I don’t see what you’re saying: The maluma/takete effect does not depend on the visual appearance of phonemes as they are articulated
Bibliographic record
Abstract
In contrast to the principle of arbitrariness, recent work has shown that language can iconically depict referents being talked about. One such example is the maluma/takete effect: an association between certain phonemes (e.g., those in maluma) and round shapes, and other phonemes (e.g., those in takete and spiky shapes). An open question has been whether this association is crossmodal (arising from phonemes' sound or kinesthetics) or unimodal (arising from phonemes' visual appearance). In the latter case, individuals may associate a person's rounded lips as they pronounce the /u/ in maluma with round shapes. We examined this hypothesis by having participants pair nonwords with shapes in either an audio-only condition (they only heard nonwords) or an audiovisual condition (they both heard nonwords and saw them articulated). We found no evidence that seeing nonwords articulated enhanced the maluma/takete effect. In fact, there was evidence that it decreased it in some cases. This was confirmed with a Bayesian analysis. These results eliminate a plausible explanation for the maluma/takete effect, as an instance of visual matching. We discuss the alternate possibility that it involves crossmodal associations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".