Parsing the role of consonants versus vowels in the classic Takete-Maluma phenomenon.
Bibliographic record
Abstract
Wolfgang Köhler (1929, Gestalt psychology, New York, NY: Liveright) famously reported a bias in people's choice of nonsense words as labels for novel objects, pointing to possible naïve expectations about language structure. Two accounts have been offered to explain this bias, one focusing on the visuomotor effects of different vowel forms and the other focusing on variation in the acoustic structure and perceptual quality of different consonants. To date, evidence in support of both effects is mixed. Moreover, the veracity of either effect has often been doubted due to perceived limitations in methodologies and stimulus materials. A novel word-construction experiment is presented to test both proposed effects using randomized word- and image-generation techniques to address previous methodological concerns. Results show that participants are sensitive to both vowel and consonant content, constructing novel words of relatively sonorant consonants and rounded vowels to label curved object images, and of relatively plosive consonants and nonrounded vowels to label jagged object images. Results point to additional influences on word construction potentially related to the articulatory affordances or constraints accompanying different word forms.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".