Using an AI creativity system to explore how aesthetic experiences are\n processed along the brains perceptual neural pathways
Bibliographic record
Abstract
With the increased sophistication of AI techniques, the application of these\nsystems has been expanding to ever newer fields. Increasingly, these systems\nare being used in modeling of human aesthetics and creativity, e.g. how humans\ncreate artworks and design products. Our lab has developed one such AI\ncreativity deep learning system that can be used to create artworks in the form\nof images and videos. In this paper, we describe this system and its use in\nstudying the human visual system and the formation of aesthetic experiences.\nSpecifically, we show how time-based AI created media can be used to explore\nthe nature of the dual-pathway neuro-architecture of the human visual system\nand how this relates to higher cognitive judgments such as aesthetic\nexperiences that rely on these divergent information streams. We propose a\ntheoretical framework for how the movement within percepts such as video clips,\ncauses the engagement of reflexive attention and a subsequent focus on visual\ninformation that are primarily processed via the dorsal stream, thereby\nmodulating aesthetic experiences that rely on information relayed via the\nventral stream. We outline our recent study in support of our proposed\nframework, which serves as the first study that investigates the relationship\nbetween the two visual streams and aesthetic experiences.\n
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".