Mismatch negativity predicts age-related declines in memory precision
Bibliographic record
Abstract
Does precision in auditory perception predict precision in subsequent memory (i.e., mnemonic discrimination) in aging? This study examined if the mismatch negativity (MMN), an electrophysiological marker of change detection and encoding, relates to age differences in mnemonic discrimination. The MMN was recorded in 92 adults (18-86 years, 47 females) in a passive oddball paradigm using tone sequences. Participants then completed a surprise recognition test for presented sequences (i.e., old targets) against novel sequences (i.e., similar lures and dissimilar foils). Across the adult lifespan, MMN amplitudes showed attenuation with increasing age, accompanied by worse performance discriminating targets from lures and foils. Across participants, smaller MMN amplitude predicted worse recognition performance. Notably, MMN amplitude partially explained age-related declines in target-lure discriminability, but not target-foil discriminability. Findings reinforce the MMN as a marker of mnemonic discrimination, and clarify how age-related declines in memory precision at retrieval may be explained by age differences at encoding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".