False memory across languages: Implicit associative response vs fuzzy trace views
Bibliographic record
Abstract
We investigated false recognition across languages using the Deese-Roediger-McDermott (DRM) paradigm. A group of English-French bilinguals studied lists of converging associates, some lists in English and some in French, and then performed a recognition test containing studied list items and nonstudied critical lures whose language matched or mismatched the language at study. Participants were instructed to answer old only if the test cue was in the same language as the studied word. The results yielded a robust false memory rate both within-language and across-languages. The effect of the study-test language shift was much larger for list items than for critical lures. This finding suggests that memory representations for critical lures contain primarily semantic gist traces and little surface information, and hence is more consistent with the fuzzy trace view than with the implicit associative response view. In sum, the study demonstrates the existence of false memory across languages, and provides information about the memory traces underlying veridical and illusory recognition.
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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 | 0.004 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".