Getting at the source of distinctive encoding effects in the DRM paradigm: evidence from signal-detection measures and source judgments
Bibliographic record
Abstract
Studying Deese-Roediger-McDermott (DRM) lists using a distinctive encoding task can reduce the DRM false memory illusion. Reductions for both distinctively encoded lists and non-distinctively encoded lists in a within-group design have been ascribed to use of a distinctiveness heuristic by which participants monitor their memories at test for distinctive-task details. Alternatively, participants might simply set a more conservative response criterion, which would be exceeded by distinctive list items more often than all other test items, including the critical non-studied items. To evaluate these alternatives, we compared a within-group who studied 5 lists by reading, 5 by anagram generation, and 5 by imagery, relative to a control group who studied all 15 lists by reading. Generation and imagery improved recognition accuracy by impairing relational encoding, but the within group did not show greater memory monitoring at test relative to the read control group. Critically, the within group's pattern of list-based source judgments provided new evidence that participants successfully monitored for distinctive-task details at test. Thus, source judgments revealed evidence of qualitative, recollection-based monitoring in the within group, to which our quantitative signal-detection measure of monitoring was blind.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".