Bibliographic record
Abstract
INTRODUCTION: Neurocognitive accounts of delusion have traditionally highlighted perceptual misrepresentation, as the primary trigger in addition to other cognitive deficits that maintain the delusion. Here, a general neurocognitive model of delusional disorder (DSM-IV) is proposed, not so much based on perceptual or cognitive deficits after right hemisphere damage as on cognitive propensities, specifically excessive inferencing (especially jumping to conclusions) and excessive reference to the self, due to left hemisphere overactivity. METHOD: The functional imaging, topographic EEG, and experimental imaging literatures on delusional disorder are reviewed, and 37 previously published cases of postunilateral lesion delusion (DSM-IV type, grandeur, persecution, jealousy, erotomania, or somatic), are reviewed and analysed multivariately. RESULTS: Functional imaging and EEG topography data were slightly more indicative of left hemisphere overactivity in delusional disorder. In addition, 73% of the postunilateral lesion cases (χ(2)=7.8, p=.005) of delusional disorder (DSM-IV type) had a right hemisphere lesion, whereas only 27% had a left hemisphere lesion. CONCLUSION: Left hemisphere release appears to be a more primary cause of delusional disorder than right hemisphere impairment, the latter merely entailing loss of inhibition of delusional beliefs. We propose that most patients with DSM-IV diagnoses of delusional disorder could be afflicted by excessive left hemisphere activity, but further research is necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".