Imagining the Glass Half-full: An Investigation of Imagery Enhanced Cognitive Bias Modification in GAD
Bibliographic record
Abstract
The tendency to interpret ambiguous information in a threatening, negative way (i.e., negative interpretation bias) contributes to the development and maintenance of chronic worry and anxiety pathology. Cognitive bias modification for interpretations (CBM-I) is an intervention that directly targets negative interpretation bias through repeated exposure to ambiguous scenarios that resolve in a benign or positive fashion. Additional research is needed to identify CBM-I procedures that yield the greatest effects for generalized anxiety disorder (GAD). The present study investigated the acute effects of using imagery to enhance CBM-I compared to standard CBM-I without imagery and a neutral control condition on interpretation bias, worry, intrusive thoughts following worry, effectiveness and concreteness of problem solving, and negative future imagery. Adults high in worry and GAD symptoms (N = 42) were randomly assigned to one of three conditions: (1) imagery enhanced CBM-I, (2) standard CBM-I, or (3) neutral control and were asked to complete their respective training for 25 minutes per day for one week. Outcomes were assessed at pre-intervention, post-intervention, and two-weeks post intervention. Participants also completed daily worry monitoring during the intervention week. All three conditions showed statistically significant improvements in most outcomes, which limits conclusions about the specificity of effects of CBM-I. There was preliminary evidence the IMG condition buffered against a worsening in interpretation bias relative to the NEUT condition, however, this was only the case for one interpretation bias outcome. Further, daily worry monitoring revealed improvements in worry over the course of the 7-day training period across all conditions. Importantly, this study was unpowered as it was interrupted due to the COVID-19 pandemic. Further data collection is required to test the hypotheses.
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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".