Bibliographic record
Abstract
The production effect is the finding that subsequent memory is better for words that are produced than for words that are not produced. Whereas the current literature demonstrates that reading aloud is the most effective form of production, the distinctiveness account used to explain the production effect predicts that there is nothing special about reading aloud per se: Other forms of vocal production that include an additional distinct element should produce even greater subsequent memory benefits than reading aloud. To test this, we presented participants with study words that they were instructed to read aloud loudly, read aloud, or read silently (Experiment 1); sing, read aloud, or read silently (Experiment 2); and sing, read aloud loudly, read aloud, or read silently (Experiment 3). We observed that both reading items aloud loudly (Experiments 1 and 3) and singing items (Experiments 2 and 3) at study resulted in greater subsequent recognition than reading items aloud in a normal voice; singing had a larger memory benefit than reading aloud loudly (Experiment 3). Our findings support the distinctiveness hypothesis by demonstrating that there are other forms of production, such as singing and reading aloud loudly that have a more pronounced effect on memory than reading aloud.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".