Atypical processing of uncertainty in individuals at risk for psychosis

Abstract

Background: Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in at-risk mental state (ARMS) individuals.Methods: Non-medicated ARMS individuals (n=13) and control participants (n=13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour -with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental 'volatility' -and used these computational quantities for analyses of fMRI data. Results: Computational modelling of ARMS individuals' behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of ARMS individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in ARMS was negatively associated with clinical measures of global functioning. Conclusions: Our results suggest a multi-faceted learning abnormality in ARMS individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high-and low-level learning signals might reflect a predisposition to delusion formation.

Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder

“…Unlike previous studies on reward [ 45 – 47 ] or volatility [ 15 ] learning in SCZ and healthy subjects at risk for psychosis [ 16 ], we did not find significant differences between SCZ and HC in this regard. The same applies to MDD patients, where one possible explanation for this negative finding is the lack of punishment for incorrect choices in our task since recent findings converge on impaired aversive learning in depression (e.g.…”

A model for learning based on the joint estimation of stochasticity and volatility

“…Our work builds directly on a rich line of theoretical and experimental work on the relationship between volatility and learning rates (Behrens et al, 2007;de Berker et al, 2016;Browning et al, 2015;Diaconescu et al, 2014;Farashahi et al, 2017;Iglesias et al, 2013;Khorsand and Soltani, 2017). There have been numerous reports of volatility effects on healthy and disordered behavioral and neural responses, often using a two-level manipulation of volatility like that from Figure 2 (Behrens et al, 2007;Brazil et al, 2017;Browning et al, 2015;Cole et al, 2020;Deserno et al, 2020;Diaconescu et al, 2020;Farashahi et al, 2017;Iglesias et al, 2013;Katthagen et al, 2018;Lawson et al, 2017;Paliwal et al, 2019;Piray et al, 2019;Powers et al, 2017;Pulcu and Browning, 2017;Soltani and Izquierdo, 2019). Our modeling suggests that it will be informative to drill deeper into these effects by augmenting this task to cross this manipulation with unpredictability so as more clearly to differentiate these two potential contributors.…”

“…All else equal, when volatility is higher, the organism is more uncertain about the cue's value (because the true value will on average have fluctuated more following each observation), and so the learning rate (the reliance on each new outcome) should be higher. A series of experiments have reported behavioral and neural signatures of these volatility effects on learning rate, and also their disruption in relation to psychiatric symptoms (Behrens et al, 2007;Brazil et al, 2017;Browning et al, 2015;Cole et al, 2020;Deserno et al, 2020;Diaconescu et al, 2020;Farashahi et al, 2017;Iglesias et al, 2013;Katthagen et al, 2018;Lawson et al, 2017;Paliwal et al, 2019;Piray et al, 2019;Powers et al, 2017;Soltani and Izquierdo, 2019). However, volatility is only one of two noise parameters in the underlying Kalman filter; the second is unpredictability, which controls how noisy are each of the outcomes (the width of the likelihood) individually.…”

Predictive processing as a systematic basis for identifying the neural correlates of consciousness

“…A further study systematically explored the dynamic interactions of predictions for conscious awareness (Meijs, Slagter et al 2018). A number of studies have applied PP to the question of aberrant conscious perception, for example, in schizophrenia (Stuke, Weilnhammer et al 2018, Cole, Diaconescu et al 2020 and autism (Skewes, Jegindø et al 2014, Lawson, Mathys et al 2017).…”