Cognitive arbitration between candidate dimensions of psychopathology
Bibliographic record
Abstract
As an alternative to the Diagnostic and Statistical Manual of Mental Disorders, transdiagnostic approaches that identify latent dimensions of psychopathology through factor analysis have gained prominence in recent years. A key critique of these approaches, however, is that they are performed at the level of symptoms only. This begs the question: are these dimensions truly more valid predictors of external outcomes than existing alternatives? Are there other ways, that are more data-driven, which can allow us to refine our definitions of clinical phenotypes? We tested this idea empirically, conducting a large-scale meta-scientific comparison of thousands of competing factor solutions that allowed us to determine if the latent structure underlying the covariation of psychiatric symptoms has robust and specific cognitive correlates. In nine independent datasets, comprising N = 7565 individuals including patients about to start mental health treatment, healthy individuals, paid and unpaid participants, a broad set of age ranges and cognitive task variants that measured model-based planning and metacognition, we found that factors with the best fit to cognition were those derived from a first-order factor analysis on the maximal number of theoretically informed self-report symptoms available. These factors ('Compulsivity and Intrusive Thought' and 'Anxious-depression') performed better than thousands of engineered alternatives and performed twice as well as traditional questionnaire total scores. Crucially, this unsupervised approached based on symptom correlation only performed on-par with a partial least squares analysis, a supervised approach to deriving factors based on cognition. These results provide evidence that unsupervised factor analysis of psychiatric symptoms is a viable method for rethinking how we define mental health and illness, affording clear opportunities for enhancing our understanding of specific underlying mechanistic processes.
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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 | 0.154 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".