Abstract shapes show affective traits
Bibliographic record
Abstract
Two experiments are presented concerning the Takete/Maluma phenomenon, which involves presenting participants with two abstract shapes (one curvy/roundish, the other angular/pointy) and two non-words (one characterised by a ‘smooth’ sound, the other by a ‘sharp’ sound). Participants tend to associate the curvy shape with the smooth sounding non-word, and the angular shape with the sharp sounding non-word. Our research expands on such phenomenon by investigating the correspondence between abstract shapes and affective traits. In experiment 1, 122 native Italian speakers were presented with nine abstract shapes and ten non-words: three characterised as sharp sounding, three as soft sounding, two as mixed sounding, and two which sounds may remotely recall the name of geometrical figures (tigano for triangolo-triangle; kiquoda for quadrato-square). Each shape was presented singularly, and participants had two tasks: 1) choose a name for the shape among the list of non-words; 2) select an affective trait that best described the shape (good, bad, angry, sad, scared, joyful, calm, pleasant, bored, melancholic). In Experiment 2, 193 native Russian speakers saw the same visual stimuli; the tasks associated with each shape were three: 1) choose a name for the shape from the list of non-words (transposed into Cyrillic); 2) choose a non-word that best describes the shape from a list of non-words (the Italian affective words transposed into Cyrillic); 3) select an affective trait that best described the shape from the list of words translated into Russian (experiment 1, task 2). Results: a) the naming task did not lead to clearcut results in either experiment; b) the assignment of affective traits (task 2 exp. 1; task 3 exp 2) were practically identical in both experiments; c) the assignment of non-words derived from Italian affective words transposed into Cyrillic (task 2 exp. 2) was basically random, not influenced by how they sound.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 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; both teacher heads agree on what is shown here.
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".