Real-world interattribute distances lead to inefficient face gender categorization.
Bibliographic record
Abstract
The processing of interattribute distances is believed to be critical for upright face categorization. A recent study by Taschereau-Dumouchel, Rossion, Schyns, and Gosselin (2010) challenged this idea by showing that participants were nearly at chance when asked to identify faces on the sole basis of real-world interattribute distances, while they were nearly perfect when all other facial cues were shown. However, it remains possible that humans are highly tuned to interattribute distances but that the information conveyed by these cues is scarce. We tested this hypothesis by contrasting the efficiencies-a measure of performance that factors out task difficulty-of 60 observers in 6 face gender categorization tasks. Our main finding is that efficiencies for faces that varied only in terms of their interattribute distances were an order of magnitude lower than efficiencies for faces that varied in all respects, except their interattribute distances, or in all respects. These results provide a definitive blow to the idea that real-world interattribute distances are critical for upright face processing. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".