Why this work is in the frame
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Bibliographic record
Abstract
Arnheim (1986) speculated that different aesthetic domains (e.g., color and music) might be related to each other through common emotional associations. We investigated this hypothesis by having participants pick from among an array of 37 colors the five colors that went best (and later the five that went worst) with each of a set of musical selections that varied in composer, tempo, and mode (major/minor). They also rated each musical selection and each color for its emotional associations (happy-sad, lively-dreary, strong-weak, angry-calm). For both orchestral music and solo piano music, systematic mappings were found between the dimensions of color and music: faster music and major mode were associated with lighter, more saturated, yellower colors, whereas slower music and minor mode were associated with darker, desaturated, bluer colors. These mappings appear to be mediated by common emotional associations, because the correlation between emotional ratings of the musical selections and emotional ratings of the colors chosen to go with them were extremely high (0.90 to 0.98) for all emotional dimensions studied (e.g., people picked happy colors to go with happy music and dreary colors to go with dreary music). Further studies using better-controlled musical stimuli (unaccompanied theme-and-variation melodies by Mozart) dissociated effects due to instrumental timbre (piano/cello), register (high/low pitch), and note density (quarter-note theme vs. eighth-note variation), as well as tempo and mode from the specific influences of different melodic and harmonic structure in the earlier studies. The mediating role of emotion was established by obtaining analogous effects when people picked the colors that went best (and worst) with faces and body poses that expressed emotions (happy-sad and angry-calm). Similarly high correlations were obtained when the emotional ratings of the faces/gestures were compared with corresponding emotional ratings of the colors chosen to go with them.
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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) | 0.006 | 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 it