Inducing the use of information for face identification
Bibliographic record
Abstract
Faghel-Soubeyrand et al. (in press) trained observers to use the facial information most correlated with skilled face-sex discrimination — the eye on the right of the face stimulus from the observer’s viewpoint — and showed that these observers’ performance increased more than that of control participants. Here, using a similar implicit induction procedure, we attempted to train observers to use the information associated with skilled — mostly the two eyes— or unskilled face identification (Tardif et al., 2018). First, participants completed 500 Bubbles trials where they were asked to identify a celebrity, to reveal their use of information pre-induction. Second, participants carried out 500 more trials of the Bubbles task, during which, unbeknownst to them, the base face stimuli were tampered with. In the best-information induction subject group, the information related to skilled face identification was made available (N=8; mean age=21.9; 2 women) and, in the worse-information induction subject group, the information related to unskilled face identification was made available (N=7; mean age=21.9; 3 women). Third, and finally, observers completed 500 more Bubbles trials to reveal their use of information post-induction. For each subject group, we computed classification images, showing the visual information used before and after the induction trials. As expected, results show that participants from the worse-information group used the mouth before and after the induction, whereas participants from the best-information group used mainly the mouth before induction and the two eyes after induction (Cluster Test: p< .05; sigma=26; tC=2.70; Sr=21901; Chauvin et al., 2005). We believe this induction procedure shows promise as a mean for individuals specifically impaired in face recognition (e.g. developmental prosopagnosics) and professionals relying on strong face processing (e.g. police officers) to improve their abilities.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".