Does disconfirmatory evidence shape safety-and danger-related beliefs of trauma-exposed individuals?
Bibliographic record
Abstract
Recent accounts of predictive processing in posttraumatic stress disorder (PTSD) suggest that trauma-exposed individuals struggle to update trauma-related hypotheses predicting danger, which may be involved in the etiology and maintenance of this disorder. Initial research supports this account, documenting an association between trauma-exposure, impaired expectation updating, and PTSD symptoms. Yet, no study to date has examined biased belief updating in PTSD using a scenario-based approach.Objective: Here, we examined the predictive processing account among trauma-exposed and non-trauma-exposed individuals using a modified Trauma-Related version of the Bias Against Disconfirmatory Evidence task.Method: The task presents both danger-and safety-related scenarios highly relevant for trauma-exposed individuals. For each scenario, participants viewed several explanations and rated their plausibility. Their ability to update their initial interpretation following new-contradictory information was assessed.Results: Preregistered analyses did not reveal any significant findings. Based on indications that our sample may not have been sufficiently powered, we conducted exploratory analyses in an extended sample of participants. These analyses yielded a significant association between reduced belief updating and PTSD symptoms which was evident for disconfirming both safety and danger scenarios. However, the effect sizes we found were in the small-to-medium range.Conclusion: Although preliminary, our current findings support initial evidence that individuals with higher PTSD symptoms show a higher resistance to update their beliefs upon new disconfirmatory evidence. Our results should be interpreted cautiously in light of the extended sample and the limitations of the current study.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".