Abstract
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, from the neurobiological to the social, allowing us to provide an information processing-based account of how specific alterations in self-self and self-environment interactions result in the experience of positive symptoms. This is key to improving how we understand the experience of psychosis. Moreover, it points us toward 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 (CSC) in early psychosis and assertive community treatment (ACT). 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 patient's experiences and response to interventions to putative neurobiology.
Highlights
While positive symptoms- such as hallucinations and delusionshave not been demonstrated to be the chief determinant of functional outcomes [1], their appropriate management, for many people on the psychotic spectrum, do appear to be of significant importance in terms of quality of life, vocational functioning, and safety [2,3,4]
We have chosen to focus on the active inference theory of hallucinations in order to provide the computational language we will use to investigate the potential mechanism of action of services such as assertive community treatment (ACT) teams and coordinated specialty care (CSC)
Active inference is a Bayesian theory [30] which, at its core, asserts that all agents function in a manner that aims to minimize the uncertainty in their model of the world
Summary
While positive symptoms- such as hallucinations and delusionshave not been demonstrated to be the chief determinant of functional outcomes [1], their appropriate management, for many people on the psychotic spectrum, do appear to be of significant importance in terms of quality of life, vocational functioning, and safety [2,3,4]. For many patients there exist a number of barriers to adherence to medication which may include: positive symptoms [11] and positive patient attitudes toward positive symptoms [12], poor insight [13], negative symptoms [11], cognitive symptoms limiting ability to obtain medications or adherence to treatment [11], medication side effects [14], or inability to afford medications, medical care, housing, or services [14] Given these complexities, as well as the host of other social and medical challenges which face patients who live with psychosis [see [15, 16]], it is no surprise that for many patients the management of positive symptoms is not a simple matter of visiting a general practitioner, picking up a prescription, and taking their medications independently at home. We will demonstrate how experiments could be designed to test the veracity of this hypothetical mechanism of action
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