Executive Function Predicts Older Adults’ Lure Discrimination Difficulties on the Mnemonic Similarity Task
Bibliographic record
Abstract
OBJECTIVES: Older adults often have difficulty remembering the details of recently encountered objects. We previously found this with the Mnemonic Similarity Task (MST). Surprisingly, the older adults' MST Lure Discrimination Index (LDI) was significantly correlated with visual acuity but not with memory or executive function. Here we ran a replication with new, larger samples of young (N = 45) and older adults (N = 70). We then combined the original and replication older adult samples (N = 108) to critically examine the relative contributions of visual acuity, memory, and executive function composite scores to LDI performance using dominance analysis. This provided, to our knowledge, the first direct statistical comparison of all 3 of these factors and their interactions on LDI. METHODS: Participants completed the MST and a battery assessing visual acuity, memory, and executive function. We examined age group differences on MST performance in the new (i.e., replication) young and older adult samples and performed multiple regression and dominance analysis on the combined older adult sample. RESULTS: Consistent with previous findings, the older adults showed significantly poorer LDI but preserved item recognition. LDI was significantly correlated with both memory and executive function but not with visual acuity. In the combined older adult sample, all 3 composites predicted LDI, but dominance analysis indicated that executive function was the most important predictor. DISCUSSION: Older adults' MST LDI difficulty may be predicted by their executive function and visual acuity. These factors should be considered when interpreting older adults' MST performance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".