Sex Differences and Cognitive Maps: Studies in the Lab don’t Always Reflect Cognitive Map Accuracy in Everyday Life
Bibliographic record
Abstract
The ability to create an accurate mental survey representation, orcognitive map, when moving through an environment varies widely across individuals, and we are still trying to understand the origins of these individual differences. Non-immersive virtual environments used to test for cognitive map accuracy in the laboratory have shown sex differences with a performance advantage for men in some studies but not others. When sex differences are demonstrated, it is unclear whether women’s performance generalizes to familiar and unfamiliar real-world environments. In Experiment 1, 98 participants explored the virtual environment Silcton and afterwards estimated directions between the landmarks in Silcton and arranged landmarks found in Silcton on a map. In addition, they reported frequently visited real-world locations and then estimated directions between them and drew a map of the locations. Men were more accurate on tests of Silcton than women were, although there was no difference between sexes for accuracy with real-world locations. Within sexes, women were more accurate with the real-world locations than Silcton, while men showed the opposite pattern. In Experiment 2, 21 women were tested with Silcton and their familiar real-world locations as in Experiment 1 but were also walked through an unfamiliar real-world area on campus and completed direction estimation and map drawing tests for the new environment. Overall, women were more accurate with the two real-world environments than Silcton, with some evidence that accuracy with the new real-world environment was more accurate than the familiar real-world locations. Overall, women’s ability to create a cognitive map of a virtual environment in the laboratory does not seem to be indicative of their ability to do the same in the real world, and care should be taken when generalizing lab results with virtual environments.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".