A New Measure of Mnemonic Discrimination Applicable to Recognition Memory Tests with Continuous Variation in Novel Stimulus Interference
Bibliographic record
Abstract
Background: Mnemonic discrimination (MD) involves distinguishing new stimuli from memories of highly similar “lure” items or events, and is a putative indirect probe of dentate gyrus functioning. MD is impaired in the elderly and in individuals with hippocampal lesions, schizophrenia, major depressive disorder, and Alzheimer’s disease. The gold-standard MD test, called the mnemonic similarity task (MST), is rarely used in clinical research. We therefore aimed to validate a novel analysis method that extracts information about MD and recognition memory in widely clinically used recognition memory tests which do not have categorical distinctions between “lures” and “foils.”Methods: By fitting a logistic function to the relationship between stimulus interference and the probability of classifying a stimulus as novel, at the single participant level, we derived participant-level indices of MD (λ) and overall recognition memory performance (Δ). We applied the novel measures to MST data from two independent datasets (N=18; N=67). Using linear mixed-effects modelling, we sought to confirm that λ predicts the MST’s lure discrimination index (LDI), while Δ predicts the MST’s overall recognition memory index (REC). Results: Across both datasets, λ predicted LDI (β=0.76, 95% CI [0.62-0.91], p<0.001), but not REC (β=-0.06, 95% CI [-0.20-0.09], p=0.438), while Δ predicted REC (β=0.93, 95% CI [0.83-1.02], p<0.001), but not LDI (β=0.06, 95% CI [-0.03-0.15], p=0197). The λ and Δ indices were not correlated.Conclusion: Our novel measure accurately indexes MD, without correlating with overall recognition memory performance. Future studies should apply it to large clinical datasets with widely used recognition memory tests, such as the California Verbal Learning Test.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".