Imaginal extinction and the vividness of mental imagery: Exploring the reduction of fear within the mind’s eye
Bibliographic record
Abstract
Patients are encouraged to produce vivid mental imagery during imaginal exposure, as it is assumed to promote fear reduction. Nevertheless, the link between fear reduction and imagery vividness is unclear. We investigated the impact of vividness on fear responses using an experimental analogue of imaginal exposure - imaginal extinction - in which conditioned fear, measured with skin conductance, is reduced through exposure to mental imagery of the conditioned stimulus. We examined (1) if task-specific vividness (high vs low) of the conditioned stimulus during imaginal extinction moderated the reduction of fear responses, and (2) if task-specific vividness influenced remaining fear responses 24 h later. Findings suggest that high vividness may be advantageous for fear reduction during imaginal extinction, but it may not influence fear responses in the longer term. A possible clinical implication is that high imagery vividness during imaginal exposure may not be vital for overall treatment outcome. As high vividness is associated with increased levels of distress, a future direction would be to explore whether similar fear reduction can be obtained with less vivid imaginal exposure and thereby make treatment tolerable for more patients.
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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 | 0.001 |
| 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".