Atypical processing of uncertainty in individuals at risk for psychosis

Abstract

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 clinical high risk (CHR) individuals. Non-medicated CHR 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. Computational modelling of CHR 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 CHR 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 CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR 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.

Keywords: At-risk mental state; Computational psychiatry; Decision-making; Hierarchical Bayesian learning; Prodromal; Volatility.

Fig. 1

Probabilistic reversal-learning task. The behavioural paradigm consisted of: (A) within trial, a pair of fractal stimuli, each paired with a reward value, requiring a decision from the participant via button press in order to obtain the reward; (B) across trials, the probabilistic contingency (dotted line) of which of the two fractal cues was most likely to yield a reward occasionally underwent ‘reversal’, the regularity of which engendered pseudo-blocks of volatility modulation (blue, violet and red panels). The reward values within trials were entirely independent of the stimulus-outcome contingencies.

Fig. 2

Hierarchical structure of the model space: perceptual models, response models and Bayesian model selection. (A) The models considered in this study have a factorial structure that can be displayed as a tree: The nodes at the first level represent the perceptual model families (RW, 2-level non-volatility HGF, 3-level HGF, and 3-level mean-reverting). The nodes at the second level represent the individual models. Two response model families were formalized under the HGF models: the mapping of beliefs-to-decisions either (i) depended dynamically on the estimated volatility of the learning environment (“Volatility + decision noise” model) or (ii) was a fixed entity over trials (“Decision noise” model). (B) Bayesian model selection (BMS) reveals M₆, the mean-reverting HGF perceptual model in combination with the “Volatility” decision model, to best explain the data.

Fig. 3

Graphical representation of the winning model combination: “mean-reverting HGF” perceptual model and the “Volatility” response model. In this graphical notation, circles represent constants and diamonds represent quantities that change in time (i.e., that carry a time/trial index). Hexagons, like diamonds, represent quantities that change in time, but additionally depend on the previous state in time in a Markovian fashion. x1 represents the cue probability, x2 the cue-outcome contingency and x3 the volatility of the cue-outcome contingency. Parameter κ determines how strongly x2 and x3 are coupled, ω determines the log-volatility or tonic component of x2, ϑ represents the volatility of x3, and m represents the mean of the drift towards which x3 regresses to in time. The response model parameter β represents the inverse decision temperature and determines the belief-to-response mapping.

Fig. 4

Behavioural parameter group differences. (A) Group differences in reversion equilibria values (m₃): larger reversion equilibria values were detected in CHR compared to controls (group: df = (1, 25), F = 4.29, p = 0.049, ${\eta }_{partial}^2=$0.15); and (B) Group-by-phase interactions of perceived environmental volatility (${\widehat{\mu }}_3$): A mixed-factor ANOVA (with Greenhouse-Geisser nonsphericity correction), which included between-subject and within-subject factors, found a significant main effect of phase and a significant group × phase interaction (phase: df = (2, 48), F = 41.68, p = 1.113e-06, ${\eta }_{partial}^2=$0.63; group × phase: df (2, 48), F = 5.71, p = 0.025, ${\eta }_{partial}^2=$0.19). See Section 3.2 in main text for details. No significant main effect of group was found (group: df = (1, 24), F = 3.42, p = 0.08, ${\eta }_{partial}^2=$0.12). Jittered raw data are plotted for each parameter. The solid red line refers to the mean, the dotted red line to the median, the grey background reflects 1 SD of the mean, and the coloured bars the 95% confidence intervals of the mean. ‘*’ refers to group differences of significance level p < 0.05.

Fig. 5

The neural representation of low-level/outcome-related precision-weighted PEs (ε₂) in CHR patients and healthy controls. (A) A representative map of significant (cluster-level FWE-corrected p < 0.05) group-level (CHR + controls) outcome-related activations modulated parametrically by ε₂, calculated via one-sample t-test (n = 25) and overlaid on an anatomical image calculated as the mean structural MRI of the whole group. (B) Significantly greater representation of ε₂-related activation in a sub-set of these and other regions in CHR patients relative to controls. Solid colour maps of group differences are binarised and indicate spatial differences between whole-brain cluster-level corrected results (red) and results corrected using the group average map in ‘(A)’ for contrast-masking (dark blue). Colour bar represents t-statistics. Axial and coronal slices are orientated in line with neurological conventions (R = right). (C) Significant negative correlation in CHR patients (n = 13) between a clinical measure of current global functioning (SIPS-GAF) and ε₂-related activation (beta-values, from the analysis as in panel ‘A’) in a region of left (L) superior dlPFC also showing significant group differences (CHR > controls, from the independent analysis as in panel ‘B’).

Fig. 6

Failures of monitoring and incorporating environmental uncertainty (volatility) in probabilistic learning by CHR relative to control individuals: prediction error response. (A) A representative map of significant (cluster-level FWE-corrected p < 0.05) group-level (CHR + controls) outcome-related activations modulated parametrically by precision-weighted volatility-related prediction error (ε₃), calculated via one-sample t-test (n = 25) and overlaid on an anatomical image calculated as the mean structural MRI of the whole group. (B) Greater neural representation of high-level/volatility-related precision-weighted PEs (ε₃) during decision feedback in healthy controls relative to CHR patients, (main) in a network of regions identified using whole-brain cluster-level correction, (inset) in a region of subgenual anterior cingulate identified under correction for an a priori anterior cingulate masque (bottom centre) and in a left midbrain region identified under correction for a dopaminergic midbrain masque (trend level p = 0.087, bottom right). Double inset, green: representative sagittal slice depicting anatomical anterior cingulate cortex masque used as search volume in statistical analysis and multiple comparison correction. Colour bars represent t-statistics.

Fig. 7

Failures of monitoring and incorporating environmental uncertainty (volatility) in probabilistic learning by CHR relative to control individuals: decision tracking. (A) A representative map of significant (cluster-level FWE-corrected p < 0.05) group-level (CHR + controls) decision-related activations modulated parametrically by estimated volatility, calculated via one-sample t-test (n = 25) and overlaid on an anatomical image calculated as the mean structural MRI of the whole group. (B) Greater neural representation of estimated volatility during probabilistic decision-making in healthy controls relative to CHR patients, (main) in a network where solid colour maps of group differences are binarised and indicate spatial differences between whole-brain cluster-level corrected results (red) and results corrected using the group average map in ‘(A)’ for contrast-masking (dark blue), and (inset) in a midbrain region identified under correction for a dopaminergic midbrain masque (p < 0.05). Colour bars represent t-statistics.