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In this article, we develop a computational model of obsessive–compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients' own actions and the unfolding of possible events. Clinically, this corresponds both to patients' difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model, including a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e., checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and overresponsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research. [ABSTRACT FROM AUTHOR]