The Feeling of the Form: Style as Dynamic ‘Textured’ Expression
Bibliographic record
Abstract
Understanding the complexities of how emotions could be implicated in the semantic (subject-matter) and the syntactic level (form/style) in art might contribute to integrating contrasting approaches regarding emotion experience and meaning. This study explores what happens when we strip away subject matter and only provide expressive information that is embedded in the physical-sensory qualities of ‘style’ of non-representational forms. What could be, if we ask artists to produce specific emotions- matières (the way in which paint—its materiality—is applied by an artist) intended to communicate specific emotion’s states to observers? Could observers share somehow these emotional artistic intentions, yielding some consistency across ratings regarding the intended meanings and the symbolic potential of the drawings? A cross-cultural study was performed (152 Canadians, 48 Greeks, 68 Japanese) using 12 non-representational, emotion-drawing stimuli of the emotion at hand. The results showed a systematic sharing of affective meaning across artists, spectators and cultures. This study serves as an illustrative case for discussion. For spectators to match the bottom up spatiotemporal derived from the syntactic, demands to go all the way down to catch up and match the stimulus impact as an ‘as if’ reciprocally created homology based on affective predictions. At this interface there is mutuality between perceiving, feeling and imaging, indicating the deep passage from expression/gesture to representation. It is discussed that there is continuity between expression and experience and agency is at its core—yet, shaped by culture it participates differentially in this iterative matching of top-down affective predictions checked against first-level bottom-up sensory-motor affective cues.
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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 | 0.003 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".