Intrusive memories: A mechanistic signature for emotional memory persistence
Bibliographic record
Abstract
Memories of negative emotional events persist more over time relative to memories for neutral information. Such persistence has been attributed to heightened encoding and consolidation processes. However, reactivation of the encoded information may also lead to reduced memory decay through rehearsal or a reconsolidation processes. Here, we tested whether involuntary intrusive memories, spontaneously arising following a stressful event and reactivating its memory, function to prevent memory decay, enhancing its persistence. Participants watched a stressful film containing scenes of aversive material. Memory for the film contents was tested immediately post-film using a visual recognition test. In the following five days, participants recorded intrusive memories of the film using a digitized diary. After 5-days, memory for the film contents was retested. Results indicate that in the immediate aftermath of film watching, participant's memory scores were similarly high for scenes that were later experienced as intrusions and scenes that did not intrude, suggesting effective encoding for all scenes. However, persistence of memory for scenes that intruded was preserved relative to memory for scenes that did not intrude, pointing to a mechanism through which negative intrusive memories persist over time. Implications for memory modification interventions in trauma-related psychopathology are discussed.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".