Neural Correlates of Covert Face Processing: fMRI Evidence from a Prosopagnosic Patient
Bibliographic record
Abstract
Brains can perceive or recognize a face even though we are subjectively unaware of the existence of that face. However, the exact neural correlates of such covert face processing remain unknown. Here, we compared the fMRI activities between a prosopagnosic patient and normal controls when they saw famous and unfamiliar faces. When compared with objects, the patient showed greater activation to famous faces in the fusiform face area (FFA) though he could not overtly recognize those faces. In contrast, the controls showed greater activation to both famous and unfamiliar faces in the FFA. Compared with unfamiliar faces, famous faces activated the controls', but not the patient's lateral prefrontal cortex (LPFC) known to be involved in familiar face recognition. In contrast, the patient showed greater activation in the bilateral medial frontal gyrus (MeFG). Functional connectivity analyses revealed that the patient's right middle fusiform gyrus (FG) showed enhanced connectivity to the MeFG, whereas the controls' middle FG showed enhanced connectivity to the LPFC. These findings suggest that the FFA may be involved in both covert and overt face recognition. The patient's impairment in overt face recognition may be due to the absence of the coupling between the right FG and the LPFC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".