The Scanpaths of Subjects with Developmental Prosopagnosia during a Face Memory Task
Bibliographic record
Abstract
The scanpaths of healthy subjects show biases towards the upper face, the eyes and the center of the face, which suggests that their fixations are guided by a feature hierarchy towards the regions most informative for face identification. However, subjects with developmental prosopagnosia have a lifelong impairment in face processing. Whether this is reflected in the loss of normal face-scanning strategies is not known. The goal of this study was to determine if subjects with developmental prosopagnosia showed anomalous scanning biases as they processed the identity of faces. We recorded the fixations of 10 subjects with developmental prosopagnosia as they performed a face memorization and recognition task, for comparison with 8 subjects with acquired prosopagnosia (four with anterior temporal lesions and four with occipitotemporal lesions) and 20 control subjects. The scanning of healthy subjects confirmed a bias to fixate the upper over the lower face, the eyes over the mouth, and the central over the peripheral face. Subjects with acquired prosopagnosia from occipitotemporal lesions had more dispersed fixations and a trend to fixate less informative facial regions. Subjects with developmental prosopagnosia did not differ from the controls. At a single-subject level, some developmental subjects performed abnormally, but none consistently across all metrics. Scanning distributions were not related to scores on perceptual or memory tests for faces. We conclude that despite lifelong difficulty with faces, subjects with developmental prosopagnosia still have an internal facial schema that guides their scanning behavior.
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 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".