Registered Replication Report
Bibliographic record
Abstract
Trying to remember something now typically improves your ability to remember it later. However, after watching a video of a simulated bank robbery, participants who verbally described the robber were 25% worse at identifying the robber in a lineup than were participants who instead listed U.S. states and capitals-this has been termed the "verbal overshadowing" effect (Schooler & Engstler-Schooler, 1990). More recent studies suggested that this effect might be substantially smaller than first reported. Given uncertainty about the effect size, the influence of this finding in the memory literature, and its practical importance for police procedures, we conducted two collections of preregistered direct replications (RRR1 and RRR2) that differed only in the order of the description task and a filler task. In RRR1, when the description task immediately followed the robbery, participants who provided a description were 4% less likely to select the robber than were those in the control condition. In RRR2, when the description was delayed by 20 min, they were 16% less likely to select the robber. These findings reveal a robust verbal overshadowing effect that is strongly influenced by the relative timing of the tasks. The discussion considers further implications of these replications for our understanding of verbal overshadowing.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reproducibility · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.041 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.455 | 0.224 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".