This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture model approach is superior in this regard to popular methods such as extremity scores, due to its incorporation of three submodels (location, dispersion, and relative composition), each of which can diagnose specific kinds of polarization and related effects. Three examples are elucidated using real data sets.