Map Formats and Landmark Cues: Their Influence on Spatial Memory and Wayfinding in Virtual Reality
Bibliographic record
Abstract
Understanding how spatial representations and environmental cues influence wayfinding and spatial memory is crucial for designing effective XR navigation systems. While most Augmented Reality (AR) aids rely on turn-by-turn guidance, they often lack survey information that supports spatial learning.We compare two informationally equivalent map formats: a conventional head-down, track-up map and SkyMap, a quasi-world-aligned, head-up display (HUD) rendered above the user. In a controlled virtual reality study simulating urban wayfinding, we also manipulate the presence of tall, visually distinctive buildings to examine the role of landmarks in spatial orientation and memory.Results show that SkyMap supports comparable performance to conventional maps across all measures, including spatial recall, challenging previous assumptions that HUD hinder spatial knowledge acquisition. Landmark presence, however, had no significant effect on wayfinding or memory. These findings highlight the potential of HUD-based survey maps for immersive navigation tasks and provide design recommendations for enhancing spatial awareness in AR interfaces.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".