Are women always better able to recognize faces? The unveiling role of exposure time
Bibliographic record
Abstract
A longer exposure time generally improves individuals' ability to recognize faces. The current research investigates whether this effect varies between genders and whether it is influenced by the gender of the exposed faces. Based on a set of four experimental studies, we advance our knowledge of face recognition, gender, gender distribution of exposed faces, and exposure time in three main ways. First, the results reveal that women are more likely than men to suffer from a decrease in face recognition ability due to a lower exposure time. Second, the findings show that when exposure time is short (vs. long) women recognize a larger proportion of same gender faces and also recognize a larger proportion of same gender faces as compared with the proportion of same gender faces recognized by men. Third, findings reveal that when individuals are only exposed to same gender faces, women recognize more faces than men regardless whether exposure time is short, or long. In short, the findings of this research suggest that insight into the interplay between gender and exposure time length is critical to appropriately determine human beings' ability to recognize faces.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".