If it is absurd, then why do you do it? The richer the obsessional experience, the more compelling the compulsion
Bibliographic record
Abstract
Mounting evidence suggests that obsessive intrusions are often accompanied and amplified by perceptual experiences of different modalities (e.g., feeling dirt on one's skin while experiencing intrusive thoughts about contamination). Pilot studies conducted online with individuals endorsing mild obsessive-compulsive symptoms have linked the co-occurrence of perceptual experiences and obsessions to the severity of subsequent compulsive behaviour as well as low insight. However, it is presently unclear whether sensory experiences accompany all types of obsessional thoughts or are restricted to certain preoccupations (e.g., contamination and aggression). The present study examined a clinical inpatient and outpatient sample with a formally diagnosed obsessive-compulsive disorder (N = 34). Perceptual properties of intrusive thoughts were assessed with the Sensory Properties of Obsessions Questionnaire. The prevalence of perception-laden obsessive thoughts was comparable with prior studies (73.5%), but the intensity was significantly greater. No association was observed between perceptual experiences and expert-rated insight. However, the severity of perception-laden obsessions predicted the frequency of and impairment associated with compulsive behaviour. This was particularly strong for obsessions about contamination. The present study confirms the high prevalence and clinical relevance of perceptual experiences that accompany obsessions and further challenges the traditional trichotomy splitting mental phenomena into thoughts, intrusions, and hallucinations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".