Bibliographic record
Abstract
Sound symbolism refers to an association between phonemes and stimuli containing particular perceptual and/or semantic elements (e.g., objects of a certain size or shape). Some of the best-known examples include the mil/mal effect (Sapir, Journal of Experimental Psychology, 12, 225-239, 1929) and the maluma/takete effect (Köhler, 1929). Interest in this topic has been on the rise within psychology, and studies have demonstrated that sound symbolic effects are relevant for many facets of cognition, including language, action, memory, and categorization. Sound symbolism also provides a mechanism by which words' forms can have nonarbitrary, iconic relationships with their meanings. Although various proposals have been put forth for how phonetic features (both acoustic and articulatory) come to be associated with stimuli, there is as yet no generally agreed-upon explanation. We review five proposals: statistical co-occurrence between phonetic features and associated stimuli in the environment, a shared property among phonetic features and stimuli; neural factors; species-general, evolved associations; and patterns extracted from language. We identify a number of outstanding questions that need to be addressed on this topic and suggest next steps for the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".