The Impact of Leading Questions on Memory Recall and Estimation
Bibliographic record
Abstract
Leading questions are known to bias memory and judgment, yet less is known about their effect on adolescents’ numerical estimations after brief exposure to a stimulus. This study tested whether the intensity of suggestive wording shifts high-school students’ recall-based estimates. Fifty-three students (ages 16–18) were assigned by intact class to one of three independent conditions: neutral (control), slightly leading (“Is there more or less than 20,000 grains of rice?”), or heavily leading (“Previous groups estimated 20,000; is that a fair estimate?”). After viewing a transparent jar (~44,000 grains) for 5 seconds and a 120-second interval, participants submitted a single numerical estimate via Google Forms. A one-way ANOVA showed a significant effect of question intensity on estimates (p = 0.004119). Relative to the neutral condition, both leading conditions shifted estimates toward the 20,000 anchor, with greater dispersion under slight leading and a narrower clustering under heavy leading. Results replicate and extend classic leading-question effects to quantitative estimation in adolescents, highlighting anchoring as a likely mechanism. Findings underscore the need for neutral phrasing when soliciting student reports or eyewitness-style recollections in school and youth-serving contexts. All procedures were conducted in accordance with school ethics guidelines, including informed consent and debriefing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".