False Recognition with the Deese-Roediger-McDermott-Reid-Solso Procedure: A Quantitative Summary
Bibliographic record
Abstract
In the Deese-Roediger-McDermott-Read-Solso (DRMRS) procedure, participants study lists of words associated with central concepts (critical themes) that are not on the lists, then their memory is tested. Based on 224 estimates, the rate of False Recognition of the nonstudied critical themes was .59 (95% confidence interval of .56 to .61), which is smaller than the Hit rate of .75 for correct recognition of studied items (95% confidence interval of .73 to .77) but greater than various rates of False Alarms for other nonstudied items (ranging from .13 to .19). Ratings of subjective confidence were similar on Hits and on False Recognitions but higher than on False Alarms, confirming that false recognition was more like correct recognition than like other errors. The results from judgments of feeling of remembering or knowing, from the effects of intervening activities (particularly recall) between study and test, and from the effects of age suggest that False Recognition occurs because the critical theme is activated along with studied items during list presentation and perhaps also during recall. Invoking fuzzy trace theory, it is argued Hits are based on verbatim traces whereas False Recognition is based on gist traces and a failure of source memory. Proposals are made for research.
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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 | 0.047 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".