Normal acquisition of expertise with greebles in two cases of acquired prosopagnosia
Bibliographic record
Abstract
Face recognition is generally thought to rely on different neurocognitive mechanisms than most types of objects, but the specificity of these mechanisms is debated. One account suggests the mechanisms are specific to upright faces, whereas the expertise view proposes the mechanisms operate on objects of high within-class similarity with which an observer has become proficient at rapid individuation. Much of the evidence cited in support of the expertise view comes from laboratory-based training experiments involving computer-generated objects called greebles that are designed to place face-like demands on recognition mechanisms. A fundamental prediction of the expertise hypothesis is that recognition deficits with faces will be accompanied by deficits with objects of expertise. Here we present two cases of acquired prosopagnosia, Herschel and Florence, who violate this prediction: Both show normal performance in a standard greeble training procedure, along with severe deficits on a matched face training procedure. Herschel and Florence also meet several response time criteria that advocates of the expertise view suggest signal successful acquisition of greeble expertise. Furthermore, Herschel's results show that greeble learning can occur without normal functioning of the right fusiform face area, an area proposed to mediate greeble expertise. The marked dissociation between face and greeble expertise undermines greeble-based claims challenging face-specificity and indicates face recognition mechanisms are not necessary for object recognition after laboratory-based training.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".