Introducing a forced choice recognition trial to the Hopkins Verbal Learning Test – Revised
Bibliographic record
Abstract
OBJECTIVE: This study was designed to replicate previous research on embedded validity indicators (EVIs) in the Hopkins Verbal Learning Test - Revised (HVLT-R) and introduce a new forced choice recognition trial (FCR). METHOD: = 14.5 years, 85% female) to either the control or experimental malingering condition, and were administered a brief battery of neuropsychological tests. RESULTS: Recognition memory based EVIs (both existing and newly introduced) effectively discriminated credible and non-credible response sets. An FCR ≤11 produced .59 sensitivity and perfect specificity to invalid responding. A Recognition Discrimination (RD) score ≤8 also produced a good combination of sensitivity (.35) and specificity (.96). The FCR trial made unique contributions to performance validity assessment above and beyond previously published EVIs. CONCLUSIONS: RD achieved ≥.90 specificity at higher cutoffs than previously reported. The newly introduced FCR trial has the potential to enhance the existing arsenal of EVIs within the HVLT-R. However, it must demonstrate its ability to differentiate genuine impairment from non-credible responding before it can be recommended for clinical use.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".