So <i>that's</i> why I don't remember: Normalising forgetting of childhood events influences false autobiographical beliefs but not memories
Bibliographic record
Abstract
We investigated changes in autobiographical belief and memory ratings for childhood events, after informing individuals that forgetting childhood events is common. Participants received false prevalence information (indicating that a particular childhood event occurred frequently in the population) plus a rationale normalizing the forgetting of childhood events; false prevalence information alone; or no manipulation, for one (Study 1) or two (Study 2) unlikely childhood events. Results demonstrated that combining prevalence information and the "forgetting rationale" substantially influenced autobiographical belief ratings, whereas prevalence information alone had no impact (Study 1) or a significantly lesser impact (Study 2) on belief ratings. Prevalence information consistently impacted plausibility ratings. No changes in memory ratings were observed. These results provide further support for a nested relationship between judgements of plausibility, belief, and memory in evaluating the occurrence of autobiographical events. Furthermore, the results suggest that some purported false memory phenomena may instead reflect the development of autobiographical false beliefs in the absence of memory.
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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 | 0.013 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".