Second-order relational face processing is applied to faces of different race and photographic contrast.
Bibliographic record
Abstract
A large body of research suggests that faces are processed by a specialized mechanism within the human visual system. This specialized mechanism is made up of subprocesses (Maurer, LeGrand, & Mondloch, 2002). One subprocess, called second- order relational processing, analyzes the metric distances between face parts. Importantly, it is well established that other-race faces and contrast-reversed faces are associated with impaired performance on numerous face processing tasks. Here, we investigated the specificity of second-order relational processing by testing how this process is applied to faces of different race and photographic contrast. Participants completed a feature displacement discrimination task, directly measuring the sensitivity to second-order relations between face parts. Across three experiments we show that, despite absolute differences in sensitivity in some conditions, inversion impaired performance in all conditions. The presence of robust inversion effects for all faces suggests that second-order relational processing can be applied to faces of different race and photographic contrast.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".