The relationship between latent state inference and (intolerance of) uncertainty

In their review, Sandhu, Xiao and Lawson (2023) outline a framework for the study of uncertainty, particularly in the context of psychopathology. The authors correctly argue that clinically-derived intolerance of uncertainty can be better understood through a computational lens. One aspect of the proposed framework is the uncertainty about the number of latent states in the environment. This is indeed an important aspect, however, as pointed out by the authors the relationship between state inference and uncertainty extends beyond the uncertainty about the number of states. In this commentary I will briefly expand on this relationship. Specifically, I will suggest that uncertainty largely determines the process of state inference and that the desire to reduce uncertainty may lead to increased tendency to identify latent structures. I will also demonstrate with simulations the importance of taking state inference into account in computational models to dissociate its contributions from uncertainty related processes.