Face detection in acquired prosopagnosia
Bibliographic record
Abstract
Detection of faces in visual scenes has received extensive attention in machine vision, but limited research has addressed face detection in humans. Here we assess face detection in six participants with acquired prosopagnosia resulting from a variety of lesions to better understand the processes and neural areas contributing to face detection and the relation of detection to other stages of face recognition. All six participants showed severe impairments on tests of facial identity recognition, confirming prosopagnosia, and participants were also extensively tested for perceptual discrimination of faces. We used structural MRI to delineate the lesions and functional MRI to show the status of the core regions of the face-processing network (OFA, FFA, STS). Two tasks requiring visual search for the presence of a face among distractor stimuli assessed detection in the patients and 12 age-matched controls. Two participants, R-AT2 and B-AT1, performed normally on both tasks. These patients had anterior temporal lesions that did not affect their core face-processing network. Two participants, R-AT1 and R-IOT4, had severe detection impairments while the performance of R-IOT1 and B-AT/IOT1 was borderline. These four subjects all showed difficulty on perceptual tasks requiring discrimination of facial identity. Except for R-AT1, all subjects had lesions to right inferior occipitotemporal cortex, with loss of the FFA and OFA in R-IOT1 and B-AT/IOT1 and loss of the FFA alone in R-IOT4. Furthermore, DTI analysis in R-AT1 suggested reduced fractional anisotropy in the region of the FFA and OFA. The association between detection and identity perception suggests that these abilities may be supported by the same processes. Impairment in these abilities correlates with damage to the core face-processing network in the right hemisphere. Face detection deficits in R-IOT4 despite preservation of the right and left OFA indicates that these regions are not sufficient on their own to support detection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".