Production improves recognition and reduces intrusions in between-subject designs: An updated meta-analysis.
Bibliographic record
Abstract
The production effect refers to the finding that words read aloud are better remembered than those read silently. This pattern has most often been explained as arising from the incorporation of sensorimotor elements into the item representation at study, which could then be used to guide performance at later test. This theoretical framework views aloud items as being distinctive in relation to silent items, and thus the effect was thought to emerge only when production was manipulated within-subjects. This claim was later challenged, and a reliable (albeit smaller) between-subject production effect has since been shown in recognition memory. Across a series of meta-analyses, we extend this earlier work, replicating the between-subject production effect for recognition, and demonstrating no such effect for overall target recall. However, supporting recent theoretical claims, we further observed an interaction between the production effect and serial position within recall, such that a production effect was observed for late time points but not early time points (a similar, albeit smaller and noncredible trend was observed for recognition). Finally, we provide evidence that production reduces off-list intrusions. In summary, production has a reliable impact on recognition memory when manipulated between-subjects, but a more complex relationship with recall performance. (PsycInfo Database Record (c) 2023 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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".