Uncovering General and Specific Relationships Between Psychopathology and Neurocognition: A Hierarchical, Dimensional Approach Using Latent Variable Models
Bibliographic record
Abstract
Discriminable relationships between psychiatric disorders and neurocognition have not been strongly established despite evidence of associations between the two. This may be due to prevailing psychiatric classification systems that define psychiatric disorders as discrete entities, contrasting with alternative empirically based approaches of hierarchical dimensional structures which may better elucidate broader and narrower relationships between psychopathology and neurocognition. In this remote study, 1000 adults with mental health concerns completed a dimensional and hierarchical measure of psychopathology and a comprehensive neurocognitive battery. A six-factor bifactor model of psychopathology and a three-factor bifactor model of neurocognition were supported by both theory and standard fit indices. Hierarchical regressions based on estimated factor scores suggested significant negative association of general psychopathology with general neurocognition, and detachment with general neurocognition and social cognition. A positive association was found between anxiety and social cognition. These preliminary findings suggest associations between psychopathology and neurocognition are largely non-specific.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".