Object recognition in acquired and developmental prosopagnosia
Bibliographic record
Abstract
Whether face and object recognition are dissociated in prosopagnosia continues to be debated: a recent review highlighted deficiencies in prior studies regarding the evidence for such a dissociation. Our goal was to study cohorts with acquired and developmental prosopagnosia with a complementary battery of tests of object recognition that address prior limitations, as well as evaluating for residual effects of object expertise. We studied 15 subjects with acquired and 12 subjects with developmental prosopagnosia on three tests: the Old/New Tests, the Cambridge Bicycle Memory Test, and the Expertise-adjusted Test of Car Recognition. Most subjects with developmental prosopagnosia were normal on the Old/New Tests: for acquired prosopagnosia, subjects with occipitotemporal lesions often showed impairments while those with anterior temporal lesions did not. Ten subjects showed a putative classical dissociation between the Cambridge Face and Bicycle Memory Tests, seven of whom had normal reaction times. Both developmental and acquired groups showed reduced car recognition on the expertise-adjusted test, though residual effects of expertise were still evident. Two subjects with developmental prosopagnosia met criteria for normal object recognition across all tests. We conclude that strong evidence for intact object recognition can be found in a few subjects but the majority show deficits, particularly those with the acquired form. Both acquired and developmental forms show residual but reduced object expertise effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".