Relating visual to verbal semantic knowledge: the evaluation of object recognition in prosopagnosia
Bibliographic record
Abstract
Assessment of face specificity in prosopagnosia is hampered by difficulty in gauging pre-morbid expertise for non-face object categories, for which humans vary widely in interest and experience. In this study, we examined the correlation between visual and verbal semantic knowledge for cars to determine if visual recognition accuracy could be predicted from verbal semantic scores. We had 33 healthy subjects and six prosopagnosic patients first rated their own knowledge of cars. They were then given a test of verbal semantic knowledge that presented them with the names of car models, to which they were to match the manufacturer. Lastly, they were given a test of visual recognition, presenting them with images of cars to which they were to provide information at three levels of specificity: model, manufacturer and decade of make. In controls, while self-ratings were only moderately correlated with either visual recognition or verbal semantic knowledge, verbal semantic knowledge was highly correlated with visual recognition, particularly for more specific levels of information. Item concordance showed that less-expert subjects were more likely to provide the most specific information (model name) for the image when they could also match the manufacturer to its name. Prosopagnosic subjects showed reduced visual recognition of cars after adjusting for verbal semantic scores. We conclude that visual recognition is highly correlated with verbal semantic knowledge, that formal measures of verbal semantic knowledge are a more accurate gauge of expertise than self-ratings, and that verbal semantic knowledge can be used to adjust tests of visual recognition for pre-morbid expertise in prosopagnosia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".