The neural correlates of covert recognition of familiar faces
Bibliographic record
Abstract
It is well established that our brains are highly sensitive to face. We can easily recognize a face even it is seriously degraded. Specially, our brains can even make a response to a face of which we can not overtly be aware. A good many of studies have reported such covert or unconscious face recognition. However, little is known about the neural correlates underlying such face processing. Patients with acquired prosopagnosia may be a good mode for the investigation of the neural mechanism of covert face processing, because they are unable to consciously identify the faces with which they were very familiar previously after the onset of their diseases. Here, we use fMRI methodology to compare the brain activities between a prosopagnosic patient and normal controls when they viewed famous face and unfamiliar face stimuli. We found that the FFA of the patient was activated only by famous faces relative to common objects stimuli. In contrast, the FFA of the normal controls was activated by both famous faces and unfamiliar faces. Further, when comparing the fMRI activities of famous face to those of unfamiliar faces, the normal controls showed enhanced activation in the lateral prefrontal cortex and right posterior parietal lobule (Figure 1). Such brain regions have been suggested to be involved in the overt processing of face identity information. In contrast, using the same contrast as the normal controls, the patient did not show enhanced activities in such regions, but instead presented greater activation in the medial prefrontal cortices (Figure 2). Our findings suggested that the FFA may be involved in both overt and covert face processing, and the patient' s impairment in overt recognition of famous faces is likely to be due to the absent activation of the lateral prefrontal cortices and right posterior parietal lobule. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".