Forgetting Numbers in Old Age: Strategy and Learning Speed Matter
Bibliographic record
Abstract
BACKGROUND: Memory intervention research with older adults has primarily focused on immediate effects of training. Little is known about whether memory training can prevent forgetting of a learned material over time. OBJECTIVE: The main purpose of this study was to investigate the effects of memory training on forgetting of numerical information in old age. In addition, the effect of speed of learning on forgetting rate was examined. METHODS: Two training programs were employed contrasting a number-consonant mnemonic strategy with a self-generated strategy. A non-practice control group was also included. There were 20 participants in each group (age range=60-83 years). Following completion of training, participants memorized six 4-digit numbers to perfection. Retention was tested after 30 min, 24 h, 7 weeks, and 8 months. RESULTS: The three groups showed equal rates of forgetting across the first two follow-up assessments. A different picture emerged for the last two occasions, with the self-generated strategy group remembering more items relative to the two other groups. Moreover, participants reaching the criterion in few trials exhibited less forgetting than slow learners. CONCLUSIONS: These data indicate that self-generated strategy training may have advantages over learning a classical mnemonic for preventing long-term forgetting of numeric materials in old age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".