Effectiveness of production and drawing as encoding techniques on recall using mixed- and pure-list designs
Bibliographic record
Abstract
We compared the benefit of production and drawing on recall of concrete and abstract words, using mixed- and pure-list designs. We varied stimulus and list types to examine whether the memory benefit from these strategies was sustained across these manipulations. For all experiments, the memory retrieval task was free recall. In Experiment 1, participants studied concrete and abstract words sequentially, with prompts to either silently-read, read aloud, write, or draw each target (intermixed). Reading aloud, writing, and drawing improved recall compared to silent reading, with drawing leading to the largest boost. Performance, however, was at floor in all but the drawing condition. In Experiment 2, the number of targets was reduced, and each strategy (between-subjects) was compared to silent-reading. We eliminated floor effects and replicated results from Experiment 1. In Experiment 3, we manipulated strategy in a pure-list-design. The drawing benefit was maintained while that from production was eliminated. In all experiments, recall was higher for concrete than abstract words that were drawn; no such effect was found for words produced. Results suggest that drawing facilitates memory by enhancing semantic elaboration, whereas the production benefit is largely perceptually based. Importantly, the memory benefit conferred by drawing at encoding, unlike production, cannot be explained by a distinctiveness account as it was relatively unaffected by study design.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".