Do Males Feel What Females Feel? Investigating the Influence of Sex on Haptic Abilities
Bibliographic record
Abstract
Interacting with objects in one’s environment is a part of life, and at all times these interactions require the use of somatosensory systems, whether it be visual, auditory, or haptic. Within visual realms, spatial abilities (SA) represent the cognitive capacity to remember & manipulate mental representations of objects to problem solve. Haptic abilities (HA) refer to the cognitive ability to acquire information & meaningfully manipulate objects through touch. Males consistently outperform females on standardized SA tests such as the Mental Rotations Test (MRT). The objective of this study is to investigate the influence of sex on HA, which is currently unknown. Given the removal of any visual spatial advantage in solely haptic tasks, it was hypothesized that although males outperform females on spatial ability tasks, this sex difference will be removed in tasks measuring HA. The MRT was used to measure SA, and a 3D version of the MRT coined the Haptic Abilities Test (HAT) was used to measure HA. The HAT was completed under two test conditions: a haptic (H) condition required the use of solely touch, while the other condition used sight & haptics (SH) to discriminate shape. Males outperformed females in the SH condition. However, this sex difference was eliminated in the purely haptic (H) HAT condition. In this condition, both sexes arrived at solutions through the differential use of haptic exploratory strategies. Given pandemic-driven migrations to online education where no haptics are possible, these findings raise concerns. Specifically, if haptics work to reduce the advantage males have over females when completing spatial tasks, the current online shift adversely affects females. Understanding the influence of sex on haptics can therefore result in more equitable learning 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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".