Bibliographic record
Abstract
The Morris Water Maze (MWM) is commonly used in research for investigating spatial cognition and hippocampal-dependent learning in nonhuman animals. One of the most consistent findings in the literature is the male advantage in spatial ability. Usually, the MWM is adapted as a virtual reality for testing in humans. In our lab, we constructed a real-world navigation task and found that by default, men implemented an allocentric strategy whereas women adopted an egocentric strategy when finding the target location. Based on these findings, the present study designed a table-top version of the MWM. The goal of each subject is to locate a hidden target by navigating on an octagonal board using different spatial navigation strategies. The allocentric condition experiment tested 60 subjects (30 women) ages 18-25 and examined their ability to find a hidden target located relative to constant environmental room cues. Results revealed that men outperformed women on this task. In the egocentric condition experiment, 60 subjects (28 women) were directed to find a target location that remained consistent with respect to their body axis and starting position in the experiment. Women outperformed men in the egocentric condition experiment. In the neutral condition, the target was located in the center of the board and could be found using either egocentric or allocentric strategy, which resulted in no sex differences. Our results suggest that when a spatial task requires competition between two sources of information, an egocentric strategy appears more prominent in women whereas an allocentric strategy appears more prominent in males. Future work may use fMRI to see if sex differences in spatial navigation will correlate to differences in functional connectivity.
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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) | 0.003 | 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".