For best results, use the eyes: Individual differences and diagnostic features in face recognition
Bibliographic record
Abstract
In recent years, the interest in individual differences in face processing ability has skyrocketed. In fact, individual differences are quite useful in better understanding the mechanisms involved in face processing, since it is thought that if a certain mechanism is important for this task, individual efficiency in using this mechanism should be correlated with face processing abilities (Yovel et al., 2014). The present study investigated how variations in the ability to perceive and recognize faces in healthy observers related to their utilization of facial features in different spatial frequency bands. Fifty participants completed a 10 choice face identification task using the Bubbles method (Gosselin & Schyns, 2001) as well as six tasks measuring face and object recognition or perception ability. The individual classification images (CIs) obtained in the bubbles task were weighted using the z-scored performance rankings in each face processing test. Our results first show that the utilization of the eye region is correlated with performance in all three face processing tasks, (p< .025; Zcriterion=3.580), specifically in intermediate to high spatial frequencies. We also show that individual differences in face-specific processing abilities (i.e. when controlling for general visual/cognitive processing ability; Royer et al., 2015) are significantly correlated with the use of the eye area, especially the left eye (p< .025; Zcriterion=3.580). Face-specific processing abilities were also significantly linked to the similarity between the individual and unweighted group CIs, meaning that those who performed best in the face recognition tests used a more consistent visual strategy. Our findings are congruent with data revealing an impaired processing of the eye region in a prosopagnosic patient (e.g. Caldara et al., 2005), indicating that the visual strategies associated with this condition are also observed in individuals at the low-end of the normal continuum of face processing ability. Meeting abstract presented at VSS 2016
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".