Cultural differences in lateral biases on aesthetic judgments: The effect of native reading direction
Bibliographic record
Abstract
Left-to right (LTR) or right-to-left (RTL) directionality bias has been proposed to influence individuals’ aesthetic preference for dynamic stimuli. Two general theoretical propositions attempt to account for this bias. One states that directionality bias is based on scanning habits due to cultural differences in native reading/writing direction, whereas the other proposition speculates that LTR motion bias occurs due to the right hemisphere’s specialization in visuospatial processing. The current study assessed the aesthetic preference bias present when native LTR and RTL readers evaluated fashion garments on the runway in LTR or RTL motion. The aim of the study was to assess aesthetic preference bias for a novel dynamic stimulus and the corresponding influence of biological and cultural factors. Native LTR and RTL readers viewed two blocks of 20 mirror-reversed video pairs with models wearing dresses on a runway. Participants indicated which dress within the mirror-reversed pair they preferred. LTR readers displayed a significant leftward aesthetic preference bias indicating a preference for dresses moving LTR. RTL readers did not display a significant aesthetic preference bias for dresses moving in either direction. These results further support the generalizability of aesthetic preference biases for novel dynamic stimuli and support seminal literature that argues the bias occurs due to a combination of hemispheric dominance and cultural differences in native reading/writing direction.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".