Anger is red, sadness is blue: Emotion depictions in abstract visual art by artists and non-artists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Through the manipulation of color and form, visual abstract art is often used to convey feelings and emotions. Here, we explored how colors and lines are used to express basic emotions and whether non-artists express emotions through art in similar ways as trained artists. Both artists and non-artists created abstract color drawings and line drawings depicting six emotions (i.e., anger, disgust, fear, joy, sadness, and wonder). To test whether people represented basic emotions in similar ways, we computationally predicted the emotion of a given drawing by comparing it to a set of references created by averaging across all other participants' drawings within each emotion category. We found that prediction accuracy was higher for color drawings than line drawings and higher for color drawings by non-artists than by artists. In a behavioral experiment, we found that people (N = 242) could also accurately infer emotions, showing the same pattern of results as our computational predictions. Further computational analyses of the drawings revealed systematic use of certain colors and line features to depict each basic emotion (e.g., anger is generally redder and more densely drawn than other emotions, sadness is more blue and contains more vertical lines). Taken together, these results imply that abstract color and line drawings are able to convey certain emotions based on their visual features, which are also used by human observers to understand the intended emotional connotation of abstract artworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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