Visualizing minimal ingroup and outgroup faces: Implications for impressions, attitudes, and behavior.
Bibliographic record
Abstract
More than 40 years of research have shown that people favor members of their ingroup in their impressions, attitudes, and behaviors. Here, we propose that people also form different mental images of minimal ingroup and outgroup members, and we test the hypothesis that differences in these mental images contribute to the well-established biases that arise from minimal group categorization. In Study 1, participants were assigned to 1 of 2 groups using a classic minimal group paradigm. Next, a reverse correlation image classification procedure was used to create visual renderings of ingroup and outgroup face representations. Subsequently, a 2nd sample naive to the face generation stage rated these faces on a series of trait dimensions. The results indicated that the ingroup face was significantly more likely than the outgroup face to elicit favorable impressions (e.g., trusting, caring, intelligent, attractive). Extending this finding, Study 2 revealed that ingroup face representations elicited more favorable implicitly measured attitudes than did outgroup representations, and Study 3 showed that ingroup faces were trusted more than outgroup faces during an economic game. Finally, Study 4 demonstrated that facial physiognomy associated with trustworthiness more closely resembled the facial structure of the average ingroup than outgroup face representation. Together, these studies suggest that minimal group distinctions can elicit different mental representations, and that this visual bias is sufficient to elicit ingroup favoritism in impressions, attitudes and behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".