One hundred and ten fundamentals of performance validity tests in neuropsychological forensic disability and related assessment I: Introduction
Bibliographic record
Abstract
This is the first of five articles on state-of-the-art conceptualization and research on the use of performance validity tests (PVTs) in forensic neuropsychological assessment. PVTs are standardized psychometric tests especially aimed at determining the extent of examinee underperformance, to the point that their cognitive test performance can be deemed invalid, and not representative of their genuine abilities. The article reviews key terms in the field, which include: PVTs; symptom validity tests (SVTs); cut scores; neuropsychological assessment; malingering/feigning and related terms; (especially invalid response set and negative response bias (NRB); the Diagnostic and Statistical Manual of Mental Disorders' (DSM-5) approach to defining malingering; test standardization; classification accuracy statistics (specificity and sensitivity); the multidimensional Malingered Neurocognitive Dysfunction (MND) framework, and the Erdodi Index (EI) multivariate framework. We conclude that PVT data might indicate invalid response set but malingering is attributed only after careful analysis of the full examinee profile, and not just on the basis of examinee PVT scores. This present five-article series stands in contrast to criticisms of the use of PVTs in FDRA by the Leonhards, as reviewed in the fifth article in the series.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".