Familiarity, but not recollection, supports the between-subject production effect in recognition memory.
Bibliographic record
Abstract
Five experiments explored the basis of the between-subjects production effect in recognition memory as represented by differences in the recollection and familiarity of produced (read aloud) and nonproduced (read silently) words. Using remember-know judgments (Experiment 1b) and a dual-process signal-detection approach applied to confidence ratings (Experiments 2b and 3), we observed that production influences familiarity but not recollection when manipulated between-subjects. This is in contrast to within-subject designs, which reveal a clear effect of production on both recollection and familiarity (Experiments 1a and 2a). Our findings resolve contention concerning apparent design effects: Whereas the within-subject production effect is subserved by separable recollective- and familiarity-based components, the between-subjects production effect is subserved by the familiarity-based component alone. Our findings support a role for the relative distinctiveness of production as a means of guiding recognition judgments (at least when manipulated within-subjects), but we also propose that production influences the strength of produced items, explaining the persistence of the effect in between-subjects designs. (PsycINFO Database Record
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".