Cognitive Bias Modification Using Mental Imagery for Depression: Developing A Novel Computerized Intervention to Change Negative Thinking Styles
Bibliographic record
Abstract
Why do some people see their glass as half-empty rather than half-full or even imagine that the glass will be filled in the future? Experimental methods can illuminate how individual differences in information processing style can profoundly impact mood or even result in disorders such as depression. A computerized cognitive bias modification intervention targeting interpretation bias in depression via positive mental imagery (CBM-I) was evaluated by investigating its impact on mental health and cognitive bias compared with a control condition. Twenty-six depressed individuals completed either positive imagery-focussed CBM-I or a control condition daily at home over one week. Outcome measures were collected pre-treatment and post-treatment and at two-week follow-up. Individuals in the positive condition demonstrated significant improvements from pre-treatment to post-treatment in depressive symptoms, cognitive bias and intrusive symptoms compared with the control condition. Improvements in depressive symptoms at two-week follow-up were at trend level. The results of this first controlled comparison of positive imagery-focussed CBM-I for depression further support the clinical potential of CBM-I and the development of a novel computerized treatment that could help patients imagine a more positive future. Broader implications concern the modification of individual differences in personality variables via their interaction with key information processing targets. Copyright © 2011 John Wiley & Sons, Ltd.
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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".