Protective effects of testing across misinformation formats in the household scene paradigm
Bibliographic record
Abstract
Many studies have demonstrated retrieval-enhanced suggestibility (RES), in which taking an initial recall test after witnessing an event increases suggestibility to subsequent misinformation introduced via a narrative. Recently, however, initial testing has been found to have a protective effect against misinformation introduced via cued-recall questions. We examined whether misinformation format (narrative vs. cued-recall questions) yields a similar dissociation in a paradigm that, to date, has consistently yielded a protective effect of testing (PET). After studying photos of household scenes (e.g., kitchen), some participants took an initial recall test. After a 48-hr delay, items not presented in the scenes (e.g., knives/plates) were suggested either via narrative or questions. Regardless of the misinformation format, we found a PET on both initial-test-conditionalised free recall and source-monitoring tests. However, initial testing also yielded memory costs, such that suggested items reported on the initial test were likely to persist on a final recall test. Thus, initial testing can protect against suggestibility, but can also precipitate memory errors when intrusions emerge on an initial test.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".