subjects were disoriented or not, suggesting a tendency for egocentric processing. However, for configuration error, when subjects were not disoriented, both corners and objects showed no difference between viewpoints. This discrepancy in results between absolute and configuration error when subjects were not disoriented suggests that subjects can maintain the relative layout of the features but not the absolute direction of the features when the testing viewpoint changed. When subjects were disoriented, configuration error was higher from the misaligned viewpoint compared to the aligned viewpoint for corners but not for objects. The manner by which different features are processed can be conceptualized as falling on different positions along a continuum between pure egocentric and pure allocentric spatial representations. Overall the availability of spatial updating and the type of spatial features can both impact the characteristics of spatial representations.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".