Verbal Repetition in the Reappraisal of Contamination-Related Thoughts
Bibliographic record
Abstract
BACKGROUND: Acceptance and Commitment Therapy advocates use of cognitive defusion techniques to reduce the distress evoked by negative thoughts, including verbal repetition (VR). In VR, a negative word is repeated until its semantic meaning is diluted (i.e. until semantic satiation is achieved). The present two studies examined whether VR is more effective than brief imaginal exposure (IE) and no intervention (CONT) in the reappraisal of contamination-related thoughts. METHOD: Participants high in contamination fears identified their most distressing thoughts and were randomly assigned to VR, IE, or CONT. A category membership decision task was also conducted to determine if VR produced semantic satiation. RESULTS: In Study 1, there was no evidence of semantic satiation. Significant reductions in negative response to the thoughts was observed immediately following VR, but not IE or CONT; however, at one-week follow-up, both VR and IE groups reported similar reductions. In Study 2, the effects of VR and IE practice between post-intervention and follow-up were examined, as well as changes in behavioural avoidance. VR was found to produce semantic satiation of contamination thoughts, and VR was associated with less negative response at follow-up relative to IE and CONT, but the degree of satiation was not associated with the decreases in negative response. Only IE produced decreases in behavioural avoidance and vigilance monitoring. CONCLUSIONS: Taken together, these results suggest that VR may have potential as an additional strategy for managing obsessional thoughts, but more research is warranted.
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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 | 0.004 |
| 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 | 0.000 |
| 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".