Effects of Different Forms of Verbal Processing on the Formation of Intrusions
Bibliographic record
Abstract
This study used the trauma film paradigm to investigate different forms of posttrauma verbal processing relevant to the formation of intrusive memories. We designed 3 experiments to investigate verbal processing that could help to reduce the formation of posttraumatic intrusions. Experiments 1 and 2 looked at the effect of several forms of verbal processing, varied in emotional foci and vantage points, on the formation of posttraumatic intrusions. Experiment 3 utilized event-related potential (ERP) technology to control emotional focus and to further examine the effect of verbal processing from different vantage points. Data produced by Experiment 1 showed that the "what-focus" group had fewer intrusions than the "why-focus" group. Experiment 2 produced no significant difference between first- and third-person vantage points. Results from the last experiment showed the what-focus group was faster to judge the colors of the words in the emotional Stroop task, and the amplitude and latency of P2 for negative words were greater than neutral words in the what-focus group. Based on the results of the experiments, participants who were led to verbalize their traumatic experiences using the what-focus and the first-person vantage point ended up with fewer intrusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".