A Creative Artificial Intelligence System to Investigate User Experience, Affect, Emotion and Creativity
Bibliographic record
Abstract
Is it possible using photographs as source (e.g., selfies of users) to affect and enhance mood, emotion and creativity by creating AI based “digital painters” that can create art painting output that is deemed to convey a certain mood from any source photograph (user face portraits, dancers in moment)? The goal of this research is to use our Creative Artificial Intelligence System (CAIS) along with our cognitive based painting algorithms (DiPaola 2013) together with additional art analysis tools (i.e., texture and palette synthesis) to parameterize a generative artistic painting process based on mood and emotion. We discuss our methods and begin to validate the work by performing two intertwined user studies that appear to support that viewers of the generated art from our CAIS agree to a high degree on a specific mood the output conveys from our 4 emotional spaces regardless of the source material: abstract, fugitive or their self portrait. This points to the conclusion that our CAIS system can automatically generate unique artworks or “aesthetic visualization” that deemed creative and have emotional qualities, which has benefits and repeatability in many interactive fields. This work has User Experience (UX) applications in computational creativity, affective aesthetic visualization, experiential learning (of art), performance visualization (dancing), and health well-being.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".