The time course of face-gender discrimination: Disentangling the use of color and luminance cues
Bibliographic record
Abstract
In a recent study using spatial Bubbles (Dupuis-Roy, et al., 2009), we identified the eyes, the eyebrows and the mouth as the most potent features for face-gender discrimination (see also Brown & Perrett, 1993; Russell, 2003, 2005; Yamaguchi, Hirukawa, & Kanazawa, 1995). Intriguingly, we found that the mouth was correlated only with rapid correct answers. Given the highly discriminative color information in this region, we hypothesized that the extraction of color and luminance cues may have different time courses. Here, we tested this possibility by sampling the chromatic and achromatic face cues independently with spatial and temporal Bubbles (see Gosselin & Schyns, 2001; Blais et al., 2009). One hundred participants (35 men) completed 600 trials of a face-gender discrimination task with briefly presented sampled faces (200ms). To create a stimulus, we first isolated the S and V channels of the HSV color space for 300 color pictures of frontal-view faces (average interpupil distance of 1.03 deg of visual angle) and adjusted the S channel so that every color was isoluminant (±5 cd/m2); then, we sampled S and V channels independently through space and time with 3D Gaussian windows (spatial std = 0.15 deg of visual angle and temporal std = 23.53 ms). The group classification image computed on the response accuracy shows that in the first 100 ms, participants used the color in the mouth region along with the luminance in the left eye-eyebrow region; and that in the last 100ms, they relied on the luminance information located in the mouth and the right eye-eyebrows. Male and female observers slightly differ in their extraction of the mouth information. Altogether, these results help to disentangle the relative role of color and luminance in face-gender discrimination.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".