From “below chance” to “a single error is one too many”: Evaluating various thresholds for invalid performance on two forced choice recognition tests
Bibliographic record
Abstract
Abstract This study was designed to empirically evaluate the classification accuracy of various definitions of invalid performance in two forced‐choice recognition performance validity tests (PVTs; FCRCVLT‐II and Test of Memory Malingering [TOMM‐2]). The proportion of at and below chance level responding defined by the binomial theory and making any errors was computed across two mixed clinical samples from the United States and Canada (N = 470) and two sets of criterion PVTs. There was virtually no overlap between the binomial and empirical distributions. Over 95% of patients who passed all PVTs obtained a perfect score. At chance level responding was limited to patients who failed ≥2 PVTs (91% of them failed 3 PVTs). No one scored below chance level on FCRCVLT‐II or TOMM‐2. All 40 patients with dementia scored above chance. Although at or below chance level performance provides very strong evidence of non‐credible responding, scores above chance level have no negative predictive value. Even at chance level scores on PVTs provide compelling evidence for non‐credible presentation. A single error on the FCRCVLT‐II or TOMM‐2 is highly specific (0.95) to psychometrically defined invalid performance. Defining non‐credible responding as below chance level scores is an unnecessarily restrictive threshold that gives most examinees with invalid profiles a Pass.
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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 | 0.024 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".