The production effect over the long term: Modeling distinctiveness using serial positions.
Bibliographic record
Abstract
The production effect is a well-established finding: If some words within a list are read aloud, that is, produced, they are better remembered than their silently read neighbors. The effect has been extensively studied with long-term memory tasks. Recently, using immediate serial recall and short-term order reconstruction, Saint-Aubin et al. (2021) reported informative interactions between the production effect and serial positions. Here, we asked whether these interactions would also be observed with the long-term memory tasks used in the field. In Experiment 1, pure and mixed lists of eight words were presented in both order reconstruction and free recall tasks, with a 30-s filled retention interval. In Experiment 2, the list length was extended to 24 words; in Experiment 3, 10-word lists were used with a 2-min retention interval. Results from all experiments aligned well with those observed in short-term memory. With mixed lists, where produced and silently read words alternated, produced items were better recalled, leading to sawtooth serial position curves. With pure lists, produced items were better recalled when studied in the last serial positions, but they were less well recalled for the primacy positions. Results were readily accounted for by the revised feature model, originally developed to explain short-term memory performance. The findings and model suggest that produced items are encoded with more item-specific, modality-related features and that this generates a relative distinctiveness advantage in short- and long-term memory. However, the richer encoding comes at a cost: It appears to disrupt rehearsal. (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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".