Reduced Sensitivity to Variation in Normality and Attractiveness for Other-Race Faces
Bibliographic record
Abstract
Adults recognize young faces and own-race faces more accurately than older and other-race faces, respectively. We recently reported that young and older adults are more sensitive to deviations from normality in young than older adult faces and that there is more between-participant variability (i.e., less consensus) in attractiveness ratings for older than young faces, suggesting that superior recognition of young adult faces is attributable to the dimensions of face space being optimized for young adult faces, presumably as the result of experience (Short & Mondloch, 2013; Short et al., 2014). In the current studies, we extended these findings to own- and other-race faces. In Experiment 1, Chinese and Caucasian adults (n= 24 per group) were shown own- and other-race face pairs in which one member of each pair was undistorted and the other had compressed or expanded features. They were asked to indicate which member of each face pair was more normal (a task that requires referencing a norm) and which was more expanded (a task that simply requires discrimination). Both Chinese and Caucasian participants were more accurate in judging the normality of own- than other-race faces, p < .001, with no effect of face race in the discrimination task, p = .60. In Experiment 2, Chinese and Caucasian adults rated the attractiveness of 40 own-race and 40 other-race faces. Consensus among Chinese adults (n = 40) did not vary as a function of face race, p = .526; testing of Caucasians is ongoing. Collectively, these results provide direct evidence that perceptual experience with own-race faces optimizes the dimensions of faces space for own-race faces. Meeting abstract presented at VSS 2015
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".