A dimensional latent variable model approach to connecting psychopathology and neurocognition hierarchies.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
= 715 female) with current mental health concerns participated in this online research study and completed questionnaires of dimensional psychopathology and comprehensive neuropsychological testing using measures with previously established latent hierarchical structures. A series of confirmatory and exploratory higher-order, bifactor, and correlated factors models were tested. Hierarchical regressions and structural models were used to test associations between psychopathology and neurocognition dimensions. An exploratory six-factor bifactor model (general psychopathology, harmful substance use, anxiety, detachment, depression, posttraumatic stress) and a confirmatory five-factor model of psychopathology (general psychopathology, internalizing, externalizing, thought, detachment plus method factor) emerged. An exploratory three-factor bifactor model of neurocognition (general neurocognition, executive function, and social cognition) was retained. Hierarchical regressions revealed a significant negative association of general psychopathology with general neurocognition. Detachment was associated with a further decrement in general neurocognition and social cognition. A positive association was found between anxiety and social cognition. Within a structural model between the five-factor bifactor model of psychopathology and three-factor bifactor model of neurocognition, only the association between detachment and general neurocognition remained significant. Higher levels of detachment are most consistently associated with decrements in general neurocognition across different models. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it