Negative Emotion Places a Boundary on Memory Malleability
Bibliographic record
Abstract
Autobiographical memories, the memories we have of our personal past, change over time ascontent is forgotten or added to the original memory trace. While decades of research hasdemonstrated the augmenting effect emotion can have on memory, even memories for verynegative experiences seem to be susceptible to change. However, it is unclear whether or notnegative emotion in day-to-day life might protect everyday memories from distortion. Here, weexamined whether the consistency with which everyday experiences are recalled differs as afunction of emotion. Participants (N=513) recalled negative and neutral events from their past attwo time points, eight weeks apart. Using human scoring and large language modelingapproaches to quantify the consistency of narrative recalls, we found that, although both negativeand neutral memories showed moderate consistency between recalls, memories for negativeevents were more consistent than memories for neutral events. While our emotional memoriesare not perfect records of the past, this work suggests that emotion reduces a memory’svulnerability to changing over time.
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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 | 0.000 |
| 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 | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".