Developing Associations to the Sounds of a Name
Bibliographic record
Abstract
Sound symbolism refers to associations between language sounds and certain perceptual or semantic properties. One well-studied example is the maluma/takete effect, in which individuals tend to associate round-sounding nonwords like maluma with round shapes, and spiky-sounding nonwords like takete with spiky shapes. This phenomenon suggests that certain sounds are perceived as better suited to particular visual shapes, and it provides a means by which language can be non-arbitrary. Research has demonstrated that sound symbolism further extends from nonwords to real first names, a phenomenon known as name sound symbolism. In addition to phonological cues, research on name sound symbolism reveals an association between a name's perceived gender and shape: femaleness is associated with roundness, whereas maleness is associated with spikiness. However, previous studies have focused on adults, leaving open the question of whether children also show these associations. The present study examined the emergence of name sound symbolism in children, considering individual differences such as age and language ability. Results indicated that adults exhibit stronger sensitivity to both name sound symbolism and gender-shape associations than children. Although the gender-shape association is present in 5- to 7-year-olds, name sound symbolism may emerge at a later age. Our results point to the possibility that the presence of semantic meanings or sociolinguistic information like gender may compete with phonological cues when processing real words, thus attenuating the sound symbolic effect. These findings have important implications on how sound symbolism operates in nonwords versus in real words.
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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 | 0.007 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".