Production increases both true and false recognition
Bibliographic record
Abstract
• Reading aloud (production) resulted in enhanced false memory rates relative to reading silently or hearing the words spoken by another voice. • Reading aloud boosted both true and false recognition, though to a smaller extent for the latter. • Production-enhanced false recognition was driven by increases in both Recollection and Familiarity based responses. • Neither the distinctiveness or strength accounts of production can fully account for the current results. • Reading aloud appears to selectively enhance gist memory (for semantic and contextual information) relative to verbatim memory (for item-specific detail) The production effect is the finding that reading information aloud enhances memory relative to reading information silently. In five experiments, we examined the influence of production on true and false memory in the DRM paradigm. In Experiments 1a, 1b, 3a, and 3b, reading aloud was compared to reading silently. In Experiment 2, reading aloud was compared to reading silently while hearing the words spoken by another voice. In all experiments, reading aloud consistently resulted in better recognition of studied words, but it also consistently resulted in more false alarms to unstudied lures that were semantically related to the studied words. We advance an argument based on current theoretical accounts of false memory wherein reading aloud selectively enhances relational or gist processing—the encoding of shared features across items—rather than item or verbatim processing—the encoding of specific details of individual items. This selective enhancement could be for the shared semantic network (gist), for the shared context of reading aloud (misattributed source memory), or for both. Thus, the benefit of production is best captured by the combination of adding new features (contextual information) together with enriching existing features (semantic information).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".