The truth is out there: Accuracy in recall of verifiable real-world events
Bibliographic record
Abstract
How accurate is memory? Although people implicitly assume that their memories faithfully represent past events, the prevailing view in research is that memories are error-prone and constructive. Yet little is known about the frequency of errors, particularly in memories for naturalistic experiences. Here, younger and older adults underwent complex real-world experiences that were nonetheless controlled and verifiable, freely recalling these experiences after days to years. As expected, memory quantity and episodic detail richness declined with increasing age and retention interval. Details that participants did recall, however, were highly accurate (93-95%) across age and time. This level of accuracy far exceeded comparatively low estimations among memory scientists and other academics in a survey. These findings suggest that details freely recalled from one-time real-world experiences can retain high correspondence to the ground truth despite significant forgetting, with higher accuracy than expected given the emphasis on fallibility in the field of memory research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".