Bayesian inference and hallucinations in schizophrenia

Siemerkus, Jakob; Tomiello, Sara; Stephan, Klaas Enno

doi:10.1093/brain/awz211

Cited by 9 publications

(6 citation statements)

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“…Computational methods in schizophrenia have mostly been generative models based on the Bayesian predictive coding framework—for example, the hierarchical Gaussian filtering approach (Powers et al ., 2017 ; Henco et al ., 2020 ; Charlton et al ., 2022 ; Sheffield et al ., 2022 ), reinforcement learning (Pratt et al ., 2021 ; Geana et al ., 2022 ), and the active inference Markov decision process model that attempts to dissect unobservable mechanistic variables based on actions taken by an agent to promote desired outcomes (Friston et al ., 2016 ). All of these approaches use a single-person task design and mainly target hallucinatory and delusional positive symptoms, reward-related decision-making, and thought and language deficits in PSZ (Siemerkus et al ., 2019 ; Deserno et al ., 2020 ; Smith et al ., 2021 ; Charlton et al ., 2022 ; Knolle et al ., 2022 ; Limongi et al ., 2022 ). A recent study examining guilt-related interpersonal dysfunction in obsessive-compulsive personality disorder using social interaction tasks applied two computational models (guilt aversion and Fehr–Schmidt inequity aversion models), and demonstrated that interpersonal dysfunction was the result of maladjustment to and poor compliance with social norms (Xiao et al ., 2022 ).…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings

et al. 2023

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Social functioning impairment is a hallmark of mental illness. As one of the most intractable and severe psychiatric disorders, schizophrenia is characterized by deficits on multiple social cognitive domains. In particular, failures of interpersonal coordination (i.e., actions that occur in spatial and temporal concordance with those of other individuals in a social setting) in schizophrenia are closely linked to significant prognostic indicators, such as symptom remission, quality of life, and employment. Despite the increasing interest in the psychological and neural mechanisms of interpersonal coordination in schizophrenia, important gaps in our understanding remain. Notably, the study of the mechanisms underpinning interpersonal coordination is currently undergoing a methodological shift, moving its focus from single-person tasks to second- and multi-person paradigms, and from model-free to model-based analyses. However, applications of these advanced approaches in schizophrenia has been slow. Here, we provide a concise update on the uses of multi-person paradigms to study interpersonal coordination and mathematical models to dissect its computational mechanisms, at both behavioral and neural levels. We then review prior studies on large-scale neuroimaging studies that explore brain correlates of general social functions and further pointed to the implementation of dissecting interpersonal coordination deficits in schizophrenia. Finally, we highlight key challenges as well as exciting opportunities for integrating research in computational psychiatry with second- and multi-person approaches in investigating interpersonal coordination deficits in schizophrenia.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings

et al. 2023

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“…This explanation for perception and action has led quite naturally to descriptions of hallucinations as perceptual inference based on overly precise priors. [19][20][21] This theory supposes that prior beliefs in perceptual stimuli are so strong that resulting inferences are resistant to the empirical absence of such stimuli. In contrast, delusions are the result of imprecise prior beliefs such that sensory attenuation fails and sensations inappropriately alter priors 22 phenomenologically this results in the attribution of meaning to events that should be trivial.…”

Section: Prediction Errors Entropy and Free-energy Minimisationmentioning

confidence: 99%

Modelling mood updating: a proof of principle study

Clark

Watson

2022

Br J Psychiatry

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Background Recent developments in computational psychiatry have led to the hypothesis that mood represents an expectation (prior belief) on the likely interoceptive consequences of action (i.e. emotion). This stems from ideas about how the brain navigates its external world by minimising an upper bound on surprisal (free energy) of sensory information and echoes developments in other perceptual domains. Aims In this paper we aim to present a simple partial observable Markov decision process that models mood updating in response to stressful or non-stressful environmental fluctuations while seeking to minimise surprisal in relation to prior beliefs about the likely interoceptive signals experienced with specific actions (attenuating or amplifying stress and pleasure signals). Method We examine how, by altering these prior beliefs we can model mood updating in depression, mania and anxiety. Results We discuss how these models provide a computational account of mood and its related psychopathology and relate it to previous research in reward processing. Conclusions Models such as this can provide hypotheses for experimental work and also open up the potential modelling of predicted disease trajectories in individual patients.

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“…In recent years, predictive processing models have arguably become the dominant approach to explain hallucinations in SSD. There are many subtypes of predictive processing models that include self-monitoring approaches ( Farrer and Franck, 2007 ; Ford and Hoffman, 2013 ) and Bayesian approaches ( Friston, 2005 ; Siemerkus et al, 2019 ). Both approaches maintain that a critical function of our brains is to make accurate predictions; to do this, our brains are on a quest to minimize prediction errors (e.g., mismatches between outcomes we anticipate vs. outcomes we actually perceive).…”

Section: Introductionmentioning

confidence: 99%

“…Self-monitoring theories emphasize the predictions that we make about the sensory consequences of our motor actions (including speech), and refer to this predictive signal as a corollary discharge ( Ford et al, 2001 ; Ford and Mathalon, 2005 , 2019 ). Meanwhile, Bayesian approaches offer a more general framework for thinking about how our prior beliefs about what will happen next (referred to as “priors”) guide our inferences in noisy or ambiguous environments ( Friston, 2005 ; Siemerkus et al, 2019 ). A central idea in Bayesian models of perception is that the initial prior is integrated and compared with new perceptual information conveyed by the sensory organs to produce the final percept (referred to as a “posterior”) ( Friston, 2005 ; Siemerkus et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, Bayesian approaches offer a more general framework for thinking about how our prior beliefs about what will happen next (referred to as “priors”) guide our inferences in noisy or ambiguous environments ( Friston, 2005 ; Siemerkus et al, 2019 ). A central idea in Bayesian models of perception is that the initial prior is integrated and compared with new perceptual information conveyed by the sensory organs to produce the final percept (referred to as a “posterior”) ( Friston, 2005 ; Siemerkus et al, 2019 ). In both self-monitoring and Bayesian theories, if there is a mismatch between the prediction (e.g., “corollary discharge” or “prior”) and the incoming information conveyed from the sensory organs to the associated brain sensory pathways, then a prediction error occurs.…”

Section: Introductionmentioning

confidence: 99%

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Hallucinations: A Functional Network Model of How Sensory Representations Become Selected for Conscious Awareness in Schizophrenia

Hare

2021

Front. Neurosci.

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Hallucinations are conscious perception-like experiences that are a common symptom of schizophrenia spectrum disorders (SSD). Current neuroscience evidence suggests several brain areas are involved in the generation of hallucinations including the sensory cortex, insula, putamen, and hippocampus. But how does activity in these regions give rise to aberrant conscious perceptions that seemingly invade ongoing conscious experience? Most existing models assume that sensory representations are sometimes spontaneously activated in the brain, and that these spontaneous activations somehow play a causal role in the generation of hallucinations. Yet, it remains unclear how these representations become selected for conscious processing. No existing theory of hallucinations has specified such a “selection mechanism.” Global Workspace (GW) theorists argue that the brain’s interconnected processors select relevant piece(s) of information for broadcasting to other brain processors, rendering the information accessible to consciousness; this process known as “ignition” is associated with synchronized activity across distributed cortical and subcortical brain regions. Yet, it remains unclear how certain information and representations become selected for conscious processing. While GW theorists maintain that attention plays an important role, they have not delineated a formal “selection mechanism.” This paper specifies a selection mechanism based upon two central hypotheses: (1) a functional network called the “salience network” plays a critical role in selecting sensory representations for conscious broadcast to the GW in normal (healthy) perception; (2) sensory representations become abnormally selected for conscious broadcast to the GW (instead of being filtered out of consciousness) in individuals with SSD that experience hallucinations.

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Bayesian inference and hallucinations in schizophrenia

Abstract: This scientific commentary refers to ‘Acquisition of visual priors and induced hallucinations in chronic schizophrenia’, by Valton et al. (doi:10.1093/brain/awz171).

Cited by 9 publications

References 10 publications

Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings

Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings

Modelling mood updating: a proof of principle study

Hallucinations: A Functional Network Model of How Sensory Representations Become Selected for Conscious Awareness in Schizophrenia

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