The impact of trauma exposure on explicit and implicit memory
Bibliographic record
Abstract
Objectives: The present study aimed to determine whether explicit and implicit memory systems are modulated by the type of content (neutral, emotional trauma-related and generally-emotional) in sexual abuse victims who did not develop PTSD, compared to non-exposed controls.Design: A mixed-factorial design with Content (neutral, trauma-related, generally-emotional) as a within-subject variable and Group (victims, controls) as a between-subject variable was used in two experiments.Methods: In both experiments, participants were required to learn three stories presented orally: a neutral, an emotional trauma-related (sexual abuse) and a generally-emotional story. In Experiment 1, participants’ memory was tested with two explicit tasks (free recall and Remember/Know/Guess) and one implicit task (word-fragment completion task). In Experiment 2, a modified version of the word-fragment completion task was presented, followed by an awareness questionnaire to ensure the implicit character of the test.Results: Victims showed lower performances with neutral contents, relative to controls, in explicit and implicit tasks. However, this difference was not observed with trauma-related contents suggesting this information is preferentially processed by trauma-exposed participants (with increased attentional resources).Conclusions: Our results show that trauma exposure may itself be associated with implicit and explicit memory alterations, even for individuals who did not develop PTSD.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".