The Impact of Authorship on Aesthetic Appreciation: A Study Comparing Human and AI-Generated Artworks
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the impact of human and AI authorship on aesthetic appreciation. The application of AI in artistic creation is discussed in terms of its use in the fields of visuals, music, and literature. An empirical study was conducted to implicitly compare AI-declared abstract artworks with human-declared artworks, using electrophysiological activity to monitor whether participants spontaneously compare the two works. Results show that a priori available information about the authorship of artworks is a key factor in aesthetic evaluation and appreciation. The neural and cognitive processes of aesthetic appreciation are explored in terms of how the human brain processes and evaluates works of art, and how creatorship influences these processes. The ethical considerations involved in using AI to create works of art are also discussed, including intellectual property rights, privacy, and social implications.
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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 | 0.001 | 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