Word and text processing in acquired prosopagnosia
Bibliographic record
Abstract
OBJECTIVE: A novel hypothesis of object recognition asserts that multiple regions are engaged in processing an object type, and that cerebral regions participate in processing multiple types of objects. In particular, for high-level expert processing, it proposes shared rather than dedicated resources for word and face perception, and predicts that prosopagnosic subjects would have minor deficits in visual word processing, and alexic subjects would have subtle impairments in face perception. In this study, we evaluated whether prosopagnosic subjects had deficits in processing either the word content or the style of visual text. METHODS: Eleven prosopagnosic subjects, 6 with unilateral right lesions and 5 with bilateral lesions, participated. In the first study, we evaluated their word length effect in reading single words. In the second study, we assessed their time and accuracy for sorting text by word content independent of style, and for sorting text by handwriting or font style independent of word content. RESULTS: Only subjects with bilateral lesions showed mildly elevated word length effects. Subjects were not slowed in sorting text by word content, but were nearly uniformly impaired in accuracy for sorting text by style. INTERPRETATION: Our results show that prosopagnosic subjects are impaired not only in face recognition but also in perceiving stylistic aspects of text. This supports a modified version of the many-to-many hypothesis that incorporates hemispheric specialization for processing different aspects of visual text.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".