The Effects of Practice on Navigation Performance and Mental Workload with a Mirror-In-The-Sky Map
Bibliographic record
Abstract
Some previous research using a new augmented reality map display called Mirror-in-the-Sky (MitS) showed that performance was worse and mental workload (MWL) greater with MitS relative to a track-up map for navigation and wayfinding tasks. The purpose of the current study was to determine—for both MitS and track-up map—how much performance improves and MWL decreases with practice in a simple navigation task. We conducted a three-session experiment in which twenty participants completed a route following task in a virtual environment. Task completion times and collisions decreased, subjective MWL decreased, and secondary task performance improved with practice. The NASA-TLX Global ratings and Detection Response Task Hit Rates showed a larger decrease in MWL with MitS than the track-up map. Additionally, means for performance and workload measures showed that differences between the MitS and track-up map decreased in the first session. In later sessions the differences between the MitS and track-up map were negligible. As such, with practice performance and MWL may be comparable to a traditional track-up map.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".