Registered report “Categorical perception of facial expressions of anger and disgust across cultures”
Bibliographic record
Abstract
Previous research has demonstrated that individuals from Western cultures exhibit categorical perception (CP) in their judgments of emotional faces. However, the extent to which this phenomenon characterises the judgments of facial expressions among East Asians remains relatively unexplored. Building upon recent findings showing that East Asians are more likely than Westerners to see a mixture of emotions in facial expressions of anger and disgust, the present research aimed to investigate whether East Asians also display CP for angry and disgusted faces. To address this question, participants from Canada and China were recruited to discriminate pairs of faces along the anger-disgust continuum. The results revealed the presence of CP in both cultural groups, as participants consistently exhibited higher accuracy and faster response latencies when discriminating between-category pairs of expressions compared to within-category pairs. Moreover, the magnitude of CP did not vary significantly across cultures. These findings provide novel evidence supporting the existence of CP for facial expressions in both East Asian and Western cultures, suggesting that CP is a perceptual phenomenon that transcends cultural boundaries. This research contributes to the growing literature on cross-cultural perceptions of facial expressions by deepening our understanding of how facial expressions are perceived categorically across cultures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".