Effects of distinctive encoding on correct and false memory:A meta-analytic review of costs and benefits and their origins in the DRM paradigm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We review and meta-analyze how distinctive encoding alters encoding and retrieval processes and, thus, affects correct and false recognition in the Deese-Roediger-McDermott (DRM) paradigm. Reductions in false recognition following distinctive encoding (e.g., generation), relative to a nondistinctive read-only control condition, reflected both impoverished relational encoding and use of a retrieval-based distinctiveness heuristic. Additional analyses evaluated the costs and benefits of distinctive encoding in within-subjects designs relative to between-group designs. Correct recognition was design independent, but in a within design, distinctive encoding was less effective at reducing false recognition for distinctively encoded lists but more effective for nondistinctively encoded lists. Thus, distinctive encoding is not entirely "cost free" in a within design. In addition to delineating the conditions that modulate the effects of distinctive encoding on recognition accuracy, we discuss the utility of using signal detection indices of memory information and memory monitoring at test to separate encoding and retrieval processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it