Recognition of static versus dynamic faces in prosopagnosia
Bibliographic record
Abstract
A striking finding in the face recognition literature is that motion improves recognition accuracy only when viewing conditions are poor. This may be due to parallel (separate) neural processing of the invariant (identity) information in the fusiform gyrus and changeable (social communication) information in the superior temporal sulcus (pSTS) (Haxby et al., 2000). The pSTS may serve as a secondary “back-up” route for the recognition of faces from identity-specific facial dynamics (O'Toole et al., 2002). This predicts that prosopagnosics with an intact pSTS may be able to recognize faces when they are presented in motion. We compared face recognition for prosopagnosics with intact STS (n=2) and neurologically intact controls (n=19). In our experiment, we used static and dynamic (speaking/expressing) faces, tested in identical and “changed” stimulus conditions (e.g., different video with hair change, etc.). Participants learned 40 faces: half from dynamic videos and half from multiple static images extracted from the videos. At test, participants made “old/new” judgments to identical and changed stimuli from the learning session and to novel faces. As expected, controls showed equivalent accuracy for static and dynamic conditions, with better performance for identical than for changed stimuli. Using the same procedure, we tested two prosopagnosic patients: MR, who has a lesion that destroyed the right OFA and FFA, and BP, who has a right anterior temporal lesion sparing these areas. For identical stimuli, MR and BP performed marginally better on static faces than on dynamic faces. For the more challenging problem of recognizing people from changed stimuli, both MR and BP performed substantially better on the dynamic faces. The motion advantage seen for MR and BP in the changed stimulus condition is consistent with the hypothesis that patients with a preserved pSTS may show better face recognition for moving faces.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.003 | 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".