Robust face detection with limited visual input does not elicit saccadic response
Bibliographic record
Abstract
Faces are extremely important stimuli for humans, and there is growing evidence that face detection and localisation occurs early in the visual hierarchy. Faces provoke saccadic responses within 100 ms after stimulus onset (Crouzet et al., 2010, J Vis), and face-selective neural representations have been identified in early visual areas (Campana et al., 2020, bioRxiv). Given that face detection may be accomplished by early, low-level visual processing, we examined whether detection and localisation can be accomplished in the absence of perceptual awareness. In a 2AFC saccadic choice task, faces and objects were presented for 8, 100, or 400 ms, followed by 400 ms phase-scrambled masking images. Observers were asked to look for the face (or object) target and to make a manual response corresponding to the target location. We predicted more accurate saccadic responses to faces compared to objects, independent of stimulus visibility. Results showed high detection accuracy even at the 8 ms SOA, with only a 20-30% reduction in visibility for face and object targets, respectively. Eye-tracking data showed that saccadic response was not necessary for accurate detection. In the critical 8 ms SOA condition, observers only made saccades on about 20% of the trials despite high manual detection accuracy. However, observers were more accurate in detecting faces, and when they did execute saccades, saccadic accuracy towards targets was above chance for faces but not for objects. These results demonstrate a remarkable robustness for face detection with very little visual input and without initiating saccadic response. Better understanding of the covert detection processes for such limited visual input may extend models of human eye movements for overt visual targeting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".