The Biasing of Figure – Ground Assignment by Shading Cues for Objects and Faces in Prosopagnosia
Bibliographic record
Abstract
Prosopagnosia is defined by impaired recognition of the identity of specific faces. Whether the perception of faces at the categorical level (recognizing that a face is a face) is also impaired to a lesser degree is unclear. We examined whether prosopagnosia is associated with impaired detection of facial contours in a bistable display, by testing a series of five prosopagnosic patients on a variation of Rubin's vase illusion, in which shading was introduced to bias perception towards either the face or the vase. We also included a control bistable display in which a disc or an aperture were the two possible percepts. With the control disc/aperture test, prosopagnosic patients did not generate a normal sigmoid function, but a U-shaped function, indicating that they perceived the shading but had difficulty in using the shading to make the appropriate figure-ground assignment. While controls still generated a sigmoid function for the vase/face test, prosopagnosic patients showed a severe impairment in using shading to make consistent perceptual assignments. We conclude that prosopagnosic patients have difficulty in using shading to segment figures from background correctly, particularly with complex stimuli like faces. This suggests that a subtler defect in face categorization accompanies their severe defect in face identification, consistent with predictions of computational models and recent data from functional imaging.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 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".