Can test list context manipulations improve recognition accuracy 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
Only test-based manipulations can be used to help people distinguish accurate from false memories once events have been encoded. In two experiments we examined how the type of studied words (weak vs strong associates, or less vs more memorable associates) and nonstudied lure words (related vs unrelated lures) on the test list affect recognition accuracy in the Deese-Roediger-McDermott paradigm. False recognition of critical lures decreased substantially in the related-lure context, but so did correct recognition of studied words. False recognition was little affected by the studied-word manipulations. In general, participants claimed to recognise critical lures as often as weak associates or less memorable studied words but less often than either strong associates or more memorable studied words. The test-list context affected how participants classified their recognition experiences but it did not systematically change their overall memory accuracy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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