Culture and the Complex Environment: Comparing the Complexity Difference between East Asians and North Americans
Bibliographic record
Abstract
Previous cultural research found that East Asian pictorial representations (e.g., paintings) contained more elements than North American ones, and that East Asians were more likely than North Americans to prefer context-rich information to context-impoverished information (Miyamoto, Nisbett, & Masuda, 2006; Masuda, Gonzalez, Kwan, & Nisbett, 2008). Four studies were conducted to examine the cultural variations of the complexity difference between East Asians and North Americans. Study 1 analyzed the posters collected at the SPSP conference and the results indicated that East Asians were more likely than North Americans to design complex posters when posters contained two or more studies; however, no cultural effect was found when posters contained a single study. In Study 2, I analyzed portal pages of governments and universities in East Asian (e.g., China, Japan, Korea) and North American societies (e.g., USA and Canada), and found that East Asian portal pages were more complex than North American ones. Based on the findings, I further investigated people’s speed in dealing with complex web information in Study 3 and simple web information in Study 4. The results showed that East Asians were faster than North Americans in dealing with information on complex WebPages, especially at the bottom of sections, but no cultural effect was found when participants were asked to perform the same tasks on simple WebPages. This research reinforced the previous cultural research on visual representations, and suggested that East Asians were more likely than North Americans to prefer to complex designs, which in turn can affect people’s patterns of attention and cognition. (255 words)
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".