  The computational underpinnings of positive psychotic symptoms have recently
received significant attention. Candidate mechanisms include some combination
of maladaptive priors and reduced updating of these priors during perception. A
potential benefit of models with such mechanisms is their ability to link
multiple levels of explanation. This is key to improving how we understand the
experience of psychosis. Moreover, it points us towards more comprehensive
avenues for therapeutic research by providing a putative mechanism that could
allow for the generation of new treatments from first principles. In order to
demonstrate this, our conceptual paper will discuss the application of the
insights from previous computational models to an important and complex set of
evidence-based clinical interventions with strong social elements, such as
coordinated specialty care clinics in early psychosis and assertive community
treatment. These interventions may include but also go beyond
psychopharmacology, providing, we argue, structure and predictability for
patients experiencing psychosis. We develop the argument that this structure
and predictability directly counteract the relatively low precision afforded to
sensory information in psychosis, while also providing the patient more access
to external cognitive resources in the form of providers and the structure of
the programs themselves. We discuss how computational models explain the
resulting reduction in symptoms, as well as the predictions these models make
about potential responses of patients to modifications or to different
variations of these interventions. We also link, via the framework of
computational models, the experiences of patients and response to interventions
to putative neurobiology.
