The impact of experienced versus non‐experienced suggestions on children's recall of repeated events
Bibliographic record
Abstract
Abstract Three experiments were conducted to test the proposition that children's suggestibility about an occurrence of a repeated event is heightened when an interviewer suggests false details that were experienced in non‐target occurrences of the event as opposed to new details that never occurred. In each experiment, children participated in a repeated event during which specific items varied each time (e.g. the children always got a sticker but the theme of the sticker was different in each occurrence). Separate biasing and memory interviews were then conducted. In Experiment 1, the interviewer merely suggested that the false details might have occurred in the event. In the remaining experiments, the suggested details were clearly linked to the target occurrence with either a contextual or temporal cue. The potential moderating effect of the child's age (Experiment 1) and the retention interval (Experiments 1 and 2) were also examined. Consistent with the initial hypothesis, suggestions about experienced (non‐target) details were more likely to be repeated by the children compared to suggestions about non‐experienced details. In Experiments 2 and 3, experienced suggestions were also more likely to inhibit children's recall of the target occurrence. The relevance and generalizability of these findings to the legal setting are discussed. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".