Does perceived race affect discrimination and recognition of ambiguous-race faces? A test of the sociocognitive hypothesis.
Bibliographic record
Abstract
Discrimination and recognition are often poorer for other-race than own-race faces. These other-race effects (OREs) have traditionally been attributed to reduced perceptual expertise, resulting from more limited experience, with other-race faces. However, recent findings suggest that sociocognitive factors, such as reduced motivation to individuate other-race faces, may also contribute. If the sociocognitive hypothesis is correct, then it should be possible to alter discrimination and memory performance for identical faces by altering their perceived race. We made identical ambiguous-race morphed faces look either Asian or Caucasian by presenting them in Caucasian or Asian face contexts, respectively. However, this perceived-race manipulation had no effect on either discrimination (Experiment 1) or memory (Experiment 2) for the ambiguous-race faces, despite the presence of the usual OREs in discrimination and recognition of unambiguous Asian and Caucasian faces in our participant population. These results provide no support for the sociocognitive hypothesis. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".