Red Facilitates Anger Perception Across Cultures
Bibliographic record
Abstract
Color is not only a perceptual attribute but also a socially embedded signal carrying psychological meaning. Previous research has shown that people tend to associate specific colors with particular emotions, with red being especially complex. On the one hand, red is commonly linked to anger. On the other hand, red can also be associated with positive emotions such as joy and celebration, particularly in Chinese culture. The present study aimed to examine whether, within this bivalent cultural context, red activates both associations simultaneously (coexistence hypothesis) or engages one over the other (competition hypothesis) across three experiments. Experiment 1 examined both Eastern/Chinese and Western/Canadian participants in a facial emotion identification task, in which they were asked to rapidly identify angry and happy emotions presented against red, green, or gray background. We found that a red background significantly facilitated the identification of angry emotions while, in most cases, impairing or having no effect on the identification of happy emotions in both groups of participants. These findings support the competition hypothesis rather than the coexistence hypothesis. Experiment 2 replicated this pattern in a new Chinese sample under reduced perceptual salience of the display. Experiment 3 further examined the extent to which stimulus factors, such as background size (full-screen vs. local), modulate these effects. We consistently found that red-anger association predominated across three experiments, which was not moderated by racial profile of the emotional faces, participants’ cultural background, or sensory-level factors. These consistent findings highlight that the robustness of red–anger association in visual-emotion processing.
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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 | 0.001 |
| 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) | 0.003 | 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".