Holistic Versus Analytic Expressions in Artworks
Bibliographic record
Abstract
Previous research has documented systematic cultural variations in adults’ cognitive processes. In particular, research on culture and aesthetics suggests that East Asian adults’ aesthetic expression tends to be holistic and context-oriented, whereas North American adults’ aesthetic expression tends to be analytic and object-oriented (Masuda, Gonzalez, Kwan, & Nisbett, 2008). However, research focusing specifically on the developmental processes of such cultural differences in children’s artworks is lacking, with the notable exception of an empirical study conducted by Rübeling et al. (2011). Our current research examined whether school-age children in Grades 1 through 6 exhibit these culturally unique patterns of expression, and if so, when. Children were asked to produce either landscape drawings (Study 1, n = 495) or landscape collages using ready-made items (Study 2, n = 376). The results indicated that children in both cultures gradually develop expressions unique to each culture. Although Grade 1 children’s artworks were still similar across cultures, artworks in Grade 2 and higher showed substantial cultural variations. Japanese children were more likely than their Canadian counterparts to place the horizon higher in the visual space and to include more pieces of information. The higher placement of the horizon is linked to the context-oriented visual attention style seen in adults’ drawings and historical paintings in East Asian cultures, as opposed to object-focused drawing styles commonly seen in North American cultures. We also report culturally similar patterns in the developmental trajectory and discuss the internalization process of culturally dominant patterns of perception.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".