Sensitization instructions can reduce the misinformation effect and improve the eyewitness confidence–accuracy relationship.
Bibliographic record
Abstract
Multiple studies have reported evidence that the misinformation effect can be reduced or even eliminated under some conditions, but these studies have typically used warnings that could not be implemented in forensic settings (e.g., telling participants/witnesses that a particular source included false information). In the present study, we investigated whether novel, ecologically valid sensitization instructions can reduce the misinformation effect. We also examined effects of the manipulation on the confidence–accuracy relationship. Across two experiments that used different stimuli and test formats, participants (total N = 422) were exposed to misinformation about a mock crime; later, half of the participants received sensitization instructions before completing a memory test. The misinformation effect was significantly smaller for participants who received the sensitization instructions. Sensitized participants also demonstrated a stronger confidence–accuracy relationship and were less overconfident at the highest level of confidence. Our findings encourage tests of the sensitization instructions under more naturalistic conditions.
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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 | 0.001 | 0.017 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".