Structure and function in acquired prosopagnosia: Lessons from a series of 10 patients with brain damage
Bibliographic record
Abstract
Acquired prosopagnosia varies in both behavioural manifestations and the location and extent of underlying lesions. We studied 10 patients with adult-onset lesions on a battery of face-processing tests. Using signal detection methods, we found that discriminative power for the familiarity of famous faces was most reduced by bilateral occipitotemporal lesions that involved the fusiform gyri, and better preserved with unilateral right-sided lesions. Tests of perception of facial structural configuration showed severe deficits with lesions that included the right fusiform gyrus, whether unilateral or bilateral. This deficit was most consistent for eye configuration, with some patients performing normally for mouth configuration. Patients with anterior temporal lesions had better configuration perception, though at least one patient showed a more subtle failure to integrate configural data from different facial regions. Facial imagery, an index of facial memories, was severely impaired by bilateral lesions that included the right anterior temporal lobe and marginally impaired by fusiform lesions alone; unilateral right fusiform lesions tended to spare imagery for facial features. These findings suggest that (I) prosopagnosia is more severe with bilateral than unilateral lesions, indicating a minor contribution of the left hemisphere to face recognition, (2) perception of facial configuration critically involves the right fusiform gyrus and (3) access to facial memories is most disrupted by bilateral lesions that also include the right anterior temporal lobe. This supports assertions that more apperceptive variants of prosopagnosia are linked to fusiform damage, whereas more associative variants are linked to anterior temporal damage. Next, we found that behavioural indices of covert recognition correlated with measures of overt familiarity, consistent with theories that covert behaviour emerges from the output of damaged neural networks, rather than alternative pathways. Finally, to probe the face specificity of the prosopagnosic defect, we tested recognition of fruits and vegetables: While face specificity was not found in most of our patients, the data of one patient suggested that this may be possible with more focal lesions of the right fusiform gyrus.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".