Navigation Experience and Mental Representations of the Environment: Do Pilots Build Better Cognitive Maps?
Bibliographic record
Abstract
A number of careers involve tasks that place demands on spatial cognition, but it is still unclear how and whether skills acquired in such applied experiences transfer to other spatial tasks. The current study investigated the association between pilot training and the ability to form a mental survey representation, or cognitive map, of a novel, ground-based, virtual environment. Undergraduate students who were engaged in general aviation pilot training and controls matched to the pilots on gender and video game usage freely explored a virtual town. Subsequently, participants performed a direction estimation task that tested the accuracy of their cognitive map representation of the town. In addition, participants completed the Object Perspective Test and rated their spatial abilities. Pilots were significantly more accurate than controls at estimating directions but did not differ from controls on the Object Perspective Test. Locations in the town were visited at a similar rate by the two groups, indicating that controls' relatively lower accuracy was not due to failure to fully explore the town. Pilots' superior performance is likely due to better online cognitive processing during exploration, suggesting the spatial updating they engage in during flight transfers to a non-aviation context.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".