A Background Layer in Aesthetic Experience: Cross‐cultural Affective Symbolism
Bibliographic record
Abstract
This study explored the role of affective/physiognomic perception in emotion perception, and its contribution to meaning construction in the absence of any representation. Following Werner's microgenetic paradigm, by peeling away the fossilized representational level in the drawings, we attempted to link both the “subjective” emotion experience of the artists, articulated in line‐drawings that functioned as the spectators’ perceptual stimulus. Subsequently, a cross‐cultural study was performed using 12 non‐representational, emotion drawings as experimental stimuli of the emotion at hand, to investigate any correspondence of feeling between spectators (152 Canadian, 48 Greek, and 72 Japanese participants) and emotions expressed in the drawings. The results suggested a relative congruence of feeling reflected in the gross classification of the stimuli, and coordination in the organization of perceived affect across cultures. Mood‐state and mood‐trait played differential roles in recognition accuracy. Greeks showed impaired classification for negative drawings, not modifying internal structures accordingly to external constraints, and being oversensitive to subjective arousal in the aversive ambiguous stimuli. Japanese coped with aversion in an implicit aesthetic manner, being task oriented while not flattening the experiential impact of the stimuli. Canadians were not insensitive to body cues, but their impact was overridden by the salience of the attended object.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".