Inducing the use of right eye enhances face-sex categorization performance.
Bibliographic record
Abstract
Face recognition ability varies tremendously among neurologically typical individuals. What causes these differences is still largely unknown. Here, we first used a data-driven experimental technique-bubbles-to measure the use of local facial information in 140 neurotypical individuals during a face-sex categorization task. We discovered that the use of the eye and eyebrow area located on the right side of the face image from the observer's viewpoint correlates positively with performance, whereas the use of the left-eye and eyebrow area correlates negatively with performance. We then tested if performance could be altered by inducing participants to use either the right- or the left-eye area. One hundred of these participants thus underwent a 1-hr session of a novel implicit training procedure aimed at inducing the use of specific facial information. Afterward, participants repeated the bubbles face-sex categorization task to assess the changes in use of information and its effect on performance. Participants that underwent right-eye induction used this facial region more than they initially did and, as expected, improved their performance more than the participants who underwent the left-eye induction. This is the first clear evidence of a causal link between the use of specific face information and face recognition ability: Use of right-eye region not only predicts but causes better face-sex categorization. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".