Age differences in effectiveness of encoding techniques on memory
Bibliographic record
Abstract
We compared the effectiveness of different encoding techniques across the adult age range. Three hundred participants: 100 younger, 100 middle-aged, and 100 older adults, were asked to encode a set of visually presented concrete and abstract words. Participants were shown target words one at a time, along with prompts (randomly and intermixed, within-subject) to either silently read, read aloud, write, or draw a picture of the target, for a duration of 10-seconds each. On a later free recall test, participants were given 2-minutes to type all the words they could remember from the encoding phase. Across age groups, we showed that drawing, writing, and reading aloud as encoding techniques yielded better memory than silently reading words, with drawing leading to the largest boost. While memory performance did decrease as age increased, it interacted with the encoding technique. Of note, there were no differences in memory performance in middle-aged compared to young adults. Importantly, age differences in memory emerged only when drawing was used as the encoding strategy, in line with previously reported age-related deficits in generating imagery, or integrating it with motoric processes. Despite this, concrete relative to abstract words that were drawn or written during encoding were better retained, regardless of age, suggesting these techniques facilitate formation of age-invariant visuo-spatial representations. Our findings suggest that whether age differences in memory emerge depends on the strategy used at encoding, and the type of information being encoded.
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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 | 0.004 |
| 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".