Bibliographic record
Abstract
This paper provides an overview of the possible function of non-arbitrary mappings between linguistic form and meaning, and presents new empirical evidence showing that shared cross-modal associations may underlie motion sound-symbolism in particular. In terms of function, several lines of empirical and theoretical evidence suggest that non-arbitrary form-meaning connections could have played a crucial role in lexical emergence during language evolution. Furthermore, the persistence of such non-arbitrariness in some areas of modern language may also be highly functional, as recent data has shown that non-arbitrary forms may help to bootstrap learning in children (Imai, Kita, Nagumo, and Okada, 2008) and adults (Nielsen and Rendall, 2012). Given the functional role of these non-arbitrary mappings between linguistic form and meaning, this paper describes new experimental data demonstrating shared mappings between non-sense words and visual motion using a direct matching task. Participants were given nonsense words that varied in terms of their voicing, reduplication, and vowel quality, and asked to change the movement of a ball to match a given word. Results show that back vowels are mapped onto slower speeds, and consonant reduplication with vowel alternation is mapped onto faster speeds. These results show a shared cross-modal association between linguistic sound and motion, which is likely leveraged in sound-symbolic systems found in natural language.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".