Timothy Friesen | Searcharxiv

arXiv2025

The Role of Affect and Priors in the Generation of Hallucinations in Early Psychosis

Background: Stress and negative affect play significant roles in developing psychosis. Bayesian analyses applied to the conditioned hallucinations (CH) task suggest that hallucinations arise when maladaptive prior beliefs outweigh sensory evidence. Prior weighting is linked to hallucination severity, yet the nature of these priors remains unclear. Negative affect may influence the strength of maladaptive priors. We hypothesized that, under stress, participants will show increased CH rates and prior weighting, with this effect more pronounced in patients. Methods: This study employs a modified CH task using valenced linguistic stimuli and stress and non-stress affective manipulations. The sample for this pilot study included those at risk for psychosis and patients with first episode psychosis (N=12) and healthy controls (N=15). The objective of this study was first to validate this affective version of the CH task and then to demonstrate an effect of affect on CH rates and prior weighting. Results: Replicating past results, patients had higher CH rates (b = 0.061, p < 0.001) and prior weighting (b = 0.097, p < 0.001) for session 1 compared to controls (n=15) across conditions. Further, runs with stress manipulations had higher prior weighting across patients and controls compared to runs with non-stress manipulations (b = 0.054, p = 0.033). Conclusions: This study validates this affective version of the CH task and provides preliminary evidence of a relationship between affective state and prior weighting. Future work will be aimed at confirming and extending these findings, with the objective of developing biomarkers of early psychosis. Key words: Schizophrenia, Affect, Priors, Computational Psychiatry, Clinical High Risk Population, Psychotic Symptoms

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arXiv2025

The Role of Affective States in Computational Psychiatry

Studying psychiatric illness has often been limited by difficulties in connecting symptoms and behavior to neurobiology. Computational psychiatry approaches promise to bridge this gap by providing formal accounts of the latent information processing changes that underlie the development and maintenance of psychiatric phenomena. Models based on these theories generate individual-level parameter estimates which can then be tested for relationships to neurobiology. In this review, we explore computational modelling approaches to one key aspect of health and illness: affect. We discuss strengths and limitations of key approaches to modelling affect, with a focus on reinforcement learning, active inference, the hierarchical gaussian filter, and drift-diffusion models. We find that, in this literature, affect is an important source of modulation in decision making, and has a bidirectional influence on how individuals infer both internal and external states. Highlighting the potential role of affect in information processing changes underlying symptom development, we extend an existing model of psychosis, where affective changes are influenced by increasing cortical noise and consequent increases in either perceived environmental instability or expected noise in sensory input, becoming part of a self-reinforcing process generating negatively valenced, over-weighted priors underlying positive symptom development. We then provide testable predictions from this model at computational, neurobiological, and phenomenological levels of description.

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