Preservation and impairment of featural and configural processing for faces as a result of prosopagnosia
Bibliographic record
Abstract
This research examines the effects of prosopagnosia on the processing of featural and configural information in faces. Featural and configural information was manipulated in a face matching task by incrementally varying the size and distance of the eyes and mouth features respectively. In a control study with visually normal adults, the featural and configural conditions for the eye and mouth regions were equated for their overall perceptual discriminability. Using the same task, we assessed featural and configural processing in two cases of acquired prosopagnosia: LR and HH. Compared to age-matched controls, both prosopagnosic patients performed normally in their ability to discern differences in the size and spacing of the mouth feature. In contrast, the two patients were selectively impaired in their ability to detect featural and configural differences in the eye region. The same pattern of results was found whether the stimulus faces were presented sequentially or simultaneously. The findings indicate that brain-damage does not necessarily result in a global impairment of face processing ability. Although LR and HH showed an impaired ability to detect differences in the eye region, they were spared in their ability to discriminate differences in the mouth region. Interestingly, the face processing deficits identified in the patients did not correspond to impairment in sensitivity to featural or configural 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".