A mega-analysis of memory reports from eight peer-reviewed false memory implantation studies
Bibliographic record
Abstract
Understanding that suggestive practices can promote false beliefs and false memories for childhood events is important in many settings (e.g., psychotherapeutic, medical, and legal). The generalisability of findings from memory implantation studies has been questioned due to variability in estimates across studies. Such variability is partly due to false memories having been operationalised differently across studies and to differences in memory induction techniques. We explored ways of defining false memory based on memory science and developed a reliable coding system that we applied to reports from eight published implantation studies (N = 423). Independent raters coded transcripts using seven criteria: accepting the suggestion, elaboration beyond the suggestion, imagery, coherence, emotion, memory statements, and not rejecting the suggestion. Using this scheme, 30.4% of cases were classified as false memories and another 23% were classified as having accepted the event to some degree. When the suggestion included self-relevant information, an imagination procedure, and was not accompanied by a photo depicting the event, the memory formation rate was 46.1%. Our research demonstrates a useful procedure for systematically combining data that are not amenable to meta-analysis, and provides the most valid estimate of false memory formation and associated moderating factors within the implantation literature to date.
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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 | 0.106 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".