Exploring Spatial Frequency and Orientation Tunings for Face Recognition in Eight Cultural Groups
Bibliographic record
Abstract
East Asians use lower spatial frequencies (SF) compared to Westerners while processing faces (Tardif et al., 2017). These differences have been attributed to culture; however, the underlying mechanism remains unclear. While many hypotheses exist (e.g. social orientation, urbanisation), having data for only two cultural groups makes generalisation difficult/iffy at best. The present study addresses this limitation by measuring SF tunings across eight cultural groups. Preliminary data was collected in Sub-Saharan Africa (n = 70), East Asia (n = 45), Eastern Europe (n = 83), English speaking countries (n = 63), Latin America (n = 89), Middle East (n=50), Southern Asia (n=72) and Western Europe (n=78). Targeted sample size is n=80 for all groups, as pre-registered on the OSF. Participants completed 600 trials of a same/different face matching task online using VPixx Pack & Go (VPixx Technologies, 2021). Target stimuli were filtered using SFO Bubbles, allowing for the sampling of all combinations of SF and orientations (Gingras et al., 2022). A weighted sum of all filters was computed to reveal SFO use for each participant as a 2D classification image. Preliminary analyses comparing the top 1% of t-scores across cultures reveal no differences in orientation tunings but reveal that Eastern and Southern Asians, as well as Sub-Saharan Africans, use lower SF compared to Western Europe/English countries. This is inconsistent with the recently proposed urbanization hypothesis (Caparos et al., 2012), according to which African cultures should show a local bias (and therefore use higher spatial frequencies). While the social orientation hypothesis is more consistent with our results, it fails to predict other visual effects, such as the Ebbinghaus illusion (Caparos et al., 2012) or eye movements (Gingras et al., in press). Other theories, applicable not only to East Asia, but to Southern Asia and Sub-Saharan Africa as well, should be explored.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".