Contact and other‐race effects in configural and component processing of faces
Bibliographic record
Abstract
Other-race faces are generally recognized more poorly than own-race faces. There has been a long-standing interest in the extent to which differences in contact contribute to this other-race effect (ORE). Here, we examined the effect of contact on two distinct aspects of face memory, memory for configuration and for components, both of which are better for own-race than other-race faces. Configural and component memory were measured using recognition memory tests with intact study faces and blurred (isolates memory for configuration) and scrambled (isolates memory for components) test faces, respectively. Our participants were a large group of ethnically Chinese individuals who had resided in Australia for varying lengths of time, from a few weeks to 26 years. We found that time in a Western country significantly (negatively) predicted the size of the ORE for configural, but not component, memory. There was also a trend for earlier age of arrival to predict smaller OREs in configural, but not component, memory. These results suggest that memory for configural information in other-race faces improves with experience with such faces. However, as found for recognition memory generally, the contact effects were small, indicating that other factors must play a substantial role in cross-race differences in face memory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".