The Role of Magical Thinking, Sensitivity, and Thought Content in Thought-Action Fusion
Bibliographic record
Abstract
Introduction: Cognitive models of obsessive-compulsive disorder (OCD) posit that maladaptive beliefs about intrusive thinking contribute to the disorder's development and maintenance. However, the findings concerning one notable belief, thought-action fusion (TAF), have been inconsistent. Current conceptualizations of TAF may conflate constructs such as magical thinking, sensitivity, and thought content that are already the subject of informative, interdisciplinary literatures. Methods: To tease apart these constructs, adult participants (N = 249) reported their trait levels of sensitivity and magical thinking, and were randomly assigned to engage with an intrusive thought in one of three content areas. We hypothesized that morality-related content would lead to heightened maladaptive outcomes, but only in combination with higher trait levels of sensitivity and magical thinking. Results: Results indicated that morality-related content, along with sensitivity to morality, played more of a prominent role in maladaptive outcomes, with magical thinking being implicated in general outcomes like worry. Discussion: These findings suggest that the link between TAF and maladaptive outcomes may depend on which TAF elements are present for an individual. Sensitivity, in tandem with other TAF elements (e.g., morality-related content, magical thinking) is predictive of divergent outcomes (e.g., worrying, urges to neutralize) and thus may be an important target of future interventions aimed at reducing TAF, worrying, and/or OC symptoms.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".