Cross-Cultural Variations in Visual Search: Exploring Attention Deployment Strategies and Novel Priming on Search Asymmetry
Bibliographic record
Abstract
Various cultures exhibit different efficiencies when searching for the same simple geometric figure. In Canada and the United States, it is more efficient to search for a long line among short lines than vice versa (typical search asymmetry: Treisman & Gormican, 1988), whereas in Japan and Taiwan, searching for a long line among short lines is equally efficient as searching for a short line among long lines (Tsai et al., 2021; Ueda et al., 2018). One explanation for this variation is the default deployment of attention; search asymmetry might be observed after changing attention deployment even for Japanese and Taiwanese participants. To investigate this hypothesis, we modulated participants’ attention deployment before a visual search using a Navon task, in which participants were presented with a large letter composed of smaller letters and responded to either the large (i.e., global attention priming) or small letter (i.e., local attention priming). The results showed that local attention priming did not change search performances, maintaining no search asymmetry for both Japanese and Taiwanese. However, global attention priming led to the opposite-direction search asymmetry (i.e., a short line among long lines was searched more efficiently compared to vice versa) only for Taiwanese. Post-hoc analysis revealed that the opposite-direction search asymmetry is specific to participants who showed longer reaction times. These novel findings suggest that Japanese and Taiwanese participants default to local attention deployment in visual search, while those with longer reaction times employed a different search strategy with global attention priming. The emergence of opposite-direction search asymmetry raises new questions about how scenes with multiple objects are perceived and how individuals determine their attentional deployment in such scenarios.
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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 | 0.007 |
| 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) | 0.003 | 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".