Bibliographic record
Abstract
People remember words they say aloud better than ones they do not, a result called the production effect. The standing explanation for the production effect is that producing a word renders it distinctive in memory and thus memorable at test. Whereas it is now clear that motoric production benefits remembering over nonproduction, and that more intense motoric production benefits remembering to a greater extent than less intense motoric production, there has been no comparison of the memorial benefit conferred by motoric versus imagined production. One reason for the gap is that the standard production-by-vocalization procedure confounds the analysis. To make the comparison, we used a production-by-typing procedure and tested memory for words that people typed, imagined typing, and did not type. Whereas participants remembered the words that they typed and imagined typing better than words that they did not, they remembered the words they typed better than the ones they imagined typing; an advantage that was consistent over tests of recognition memory and source discrimination. We conclude that motoric production is a sufficient and facilitative (but not a necessary) condition to observe the production effect. We explain our results by a sensory feedback account of the production effect and sketch a computational framework to implement that approach.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".