Even you, greebles? Normal greeble performance in acquired prosopagnosia supports face specificity
Bibliographic record
Abstract
A prominent account of face processing suggests that face recognition depends on generic mechanisms involved in processing object classes for which individuals have developed expertise. Many laboratory studies of expertise have used a multi-session training paradigm designed to develop expertise with computer-generated stimuli known as greebles. If the recognition of faces and greebles following training depend on the same mechanisms, impairments with faces should be accompanied by impairments with the acquisition of greeble expertise. Contrary to this prediction, we present two cases of acquired prosopagnosia who exhibit normal greeble learning. Florence (female, 29, with a right anterior temporal resection for epilepsy) and Herschel (male, 55, with right occipitotemporal lesions following several strokes) completed an eight-day greeble training procedure used in previous studies. Their accuracy and response times were similar to those of age-matched control participants. In addition, by the end of the training procedure, both Florence and Herschel fulfilled the criterion expertise researchers claim signals successful acquisition of greeble expertise: comparable response times for greeble recognition at the family and individual level. As expected, Florence and Herschel failed to match controls’ learning profile in a follow-up training procedure with faces, demonstrating a dissociation between face and greeble expertise. In addition, Herschel’s lesion disrupted his right fusiform face area (FFA) so his results show that greeble learning can occur without an intact FFA. In sum, our findings are inconsistent with claims from the greeble literature challenging face-specificity, and indicate that distinct mechanisms are used for face recognition and the object recognition processes used in greeble training procedures. Meeting abstract presented at VSS 2013
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".