Implanting False Autobiographical Memories for Repeated Events
Bibliographic record
Abstract
Research to date has exclusively focused on the implantation of false memories for single events. The current experiment is the first proof of concept that false memories can be implanted for repeated autobiographical experiences using an adapted false memory implantation paradigm. We predicted that false memory implantation approaches for repeated events would generate fewer false memories compared to the classic implantation method for single events. We assigned students to one of three implantation conditions in our study: Standard, Repeated, and Gradual. Participants underwent three interview sessions with a 1-week interval between sessions. In the Standard condition, we exposed participants to a single-event implantation method in all three interviews. In the Repeated condition, participants underwent a repeated-event implantation method in the three interviews. The Gradual condition also consisted of a repeated-event implantation method, however, in the first interview alone, we suggested to participants that they had experienced the false narrative once. Surprisingly, within our sample, false memories rates in the Standard condition were not higher compared to the Repeated and Gradual conditions. Although sometimes debated, our results imply that false memories for repeated events can be implanted in lab conditions, likely with the same ease as false memories for single events.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".