The Production Effect Interacts With Serial Positions
Bibliographic record
Abstract
Reading some words aloud during presentation, that is, producing them, and reading other words silently generate a large memory advantage for words that are produced. This robust within-list production effect is in contrast with the between-lists condition in which all words are read aloud or silently. In a between-lists condition, produced items are better recognized, but not better recalled. The lack of a between-lists production effect with recall tasks has often been presented as one of its defining characteristics and as a benchmark for evaluating models. Recently, Cyr et al. (2021) showed that this occurs because item production interacts with serial positions: Produced items are less well recalled on the first serial positions than silently read items, while the reverse pattern is observed for the recency portion of the curve. However, this pattern was observed with a repeated-measures design, and it may be a by-product of compensatory processes under the control of participants. Here, using a between-participants design, we observed the predicted interaction between production and serial positions. The results further support the Revised Feature Model (RFM) suggesting that produced items are encoded with more modality-dependent distinctive features, therefore benefiting recall. However, the production of the additional distinctive features would disrupt rehearsal.
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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 | 0.006 | 0.040 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".