Forgetting faces in a crowd: Faster memory decay for other-race faces?
Bibliographic record
Abstract
Adults recognize own-race faces more accurately than other-race faces, a pattern called the other-race effect. We previously reported that Caucasian adults were more sensitive to differences among faces in both feature shape (e.g., eyes) and feature spacing (e.g., the distance between the eyes) for Caucasian faces than for Chinese faces. However these effects were surprisingly small (M difference = 5.6% and 9.9%) given the difficulty adults experience in recognizing other-race faces on a daily basis. Here we tested whether storage is better for own-race than other-race faces by varying the delay (1s, 5s, or 10s) in a delayed match-to-sample task. In Study 1 (n=24) we used featural and spatial manipulations of a single identity per race and a blank screen was presented during the delay. There was an effect of delay for both face sets, ps ps [[gt]] .5. To more closely mimic the real world, in Study 2 (n=24) we presented altered versions of two identities per race and a screen comprised of multiple Chinese and Caucasian faces was presented during the delay. The effect of delay and the face race x delay interaction were significant only for the spacing set, ps [[lt]].02. The drop in accuracy in the 10s-delay condition relative to the 1s-delay condition was larger for other-race (12%) than for own-race faces (6%). Collectively, these results suggest that the own-race advantage for feature shape occurs at the encoding stage, whereas the own-race advantage for the spacing set may occur at both encoding and storage (see Freire et al., 2000 for similar analyses of the inversion effect). The own-race advantage may be small in lab studies because cues that adults might normally rely on when encoding other-race faces (e.g., hair, clothing) are removed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".