Automatic Mechanisms for Social Attention Are Culturally Penetrable
Bibliographic record
Abstract
Are mechanisms for social attention influenced by culture? Evidence that social attention is triggered automatically by bottom-up gaze cues and is uninfluenced by top-down verbal instructions may suggest it operates in the same way everywhere. Yet considerations from evolutionary and cultural psychology suggest that specific aspects of one's cultural background may have consequence for the way mechanisms for social attention develop and operate. In more interdependent cultures, the scope of social attention may be broader, focusing on more individuals and relations between those individuals. We administered a multi-gaze cueing task requiring participants to fixate a foreground face flanked by background faces and measured shifts in attention using eye tracking. For European Americans, gaze cueing did not depend on the direction of background gaze cues, suggesting foreground gaze alone drives automatic attention shifting; for East Asians, cueing patterns differed depending on whether the foreground cue matched or mismatched background cues, suggesting foreground and background gaze information were integrated. These results demonstrate that cultural background influences the social attention system by shifting it into a narrow or broad mode of operation and, importantly, provides evidence challenging the assumption that mechanisms underlying automatic social attention are necessarily rigid and impenetrable to culture.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".