Does Prosopagnosia Take the Eyes Out of Face Representations? Evidence for a Defect in Representing Diagnostic Facial Information following Brain Damage
Bibliographic record
Abstract
One of the most impressive disorders following brain damage to the ventral occipitotemporal cortex is prosopagnosia, or the inability to recognize faces. Although acquired prosopagnosia with preserved general visual and memory functions is rare, several cases have been described in the neuropsychological literature and studied at the functional and neural level over the last decades. Here we tested a brain-damaged patient (PS) presenting a deficit restricted to the category of faces to clarify the nature of the missing and preserved components of the face processing system when it is selectively damaged. Following learning to identify 10 neutral and happy faces through extensive training, we investigated patient PS's recognition of faces using Bubbles, a response classification technique that sampled facial information across the faces in different bandwidths of spatial frequencies [Gosselin, F., & Schyns, P. E., Bubbles: A technique to reveal the use of information in recognition tasks. Vision Research, 41, 2261-2271, 2001]. Although PS gradually used less information (i.e., the number of bubbles) to identify faces over testing, the total information required was much larger than for normal controls and decreased less steeply with practice. Most importantly, the facial information used to identify individual faces differed between PS and controls. Specifically, in marked contrast to controls, PS did not use the optimal eye information to identify familiar faces, but instead the lower part of the face, including the mouth and the external contours, as normal observers typically do when processing unfamiliar faces. Together, the findings reported here suggest that damage to the face processing system is characterized by an inability to use the information that is optimal to judge identity, focusing instead on suboptimal information.
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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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".