Examining Moral Injury using a Predictive Processing Framework
Bibliographic record
Abstract
Moral injury describes the set of psychological symptoms resulting from traumatic experiences that violate one’s moral presuppositions. Such disruption occurs when an individual encounters information from the environment that cannot be reconciled with the fundamental assumptions underlying their predictive models of the world. Examination of predictive models has been rapidly developing within cognitive science, with the predictive processing framework emerging as a central paradigm. Predictive processing entails estimations of sensory uncertainty scaffolded by previous predictions and modified by attention. This model describes cognition as seeking to minimize sensory prediction error using dynamic interactions between top-down and bottom-up processes. Therefore, the predictive processing framework may be fruitfully used to examine psychological changes related to moral injury. Towards this end, we will consider moral injury as a form of belief updating, dysregulation in precision estimates of predictive models, and a breakdown in what Ramstead et al. (2016) call ‘regimes of shared attention.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".