The production effect: Delineation of a phenomenon.
Bibliographic record
Abstract
In 8 recognition experiments, we investigated the production effect-the fact that producing a word aloud during study, relative to simply reading a word silently, improves explicit memory. Experiments 1, 2, and 3 showed the effect to be restricted to within-subject, mixed-list designs in which some individual words are spoken aloud at study. Because the effect was not evident when the same repeated manual or vocal overt response was made to some words (Experiment 4), producing a subset of studied words appears to provide additional unique and discriminative information for those words-they become distinctive. This interpretation is supported by observing a production effect in Experiment 5, in which some words were mouthed (i.e., articulated without speaking); in Experiment 6, in which the materials were pronounceable nonwords; and even in Experiment 7, in which the already robust generation effect was incremented by production. Experiment 8 incorporated a semantic judgment and showed that the production effect was not due to "lazy reading" of the words studied silently. The distinctiveness that accrues to the records of produced items at the time of study is useful at the time of test for discriminating these produced items from other items. The production effect represents a simple but quite powerful mechanism for improving memory for selected information.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".