Drawing compared to writing in a diary enhances recall of autobiographical memories
Bibliographic record
Abstract
Drawing at encoding has been shown to improve later recall of to-be-remembered words, pictures, and academic terms, compared to when one simply writes out the target information. Here we examined whether drawing in a diary, compared to writing in it, differentially improved later memorability of personal autobiographical events, and whether aging influenced the magnitude of this effect. Thirty younger and thirty older adults were given a diary booklet, and instructed to use it to reminisce about a random daily event of their choosing, on 12 days within a two-week period. They recorded the event on each day in one of two ways, counterbalanced within-subject: by writing about or by drawing a picture of the event. Participants also generated a keyword descriptor for each event. After the two-week period, participants were cued using their keyword descriptors to recall each autobiographical memory by writing it down. Self-reported match accuracy, between reminisced and recalled events was tabulated. Across age groups, match accuracy was significantly higher for those drawn than written during the reminiscing phase. In addition, we compared the word count, level of detail, visual imagery, and point of view of the recalled memories. Self-reports of the level of detail in the recalled memory were better maintained for events drawn compared to written during the reminiscing stage, and better maintained in older than younger adults. Our findings suggest the use of drawing, while reminiscing within a diary format, enhances accuracy and quality of later recollections.
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".