Voice cues are used in a similar way by blind and sighted adults when assessing women’s body size
Bibliographic record
Abstract
Humans' ability to gauge another person's body size from their voice alone may serve multiple functions ranging from threat assessment to speaker normalization. However, how this ability is acquired remains unknown. In two experiments we tested whether sighted, congenitally blind and late blind adults could accurately judge the relative heights of women from paired voice stimuli, and importantly, whether errors in size estimation varied with task difficulty across groups. Both blind (n = 56) and sighted (n = 61) listeners correctly judged women's relative heights on approximately 70% of low difficulty trials, corroborating previous findings for judging men's heights. However, accuracy dropped to chance levels for intermediate difficulty trials and to 25% for high difficulty trials, regardless of the listener's sightedness, duration of vision loss, sex, or age. Thus, blind adults estimated women's height with the same degree of accuracy, but also the same pattern of errors, as did sighted controls. Our findings provide further evidence that visual experience is not necessary for accurate body size estimation. Rather, both blind and sighted listeners appear to follow a general rule, mapping low auditory frequencies to largeness across a range of contexts. This sound-size mapping emerges without visual experience, and is likely very important for humans.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".