Bibliographic record
Abstract
Abstract Some people feel emotions when they look at abstract art. This article presents a ‘simulation’ theory that predicts which emotions they will experience, including those based on their aesthetic reactions. It also explains the mental processes underlying these emotions. This new theory embodies two precursors: an account of how mental models represent perceptions, descriptions, and self-reflections, and an account of the communicative nature of emotions, which distinguishes between basic emotions that can be experienced without knowledge of their objects or causes, and complex emotions that are founded on basic ones, but that include propositional contents. The resulting simulation theory predicts that abstract paintings can evoke the basic emotions of happiness, sadness, anger, and anxiety, and that they do so in several ways. In mimesis, models simulate the actions and gestures of people in emotional states, elicited from cues in the surface of paintings, and that in turn evoke basic emotions. Other basic emotions depend on synaesthesia, and both association and projection can yield complex emotions. Underlying viewers’ awareness of looking at a painting is a mental model of themselves in that relation with the painting. This self-reflective model has access to knowledge, enabling people to evaluate the work, and to experience an aesthetic emotion, such as awe or revulsion. The comments of artists and critics, and experimental results support the theory.
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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 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".