The relationship between inflated personal responsibility and exaggerated danger expectancies in obsessive-compulsive concerns.

The association between responsibility for a negative outcome, perceived severity of the outcome and perceived likelihood of the outcome was examined in a sample of 70 undergraduate students. Participants were asked to rate the likelihood and severity of 10 negative outcomes, five related to contamination and five related to checking. Thirty-eight participants completed a version of the questionnaire that presented the subject as responsible for the action that may lead to a negative outcome ('personally responsible' group). The remaining 32 completed a version of the questionnaire that presented someone else performing the actions that may lead to a negative outcome ('other responsible' group). Significant differences emerged between the personally responsible and other responsible groups for severity of outcome ratings but not for likelihood of outcome ratings. Specifically, for both washing and checking concerns, participants in the personally responsible group rated the severity of the potential negative outcome as greater than did those in the other responsible group. The results support the claimed general tendency for individuals to regard an outcome as more aversive if they are personally responsible for that outcome, rather than someone else being responsible. The results suggest that, in general, increasing perceptions of personal responsibility will increase cost or severity estimates in subjective danger calculations, and that responsibility may influence OCD phenomena in this way. Finally, the results suggest that attempts to manipulate responsibility in the laboratory may be confounded by necessarily impacting on cost estimates, and therefore on danger expectancies.

Prediction of dose-response relations based on patient characteristics.

The recent discussion of evidence-based, adaptive treatment planning highlights the need for models for the prediction of courses of treatment response. We combine a dose-response model with growth curve modeling to determine dose-response relations for well-being, symptoms, and functioning. Hierarchical linear modeling was used to model each patient's expected course of improvement. The resulting predictions were cross-validated on two samples of psychotherapy outpatients. The results give further empirical support for the dose-response model and the phase model of psychotherapy as well as for the usefulness of patient treatment response profiling for individual treatment management.