A mirror in the sky: the effects of map format and user expertise on navigation performance and mental workload
Bibliographic record
Abstract
A novel map display concept named Mirror in the Sky (MitS) has been introduced to improve performance and reduce workload in navigation tasks. However, this display will be novel to most users and as such, an evaluation of MitS in comparison with more conventional map formats is warranted. This study investigated the effects of map display format (MitS vs. north-up and track-up maps) and user expertise on mental workload (MWL) and performance, using both soldiers (experts) and civilians (novices) as participants. Participants followed a prescribed route to a destination in a virtual environment (route following task) while also performing a secondary task (detection response task). Soldiers generally performed better than civilians. Soldiers reported a higher MWL with MitS than with the north-up map, whereas civilians reported a higher MWL with MitS than with the track-up map. Regardless of user expertise, there were performance and workload challenges with MitS, despite its potential. Practitioner summary: A new map display concept called Mirror in the Sky (MitS) was compared with two conventional map formats: a north-up and track-up map. The experiment tested soldier and civilian users in a route following task. Both groups got further into the route and had fewer obstacle collisions with north-up and track-up maps than they did with the MitS map. MWL measures generally indicated higher workload with MitS. Abbreviations: MitS: mirror in the sky; FFOV: forward field of view; AR: augmented reality; MWL: mental workload; VR: virtual reality; HF: human factors; HR: heart rate; HRV: heart rate variability; DRT: detection response task; DRDC: defence research and development Canada; VE: virtual environment; RT: response time; ANOVA: analysis of variance
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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".