Diminutive Digits Discern Delicate Details: Fingertip Size and the Sex Difference in Tactile Spatial Acuity
Bibliographic record
Abstract
We have observed that passive tactile spatial acuity, the ability to resolve the spatial structure of surfaces pressed upon the skin, differs subtly but consistently between the sexes, with women able to perceive finer surface detail than men. Eschewing complex central explanations, we hypothesized that this sex difference in somatosensory perception might result from simple physical differences between the fingers of women and men. To investigate, we tested 50 women and 50 men on a tactile grating orientation task and measured the surface area of the participants' index fingertips. In subsets of participants, we additionally measured finger skin compliance and optically imaged the fingerprint microstructure to count sweat pores. We show here that tactile perception improves with decreasing finger size, and that this correlation fully explains the better perception of women, who on average have smaller fingers than men. Indeed, when sex and finger size are both considered in statistical analyses, only finger size predicts tactile acuity. Thus, a man and a woman with fingers of equal size will, on average, enjoy equal tactile acuity. We further show that sweat pores, and presumably the Merkel receptors beneath them, are packed more densely in smaller fingers.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".