Remembering makes evidence compelling: Retrieval from memory can give rise to the illusion of truth.
Bibliographic record
Abstract
The illusion of truth is traditionally described as the increase in perceived validity of statements when they are repeated (Hasher, Goldstein, & Toppino, 1977). However, subsequent work has demonstrated that the effect can arise due to the increased familiarity or fluency afforded by repetition and not necessarily to repetition per se. We examine the case of information retrieved from memory. Recently experienced information is expected to be subsequently reexperienced as more fluent and familiar than novel information (Jacoby, 1983; Jacoby & Dallas, 1981). Therefore, the possibility exists that information retrieved from memory, because it is subjectively re-experienced at retrieval, would be more fluent or familiar than when it was first learned and would thus lead to an increase in perceived validity. Using a method to indirectly poll the perceived truth of factual statements, our experiment demonstrated that information retrieved from memory does indeed give rise to an illusion of truth. The effect was larger than when statements were explicitly repeated twice and was of comparable size to when statements were repeated 4 times. We conclude that memory retrieval is a powerful method for increasing the perceived validity of statements (and subsequent illusion of truth) and that the illusion of truth is a robust effect that can be observed even without directly polling the factual statements in question.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".