Belief updating in psychosis, depression and anxiety disorders: A systematic review across computational modelling approaches

Lack of optimistic bias during social evaluation learning reflects reduced positive self-beliefs in depression and social anxiety, but via distinct mechanisms

Processing social feedback optimistically may maintain positive self-beliefs and stable social relationships. Conversely, a lack of this optimistic bias in depression and social anxiety may perpetuate negative self-beliefs and maintain symptoms. Research investigating this mechanism is scarce, however, and the mechanisms by which depressed and socially anxious individuals respond to social evaluation may also differ. Using a range of computational approaches in two large datasets (mega-analysis of previous studies, n = 450; pre-registered replication study, n = 807), we investigated how depression (PHQ-9) and social anxiety (BFNE) symptoms related to social evaluation learning in a computerized task. Optimistic bias (better learning of positive relative to negative evaluations) was found to be negatively associated with depression and social anxiety. Structural equation models suggested this reflected a heightened sensitivity to negative social feedback in social anxiety, whereas in depression it co-existed with a blunted response to positive social feedback. Computational belief-based learning models further suggested that reduced optimism was driven by less positive trait-like self-beliefs in both depression and social anxiety, with some evidence for a general blunting in belief updating in depression. Recognizing such transdiagnostic similarities and differences in social evaluation learning across disorders may inform approaches to personalizing treatment.

Individual differences in belief updating and phasic arousal are related to psychosis proneness

Many decisions entail the updating of beliefs about the state of the environment by accumulating noisy sensory evidence. This form of probabilistic reasoning may go awry in psychosis. Computational theory shows that optimal belief updating in environments subject to hidden changes in their state requires a dynamic modulation of the evidence accumulation process. Recent empirical findings implicate transient responses of pupil-linked central arousal systems to individual evidence samples in this modulation. Here, we analyzed behavior and pupil responses during evidence accumulation in a changing environment in a community sample of human participants. We also assessed their subclinical psychotic experiences (psychosis proneness). Participants most prone to psychosis showed overall less flexible belief updating profiles, with diminished behavioral impact of evidence samples occurring late during decision formation. These same individuals also exhibited overall smaller pupil responses and less reliable pupil encoding of computational variables governing the dynamic belief updating. Our findings provide insights into the cognitive and physiological bases of psychosis proneness and open paths to unraveling the pathophysiology of psychotic disorders.

Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication and extension of previous results

Recent Bayesian theories of interoception suggest that perception of bodily states rests upon a precision-weighted integration of afferent signals and prior beliefs. In a previous study, we fit a computational model of perception to behavior on a heartbeat tapping task to test whether aberrant precision-weighting could explain misestimation of cardiac states in psychopathology. We found that, during an interoceptive perturbation designed to amplify afferent signal precision (inspiratory breath-holding), healthy individuals increased the precision-weighting assigned to ascending cardiac signals (relative to resting conditions), while individuals with anxiety, depression, substance use disorders, and/or eating disorders did not. In this pre-registered study, we aimed to replicate and extend our prior findings in a new transdiagnostic patient sample (N = 285) similar to the one in the original study. As expected, patients in this new sample were also unable to adjust beliefs about the precision of cardiac signals - preventing the ability to accurately perceive changes in their cardiac state. Follow-up analyses combining samples from the previous and current study (N = 719) also afforded power to identify group differences between narrower diagnostic categories, and to examine predictive accuracy when logistic regression models were trained on one sample and tested on the other. With this confirmatory evidence in place, future studies should examine the utility of interoceptive precision measures in predicting treatment outcomes and test whether these computational mechanisms might represent novel therapeutic targets.

Temporal stability of Bayesian belief updating in perceptual decision-making

Bayesian inference suggests that perception is inferred from a weighted integration of prior contextual beliefs with current sensory evidence (likelihood) about the world around us. The perceived precision or uncertainty associated with prior and likelihood information is used to guide perceptual decision-making, such that more weight is placed on the source of information with greater precision. This provides a framework for understanding a spectrum of clinical transdiagnostic symptoms associated with aberrant perception, as well as individual differences in the general population. While behavioral paradigms are commonly used to characterize individual differences in perception as a stable characteristic, measurement reliability in these behavioral tasks is rarely assessed. To remedy this gap, we empirically evaluate the reliability of a perceptual decision-making task that quantifies individual differences in Bayesian belief updating in terms of the relative precision weighting afforded to prior and likelihood information (i.e., sensory weight). We analyzed data from participants (n = 37) who performed this task twice. We found that the precision afforded to prior and likelihood information showed high internal consistency and good test-retest reliability (ICC = 0.73, 95% CI [0.53, 0.85]) when averaged across participants, as well as at the individual level using hierarchical modeling. Our results provide support for the assumption that Bayesian belief updating operates as a stable characteristic in perceptual decision-making. We discuss the utility and applicability of reliable perceptual decision-making paradigms as a measure of individual differences in the general population, as well as a diagnostic tool in psychiatric research.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

Exploring the relationship between frequent cannabis use, belief updating under uncertainty and psychotic-like symptoms

Background

Cannabis users present an important group for investigating putative mechanisms underlying psychosis, as cannabis-use is associated with an increased risk of psychosis. Recent work suggests that alterations in belief-updating under uncertainty underlie psychosis. We therefore compared belief updating under uncertainty between cannabis and non-cannabis users.

Methods

49 regular cannabis users and 52 controls completed the Space Game, via an online platform used for behavioral testing. In the task, participants were asked to predict the location of the stimulus based on previous information, under different uncertainty conditions. Mixed effects models were used to identify significant predictors of mean score, confidence, performance error and learning rate.

Results

Both groups showed decreased confidence in high noise conditions, and increased belief updating in more volatile conditions, suggesting that they could infer the degree and sources of uncertainty. There were no significant effects of group on any of the performance indices. However, within the cannabis group, frequent users showed worse performance than less frequent users.

Conclusion

Belief updating under uncertainty is not affected by cannabis use status but could be impaired in those who use cannabis more frequently. This finding could show a similarity between frequent cannabis use and psychosis risk, as predictors for abnormal belief-updating.

Information processing style and institutional trust as factors of COVID vaccine hesitancy

This study investigates the factors contributing to COVID vaccine hesitancy. Vaccine hesitancy has commonly been attributed to susceptibility to misinformation and linked to particular socio-demographic factors and personality traits. We present a new perspective, emphasizing the interplay between individual cognitive styles and perceptions of public health institutions. In January 2020, before the COVID-19 pandemic, 318 participants underwent a comprehensive assessment, including self-report measures of personality and clinical characteristics, as well as a behavioral task that assessed information processing styles. During 2021, attitudes towards vaccines, scientists, and the CDC were measured at three time points (February-October). Panel data analysis and structural equation modeling revealed nuanced relationships between these measures and information processing styles over time. Trust in public health institutions, authoritarian submission, and lower information processing capabilities together contribute to vaccine acceptance. Information processing capacities influenced vaccination decisions independently from the trust level, but their impact was partially mediated by authoritarian tendencies. These findings underscore the multifactorial nature of vaccine hesitancy, which emerges as a product of interactions between individual cognitive styles and perceptions of public health institutions. This novel perspective provides valuable insights into the underlying mechanisms that drive this complex phenomenon.

Belief Updating, Childhood Maltreatment, and Paranoia in Schizophrenia-Spectrum Disorders

BACKGROUND AND HYPOTHESIS

Exposure to childhood maltreatment-a risk factor for psychosis is associated with paranoia-may impact one's beliefs about the world and how beliefs are updated. We hypothesized that increased exposure to childhood maltreatment is related to volatility-related belief updating, specifically higher expectations of volatility, and that these relationships are strongest for threat-related maltreatment. Additionally, we tested whether belief updating mediates the relationship between maltreatment and paranoia.

STUDY DESIGN

Belief updating was measured in 75 patients with schizophrenia-spectrum disorders and 76 nonpsychiatric controls using a 3-option probabilistic reversal learning (3PRL) task. A Hierarchical Gaussian Filter (HGF) was used to estimate computational parameters of belief updating, including prior expectations of volatility (μ03). The Childhood Trauma Questionnaire (CTQ) was used to assess cumulative maltreatment, threat, and deprivation exposure. Paranoia was measured using the Positive and Negative Syndrome Scale (PANSS) and the revised Green et al. Paranoid Thoughts Scale (R-GPTS).

RESULTS

Greater exposure to childhood maltreatment is associated with higher prior expectations of volatility in the whole sample and in individuals with schizophrenia-spectrum disorders. This was specific to threat-related maltreatment, rather than deprivation, in schizophrenia-spectrum disorders. Paranoia was associated with both exposure to childhood maltreatment and volatility priors, but we did not observe a significant indirect effect of volatility priors on the relationship between maltreatment and paranoia.

CONCLUSIONS

Our study suggests that individuals with schizophrenia-spectrum disorders who were exposed to threatening experiences during childhood expect their environment to be more volatile, potentially facilitating aberrant belief updating and conferring risk for paranoia.

Catch the drift: Depressive symptoms track neural response during more efficient decision-making for negative self-referents

Background

Adolescence is a time of heightened risk for developing depression and also a critical period for the development and integration of self-identity. Despite this, the relation between the neurophysiological correlates of self-referential processing and major depressive symptoms in youth is not well understood. Here, we leverage computational modeling of the self-referential encoding task (SRET) to identify behavioral moderators of the association between the posterior late positive potential (LPP), an event-related potential associated with emotion regulation, and youth self-reported symptoms of depression. Specifically, within a drift-diffusion framework, we evaluated whether the association between the posterior LPP and youth symptoms of major depression was moderated by drift rate, a parameter reflecting processing efficiency during self-evaluative decisions.

Methods

A sample of 106 adolescents, aged 12 to 17 (53% male; Mage = 14.49, SD = 1.70), completed the SRET with concurrent high-density electroencephalography and self-report measures of depression and anxiety.

Results

Findings indicated a significant moderation: for youth showing greater processing efficiency (drift rate) when responding to negative compared to positive words, larger posterior LPPs predicted greater depressive symptom severity.

Limitations

We relied on a community sample and our study was cross-sectional in nature. Future longitudinal work with clinically depressed youth would be beneficial.

Conclusions

Our results suggest a neurobehavioral model of adolescent depression wherein efficient processing of negative information co-occurs with increased demands on affective self-regulation. Our findings also have clinical relevance; youth's neurophysiological response (posterior LPP) and performance during the SRET may serve as a novel target for tracking treatment-related changes in one's self-identity.

Computational mechanisms of belief updating in relation to psychotic-like experiences

Introduction

Psychotic-like experiences (PLEs) may occur due to changes in weighting prior beliefs and new evidence in the belief updating process. It is still unclear whether the acquisition or integration of stable beliefs is altered, and whether such alteration depends on the level of environmental and belief precision, which reflects the associated uncertainty. This motivated us to investigate uncertainty-related dynamics of belief updating in relation to PLEs using an online study design.

Methods

We selected a sample (n = 300) of participants who performed a belief updating task with sudden change points and provided self-report questionnaires for PLEs. The task required participants to observe bags dropping from a hidden helicopter, infer its position, and dynamically update their belief about the helicopter's position. Participants could optimize performance by adjusting learning rates according to inferred belief uncertainty (inverse prior precision) and the probability of environmental change points. We used a normative learning model to examine the relationship between adherence to specific model parameters and PLEs.

Results

PLEs were linked to lower accuracy in tracking the outcome (helicopter location) (β = 0.26 ± 0.11, p = 0.018) and to a smaller increase of belief precision across observations after a change point (β = -0.003 ± 0.0007, p < 0.001). PLEs were related to slower belief updating when participants encountered large prediction errors (β = -0.03 ± 0.009, p = 0.001). Computational modeling suggested that PLEs were associated with reduced overall belief updating in response to prediction errors (βPE = -1.00 ± 0.45, p = 0.028) and reduced modulation of updating at inferred environmental change points (βCPP = -0.84 ± 0.38, p = 0.023).

Discussion

We conclude that PLEs are associated with altered dynamics of belief updating. These findings support the idea that the process of balancing prior belief and new evidence, as a function of environmental uncertainty, is altered in PLEs, which may contribute to the development of delusions. Specifically, slower learning after large prediction errors in people with high PLEs may result in rigid beliefs. Disregarding environmental change points may limit the flexibility to establish new beliefs in the face of contradictory evidence. The present study fosters a deeper understanding of inferential belief updating mechanisms underlying PLEs.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.