Deductive and inductive reasoning in obsessive‐compulsive disorder
Bibliographic record
Abstract
OBJECTIVES: This study tested the hypothesis that people with obsessive-compulsive disorder (OCD) show an inductive reasoning style distinct from people with generalized anxiety disorder (GAD) and from participants in a non-anxious (NA) control group. DESIGN: The experimental procedure consisted of administering a range of six deductive and inductive tasks and a probabilistic task in order to compare reasoning processes between groups. METHODS: Recruitment was in the Montreal area within a French-speaking population. The participants were 12 people with OCD, 12 NA controls and 10 people with GAD. Participants completed a series of written and oral reasoning tasks including the Wason Selection Task, a Bayesian probability task and other inductive tasks, designed by the authors. RESULTS: There were no differences between groups in deductive reasoning. On an inductive "bridging task", the participants with OCD always took longer than the NA control and GAD groups to infer a link between two statements and to elaborate on this possible link. The OCD group alone showed a significant decrease in their degree of conviction about an arbitrary statement after inductively generating reasons to support this statement. Differences in probabilistic reasoning replicated those of previous authors. CONCLUSIONS: The results pinpoint the importance of examining inference processes in people with OCD in order to further refine the clinical applications of behavioural-cognitive therapy for this disorder.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".