The production effect in recognition memory: Weakening strength can strengthen distinctiveness.
Bibliographic record
Abstract
Producing items (e.g., by saying them aloud or typing them) can improve recognition memory. To evaluate whether production increases item distinctiveness and/or memory strength we compared this effect as a function of the percentage of items that participants typed at encoding (i.e., 0%, 20%, 50%, 80%, and 100%). Experiment 1 revealed a strength-based pattern: The production effect was similar across pure-list (i.e., 0% vs. 100%) and mixed-list (i.e., 20%, 50%, 80%) designs, and there was no observed influence of statistical distinctiveness (i.e., 20% vs. 80%). In Experiment 2, we increased the study time for unproduced items to minimise the strength difference between produced and unproduced items. The manipulation attenuated the pure-list effect without eliminating the mixed-list effect, providing support for the inference that the mixed-list effect reflects distinctiveness. An influence of statistical distinctiveness also emerged: The mixed-list effect was larger when participants produced only 20%, rather than 80%, of the items. These findings suggest that both strength and distinctiveness contribute to the production effect in recognition. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".