Comparing the influence of doodling, drawing, and writing at encoding on memory.
Bibliographic record
Abstract
The purpose of the present study was to determine the extent to which doodling, which we define as drawing that is semantically unrelated to to-be-remembered information, enhances memory performance. In Experiment 1, participants heard auditorily presented lists of categorized words. They were asked to either doodle, draw a picture of, or write out, each item while listening to the target words. Participants showed poorer free recall for words encoded while free-form doodling compared to words that were drawn or written, with drawing resulting in the best performance. In Experiment 2, target words were embedded in a narrative story to better resemble a real-world situation in which one might doodle. Participants monitored each auditorily presented narrative while either free-form doodling, drawing, or writing in response to the target words. As in Experiment 1, doodling led to the poorest subsequent recall for targets compared to drawing or writing during encoding. In Experiment 3, we used a structured doodling task at encoding, such that participants shaded in geometric shapes printed on paper rather than create their own doodles. Structured doodling led to similar levels of recall compared to simply writing. Creating a drawing of the words at encoding, rather than doodling, once again enhanced recall significantly. Taken together, these findings indicate that unlike task-relevant drawing, structured doodling during study provides no benefits to free recall, and free-form doodling leads to memory costs. (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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| 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".