Sex differences in spatial tasks: Considering environmental factors, navigation strategies, and age
Bibliographic record
Abstract
Sex differences in navigation have been a topic of investigation for decades and has been subjected to various contradictory findings and debates. The aim of this work was to compare the spatial memory of men and women tested in various different types of spatial tasks, while controlling for navigation strategies and aging. It is generally thought that men outperform women in navigation and that women have higher scores on object location tasks. However, many studies fail to control for different factors that may bias one sex or the other. We aggregated the data of 465 participants (349 young adults, 127 older adults) who took part in various studies conducted in our laboratory, which include both published and original unpublished data, in order to investigate sex differences. In these studies, we used a number of different paradigms: virtual radial arm mazes, a virtual wayfinding task, an object location task, a virtual Morris Water Maze, and the invisible sensor task which is a real-life model of the Morris Water Maze. While our results may seem discordant at first glance, they demonstrate that several factors can impact the performance of men and women on spatial tasks, including spontaneous navigation strategies, environmental characteristics, and age. We replicated findings showing that women favor proximal landmarks compared to men who favor distal landmarks, women have better memory than men for the position of objects in the absence of reference frames, but they will have poorer scores when navigation requires specific angles, distances and polar coordinates. Moreover, we found that in aging, women who avoid the use of landmarks when navigating a radial maze show stronger reliance on these non-spatial strategies than men. On the other hand, women who rely on landmarks, do so to the same extent as men. Our findings highlight the need to carefully take into consideration these factors in order to produce a more harmonious understanding of sex differences in navigation. Finally, the interaction between spontaneous navigation strategies, sex, and age is discussed in terms of its implications for risk of Alzheimer’s disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".