An eye for the I: Preferential attention to the eyes of ingroup members.
Bibliographic record
Abstract
Human faces, and more specifically the eyes, play a crucial role in social and nonverbal communication because they signal valuable information about others. It is therefore surprising that few studies have investigated the impact of intergroup contexts and motivations on attention to the eyes of ingroup and outgroup members. Four experiments investigated differences in eye gaze to racial and novel ingroups using eye tracker technology. Whereas Studies 1 and 3 demonstrated that White participants attended more to the eyes of White compared to Black targets, Study 2 showed a similar pattern of attention to the eyes of novel ingroup and outgroup faces. Studies 3 and 4 also provided new evidence that eye gaze is flexible and can be meaningfully influenced by current motivations. Specifically, instructions to individuate specific social categories increased attention to the eyes of target group members. Furthermore, the latter experiments demonstrated that preferential attention to the eyes of ingroup members predicted important intergroup biases such as recognition of ingroup over outgroup faces (i.e., the own-race bias; Study 3) and willingness to interact with outgroup members (Study 4). The implication of these findings for general theorizing on face perception, individuation processes, and intergroup relations are discussed.
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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 | 0.002 |
| 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 | 0.000 |
| 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".