A Naturalistic Paradigm to Investigate Postencoding Neural Activation Patterns in Relation to Subsequent Voluntary and Intrusive Recall of Distressing Events
Bibliographic record
Abstract
BACKGROUND: While neuroimaging has provided insights into the formation of episodic memories in relation to voluntary memory recall, less is known about neural mechanisms that cause memories to occur involuntarily, for example, as intrusive memories of trauma. Here, we investigated brain activity shortly after viewing distressing events as a function of whether memories for those events later intruded involuntarily. The postencoding period is particularly important because it is a period when clinical interventions could be applied. METHODS: A total of 32 healthy volunteers underwent functional magnetic resonance imaging while viewing distressing film clips, interspersed with 5 minutes of awake (postencoding) rest. Voluntary memories of the films were assessed using free recall and verbal and visual recognition tests after a week, while intrusive (involuntary) memories were recorded in a diary throughout that week. RESULTS: When analyzing functional magnetic resonance imaging responses related to watching the films, we replicated findings that those "hotspots" (salient moments within the films) that would later become intrusive memories elicited higher activation in parts of the brain's salience network. Surprisingly, while the postencoding persistence of multivoxel correlation structures associated with entire film clips predicted subsequent voluntary recall, there was no evidence that they predicted subsequent intrusions. CONCLUSIONS: Results replicate findings regarding the formation of intrusive memories during encoding and extend findings regarding the consolidation of information in postencoding rest in relation to voluntary memory. While we provided a first step using a naturalistic paradigm, further research is needed to elucidate the role of postencoding neural processes in the development of intrusive memories.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".