Drawing enhances item information but undermines sequence information in memory.
Bibliographic record
Abstract
Drawing a picture of the referent of a word produces considerably better recall and recognition of that word than does a baseline condition, such as repeatedly writing the word, a phenomenon referred to as the drawing effect. Although the drawing effect has been the focus of much recent research, it is not yet clear what underlies the beneficial effects of drawing to memory. In 3 experiments, we explored the roles of item and order information following drawing versus silent reading and produced 2 important findings. First, the drawing effect in recall was substantially larger when the 2 conditions were intermixed in a single list compared to appearing in separate lists-in other words, drawing produced a design effect. Second, the studied order was better retained for silent reading compared to drawing in pure lists. These findings are consistent with the item-order account: Memory for the order of drawn lists is poorer because the elaborative act of drawing disrupts the encoding of interitem associations (i.e., relational information) between all list items. Thus, the selection of an encoding task should be informed by how that memory might be used in the future; if one wants to remember individual items, then an elaborative task such as drawing is recommended; if one wants to remember their sequence, it is likely better to read silently. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".