Impact of perceived interpersonal similarity on attention to the eyes of same-race and other-race faces
Bibliographic record
Abstract
One reason for the persistence of racial discrimination may be anticipated dissimilarity with racial outgroup members that prevent meaningful interactions. In the present research, we investigated whether perceived similarity would impact the processing of same-race and other-race faces. Specifically, in two experiments, we varied the extent to which White participants were ostensibly similar to targets via bogus feedback on a personality test. With an eye tracker, we measured the effect of this manipulation on attention to the eyes, a critical region for person perception and face memory. In Experiment 1, we monitored the impact of perceived interpersonal similarity on White participants' attention to the eyes of same-race White targets. In Experiment 2, we replicated this procedure, but White participants were presented with either same-race White targets or other-race Black targets in a between-subjects design. The pattern of results in both experiments indicated a positive linear effect of similarity-greater perceived similarity between participants and targets predicted more attention to the eyes of White and Black faces. The implications of these findings related to top-down effects of perceived similarity for our understanding of basic processes in face perception, as well as 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 | 0.003 |
| 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 | 0.000 |
| 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".