The Predictive Coding Account of Psychosis

Learned uncertainty: The free energy principle in anxiety

Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness. Our account is simple: anxiety can be formalized as learned uncertainty. A biological system, having had persistent uncertainty in its past, will expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity—which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms.

The world seems different in a social context: A neural network analysis of human experimental data

Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience.

Familial aggregation of synaesthesia with autism (but not schizophrenia)

INTRODUCTION

This study determines whether there is a familial aggregation between synaesthesia and two neuropsychiatric conditions (autism and schizophrenia). METHOD We examined the prevalence of autism and schizophrenia among synaesthetes and non-synaesthetic controls, and among their first-degree relatives.

RESULTS

As predicted, autism occurred at elevated levels among synaesthetes and-we document for the first time-amongst their relatives. This was not found for schizophrenia, where a link may be expected, or in a control condition (type 1 diabetes) where we had no a priori reason to assume a link. Synaesthetes, compared to controls, were also more likely to have other synaesthetes in their family. People with three or more types of synaesthesia were more likely (compared to synaesthetes with fewer types) to have synaesthetic relatives and to report autism in themselves. People with two or more types of synaesthesia (compared to synaesthetes with only one type) were more likely to report familial autism.

CONCLUSIONS

The results suggest a shared genetic predisposition between synaesthesia and autism, and more extreme synaesthetes may tend to hail from more neurodiverse families.

'An experience of meaning': A 20-year prospective analysis of delusional realities in schizophrenia and affective psychoses

Delusions are transdiagnostic and heterogeneous phenomena with varying degrees of intensity, stability, and dimensional attributes where the boundaries between everyday beliefs and delusional beliefs can be experienced as clearly demarcated, fuzzy, or indistinguishable. This highlights the difficulty in defining delusional realities. All individuals in the current study were evaluated at index and at least one of six subsequential follow-ups over 20 years in the Chicago Longitudinal Study. We assessed 16 distinct delusions categorized as thought or thematic delusions. We also examined the probability of recurrence and the relationships between delusions and hallucinations, depression, anxiety, and negative symptoms. The sample consisted of 262 individuals with schizophrenia vs. affective psychosis. Thought delusions were significantly different between groups at all follow-up evaluations except the 20-year timepoint. Thematic delusions were more common than thought delusions and show a significant decreasing pattern. In general, delusional content varied over time. Referential, persecutory, and thought dissemination delusions show the highest probability of recurrence. Hallucinations were the strongest indicator for thought, thematic, and overall delusions. The formation and maintenance of delusions were conceptualized as a multimodal construct consisting of sensory, perceptual, emotional, social, and somatic embodiment of an "experience of meanings". Given the significant associations between delusions and hallucinations, future work incorporating participatory research is needed to better define and align subjective and objective perspectives. Our research also points to the need for future clinical interventions that specifically evaluate and target the coexistence and entanglement of delusions and hallucinations.

Hypersensitivity to passive voice hearing in hallucination proneness

Voices are a complex and rich acoustic signal processed in an extensive cortical brain network. Specialized regions within this network support voice perception and production and may be differentially affected in pathological voice processing. For example, the experience of hallucinating voices has been linked to hyperactivity in temporal and extra-temporal voice areas, possibly extending into regions associated with vocalization. Predominant self-monitoring hypotheses ascribe a primary role of voice production regions to auditory verbal hallucinations (AVH). Alternative postulations view a generalized perceptual salience bias as causal to AVH. These theories are not mutually exclusive as both ascribe the emergence and phenomenology of AVH to unbalanced top-down and bottom-up signal processing. The focus of the current study was to investigate the neurocognitive mechanisms underlying predisposition brain states for emergent hallucinations, detached from the effects of inner speech. Using the temporal voice area (TVA) localizer task, we explored putative hypersalient responses to passively presented sounds in relation to hallucination proneness (HP). Furthermore, to avoid confounds commonly found in in clinical samples, we employed the Launay-Slade Hallucination Scale (LSHS) for the quantification of HP levels in healthy people across an experiential continuum spanning the general population. We report increased activation in the right posterior superior temporal gyrus (pSTG) during the perception of voice features that positively correlates with increased HP scores. In line with prior results, we propose that this right-lateralized pSTG activation might indicate early hypersensitivity to acoustic features coding speaker identity that extends beyond own voice production to perception in healthy participants prone to experience AVH.

The computational relationship between reinforcement learning, social inference, and paranoia

Theoretical accounts suggest heightened uncertainty about the state of the world underpin aberrant belief updates, which in turn increase the risk of developing a persecutory delusion. However, this raises the question as to how an agent’s uncertainty may relate to the precise phenomenology of paranoia, as opposed to other qualitatively different forms of belief. We tested whether the same population (n = 693) responded similarly to non-social and social contingency changes in a probabilistic reversal learning task and a modified repeated reversal Dictator game, and the impact of paranoia on both. We fitted computational models that included closely related parameters that quantified the rigidity across contingency reversals and the uncertainty about the environment/partner. Consistent with prior work we show that paranoia was associated with uncertainty around a partner’s behavioural policy and rigidity in harmful intent attributions in the social task. In the non-social task we found that pre-existing paranoia was associated with larger decision temperatures and commitment to suboptimal cards. We show relationships between decision temperature in the non-social task and priors over harmful intent attributions and uncertainty over beliefs about partners in the social task. Our results converge across both classes of model, suggesting paranoia is associated with a general uncertainty over the state of the world (and agents within it) that takes longer to resolve, although we demonstrate that this uncertainty is expressed asymmetrically in social contexts. Our model and data allow the representation of sociocognitive mechanisms that explain persecutory delusions and provide testable, phenomenologically relevant predictions for causal experiments.

Pharmacological, neural, and psychological mechanisms underlying psychedelics: A critical review

This paper provides a critical review of several possible mechanisms at different levels of analysis underlying the effects and therapeutic potential of psychedelics. At the (1) biochemical level, psychedelics primarily affect the 5-HT_2A receptor, increase neuroplasticity, offer a critical period for social reward learning, and have anti-inflammatory properties. At the (2) neural level, psychedelics have been associated with reduced efficacy of thalamo-cortical filtering, the loosening of top-down predictive signaling and an increased sensitivity to bottom-up prediction errors, and activation of the claustro-cortical-circuit. At the (3) psychological level, psychedelics have been shown to induce altered and affective states, they affect cognition, induce belief change, exert social effects, and can result in lasting changes in behavior. We outline the potential for a unifying account of the mechanisms underlying psychedelics and contrast this with a model of pluralistic causation. Ultimately, a better understanding of the specific mechanisms underlying the effects of psychedelics could allow for a more targeted therapeutic approach. We highlight current challenges for psychedelic research and provide a research agenda to foster insight in the causal-mechanistic pathways underlying the efficacy of psychedelic research and therapy.

Autistic-Like Traits and Positive Schizotypy as Diametric Specializations of the Predictive Mind

According to the predictive-processing framework, only prediction errors (rather than all sensory inputs) are processed by an organism's perceptual system. Prediction errors can be weighted such that errors from more reliable sources will be more influential in updating prior beliefs. It has previously been argued that autism-spectrum conditions can be understood as resulting from a predictive-processing mechanism in which an inflexibly high weight is given to sensory-prediction errors that results in overfitting their predictive models to the world. Deficits in executive functioning, theory of mind, and central coherence are all argued to flow naturally from this core underlying mechanism. The diametric model of autism and psychosis suggests a simple extension of this hypothesis. If people on the autism spectrum give an inflexibly high weight to sensory input, could it be that people with a predisposition to psychosis (i.e., people high in positive schizotypy) give an inflexibly low weight to sensory input? In this article I argue that evidence strongly supports this hypothesis. An inflexibly low weight given to sensory input can explain such disparate features of positive schizotypy as increased exploratory behavior, apophenia, hyper theory of mind, hyperactive imagination, attentional differences, and having idiosyncratic worldviews.

Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes

Schizophrenia is a severe mental disorder whose neural basis remains difficult to ascertain. Among the available pathophysiological theories, recent work has pointed towards subtle perturbations in the excitation-inhibition (E/I) balance within different neural circuits. Computational approaches have suggested interesting mechanisms that can account for both E/I imbalances and psychotic symptoms. Based on hierarchical neural networks propagating information through a message-passing algorithm, it was hypothesized that changes in the E/I ratio could cause a "circular belief propagation" in which bottom-up and top-down information reverberate. This circular inference (CI) was proposed to account for the clinical features of schizophrenia. Under this assumption, this paper examined the impact of CI on network dynamics in light of brain imaging findings related to psychosis. Using brain-inspired graphical models, we show that CI causes overconfidence and overactivation most specifically at the level of connector hubs (e.g., nodes with many connections allowing integration across networks). By also measuring functional connectivity in these graphs, we provide evidence that CI is able to predict specific changes in modularity known to be associated with schizophrenia. Altogether, these findings suggest that the CI framework may facilitate behavioral and neural research on the multifaceted nature of psychosis.

A generative framework for the study of delusions

Despite the ubiquity of delusional information processing in psychopathology and everyday life, formal characterizations of such inferences are lacking. In this article, we propose a generative framework that entails a computational mechanism which, when implemented in a virtual agent and given new information, generates belief updates (i.e., inferences about the hidden causes of the information) that resemble those seen in individuals with delusions. We introduce a particular form of Dirichlet process mixture model with a sampling-based Bayesian inference algorithm. This procedure, depending on the setting of a single parameter, preferentially generates highly precise (i.e. over-fitting) explanations, which are compartmentalized and thus can co-exist despite being inconsistent with each other. Especially in ambiguous situations, this can provide the seed for delusional ideation. Further, we show by simulation how the excessive generation of such over-precise explanations leads to new information being integrated in a way that does not lead to a revision of established beliefs. In all configurations, whether delusional or not, the inference generated by our algorithm corresponds to Bayesian inference. Furthermore, the algorithm is fully compatible with hierarchical predictive coding. By virtue of these properties, the proposed model provides a basis for the empirical study and a step toward the characterization of the aberrant inferential processes underlying delusions.

Everything is connected: Inference and attractors in delusions

Delusions are, by popular definition, false beliefs that are held with certainty and resistant to contradictory evidence. They seem at odds with the notion that the brain at least approximates Bayesian inference. This is especially the case in schizophrenia, a disorder thought to relate to decreased - rather than increased - certainty in the brain's model of the world. We use an active inference Markov decision process model (a Bayes-optimal decision-making agent) to perform a simple task involving social and non-social inferences. We show that even moderate changes in some model parameters - decreasing confidence in sensory input and increasing confidence in states implied by its own (especially habitual) actions - can lead to delusions as defined above. Incorporating affect in the model increases delusions, specifically in the social domain. The model also reproduces some classic psychological effects, including choice-induced preference change, and an optimism bias in inferences about oneself. A key observation is that no change in a single parameter is both necessary and sufficient for delusions; rather, delusions arise due to conditional dependencies that create 'basins of attraction' which trap Bayesian beliefs. Simulating the effects of antidopaminergic antipsychotics - by reducing the model's confidence in its actions - demonstrates that the model can escape from these attractors, through this synthetic pharmacotherapy.

Computational mechanisms underlying illusion of control in delusional individuals

Humans navigate complex situations that require the accurate estimation of the controllability of the environment. Aberrant controllability computation might lead to maladaptive behaviors and poor mental health outcomes. Illusion of control, which refers to a heightened sense of control while the environment is uncontrollable, is one such manifestation and has been conceptually associated with delusional ideation. Nevertheless, this association has not yet been formally characterized in a computational framework. To address this, we used a computational psychiatry approach to quantify illusion of control in human participants with high (n = 125) or low (n = 126) trait delusion. Participants played a two-party exchange game in which their choices either did ("Controllable condition") or did not ("Uncontrollable condition") influence the future monetary offers made by simulated partners. We found that the two groups behaved similarly in model-agnostic measures (i.e., offer size, rejection rate). However, computational modeling revealed that compared to the low trait delusion group, the high delusion group overestimated their influence ("expected influence" parameter) over the offers made by their partners under the Uncontrollable condition. Highly delusional individuals also reported a stronger sense of control than those with low trait delusion in the Uncontrollable condition. Furthermore, the expected influence parameter and self-reported beliefs about controllability were significantly correlated in the Controllable condition in individuals with low trait delusion, whereas this relationship was diminished in those with high trait delusion. Collectively, these findings demonstrate that delusional ideation is associated with aberrant computation of and belief about environmental controllability, as well as a belief-behavior disconnect.

Rethinking delusions: A selective review of delusion research through a computational lens

Delusions are rigid beliefs held with high certainty despite contradictory evidence. Notwithstanding decades of research, we still have a limited understanding of the computational and neurobiological alterations giving rise to delusions. In this review, we highlight a selection of recent work in computational psychiatry aimed at developing quantitative models of inference and its alterations, with the goal of providing an explanatory account for the form of delusional beliefs in psychosis. First, we assess and evaluate the experimental paradigms most often used to study inferential alterations in delusions. Based on our review of the literature and theoretical considerations, we contend that classic draws-to-decision paradigms are not well-suited to isolate inferential processes, further arguing that the commonly cited 'jumping-to-conclusion' bias may reflect neither delusion-specific nor inferential alterations. Second, we discuss several enhancements to standard paradigms that show promise in more effectively isolating inferential processes and delusion-related alterations therein. We further draw on our recent work to build an argument for a specific failure mode for delusions consisting of prior overweighting in high-level causal inferences about partially observable hidden states. Finally, we assess plausible neurobiological implementations for this candidate failure mode of delusional beliefs and outline promising future directions in this area.

Perceptual pathways to hallucinogenesis

Recent advances in computational psychiatry have provided unique insights into the neural and cognitive underpinnings of psychotic symptoms. In particular, a host of new data has demonstrated the utility of computational frameworks for understanding how hallucinations might arise from alterations in typical perceptual processing. Of particular promise are models based in Bayesian inference that link hallucinatory perceptual experiences to latent states that may drive them. In this piece, we move beyond these findings to ask: how and why do these latent states arise, and how might we take advantage of heterogeneity in that process to develop precision approaches to the treatment of hallucinations? We leverage specific models of Bayesian inference to discuss components that might lead to the development of hallucinations. Using the unifying power of our model, we attempt to place disparate findings in the study of psychotic symptoms within a common framework. Finally, we suggest directions for future elaboration of these models in the service of a more refined psychiatric nosology based on predictable, testable, and ultimately treatable information processing derangements.

The promise of layer-specific neuroimaging for testing predictive coding theories of psychosis

Predictive coding potentially provides an explanatory model for understanding the neurocognitive mechanisms of psychosis. It proposes that cognitive processes, such as perception and inference, are implemented by a hierarchical system, with the influence of each level being a function of the estimated precision of beliefs at that level. However, predictive coding models of psychosis are insufficiently constrained-any phenomenon can be explained in multiple ways by postulating different changes to precision at different levels of processing. One reason for the lack of constraint in these models is that the core processes are thought to be implemented by the function of specific cortical layers, and the technology to measure layer specific neural activity in humans has until recently been lacking. As a result, our ability to constrain the models with empirical data has been limited. In this review we provide a brief overview of predictive processing models of psychosis and then describe the potential for newly developed, layer specific neuroimaging techniques to test and thus constrain these models. We conclude by discussing the most promising avenues for this research as well as the technical and conceptual challenges which may limit its application.

Revisiting the seven pillars of RDoC

BACKGROUND

In 2013, a few years after the launch of the National Institute of Mental Health's Research Domain Criteria (RDoC) initiative, Cuthbert and Insel published a paper titled "Toward the future of psychiatric diagnosis: the seven pillars of RDoC." The RDoC project is a translational research effort to encourage new ways of studying psychopathology through a focus on disruptions in normal functions (such as reward learning or attention) that are defined jointly by observable behavior and neurobiological measures. The paper outlined the principles of the RDoC research framework, including emphases on research that acquires data from multiple measurement classes to foster integrative analyses, adopts dimensional approaches, and employs novel methods for ascertaining participants and identifying valid subgroups.

DISCUSSION

To mark the first decade of the RDoC initiative, we revisit the seven pillars and highlight new research findings and updates to the framework that are related to each. This reappraisal emphasizes the flexible nature of the RDoC framework and its application in diverse areas of research, new findings related to the importance of developmental trajectories within and across neurobehavioral domains, and the value of computational approaches for clarifying complex multivariate relations among behavioral and neurobiological systems.

CONCLUSION

The seven pillars of RDoC have provided a foundation that has helped to guide a surge of new studies that have examined neurobehavioral domains related to mental disorders, in the service of informing future psychiatric nosology. Building on this footing, future areas of emphasis for the RDoC project will include studying central-peripheral interactions, developing novel approaches to phenotyping for genomic studies, and identifying new targets for clinical trial research to facilitate progress in precision psychiatry.

Therapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Biobehavioural Synchrony in Musculoskeletal Care

Touch is recognised as crucial for survival, fostering cooperative communication, accelerating recovery, reducing hospital stays, and promoting overall wellness and the therapeutic alliance. In this hypothesis and theory paper, we present an entwined model that combines touch for alignment and active inference to explain how the brain develops "priors" necessary for the health care provider to engage with the patient effectively. We appeal to active inference to explain the empirically integrative neurophysiological and behavioural mechanisms that underwrite synchronous relationships through touch. Specifically, we offer a formal framework for understanding - and explaining - the role of therapeutic touch and hands-on care in developing a therapeutic alliance and synchrony between health care providers and their patients in musculoskeletal care. We first review the crucial importance of therapeutic touch and its clinical role in facilitating the formation of a solid therapeutic alliance and in regulating allostasis. We then consider how touch is used clinically - to promote cooperative communication, demonstrate empathy, overcome uncertainty, and infer the mental states of others - through the lens of active inference. We conclude that touch plays a crucial role in achieving successful clinical outcomes and adapting previous priors to create intertwined beliefs. The ensuing framework may help healthcare providers in the field of musculoskeletal care to use hands-on care to strengthen the therapeutic alliance, minimise prediction errors (a.k.a., free energy), and thereby promote recovery from physical and psychological impairments.

COVID-19 conspiracy ideation is associated with the delusion proneness trait and resistance to update of beliefs

The rapid spread of conspiracy ideas associated with the recent COVID-19 pandemic represents a major threat to the ongoing and coming vaccination programs. Yet, the cognitive factors underlying the pandemic-related conspiracy beliefs are not well described. We hypothesized that such cognitive style is driven by delusion proneness, a trait phenotype associated with formation of delusion-like beliefs that exists on a continuum in the normal population. To probe this hypothesis, we developed a COVID-19 conspiracy questionnaire (CCQ) and assessed 577 subjects online. Their responses clustered into three factors that included Conspiracy, Distrust and Fear/Action as identified using principal component analysis. We then showed that CCQ (in particular the Conspiracy and Distrust factors) related both to general delusion proneness assessed with Peter's Delusion Inventory (PDI) as well as resistance to belief update using a Bias Against Disconfirmatory Evidence (BADE) task. Further, linear regression and pathway analyses suggested a specific contribution of BADE to CCQ not directly explained by PDI. Importantly, the main results remained significant when using a truncated version of the PDI where questions on paranoia were removed (in order to avoid circular evidence), and when adjusting for ADHD- and autistic traits (that are known to be substantially related to delusion proneness). Altogether, our results strongly suggest that pandemic-related conspiracy ideation is associated with delusion proneness trait phenotype.

Simulating Developmental and Individual Differences of Drawing Behavior in Children Using a Predictive Coding Model

Predictive coding has recently been proposed as a mechanistic approach to explain human perception and behavior based on the integration of perceptual stimuli (bottom-up information) and the predictions about the world based on previous experience (top-down information). However, the gap between the computational accounts of cognition and evidence of behavioral studies remains large. In this study, we used a computational model of drawing based on the mechanisms of predictive coding to systematically investigate the effects of the precision of top-down and bottom-up information when performing a drawing completion task. The results indicated that sufficient precision of both signals was required for the successful completion of the stimuli and that a reduced precision in either sensory or prediction (i.e., prior) information led to different types of atypical drawing behavior. We compared the drawings produced by our model to a dataset of drawings created by children aged between 2 and 8 years old who drew on incomplete drawings. This comparison revealed that a gradual increase in children's precision of top-down and bottom-up information as they develop effectively explains the observed change of drawing style from scribbling toward representational drawing. Furthermore, individual differences that are prevalent in children's drawings, might arise from different developmental pathways regarding the precision of these two signals. Based on these findings we propose a theory of how both general and individual development of drawing could be explained in a unified manner within the framework of predictive coding.

A Predictive Processing Model of Episodic Memory and Time Perception

Human perception and experience of time are strongly influenced by ongoing stimulation, memory of past experiences, and required task context. When paying attention to time, time experience seems to expand; when distracted, it seems to contract. When considering time based on memory, the experience may be different than what is in the moment, exemplified by sayings like "time flies when you're having fun." Experience of time also depends on the content of perceptual experience-rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions among attention, memory, and perceptual stimulation is a likely reason that an overarching theory of time perception has been difficult to achieve. Here, we introduce a model of perceptual processing and episodic memory that makes use of hierarchical predictive coding, short-term plasticity, spatiotemporal attention, and episodic memory formation and recall, and apply this model to the problem of human time perception. In an experiment with approximately 13,000 human participants, we investigated the effects of memory, cognitive load, and stimulus content on duration reports of dynamic natural scenes up to about 1 minute long. Using our model to generate duration estimates, we compared human and model performance. Model-based estimates replicated key qualitative biases, including differences by cognitive load (attention), scene type (stimulation), and whether the judgment was made based on current or remembered experience (memory). Our work provides a comprehensive model of human time perception and a foundation for exploring the computational basis of episodic memory within a hierarchical predictive coding framework.

'Regarding the scientific viewpoint in psychiatry', lecture by Carl Wernicke (1880)

In 1880 Carl Wernicke gave this plenary lecture at an annual meeting of German physicians and natural scientists. He used principles from his 1874 aphasia monograph to build a neural model of mental illness. He proposed that the brain keeps a record of experiences in distinct areas of the sensory and motor cortices in the form of memory images, which allows for recognition of objects and the planning of motor acts. He conjectured that imperfections, partial defects and complete loss of such memory images lead, respectively, to mild, moderate and severe forms of psychopathology in sensory and motor realms. The lecture is an early presentation of Wernicke's system of psychiatry. Several of his concepts have remained relevant in contemporary neuroscience.

Do psychedelics change beliefs?

Renewed interest in psychedelics has reignited the debate about whether and how they change human beliefs. In both the clinical and social-cognitive domains, psychedelic consumption may be accompanied by profound, and sometimes lasting, belief changes. We review these changes and their possible underlying mechanisms. Rather than inducing de novo beliefs, we argue psychedelics may instead change the impact of affect and of others' suggestions on how beliefs are imputed. Critically, we find that baseline beliefs (in the possible effects of psychedelics, for example) might color the acute effects of psychedelics as well as longer-term changes. If we are to harness the apparent potential of psychedelics in the clinic and for human flourishing more generally, these possibilities must be addressed empirically.

The role of cognitive control in the positive symptoms of psychosis

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Mechanisms underlying positive symptoms in psychosis are unclear.

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Differential fMRI activity present in left amygdala, pallidum and thalamus in high positive symptom patients compared to low.

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Lower activity in SMA/pre-SMA also present in high symptom patients.

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We suggest poor integration of social-emotional information with reward feedback.

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Results may be important for guiding treatment strategies to prevent chronic illness.

Background

Positive symptoms of psychosis (e.g., hallucinations) often limit everyday functioning and can persist despite adequate antipsychotic treatment. We investigated whether poor cognitive control is a mechanism underlying these symptoms.

Methods

97 patients with early psychosis (30 with high positive symptoms (HS) and 67 with low positive symptoms (LS)) and 40 healthy controls (HC) underwent fMRI whilst performing a reward learning task with two conditions; low cognitive demand (choosing between neutral faces) and high cognitive demand (choosing between angry and happy faces – shown to induce an emotional bias). Decision and feedback phases were examined.

Results

Both patient groups showed suboptimal learning behaviour compared to HC and altered activity within a core reward network including occipital/lingual gyrus (decision), rostral Anterior Cingulate Cortex, left pre-central gyrus and Supplementary Motor Cortex (feedback). In the low cognitive demand condition, HS group showed significantly reduced activity in Supplementary Motor Area (SMA)/pre-SMA during the decision phase whilst activity was increased in LS group compared to HC. Recruitment of this region suggests a top-down compensatory mechanism important for control of positive symptoms. With additional cognitive demand (emotional vs. neutral contrast), HS patients showed further alterations within a subcortical network (increased left amygdala activity during decisions and reduced left pallidum and thalamus activity during feedback) compared to LS patients.

Conclusions

The findings suggest a core reward system deficit may be present in both patient groups, but persistent positive symptoms are associated with a specific dysfunction within a network needed to integrate social-emotional information with reward feedback.

Enhanced Instructed Fear Learning in Delusion-Proneness

Psychosis is associated with distorted perceptions and deficient bottom-up learning such as classical fear conditioning. This has been interpreted as reflecting imprecise priors in low-level predictive coding systems. Paradoxically, overly strong beliefs, such as overvalued beliefs and delusions, are also present in psychosis-associated states. In line with this, research has suggested that patients with psychosis and associated phenotypes rely more on high-order priors to interpret perceptual input. In this behavioural and fMRI study we studied two types of fear learning, i.e., instructed fear learning mediated by verbal suggestions about fear contingencies and classical fear conditioning mediated by low level associative learning, in delusion proneness—a trait in healthy individuals linked to psychotic disorders. Subjects were shown four faces out of which two were coupled with an aversive stimulation (CS+) while two were not (CS-) in a fear conditioning procedure. Before the conditioning, subjects were informed about the contingencies for two of the faces of each type, while no information was given for the two other faces. We could thereby study the effect of both classical fear conditioning and instructed fear learning. Our main outcome variable was evaluative rating of the faces. Simultaneously, fMRI-measurements were performed to study underlying mechanisms. We postulated that instructed fear learning, measured with evaluative ratings, is stronger in psychosis-related phenotypes, in contrast to classical fear conditioning that has repeatedly been shown to be weaker in these groups. In line with our hypothesis, we observed significantly larger instructed fear learning on a behavioural level in delusion-prone individuals (n = 20) compared to non-delusion-prone subjects (n = 23; n = 20 in fMRI study). Instructed fear learning was associated with a bilateral activation of lateral orbitofrontal cortex that did not differ significantly between groups. However, delusion-prone subjects showed a stronger functional connectivity between right lateral orbitofrontal cortex and regions processing fear and pain. Our results suggest that psychosis-related states are associated with a strong instructed fear learning in addition to previously reported weak classical fear conditioning. Given the similarity between nocebo paradigms and instructed fear learning, our results also have an impact on understanding why nocebo effects differ between individuals.

Understanding source monitoring subtypes and their relation to psychosis: a systematic review and meta-analysis

AIMS

Source monitoring (SM) is the metacognitive ability to determine the origin of one's experiences. SM is altered in primary psychiatric psychosis, although relationships between SM subtypes, other cognitive domains and symptoms are unclear. Our aims were to synthesize evidence comparing psychosis -with and without hallucinations- and healthy controls classifying SM subtypes by source discrimination (internal/external/reality monitoring) and stimulus modality (visual/auditory/imagined/performed).

METHODS

This systematic review adopted Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Meta-analyses Of Observational Studies in Epidemiology and Population, Intervention, Comparison and Outcomes guidelines. Core demographical and clinical parameters were extracted. Newcastle-Ottawa Scale was used as quality check. SM differences between (i) psychosis patients versus healthy controls and (ii) patients with versus without hallucinations were investigated via random-effect model meta-analysis. The primary effect size measure was standardized mean difference (SMD) in each SM subtype performance (error or accuracy). Heterogeneity, publication biases and meta-regressions were assessed.

RESULTS

Five thousand two hundred and fifty-six records were screened to finally include 44 studies (1566 patients, 1175 controls). Mean Newcastle-Ottawa score was 7.41 out of 9. Few studies measured SM associations with cognition (n = 9) and symptoms (n = 19), with heterogeneous findings. SM performance across all measures was reduced in psychosis versus healthy controls (SMD = 0.458). Internal SM (SMD: errors = 0.513; accuracy = 0.733) and imagined stimuli (SMD: errors = 0.688; accuracy = 0.978) were specifically impaired. Patients with versus without hallucinations showed SM deficits only for externalizing (SMD = 0.410) and imagined/auditory (SMD = 0.498/0.277) errors.

CONCLUSION

The proposed classifications highlight specific SM deficits for internal/imagined stimuli in psychosis, providing evidence-based indications to design and interpret future studies.

I overthink—Therefore I am not: An active inference account of altered sense of self and agency in depersonalisation disorder

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This paper examines potential mechanisms behind atypical sense of self and agency in Depersonalisation Disorder (DPD).

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Using Active Inference, we argue that aberrant somatosensory attenuation and precision weighting underlies DPD.

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Failures of somatosensory attenuation may disrupt the sense of agency and control over one’s perceptions and actions.

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In DPD, individuals may believe ‘another agent’ is controlling their thoughts, perceptions or actions.

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Unlike psychosis however, in DPD the individual maintains the full insight that the ‘other agent’ is ‘me’ (the self).

This paper considers the phenomenology of depersonalisation disorder, in relation to predictive processing and its associated pathophysiology. To do this, we first establish a few mechanistic tenets of predictive processing that are necessary to talk about phenomenal transparency, mental action, and self as subject. We briefly review the important role of ‘predicting precision’ and how this affords mental action and the loss of phenomenal transparency. We then turn to sensory attenuation and the phenomenal consequences of (pathophysiological) failures to attenuate or modulate sensory precision. We then consider this failure in the context of depersonalisation disorder. The key idea here is that depersonalisation disorder reflects the remarkable capacity to explain perceptual engagement with the world via the hypothesis that “I am an embodied perceiver, but I am not in control of my perception”. We suggest that individuals with depersonalisation may believe that ‘another agent’ is controlling their thoughts, perceptions or actions, while maintaining full insight that the ‘other agent’ is ‘me’ (the self). Finally, we rehearse the predictions of this formal analysis, with a special focus on the psychophysical and physiological abnormalities that may underwrite the phenomenology of depersonalisation.

The shallow cognitive map hypothesis: A hippocampal framework for thought disorder in schizophrenia

Memories are not formed in isolation. They are associated and organized into relational knowledge structures that allow coherent thought. Failure to express such coherent thought is a key hallmark of Schizophrenia. Here we explore the hypothesis that thought disorder arises from disorganized Hippocampal cognitive maps. In doing so, we combine insights from two key lines of investigation, one concerning the neural signatures of cognitive mapping, and another that seeks to understand lower-level cellular mechanisms of cognition within a dynamical systems framework. Specifically, we propose that multiple distinct pathological pathways converge on the shallowing of Hippocampal attractors, giving rise to disorganized Hippocampal cognitive maps and driving conceptual disorganization. We discuss the available evidence at the computational, behavioural, network, and cellular levels. We also outline testable predictions from this framework, including how it could unify major chemical and psychological theories of schizophrenia and how it can provide a rationale for understanding the aetiology and treatment of the disease.

The perceived salience of vocal emotions is dampened in non-clinical auditory verbal hallucinations

Introduction: Auditory verbal hallucinations (AVH) are a cardinal symptom of schizophrenia but are also reported in the general population without need for psychiatric care. Previous evidence suggests that AVH may reflect an imbalance of prior expectation and sensory information, and that altered salience processing is characteristic of both psychotic and non-clinical voice hearers. However, it remains to be shown how such an imbalance affects the categorisation of vocal emotions in perceptual ambiguity.Methods: Neutral and emotional nonverbal vocalisations were morphed along two continua differing in valence (anger; pleasure), each including 11 morphing steps at intervals of 10%. College students (N = 234) differing in AVH proneness (measured with the Launay-Slade Hallucination Scale) evaluated the emotional quality of the vocalisations.Results: Increased AVH proneness was associated with more frequent categorisation of ambiguous vocalisations as 'neutral', irrespective of valence. Similarly, the perceptual boundary for emotional classification was shifted by AVH proneness: participants needed more emotional information to categorise a voice as emotional.Conclusions: These findings suggest that emotional salience in vocalisations is dampened as a function of increased AVH proneness. This could be related to changes in the acoustic representations of emotions or reflect top-down expectations of less salient information in the social environment.

Evidence From Imaging Resilience Genetics for a Protective Mechanism Against Schizophrenia in the Ventral Visual Pathway

INTRODUCTION

Illuminating neurobiological mechanisms underlying the protective effect of recently discovered common genetic resilience variants for schizophrenia is crucial for more effective prevention efforts. Current models implicate adaptive neuroplastic changes in the visual system and their pro-cognitive effects as a schizophrenia resilience mechanism. We investigated whether common genetic resilience variants might affect brain structure in similar neural circuits.

METHOD

Using structural magnetic resonance imaging, we measured the impact of an established schizophrenia polygenic resilience score (PRSResilience) on cortical volume, thickness, and surface area in 101 healthy subjects and in a replication sample of 33 224 healthy subjects (UK Biobank).

FINDING

We observed a significant positive whole-brain correlation between PRSResilience and cortical volume in the right fusiform gyrus (FFG) (r = 0.35; P = .0004). Post-hoc analyses in this cluster revealed an impact of PRSResilience on cortical surface area. The replication sample showed a positive correlation between PRSResilience and global cortical volume and surface area in the left FFG.

CONCLUSION

Our findings represent the first evidence of a neurobiological correlate of a genetic resilience factor for schizophrenia. They support the view that schizophrenia resilience emerges from strengthening neural circuits in the ventral visual pathway and an increased capacity for the disambiguation of social and nonsocial visual information. This may aid psychosocial functioning, ameliorate the detrimental effects of subtle perceptual and cognitive disturbances in at-risk individuals, and facilitate coping with the cognitive and psychosocial consequences of stressors. Our results thus provide a novel link between visual cognition, the vulnerability-stress concept, and schizophrenia resilience models.

Perceptual reality monitoring: Neural mechanisms dissociating imagination from reality

There is increasing evidence that imagination relies on similar neural mechanisms as externally triggered perception. This overlap presents a challenge for perceptual reality monitoring: deciding what is real and what is imagined. Here, we explore how perceptual reality monitoring might be implemented in the brain. We first describe sensory and cognitive factors that could dissociate imagery and perception and conclude that no single factor unambiguously signals whether an experience is internally or externally generated. We suggest that reality monitoring is implemented by higher-level cortical circuits that evaluate first-order sensory and cognitive factors to determine the source of sensory signals. According to this interpretation, perceptual reality monitoring shares core computations with metacognition. This multi-level architecture might explain several types of source confusion as well as dissociations between simply knowing whether something is real and actually experiencing it as real. We discuss avenues for future research to further our understanding of perceptual reality monitoring, an endeavour that has important implications for our understanding of clinical symptoms as well as general cognitive function.

Association Between Failures in Perceptual Updating and the Severity of Psychosis in Schizophrenia

IMPORTANCE

Recent accounts suggest that delusions and hallucinations may result from alterations in how prior knowledge is integrated with new information, but experimental evidence supporting this idea has been complex and inconsistent. Evidence from a simpler perceptual task would make clear whether psychotic symptoms are associated with overreliance on prior information and impaired updating.

OBJECTIVE

To investigate whether individuals with schizophrenia or schizoaffective disorder (PSZ) and healthy control individuals (HCs) differ in the ability to update their beliefs based on evidence in a relatively simple perceptual paradigm.

DESIGN, SETTING, AND PARTICIPANTS

This case-control study included individuals who met DSM-IV criteria for PSZ and matched HC participants in 2 independent samples. The PSZ group was recruited from the Maryland Psychiatric Research Center, Yale University, and community clinics, and the HC group was recruited from the community. To test perceptual updating, a random dot kinematogram paradigm was implemented in which dots moving coherently in a single direction were mixed with randomly moving dots. On 50% of trials, the direction of coherent motion changed by 90° midway through the trial. Participants were asked to report the direction perceived at the end of the trial. The Peters Delusions Inventory and Brief Psychiatric Rating Scale (BPRS) were used to quantify the severity of positive symptoms. Data were collected from September 2018 to March 2020 and were analyzed from approximately March 2020 to March 2021.

MAIN OUTCOMES AND MEASURES

Critical measures included the proportion of responses centered around the initial direction vs the subsequent changed direction and the overall precision of motion perception and reaction times.

RESULTS

A total of 48 participants were included in the PSZ group (31 [65%] male; mean [SD] age, 36.56 [9.76] years) and 36 in the HC group (22 [61%] male; mean [SD] age, 35.67 [10.74] years) in the original sample. An independent replication sample included 42 participants in the PSZ group (29 [69%] male; mean [SD] age, 33.98 [11.03] years) and 34 in the HC group (20 [59%] male; mean [SD] age, 34.29 [10.44] years). In line with previous research, patients with PSZ were less precise and had slower reaction times overall. The key finding was that patients with PSZ were significantly more likely (original sample: mean, 27.88 [95% CI, 24.19-31.57]; replication sample: mean, 26.70 [95% CI, 23.53-29.87]) than HC participants (original sample: mean, 18.86 [95% CI, 16.56-21.16]; replication sample: mean, 15.67 [95% CI, 12.61-18.73]) to report the initial motion direction rather than the final one. Moreover, the tendency to report the direction of initial motion correlated with the degree of conviction on the Peters Delusions Inventory (original sample: r = 0.32 [P = .05]; replication sample: r = 0.30 [P = .05]) and the Brief Psychiatric Rating Scale Reality Distortion score (original sample: r = 0.55 [P = .001]; replication sample: r = 0.35 [P = .03]) and severity of hallucinations (original sample: r = 0.39 [P = .02]; replication sample: r = 0.30 [P = .05]).

CONCLUSIONS AND RELEVANCE

The findings of this case-control study suggest that the severity of psychotic symptoms is associated with a tendency to overweight initial information over incoming sensory evidence. These results are consistent with predictive coding accounts of the origins of positive symptoms and suggest that deficits in very elementary perceptual updating may be a critical mechanism in psychosis.

GABAA and NMDA receptor density alterations and their behavioral correlates in the gestational methylazoxymethanol acetate model for schizophrenia

Hippocampal hyperactivity driven by GABAergic interneuron deficits and NMDA receptor hypofunction is associated with the hyperdopaminergic state often observed in schizophrenia. Furthermore, previous research in the methylazoxymethanol acetate (MAM) rat model has demonstrated that repeated peripubertal diazepam administration can prevent the emergence of adult hippocampal hyperactivity, dopamine-system hyperactivity, and associated psychosis-relevant behaviors. Here, we sought to characterize hippocampal GABAA and NMDA receptors in MAM-treated rats and to elucidate the receptor mechanisms underlying the promising effects of peripubertal diazepam exposure. Quantitative receptor autoradiography was used to measure receptor density in the dorsal hippocampus CA1, ventral hippocampus CA1, and ventral subiculum. Specifically, [3H]-Ro15-4513 was used to quantify the density of α5GABAA receptors (α5GABAAR), [3H]-flumazenil to quantify α1-3;5GABAAR, and [3H]-MK801 to quantify NMDA receptors. MAM rats exhibited anxiety and schizophrenia-relevant behaviors as measured by elevated plus maze and amphetamine-induced hyperlocomotion (AIH), although diazepam only partially rescued these behaviors. α5GABAAR density was reduced in MAM-treated rats in all hippocampal sub-regions, and negatively correlated with AIH. Ventral hippocampus CA1 α5GABAAR density was positively correlated with anxiety-like behavior. Dorsal hippocampus CA1 NMDA receptor density was increased in MAM-treated rats, and positively correlated with AIH. [3H]-flumazenil revealed no significant effects. Finally, we found no significant effect of diazepam treatment on receptor densities, potentially related to the only partial rescue of schizophrenia-relevant phenotypes. Overall, our findings provide first evidence of α5GABAAR and NMDA receptor abnormalities in the MAM model, suggesting that more selective pharmacological agents may become a novel therapeutic mechanism in schizophrenia.

A review of risky decision-making in psychosis-spectrum disorders

The investigation of risky decision-making has a prominent place in clinical science, with sundry behavioral tasks aimed at empirically quantifying the psychological construct of risk-taking. However, use of differing behavioral tasks has resulted in lack of agreement on risky decision-making within psychosis-spectrum disorders, as findings fail to converge upon the typical, binary conceptualization of increased risk-seeking or risk-aversion. The current review synthesizes the behavioral, risky decision-making literature to elucidate how specific task parameters may contribute to differences in task performance, and their associations with psychosis symptomatology and cognitive functioning. A paring of the literature suggests that: 1) Explicit risk-taking may be characterized by risk imperception, evidenced by less discrimination between choices of varying degrees of risk, potentially secondary to cognitive deficits. 2) Ambiguous risk-taking findings are inconclusive with few published studies. 3) Uncertain risk-taking findings, consistently interpreted as more risk-averse, have not parsed risk attitudes from confounding processes that may impact decision-making (e.g. risk imperception, reward processing, motivation). Thus, overgeneralized interpretations of task-specific risk-seeking/aversion should be curtailed, as they may fail to appropriately characterize decision-making phenomena. Future research in psychosis-spectrum disorders would benefit from empirically isolating contributions of specific processes during risky decision-making, including the newly hypothesized risk imperception.

The phenomenology of auditory verbal hallucinations in schizophrenia and the challenge from pseudohallucinations

In trying to make sense of the extensive phenomenological variation of first-personal reports on auditory verbal hallucinations, the concept of pseudohallucination is originally introduced to designate any hallucinatory-like phenomena not exhibiting some of the paradigmatic features of "genuine" hallucinations. After its introduction, Karl Jaspers locates the notion of pseudohallucinations into the auditory domain, appealing to a distinction between hallucinatory voices heard within the subjective inner space (pseudohallucination) and voices heard in the outer external space (real hallucinations) with differences in their sensory richness. Jaspers' characterization of the term has been the target of a number of phenomenological, conceptual and empirically-based criticisms. From this latter point of view, it has been claimed that the concept cannot capture distinct phenomena at the neurobiological level. Over the last years, the notion of pseudohallucination seems to be falling into disuse as no major diagnostic system seems to refer to it. In this paper, we propose that even if the concept of pseudohallucination is not helpful to differentiate distinct phenomena at the neurobiological level, the inner/outer distinction highlighted by Jaspers' characterization of the term still remains an open explanatory challenge for dominant theories about the neurocognitive origin of auditory verbal hallucinations. We call this, "the challenge from pseudohallucinations". After exploring this issue in detail, we propose some phenomenological, conceptual, and empirical paths for future research that might help to build up a more contextualized and dynamic view of auditory verbal hallucinatory phenomena.

Models of Dynamic Belief Updating in Psychosis-A Review Across Different Computational Approaches

To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.

The Transdiagnostic Relevance of Self-Other Distinction to Psychiatry Spans Emotional, Cognitive and Motor Domains

Self-other distinction refers to the ability to distinguish between our own and other people's physical and mental states (actions, perceptions, emotions etc.). Both the right temporo-parietal junction and brain areas associated with the human mirror neuron system are likely to critically influence self-other distinction, given their respective contributions to theory of mind and embodied empathy. The degree of appropriate self-other distinction will vary according to the exact social situation, and how helpful it is to feel into, or remain detached from, another person's mental state. Indeed, the emotional resonance that we can share with others affords the gift of empathy, but over-sharing may pose a downside, leading to a range of difficulties from personal distress to paranoia, and perhaps even motor tics and compulsions. The aim of this perspective paper is to consider how evidence from behavioral and neurophysiological studies supports a role for problems with self-other distinction in a range of psychiatric symptoms spanning the emotional, cognitive and motor domains. The various signs and symptoms associated with problematic self-other distinction comprise both maladaptive and adaptive (compensatory) responses to dysfunction within a common underlying neuropsychological mechanism, compelling the adoption of more holistic transdiagnostic therapeutic approaches within Psychiatry.

A computational lens on menopause-associated psychosis

Psychotic episodes are debilitating disease states that can cause extreme distress and impair functioning. There are sex differences that drive the onset of these episodes. One difference is that, in addition to a risk period in adolescence and early adulthood, women approaching the menopause transition experience a second period of risk for new-onset psychosis. One leading hypothesis explaining this menopause-associated psychosis (MAP) is that estrogen decline in menopause removes a protective factor against processes that contribute to psychotic symptoms. However, the neural mechanisms connecting estrogen decline to these symptoms are still not well understood. Using the tools of computational psychiatry, links have been proposed between symptom presentation and potential algorithmic and biological correlates. These models connect changes in signaling with symptom formation by evaluating changes in information processing that are not easily observable (latent states). In this manuscript, we contextualize the observed effects of estrogen (decline) on neural pathways implicated in psychosis. We then propose how estrogen could drive changes in latent states giving rise to cognitive and psychotic symptoms associated with psychosis. Using computational frameworks to inform research in MAP may provide a systematic method for identifying patient-specific pathways driving symptoms and simultaneously refine models describing the pathogenesis of psychosis across all age groups.