False Recall in the Deese–Roediger–McDermott Paradigm: The Roles of Gist and Associative Strength
Why this work is in the frame
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Bibliographic record
Abstract
Theories of false memories, particularly in the Deese-Roediger-McDermott (DRM) paradigm, focus on word association strength and gist. Backward associative strength (BAS) is a strong predictor of false recall in this paradigm. However, other than being defined as a measure of association between studied list words and falsely recalled nonpresented critical words, there is little understanding of this variable. In Experiment 1, we used a knowledge-type taxonomy to classify the semantic relations in DRM stimuli. These knowledge types predicted false-recall probability, as well as BAS itself, with the most important being situation features, synonyms, and taxonomic relations. In three subsequent experiments, we demonstrated that lists composed solely of situation features can elicit a gist and produce false memories, particularly when monitoring processes are made more difficult. Our results identify the semantic factors that underlie BAS and suggest how considering semantic relations leads to a better understanding of gist formation.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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