Reply to: Not just the alveolar trill, but all “r-like” sounds are associated with roughness across languages, pointing to a more general link between sound and touch
Bibliographic record
Abstract
In Winter et al. 1 , we reported evidence for a cross-modal iconic association between the trilled r sound and the sensory dimension of roughness in spoken vocabularies. One of our studies showed that the word ‘rough’ is more likely to contain an r than the word ‘smooth’ across 332 languages, but only when the language has a trilled r . Anselme, Pellegrino & Dediu 2 (APD) present a reanalysis of our data based on a more rigorous coding of trilled versus non-trilled r . They confirm the link between trilled r and roughness but find an equally strong effect for languages with a non-trilled r , suggesting that the unique properties of trills ‘cannot be the main cause to this tactile-sound association.’ We question this conclusion. In making our case, we point out an important methodological dilemma: while pre-existing cross-linguistic data sets are often noisy, using manual coding to reduce this noise may introduce biases that distort the results. Indeed, a simulation shows that the effect size for languages with non-trilled r in APD’s analysis is larger than expected under an unbiased approach to recoding. While APD convincingly demonstrate the existence of an effect for non-trilled r , it is not clear that the effect is as strong as that for trilled r . It remains possible that trills play a distinctive role in carrying an iconic association with roughness across vocabularies.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.029 | 0.041 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".