Dynamic stimuli: Accentuating aesthetic preference biases
Bibliographic record
Abstract
Despite humans' preference for symmetry, artwork often portrays asymmetrical characteristics that influence the viewer's aesthetic preference for the image. When presented with asymmetrical images, aesthetic preference is often given to images whose content flows from left-to-right and whose mass is located on the right of the image. Cerebral lateralization has been suggested to account for the left-to-right directionality bias; however, the influence of cultural factors, such as scanning habits, on aesthetic preference biases is debated. The current research investigates aesthetic preference for mobile objects and landscapes, as previous research has found contrasting preference for the two image types. Additionally, the current experiment examines the effects of dynamic movement on directionality preference to test the assumption that static images are perceived as aesthetically equivalent to dynamic images. After viewing mirror-imaged pairs of pictures and videos, right-to-left readers failed to show a preference bias, whereas left-to-right readers preferred stimuli with left-to-right directionality regardless of the location of the mass. The directionality bias in both reading groups was accentuated by the videos, but the bias was significantly stronger in left-to-right readers. The findings suggest that scanning habits moderate the leftward bias resulting from hemispheric specialization and that dynamic stimuli further fluent visual processing.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".