Explaining Delusions: Reducing Uncertainty Through Basic and Computational Neuroscience

How dopamine shapes trust beliefs

Learning whom to trust is integral for healthy relationships and social cohesion, and atypicalities in trust learning are common across a range of clinical conditions, including schizophrenia spectrum disorders, Parkinson's disease, and depression. Persecutory delusions - rigid, unfounded beliefs that others are intending to harm oneself - significantly impact affected individuals' lives as they are associated with a range of negative health outcomes, including suicidal behaviour and relapse. Recent advances in computational modelling and psychopharmacology have significantly extended our understanding of the brain bases of dynamic trust learning, and the neuromodulator dopamine has been suggested to play a key role in this. However, the specifics of this role on a computational and neurobiological level remain to be fully established. The current review article provides a comprehensive summary of novel conceptual developments and empirical findings regarding the computational role of dopamine in social learning processes. Research findings strongly suggest a conceptual shift, from dopamine as a reward mechanism to a teaching signal indicating which information is relevant for learning, and shed light on the neurocomputational mechanisms via which antipsychotics may alleviate symptoms of aberrant social learning processes such as persecutory delusions. Knowledge gaps and inconsistencies in the extant literature are examined and the most pressing issues highlighted, laying the foundation for future research that will further advance our understanding of the neuromodulation of social belief updating processes.

Embracing variability in the search for biological mechanisms of psychiatric illness

Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of patient and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that underpins psychiatric populations. Embracing such variability is necessary to understand pathophysiological mechanisms and develop more targeted and effective treatments.

Delusional Unreality and Predictive Processing

BACKGROUND

Phenomenological psychopathologists have recently highlighted how people with delusions experience multiple realities (delusional and non-delusional) and have suggested this double bookkeeping cannot be explained via predictive processing. Here, we present data from Kamin blocking and extinction learning that show how predictive processing might, in principle, explain a pervasive sense of dual reality.

METHODS

This cross-sectional study involved three participant groups: patients with schizophrenia (SZ; n=42), healthy participants with elevated esoteric beliefs (EEB; clairaudient psychics; n=31), and heathy controls (with neither illness nor significant delusional ideation, n=62). We examined belief formation using a Kamin blocking causal learning task with extinction, and delusions with the 40-item Peters Delusion Inventory, specifically the unreality item: "Do things around you ever feel unreal, as though it was all part of an experiment?" as a proxy for unreality experiences and beliefs. A clinician also assessed symptoms with a structured clinical interview.

RESULTS

Some people with schizophrenia did not report a sense of unreality, and some people with EEB (but no psychotic illness) reported unreality experiences. No HC endorsed them (despite endorsing other delusion-like beliefs). Unreality experiences in clinical delusions and non-clinical delusion-like beliefs were associated with different types of aberrant prediction error processing.

CONCLUSIONS

These data suggest how predictive processing might explain the sense of unreality. They indicate that different prediction error dysfunctions are associated with delusions with different contents. In this case we have used predictive processing to address a salient issue raised by phenomenological colleagues, namely the impact of psychosis on experiences of and beliefs about reality.

Bridging perspectives - A review and synthesis of 53 theoretical models of delusions

The degree to which numerous existing models of delusion formation disagree or propose common mechanisms remains unclear. To achieve a comprehensive understanding of delusion aetiology, we summarised 53 theoretical models of delusions extracted from a systematic literature search. We identified central aspects and unique or overarching features of five core perspectives: cognitive (n = 22), associative learning (n = 4), social (n = 6), neurobiological (n = 6), and Bayesian inference (n = 15). These perspectives differ in foci and mechanistic explanations. Whereas some postulate delusions to result from associative and operant learning, others assume a disbalance in the integration of prior beliefs and sensory input or emphasise the relevance of information processing biases. Postulated moderators range from maladaptive generalised beliefs over neurocognitive impairment to dopamine, stress, and affective dysregulation. The models also differ in whether they attempt to explain delusion formation in general or the delusional content (i.e., persecutory). Finally, some models postulate functional aspects of delusions, such as insight relief. Despite their differences, the perspectives converge on the idea that delusions form as an explanation for an experienced ambiguity. Building on this common ground, we propose an integrative framework incorporating essential mechanistic explanations from each perspective and discuss its implications for research and clinical practice.

A prefrontal thalamocortical readout for conflict-related executive dysfunction in schizophrenia

Executive dysfunction is a prominent feature of schizophrenia and may drive core symptoms. Dorsolateral prefrontal cortex (dlPFC) deficits have been linked to schizophrenia executive dysfunction, but mechanistic details critical for treatment development remain unclear. Here, capitalizing on recent animal circuit studies, we develop a task predicted to engage human dlPFC and its interactions with the mediodorsal thalamus (MD). We find that individuals with schizophrenia exhibit selective performance deficits when attention is guided by conflicting cues. Task performance correlates with lateralized MD-dlPFC functional connectivity, identifying a neural readout that predicts susceptibility to conflict during working memory in a larger independent schizophrenia cohort. In healthy subjects performing a probabilistic reversal task, this MD-dlPFC network predicts switching behavior. Overall, our three independent experiments introduce putative biomarkers for executive function in schizophrenia and highlight animal circuit studies as inspiration for the development of clinically relevant readouts.

Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.

Different learning aberrations relate to delusion-like beliefs with different contents

The prediction error account of delusions has had success. However, its explanation of delusions with different contents has been lacking. Persecutory delusions and paranoia are the common unfounded beliefs that others have harmful intentions towards us. Other delusions include believing that one's thoughts or actions are under external control or that events in the world have specific personal meaning. We compare learning in two different cognitive tasks, probabilistic reversal learning and Kamin blocking, that have relationships to paranoid and non-paranoid delusion-like beliefs, respectively. We find that clinical high-risk status alone does not result in different behavioural results in the probabilistic reversal learning task but that an individual's level of paranoia is associated with excessive switching behaviour. During the Kamin blocking task, paranoid individuals learned inappropriately about the blocked cue. However, they also had decreased learning about the control cue, suggesting more general learning impairments. Non-paranoid delusion-like belief conviction (but not paranoia) was associated with aberrant learning about the blocked cue but intact learning about the control cue, suggesting specific impairments in learning related to cue combination. We fit task-specific computational models separately to behavioural data to explore how latent parameters vary within individuals between tasks and how they can explain symptom-specific effects. We find that paranoia is associated with low learning rates in the probabilistic reversal learning task and the blocking task. Non-paranoid delusion-like belief conviction is instead related to parameters controlling the degree and direction of similarity between cue updating during simultaneous cue presentation. These results suggest that paranoia and other delusion-like beliefs involve dissociable deficits in learning and belief updating, which, given the transdiagnostic status of paranoia, might have differential utility in predicting psychosis.

Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia

Beliefs-attitudes toward some state of the environment-guide action selection and should be robust to variability but sensitive to meaningful change. Beliefs about volatility (expectation of change) are associated with paranoia in humans, but the brain regions responsible for volatility beliefs remain unknown. The orbitofrontal cortex (OFC) is central to adaptive behavior, whereas the magnocellular mediodorsal thalamus (MDmc) is essential for arbitrating between perceptions and action policies. We assessed belief updating in a three-choice probabilistic reversal learning task following excitotoxic lesions of the MDmc (n = 3) or OFC (n = 3) and compared performance with that of unoperated monkeys (n = 14). Computational analyses indicated a double dissociation: MDmc, but not OFC, lesions were associated with erratic switching behavior and heightened volatility belief (as in paranoia in humans), whereas OFC, but not MDmc, lesions were associated with increased lose-stay behavior and reward learning rates. Given the consilience across species and models, these results have implications for understanding paranoia.

Assumed shared belief about conspiracy theories in social networks protects paranoid individuals against distress

Paranoia is the belief that others intend you harm. It is related to conspiracy theories, wherein those others represent an organized faction, coordinating the harm against self and others, and violating societal norms. Current psychological studies of paranoid conspiracy theorizing focus either on the individual or their broader social network. Likewise, theories of belief formation and updating often contain individual level processes as well as broader interpersonal and organizational factors. Here we examine paranoia and conspiracy theorizing in terms of individual behavioral predictors (performance on a probabilistic reversal learning task which assays belief updating) as well as social sensing: we ask participants to report the features of their social network, including whether their friends and acquaintances share their paranoid conspiratorial beliefs. We find that people who believe paranoid conspiracy theories expect more volatility during the task. They also assume that members of their social network share their paranoid beliefs. Critically, those participants with larger social networks and greater assumed shared belief tend to harbor their conspiratorial beliefs with less emotional distress and expect less volatility in the task. This is evidence that, like political and religious beliefs, conspiracy theories may flourish under a sacred canopy of belief consensus. These data suggest that friends and acquaintances may serve as sources of credulity and moving between them may sustain conspiracy beliefs when there is detraction. This hybrid individual/social account may shed light on clinical paranoia and persecutory delusion, wherein disability is defined normatively, and social supports are fewer.

Aberrant adapting of beliefs under stress: a mechanism relevant to the formation of paranoia?

BACKGROUND

Difficulties in the ability to adapt beliefs in the face of new information are associated with psychosis and its central symptom - paranoia. As cognitive processes and psychotic symptoms are both known to be sensitive to stress, the present study investigated the exact associations between stress, adapting of beliefs [reversal learning (RL), bias against disconfirmatory evidence (BADE), and jumping to conclusions (JTC)] and paranoia. We hypothesized that paranoia would increase under stress and that difficulties in adapting of beliefs would mediate or moderate the link between stress and paranoia. Furthermore, we hypothesized that the investigated effects would be strongest in the group of individuals diagnosed with a psychotic disorder.

METHODS

We exposed 155 participants (38 diagnosed with a psychotic disorder, 40 individuals with attenuated psychotic symptoms, 39 clinical controls diagnosed with an obsessive-compulsive disorder, and 38 healthy controls) to a control condition and a stress condition, in which we assessed their levels of paranoia and their ability to adapt beliefs. We applied multilevel models to analyze the data.

RESULTS

Paranoia was higher in the stress condition than in the control condition, b = 1.142, s.e. = 0.338, t(150) = 3.381, p < 0.001. RL, BADE, and JTC did not differ between conditions and did not mediate or moderate the association between stress and paranoia (all p_s > 0.05).

CONCLUSIONS

The results support the assumption that stress triggers paranoia. However, the link between stress and paranoia does not seem to be affected by the ability to adapt beliefs.

Formalising social representation to explain psychiatric symptoms

Recent work in social cognition has moved beyond a focus on how people process social rewards to examine how healthy people represent other agents and how this is altered in psychiatric disorders. However, formal modelling of social representation has not kept pace with these changes, impeding our understanding of how core aspects of social cognition function, and fail, in psychopathology. Here, we suggest that belief-based computational models provide a basis for an integrated sociocognitive approach to psychiatry, with the potential to address important but unexamined pathologies of social representation, such as maladaptive schemas and illusory social agents.

Circuit-Based Approaches to Understanding Corticostriatothalamic Dysfunction Across the Psychosis Continuum

Dopamine is known to play a role in the pathogenesis of psychotic symptoms, but the mechanisms driving dopaminergic dysfunction in psychosis remain unclear. Considerable attention has focused on the role of corticostriatothalamic (CST) circuits, given that they regulate and are modulated by the activity of dopaminergic cells in the midbrain. Preclinical studies have proposed multiple models of CST dysfunction in psychosis, each prioritizing different brain regions and pathophysiological mechanisms. A particular challenge is that CST circuits have undergone considerable evolutionary modification across mammals, complicating comparisons across species. Here, we consider preclinical models of CST dysfunction in psychosis and evaluate the degree to which they are supported by evidence from human resting-state functional magnetic resonance imaging studies conducted across the psychosis continuum, ranging from subclinical schizotypy to established schizophrenia. In partial support of some preclinical models, human studies indicate that dorsal CST and hippocampal-striatal functional dysconnectivity are apparent across the psychosis spectrum and may represent a vulnerability marker for psychosis. In contrast, midbrain dysfunction may emerge when symptoms warrant clinical assistance and may thus be a trigger for illness onset. The major difference between clinical and preclinical findings is the strong involvement of the dorsal CST in the former, consistent with an increasing prominence of this circuitry in the primate brain. We close by underscoring the need for high-resolution characterization of phenotypic heterogeneity in psychosis to develop a refined understanding of how the dysfunction of specific circuit elements gives rise to distinct symptom profiles.

Belief Updating and Paranoia in Individuals With Schizophrenia

BACKGROUND

Persecutory delusions are among the most common delusions in schizophrenia and represent the extreme end of the paranoia continuum. Paranoia is accompanied by significant worry and distress. Identifying cognitive mechanisms underlying paranoia is critical for advancing treatment. We hypothesized that aberrant belief updating, which is related to paranoia in human and animal models, would also contribute to persecutory beliefs in individuals with schizophrenia.

METHODS

Belief updating was assessed in 42 participants with schizophrenia and 44 healthy control participants using a 3-option probabilistic reversal learning task. Hierarchical Gaussian Filter was used to estimate computational parameters of belief updating. Paranoia was measured using the Positive and Negative Syndrome Scale and the revised Green et al. Paranoid Thoughts Scale. Unusual thought content was measured with the Psychosis Symptom Rating Scale and the Peters et al. Delusions Inventory. Worry was measured using the Dunn Worry Questionnaire.

RESULTS

Paranoia was significantly associated with elevated win-switch rate and prior beliefs about volatility both in schizophrenia and across the whole sample. These relationships were specific to paranoia and did not extend to unusual thought content or measures of anxiety. We observed a significant indirect effect of paranoia on the relationship between prior beliefs about volatility and worry.

CONCLUSIONS

This work provides evidence that relationships between belief updating parameters and paranoia extend to schizophrenia, may be specific to persecutory beliefs, and contribute to theoretical models implicating worry in the maintenance of persecutory delusions.

Meta-analysis of human prediction error for incentives, perception, cognition, and action

Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species and has inspired the construction of artificial intelligence that can outperform humans. However, there is still much to learn. Here, we leverage the wealth of human PE data acquired in the functional neuroimaging setting in service of a deeper understanding, using an MKDA (multi-level kernel-based density) meta-analysis. Studies were identified with Google Scholar, and we included studies with healthy adult participants that reported activation coordinates corresponding to PEs published between 1999-2018. Across 264 PE studies that have focused on reward, punishment, action, cognition, and perception, consistent with domain-general theoretical models of prediction error we found midbrain PE signals during cognitive and reward learning tasks, and an insula PE signal for perceptual, social, cognitive, and reward prediction errors. There was evidence for domain-specific error signals--in the visual hierarchy during visual perception, and the dorsomedial prefrontal cortex during social inference. We assessed bias following prior neuroimaging meta-analyses and used family-wise error correction for multiple comparisons. This organization of computation by region will be invaluable in building and testing mechanistic models of cognitive function and dysfunction in machines, humans, and other animals. Limitations include small sample sizes and ROI masking in some included studies, which we addressed by weighting each study by sample size, and directly comparing whole brain vs. ROI-based results.

Reinforcement learning detuned in addiction: integrative and translational approaches

Suboptimal decision-making strategies have been proposed to contribute to the pathophysiology of addiction. Decision-making, however, arises from a collection of computational components that can independently influence behavior. Disruptions in these different components can lead to decision-making deficits that appear similar behaviorally, but differ at the computational, and likely the neurobiological, level. Here, we discuss recent studies that have used computational approaches to investigate the decision-making processes underlying addiction. Studies in animal models have found that value updating following positive, but not negative, outcomes is predictive of drug use, whereas value updating following negative, but not positive, outcomes is disrupted following drug self-administration. We contextualize these findings with studies on the circuit and biological mechanisms of decision-making to develop a framework for revealing the biobehavioral mechanisms of addiction.

Schizophrenia

Schizophrenia, characterised by psychotic symptoms and in many cases social and occupational decline, remains an aetiological and therapeutic challenge. Contrary to popular belief, the disorder is modestly more common in men than in women. Nor is the outcome uniformly poor. A division of symptoms into positive, negative, and disorganisation syndromes is supported by factor analysis. Catatonic symptoms are not specific to schizophrenia and so-called first rank symptoms are no longer considered diagnostically important. Cognitive impairment is now recognised as a further clinical feature of the disorder. Lateral ventricular enlargement and brain volume reductions of around 2% are established findings. Brain functional changes occur in different subregions of the frontal cortex and might ultimately be understandable in terms of disturbed interaction among large-scale brain networks. Neurochemical disturbance, involving dopamine function and glutamatergic N-methyl-D-aspartate receptor function, is supported by indirect and direct evidence. The genetic contribution to schizophrenia is now recognised to be largely polygenic. Birth and early life factors also have an important aetiological role. The mainstay of treatment remains dopamine receptor-blocking drugs; a psychological intervention, cognitive behavioural therapy, has relatively small effects on symptoms. The idea that schizophrenia is better regarded as the extreme end of a continuum of psychotic symptoms is currently influential. Other areas of debate include cannabis and childhood adversity as causative factors, whether there is progressive brain change after onset, and the long-term success of early intervention initiatives.

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.

Paranoia and belief updating during the COVID-19 crisis

The COVID-19 pandemic has made the world seem less predictable. Such crises can lead people to feel that others are a threat. Here, we show that the initial phase of the pandemic in 2020 increased individuals' paranoia and made their belief updating more erratic. A proactive lockdown made people's belief updating less capricious. However, state-mandated mask-wearing increased paranoia and induced more erratic behaviour. This was most evident in states where adherence to mask-wearing rules was poor but where rule following is typically more common. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable. People who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines and the QAnon conspiracy theories. These beliefs were associated with erratic task behaviour and changed priors. Taken together, we found that real-world uncertainty increases paranoia and influences laboratory task behaviour.

Conspiratorial Thinking During COVID-19: The Roles of Paranoia, Delusion-Proneness, and Intolerance of Uncertainty

The COVID-19 global pandemic has left many feeling a sense of profound uncertainty about their world, safety, and livelihood. Sources espousing misinformation and conspiracy theories frequently offer information that can help make sense of this uncertainty. Individuals high in intolerance of uncertainty (IU) may be particularly impacted by the impoverished epistemic environment and may thus be more drawn to conspiratorial thinking (CT). In the present work, we show across 2 studies (N = 519) that COVID-19-specific CT is associated with higher levels of IU as well as delusion-proneness, and paranoia. Furthermore, delusion-proneness and paranoia explained the relationship between IU and CT and emerged as independent partial correlates of CT even when controlling for other facets of schizotypy. In contrast, anxiety did not explain the relationship between IU and CT. Overall, our findings highlight the importance of individual differences in IU, delusion-proneness and paranoia in the development of CT in the context of the acute uncertainty of a global crisis, in which conspiracy theories are more prevalent and salient. Informational intervention designs may benefit from leveraging the body of work demonstrating the efficacy of targeting IU to incite meaningful changes in thinking.

Neural Substrates of Incidental Associations and Mediated Learning: The Role of Cannabinoid Receptors

The ability to form associations between different stimuli in the environment to guide adaptive behavior is a central element of learning processes, from perceptual learning in humans to Pavlovian conditioning in animals. Like so, classical conditioning paradigms that test direct associations between low salience sensory stimuli and high salience motivational reinforcers are extremely informative. However, a large part of everyday learning cannot be solely explained by direct conditioning mechanisms - this includes to a great extent associations between individual sensory stimuli, carrying low or null immediate motivational value. This type of associative learning is often described as incidental learning and can be captured in animal models through sensory preconditioning procedures. Here we summarize the evolution of research on incidental and mediated learning, overview the brain systems involved and describe evidence for the role of cannabinoid receptors in such higher-order learning tasks. This evidence favors a number of contemporary hypotheses concerning the participation of the endocannabinoid system in psychosis and psychotic experiences and provides a conceptual framework for understanding how the use of cannabinoid drugs can lead to altered perceptive states.

Unlocking the reinforcement-learning circuits of the orbitofrontal cortex

Neuroimaging studies have consistently identified the orbitofrontal cortex (OFC) as being affected in individuals with neuropsychiatric disorders. OFC dysfunction has been proposed to be a key mechanism by which decision-making impairments emerge in diverse clinical populations, and recent studies employing computational approaches have revealed that distinct reinforcement-learning mechanisms of decision-making differ among diagnoses. In this perspective, we propose that these computational differences may be linked to select OFC circuits and present our recent work that has used a neurocomputational approach to understand the biobehavioral mechanisms of addiction pathology in rodent models. We describe how combining translationally analogous behavioral paradigms with reinforcement-learning algorithms and sophisticated neuroscience techniques in animals can provide critical insights into OFC pathology in biobehavioral disorders. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Leveraging Basic Science for the Clinic-From Bench to Bedside

Importance

The tools and insights of behavioral neuroscience grow apace, yet their clinical application is lagging.

Observations

This article suggests that associative learning theory may be the algorithmic bridge to connect a burgeoning understanding of the brain with the challenges to the mind with which all clinicians and researchers are concerned.

Conclusions and Relevance

Instead of giving up, talking past one another, or resting on the laurels of face validity, a consilient and collaborative approach is suggested: visiting laboratory meetings and clinical rounds and attempting to converse in the language of behavior and cognition to better understand and ultimately treat patients.

Paranoia and belief updating during a crisis

The 2019 coronavirus (COVID-19) pandemic has made the world seem unpredictable. During such crises we can experience concerns that others might be against us, culminating perhaps in paranoid conspiracy theories. Here, we investigate paranoia and belief updating in an online sample (N=1,010) in the United States of America (U.S.A). We demonstrate the pandemic increased individuals’ self-rated paranoia and rendered their task-based belief updating more erratic. Local lockdown and reopening policies, as well as culture more broadly, markedly influenced participants’ belief-updating: an early and sustained lockdown rendered people’s belief updating less capricious. Masks are clearly an effective public health measure against COVID-19. However, state-mandated mask wearing increased paranoia and induced more erratic behaviour. Remarkably, this was most evident in those states where adherence to mask wearing rules was poor but where rule following is typically more common. This paranoia may explain the lack of compliance with this simple and effective countermeasure. Computational analyses of participant behaviour suggested that people with higher paranoia expected the task to be more unstable, but at the same time predicted more rewards. In a follow-up study we found people who were more paranoid endorsed conspiracies about mask-wearing and potential vaccines – again, mask attitude and conspiratorial beliefs were associated with erratic task behaviour and changed priors. Future public health responses to the pandemic might leverage these observations, mollifying paranoia and increasing adherence by tempering people’s expectations of other’s behaviour, and the environment more broadly, and reinforcing compliance.

Derationalizing Delusions

Because of the traditional conceptualization of delusion as “irrational belief,” cognitive models of delusions largely focus on impairments to domain-general reasoning. Nevertheless, current rationality-impairment models do not account for the fact that (a) equivalently irrational beliefs can be induced through adaptive social cognitive processes, reflecting social integration rather than impairment; (b) delusions are overwhelmingly socially themed; and (c) delusions show a reduced sensitivity to social context both in terms of how they are shaped and how they are communicated. Consequently, we argue that models of delusions need to include alteration to coalitional cognition—processes involved in affiliation, group perception, and the strategic management of relationships. This approach has the advantage of better accounting for both content (social themes) and form (fixity) of delusion. It is also supported by the established role of mesolimbic dopamine in both delusions and social organization and the ongoing reconceptualization of belief as serving a social organizational function.

Using internal memory representations in associative learning to study hallucination-like phenomenon

Studies of Pavlovian conditioning have enriched our understanding of how relations among events can adaptively guide behavior through the formation and use of internal mental representations. In this review, we illustrate how internal representations flexibly integrate new updated information in reinforcer revaluation to influence relationships to impact actions and outcomes. We highlight representation-mediated learning to show the similarities in properties and functions between internally generated and directly activated representations, and how normal perception of internal representations could contribute to hallucinations. Converging evidence emerges from recent behavioral and neural activation studies using animal models of schizophrenia as well as clinical studies in patients to support increased tendencies in these populations to evoke internal representations from prior associative experience that approximate hallucination-like percepts. The heightened propensity is dependent on dopaminergic activation which is known to be sensitive to hippocampal overexcitability, a condition that has been observed in patients with psychosis. This presents a network that overlaps with cognitive neural circuits and offers a fresh approach for the development of therapeutic interventions targeting psychosis.

Enhancing Psychosis Risk Prediction Through Computational Cognitive Neuroscience

Research suggests that early identification and intervention with individuals at clinical high risk (CHR) for psychosis may be able to improve the course of illness. The first generation of studies suggested that the identification of CHR through the use of specialized interviews evaluating attenuated psychosis symptoms is a promising strategy for exploring mechanisms associated with illness progression, etiology, and identifying new treatment targets. The next generation of research on psychosis risk must address two major limitations: (1) interview methods have limited specificity, as recent estimates indicate that only 15%-30% of individuals identified as CHR convert to psychosis and (2) the expertise needed to make CHR diagnosis is only accessible in a handful of academic centers. Here, we introduce a new approach to CHR assessment that has the potential to increase accessibility and positive predictive value. Recent advances in clinical and computational cognitive neuroscience have generated new behavioral measures that assay the cognitive mechanisms and neural systems that underlie the positive, negative, and disorganization symptoms that are characteristic of psychotic disorders. We hypothesize that measures tied to symptom generation will lead to enhanced sensitivity and specificity relative to interview methods and the cognitive intermediate phenotype measures that have been studied to date that are typically indicators of trait vulnerability and, therefore, have a high false positive rate for conversion to psychosis. These new behavioral measures have the potential to be implemented on the internet and at minimal expense, thereby increasing accessibility of assessments.

Paranoia as a deficit in non-social belief updating

Paranoia is the belief that harm is intended by others. It may arise from selective pressures to infer and avoid social threats, particularly in ambiguous or changing circumstances. We propose that uncertainty may be sufficient to elicit learning differences in paranoid individuals, without social threat. We used reversal learning behavior and computational modeling to estimate belief updating across individuals with and without mental illness, online participants, and rats chronically exposed to methamphetamine, an elicitor of paranoia in humans. Paranoia is associated with a stronger prior on volatility, accompanied by elevated sensitivity to perceived changes in the task environment. Methamphetamine exposure in rats recapitulates this impaired uncertainty-driven belief updating and rigid anticipation of a volatile environment. Our work provides evidence of fundamental, domain-general learning differences in paranoid individuals. This paradigm enables further assessment of the interplay between uncertainty and belief-updating across individuals and species.

Everyone has had fleeting concerns that others might be against them at some point in their lives. Sometimes these concerns can escalate into paranoia and become debilitating. Paranoia is a common symptom in serious mental illnesses like schizophrenia. It can cause extreme distress and is linked with an increased risk of violence towards oneself or others. Understanding what happens in the brains of people experiencing paranoia might lead to better ways to treat or manage it.

Some experts argue that paranoia is caused by errors in the way people assess social situations. An alternative idea is that paranoia stems from the way the brain forms and updates beliefs about the world. Now, Reed et al. show that both people with paranoia and rats exposed to a paranoia-inducing substance expect the world will change frequently, change their minds often, and have a harder time learning in response to changing circumstances.

In the experiments, human volunteers with and without psychiatric disorders played a game where the best choices change. Then, the participants completed a survey to assess their level of paranoia. People with higher levels of paranoia predicted more changes would occur and made less predictable choices. In a second set of experiments, rats were put in a cage with three holes where they sometimes received sugar rewards. Some of the rats received methamphetamine, a drug that causes paranoia in humans. Rats given the drug also expected the location of the sugar reward would change often. The drugged animals had harder time learning and adapting to changing circumstances.

The experiments suggest that brain processes found in both rats, which are less social than humans, and humans contribute to paranoia. This suggests paranoia may make it harder to update beliefs. This may help scientists understand what causes paranoia and develop therapies or drugs that can reduce paranoia. This information may also help scientists understand why during societal crises like wars or natural disasters humans are prone to believing conspiracies. This is particularly important now as the world grapples with climate change and a global pandemic. Reed et al. note paranoia may impede the coordination of collaborative solutions to these challenging situations.

Systems Neuroscience of Psychosis: Mapping Schizophrenia Symptoms onto Brain Systems

Schizophrenia research has been in a deadlock for many decades. Despite important advances in clinical treatment, there are still major concerns regarding long-term psychosocial reintegration and disease management, biological heterogeneity, unsatisfactory predictors of individual course and treatment strategies, and a confusing variety of controversial theories about its etiology and pathophysiological mechanisms. In the present perspective on schizophrenia research, we first discuss a methodological pitfall in contemporary schizophrenia research inherent in the attempt to link mental phenomena with the brain: we claim that the time-honored phenomenological method of defining mental symptoms should not be contaminated with the naturalistic approach of modern neuroscience. We then describe our Systems Neuroscience of Psychosis (SyNoPsis) project, which aims to overcome this intrinsic problem of psychiatric research. Considering schizophrenia primarily as a disorder of interindividual communication, we developed a neurobiologically informed semiotics of psychotic disorders, as well as an operational clinical rating scale. The novel psychopathology allows disentangling the clinical manifestations of schizophrenia into behavioral domains matching the functions of three well-described higher-order corticobasal brain systems involved in interindividual human communication, namely, the limbic, associative, and motor loops, including their corticocortical sensorimotor connections. The results of several empirical studies support the hypothesis that the proposed three-dimensional symptom structure, segregated into the affective, the language, and the motor domain, can be specifically mapped onto structural and functional abnormalities of the respective brain systems. New pathophysiological hypotheses derived from this brain system-oriented approach have helped to develop and improve novel treatment strategies with noninvasive brain stimulation and practicable clinical parameters. In clinical practice, the novel psychopathology allows confining the communication deficits of the individual patient, shifting attention from the symptoms to the intact resources. We have studied this approach and observed important advantages for therapeutic alliances, personalized treatment, and de-escalation strategies. Future studies will further conjoin clinical definitions of psychotic symptoms with brain structures and functions, and disentangle structural and functional deficit patterns within these systems to identify neurobiologically distinct subsyndromes. Neurobiologically homogeneous patient groups may provide new momentum for treatment research. Finally, lessons learned from schizophrenia research may contribute to developing a comprehensive perspective on human experience and behavior that integrates methodologically distinct, but internally consistent, insights from humanities and neuroscience.

Postpartum psychiatric disorders

Pregnancy is a complex and vulnerable period that presents a number of challenges to women, including the development of postpartum psychiatric disorders (PPDs). These disorders can include postpartum depression and anxiety, which are relatively common, and the rare but more severe postpartum psychosis. In addition, other PPDs can include obsessive-compulsive disorder, post-traumatic stress disorder and eating disorders. The aetiology of PPDs is a complex interaction of psychological, social and biological factors, in addition to genetic and environmental factors. The goals of treating postpartum mental illness are reducing maternal symptoms and supporting maternal-child and family functioning. Women and their families should receive psychoeducation about the illness, including evidence-based discussions about the risks and benefits of each treatment option. Developing effective strategies in global settings that allow the delivery of targeted therapies to women with different clinical phenotypes and severities of PPDs is essential.

A greater tendency for representation mediated learning in a ketamine mouse model of schizophrenia

Representation mediated learning is a behavioral paradigm that could be used to potentially capture psychotic symptoms including hallucinations and delusions in schizophrenia. In studies of mediated learning, representations of prior experience can enter into current associations. Using a ketamine model of schizophrenia, we investigated whether mice exposed to ketamine during late adolescence subsequently showed an increased tendency to use a representation of a prior gustatory experience to form associations in learning. Mice were given prior experience of an odor and a taste presented together. The odor was subsequently presented alone with gastrointestinal illness induced by a lithium chloride injection. A consumption test was then given to assess whether the taste, despite its absence during conditioning, entered into an association with the induced illness. Such learning would be mediated via a representation of the taste activated by the odor. Our results showed that control mice displayed no aversion to the taste following the procedures just described, but mice that had been treated developmentally with ketamine exhibited a significant taste aversion, suggesting a greater propensity for mediated learning. Complementary to that finding, ketamine-exposed mice also showed a greater susceptibility to mediated extinction. Chronic treatment with the antipsychotic drug, risperidone, in ketamine-exposed mice attenuated mediated learning, a finding that may be related to its known efficacy in reducing the positive symptoms of schizophrenia. These data provide a setting with potential relevance to preclinical research on schizophrenia, to study the neural mechanisms underlying a propensity for aberrant associations and assessment of therapeutics. (PsycINFO Database Record

Meta-analytic evidence for altered mesolimbic responses to reward in schizophrenia

Dysfunction of reward-related neural circuitry in schizophrenia (SCZ) has been widely reported, and may provide insight into the motivational and cognitive disturbances that characterize the disorder. Although previous meta-analyses of reward learning paradigms in SCZ have been performed, a meta-analysis of whole-brain coordinate maps in SCZ alone has not been conducted. In this study, we performed an activation likelihood estimate (ALE) meta-analysis, and performed a follow-up analysis of functional connectivity and functional decoding of identified regions. We report several salient findings that extend prior work in this area. First, an alteration in reward-related activation was observed in the right ventral striatum, but this was not solely driven by hypoactivation in the SCZ group compared to healthy controls. Second, the region was characterized by functional connectivity primarily with the lateral prefrontal cortex and pre-supplementary motor area (preSMA), as well as subcortical regions such as the thalamus which show structural deficits in SCZ. Finally, although the meta-analysis showed no regions outside the ventral striatum to be significantly altered, regions with higher functional connectivity with the ventral striatum showed a greater number of subthreshold foci. Together, these findings confirm the alteration of ventral striatal function in SCZ, but suggest that a network-based approach may assist future analysis of the functional underpinnings of the disorder.