When does memory monitoring succeed versus fail? Comparing item-specific and relational encoding in the DRM paradigm.
Bibliographic record
Abstract
We compared the effects of item-specific versus relational encoding on recognition memory in the Deese-Roediger-McDermott paradigm. In Experiment 1, we directly compared item-specific and relational encoding instructions, whereas in Experiments 2 and 3 we biased pleasantness and generation tasks, respectively, toward one or the other type of processing. A read condition was tested in each experiment for comparison purposes. Across experiments, item-specific and relational encoding both boosted correct recognition relative to reading, but only item-specific encoding typically reduced false recognition. Signal-detection measures revealed that less information was encoded about critical items after item-specific than after relational encoding. In contrast, item-specific and relational encoding led to equivalent increases in strategic monitoring at test (e.g., use of a distinctiveness heuristic). Thus, monitoring at test was less successful after relational than item-specific encoding because more information had been encoded about critical lures.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".