Prediction-based false memory: Unconfirmed prediction can result in robust false memories
Bibliographic record
Abstract
A growing body of literature suggests a powerful role of predictions on memory through prediction violation and prediction confirmation. Violation appears to enhance memory for the event violating the prediction, meanwhile, confirmation boosts memory for the predicted event instead. Crucially, however, the effect of prediction by itself has not been identified as it has typically been studied with its violation or confirmation. Here, we demonstrate the power of explicit predictions on memory by isolating it from its direct violation and confirmation. In a series of experiments, participants were presented with a real-world object along with three characters and they predicted which character the object belonged to. Upon prediction, participants received either visual confirmation (predicted character showing the item), visual rebuttal (another character showing the item) or no feedback (none of the characters showing the item) with regard to their prediction. When their memory was tested, participants were more likely to falsely remember that their predicted character showed them the item than the other characters did, even when no feedback was provided. This false memory was not eliminated by visual rebuttal and it was not weakened when participants had a strong item memory. Experiments 2-4 eliminated action (selecting a predicted character) as an alternative explanation and demonstrated that this prediction-based false memory could be modulated through indirect prediction confirmation and rebuttal. Taken together, our findings show that explicit predictions can be sufficient to induce false memory of predicted events that are robust enough to withstand its direct rebuttal.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".