Efficiency and equivalent internal noise for own- and other-race face recognition suggest qualitatively similar processing
Bibliographic record
Abstract
Identification of faces of a non-native race is drastically impaired compared to performance with own-race faces (Hancock & Rhodes, 2008; Meissner & Brigham, 2001). It is known that differential experience brings about this effect yet it is not clear how experience, or the lack thereof, with a particular race impacts neural processing of faces. It has been suggested that unlike own-race faces, other-race faces do not benefit from expert holistic processes but are recognized via general-purpose processes in a piece-meal fashion similar to other, non-face, visual forms (Rossion, 2008; Tanaka, Kiefer, & Bukach, 2004). Alternatively, the difference may stem from quantitative, rather than qualitative, changes in processing. Under this hypothesis, the neural computation that leads to recognition itself is the same in both own- and other-race faces; but in the case of other-race faces the input is noisier due to reduced experience. In order to discriminate between these alternative models we measured contrast recognition thresholds in a 5AFC paradigm for three classes of stimuli: a) own-race faces b) other-race faces, and c) houses. Contrast thresholds were measured in white noise and no-noise conditions. High-noise efficiency and equivalent internal noise for the human observers across the three stimulus conditions were computed relative to an ideal observer that performed the same tasks. We predicted lower efficiencies for houses, which are recognized via general-purpose processes, compared to own-race faces, which are recognized via expert holistic processes. We found both own-race and other-race efficiencies to be significantly higher than that for houses. Efficiencies did not differ between the two face conditions but equivalent input noise was higher for other-race faces. These results do not support the idea of distinct processing styles for own-vs. other-race faces. Instead, they suggest qualitatively similar processing regardless of race. Meeting abstract presented at VSS 2016
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".