Americans weigh an attended emotion more than Koreans in overall mood judgments
Bibliographic record
Abstract
Face ensemble coding is the perceptual ability to create a quick and overall impression of a group of faces, triggering social and behavioral motivations towards other people (approaching friendly people or avoiding an angry mob). Cultural differences in this ability have been reported, such that Easterners are better at face ensemble coding than Westerners are. The underlying mechanism has been attributed to differences in processing styles, with Easterners allocating attention globally, and Westerners focusing on local parts. However, the remaining question is how such default attention mode is influenced by salient information during ensemble perception. We created visual displays that resembled a real-world social setting in which one individual in a crowd of different faces drew the viewer's attention while the viewer judged the overall emotion of the crowd. In each trial, one face in the crowd was highlighted by a salient cue, capturing spatial attention before the participants viewed the entire group. American participants' judgment of group emotion more strongly weighed the attended individual face than Korean participants, suggesting a greater influence of local information on global perception. Our results showed that different attentional modes between cultural groups modulate social-emotional processing underlying people's perceptions and attributions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".