Where do I go from here?: Spatial navigation strategy and disorientation when switching environments
Bibliographic record
Abstract
Deciding which way to turn when exiting a building requires one to be oriented with respect to the wider environment, but it remains unclear how spatial representations are updated to facilitate the switch from one space to another. Here, we consider two strategies: (1) egocentric: environmental locations are self-referenced; and (2) allocentric: positional information referenced to external cues. Participants viewed a walkthrough a 3D rendered city street intermediately entering and exiting an indoor space. We manipulated complexity of the indoor path to induce low or high disorientation, and mirror-reversed the street view in half the trials to dissociate between the strategies. Upon return, participants chose a turn as to continue in the tasked direction, and verified chosen direction after viewing the street end. The results showed that the street view was initially disregarded, suggesting a preference for the egocentric strategy in directional choices. However, responses were corrected based on the subsequent view, indicating a shift towards adherence to the allocentric strategy. The pattern of results also points to a directional conflict between egocentric and allocentric representations. Thus, although both strategies were engaged when switching environments, their implementation was largely asynchronous: egocentric dominated early and allocentric dominated subsequent control of navigation.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".