Creating a recollection-based memory through drawing.
Bibliographic record
Abstract
Drawing a picture of to-be-remembered information substantially boosts memory performance in free-recall tasks. In the current work, we sought to test the notion that drawing confers its benefit to memory performance by creating a detailed recollection of the encoding context. In Experiments 1 and 2, we demonstrated that for both pictures and words, items that were drawn by the participant at encoding were better recognized in a later test than were words that were written out. Moreover, participants' source memory (in this experiment, correct identification of whether the word was drawn or written) was superior for items drawn relative to written at encoding. In Experiments 3A and 3B, we used a remember-know paradigm to demonstrate again that drawn words were better recognized than written words, and further showed that this effect was driven by a greater proportion of recollection-, rather than familiarity-based responses. Lastly, in Experiment 4 we implemented a response deadline procedure, and showed that when recognition responses were speeded, thereby reducing participants' capacity for recollection, the benefit of drawing was substantially smaller. Taken together, our findings converge on the idea that drawing improves memory as a result of providing vivid contextual information which can be later called upon to aid retrieval. (PsycINFO Database Record
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".