The Impact of Spontaneity and Presentation Mode on the Ingroup Advantage in Recognizing Angry and Disgusted Facial Expressions
Bibliographic record
Abstract
Previous research has found that individuals are more accurate at recognizing facial expressions of individuals from their own cultural background than those from a different cultural background, known as the ingroup advantage. However, most studies investigating the ingroup advantage have primarily focused on posed and static facial expressions, paying less attention to spontaneous and dynamic facial expressions. To investigate whether the ingroup advantage is influenced by spontaneity (posed and spontaneous) and presentation mode (static and dynamic) of facial expressions, we recruited participants from China, Canada, and the Netherlands to recognize posed and spontaneous facial expressions of anger and disgust displayed by Chinese and Dutch models (Experiment 1), as well as static and dynamic facial expressions (Experiment 2). The results showed that, in most cases, there was an ingroup advantage in the recognition of both posed and spontaneous expressions, with the ingroup advantage being significantly higher for posed expressions compared to spontaneous expressions. Additionally, an ingroup advantage was observed in the recognition of both static and dynamic expressions, although there was no significant difference between the two overall. These findings suggest that the ingroup advantage in facial expression recognition is influenced by the spontaneity of the expressions, but may not be affected by the mode of expression presentation. The implications of this research are significant in expanding our understanding of the ingroup advantage and deepening our knowledge of cross-cultural facial expression recognition.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".