Facial attraction: a study of the aesthetic dimension of face processing in prosopagnosia
Bibliographic record
Abstract
Background: Attractiveness is the perception of a facial property that has seldom been investigated in patient populations. On the one hand, it is a social signal, and thus may depend on processing in the superior temporal sulcus, but on the other it seems likely that attractiveness may depend on elements of facial structure that do not change with dynamic shifts in expression or gaze, and thus processed by the fusiform face area. Objective: We investigated the status of facial attractiveness perception in prosopagnosia, an impairment in the recognition of another temporally invariant property of faces, their identity. We hypothesized that if attractiveness is processed by regions that encode the temporally invariant properties of faces, such as the fusiform face area, then these patients should be universally impaired on this function. Method: We studied eight prosopagnosic subjects in two separate tasks: one testing their explicit rating of attractiveness and the other an attractiveness-motivated keypress behaviour. Results: Both tasks showed that the prosopagnosics were impaired in processing facial attractiveness. Residual but impaired attractiveness. Residual but impaired attractiveness perception was found only in subjects with unilateral right-sided occipitotemporal lesions or anterior temporal lesions. Measures of perception of facial attraction also correlated with measures of residual familiarity for famous faces.Conclusion: The processing of facial attraction requires participation of the same neural structures that encode facial identity.
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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 | 0.001 |
| 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 | 0.001 |
| 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".