Culture shapes spatial frequency tuning for face identification.
Bibliographic record
Abstract
Many studies have revealed cultural differences in the way Easterners and Westerners attend to their visual world. It has been proposed that these cultural differences reflect the utilization of different processes, namely holistic processes by Easterners and analytical processes by Westerners. In the face processing literature, eye movement studies have revealed different fixation patterns for Easterners and Westerners that are congruent with a broader spread of attention by Easterners: compared with Westerners, Easterners tend to fixate more toward the center of the face even if they need the information provided by the eyes and mouth. Although this cultural difference could reflect an impact of culture on the visual mechanisms underlying face processing, this interpretation has been questioned by the finding that Easterners and Westerners do not differ on the location of their initial fixations, that is, those that have been shown as being sufficient for face recognition. Because a broader spread of attention is typically linked with the reduced sensitivity to higher spatial frequency, the present study directly compared the spatial frequency tuning of Easterners (Chinese) and Westerners (Canadians) in 2 face recognition tasks (Experiment 1 and 2), along with their general low-level sensitivity to spatial frequencies (Experiment 3). Consistent with our hypothesis, Chinese participants were tuned toward lower spatial frequencies than Canadians participants during the face recognition tasks, despite comparable low-level contrast sensitivity functions. These results strongly support the hypothesis that culture impacts the nature of the visual information extracted during face recognition. (PsycINFO Database Record
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".