Cognitive maps integrating locations but missing orientations in across-boundary environments.
Bibliographic record
Abstract
People often struggle to accurately point to locations across boundaries, such as pointing to campus buildings while seated inside a lecture room. This difficulty challenges the existence of a cognitive map with integrated representations of places across boundaries. In this project, we distinguished between a cognitive map comprising integrated representations of locations and one comprising integrated representations of orientations. We hypothesized that the across-boundary pointing difficulty might originate from a cognitive map lacking integrated orientations. Using an immersive virtual reality head-mounted display, participants were presented with panoramic photos taken indoors or outdoors of six campus buildings. After familiarizing themselves with their location as indicated by the panorama photo, participants were instructed to face a specific direction indicated by an arrow in the environment. They were then asked to point to five additional campus buildings. Participants' represented locations and headings for each testing view were calculated by maximizing the similarity between their pointing directions and their represented directions from a given location and heading. The results revealed that absolute pointing errors were significantly larger indoors than outdoors. This indoor-outdoor difference was primarily attributed to differences in estimating headings rather than differences in estimating positions. Furthermore, systematic positional shifts were observed in individual test views. These shifts were consistent between indoor and outdoor views of the same buildings but did not show consistency between indoor and outdoor views of different buildings. This suggests that individuals may develop a cognitive map of distorted but globally consistent representations of locations across boundaries. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".