Evidence from paranoid schizophrenia for more than one component of theory of mind
Bibliographic record
Abstract
We previously reported finding that performance was impaired on four out of five theory of mind (ToM) tests in a group of 21 individuals diagnosed with paranoid schizophrenia (pScz), relative to a non-clinical group of 29 individuals (Scherzer et al., 2012). Only the Reading the Mind in the Eyes Test did not distinguish between groups. A principal components analysis revealed that the results on the ToM battery could be explained by one general ToM factor with the possibility of a latent second factor. As well, the tests were not equally sensitive to the pathology. There was also overmentalization in some ToM tests and under-mentalisation in others. These results led us to postulate that there is more than one component to ToM. We hypothesized that correlations between the different EF measures and ToM tests would differ sufficiently within and between groups to support this hypothesis. We considered the relationship between the performance on eight EF tests and five ToM tests in the same diagnosed and non-clinical individuals as in the first study. The ToM tests shared few EF correlates and each had its own best EF predictor. These findings support the hypothesis of multiple ToM components.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".