Increasing belief in the experience of an invasive procedure that never happened: the role of plausibility and schematicity
Bibliographic record
Abstract
Abstract Numerous studies have increased people's confidence in the occurrence of various childhood events, however, Pezdek, Finger, and Hodge (1997) were able to successfully increase participants' confidence in one event (e.g., being lost in a mall), but not another (e.g., having received an enema). Two experiments considered two factors, plausibility and schematicity, as explanations for this differential suggestibility. In Experiment 1, participants completed a questionnaire regarding the likelihood of experiencing various childhood events, including receiving an enema. Two weeks later, they were given schematic or plausibility information about enemas, or both, or neither. Finally, participants again completed the previous questionnaire regarding childhood experiences. Only plausibility increased participants' beliefs that they had experienced an enema during childhood. In Experiment 2, participants were additionally asked about whether they had a memory of the event. While participants still responded with greater confidence that they had experienced an enema when given plausibility information, it did not increase their memory for the event, and schematicity actually decreased reported memory for the experience. The potential implications of these findings for the formation of false memories of sexual abuse are considered. 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 | 0.002 | 0.035 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".