Age differences in the precision of memory at short and long delays
Bibliographic record
Abstract
Age differences are well established for many memory tasks assessing both short-term and long-term memory. However, how age differences in performance vary with increasing delay between study and test is less clear. Here we report two experiments in which participants studied a continuous sequence of object-location pairings. Test events were intermixed such that participants were asked to recall the precise location of an object following a variable delay. Older adults exhibit a greater degree of error (distance between studied and recalled locations) relative to younger adults at short (0-2 intervening events) and longer delays (10-25 intervening events). Mixture modeling of the distribution of recall error suggests that older adults do not fail to recall information at a significantly higher rate than younger adults. Instead, what they do recall appears to be less precise. Follow up analyses demonstrate that this age difference emerges following only one or two intervening events between study and test. These findings are consistent with the suggestion that aging does not greatly impair recall from the focus of attention but age differences emerge once information is displaced from this highly accessible state. Further, we suggest that age differences in the precision of memory, but not the probability of successful recall, may be due to the use of more gist-like representations in this task.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".