Perceptual Expertise Effects Are Not All or None: Spatially Limited Perceptual Expertise for Faces in a Case of Prosopagnosia
Bibliographic record
Abstract
We document a seemingly unique case of severe prosopagnosia, L. R., who suffered damage to his anterior and inferior right temporal lobe as a result of a motor vehicle accident. We systematically investigated each of three factors associated with expert face recognition: fine-level discrimination, holistic processing, and configural processing (Experiments 1-3). Surprisingly, L. R. shows preservation of all three of these processes; that is, his performance in these experiments is comparable to that of normal controls. However, L. R. is only able to apply these processes over a limited spatial extent to the fine-level detail within faces. Thus, when the location of a given change is unpredictable (Experiment 3), L. R. exhibits normal detection of features and spatial configurations only for the lower half of each face. Similarly, when required to divide his attention over multiple face features, L. R. is able to determine the identity of only a single feature (Experiment 4). We discuss these results in the context of forming a better understanding of prosopagnosia and the mechanisms used in face recognition and visual expertise. We conclude that these mechanisms are not "all-or-none," but rather can be impaired incrementally, such that they may remain functional over a restricted spatial area. This conclusion is consistent with previous research suggesting that perceptual expertise is acquired in a spatially incremental manner [Gauthier, I., & Tarr, M. J. Unraveling mechanisms for expert object recognition: Bridging brain activity and behavior. Journal of Experimental Psychology: Human Perception & Performance, 28, 431-446, 2002].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".