Direct measurement of spontaneous strategy selection in a virtual morris water maze shows females choose an allocentric strategy at least as often as males do.
Bibliographic record
Abstract
Considerable evidence indicates that males navigate large-scale space better than females, and some have previously attributed this difference to a greater ability of males to select or use an allocentric (cognitive mapping) navigational strategy. We directly tested this proposal by having males and females navigate in an "ambiguous" virtual Morris water maze environment that permitted participants to choose and use either an allocentric or an egocentric strategy. A novel probe trial at the end of training revealed which strategy each participant had been using and showed that the strategy selected by the greatest number of males and females was allocentric, and that this bias was even greater for females. Traditional measures of navigational performance (distance, latency, probe dwell time) indicated that overall, males were more efficient than females. However, this gender difference was not related to strategy choice: males were better than females regardless of strategy, though the difference was significant only in those navigating allocentrically. These data indicate that while males may navigate allocentrically more efficiently than females, this does not account for the male advantage in navigation. The data also indicate that under specific circumstances, females may also prefer and use an allocentric strategy to navigate. These findings have implications for theories regarding the differential use of the hippocampus by men and women.
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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 | 0.001 |
| 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) | 0.004 | 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".