A Perceptual Inference Mechanism for Hallucinations Linked to Striatal Dopamine

Uncovering the latent structure of human time perception

One of the ongoing controversies in interval timing concerns whether human time perception relies on multiple distinct mechanisms. This debate centres around whether subsecond and suprasecond timing may be attributed to a single semi-uniform timing system or separate and interacting cognitive systems. Whereas past studies offer valuable insights, this study overcomes previous limitations by adopting multiple convergent statistical approaches in a design with strong statistical power. We conducted two online experiments involving participants reproducing temporal intervals ranging from 400 to 2400 ms (Experiment 1; N = 302) and 1000 to 2000 ms (Experiment 2; N = 302). We contrasted the application of exploratory factor analysis and structural equation modelling to differentiate distinct latent structures underlying duration reproduction patterns. Additionally, we compared the model outcomes with results from changepoint analysis models fitted to individual participants' data. In both experiments, these analyses yielded evidence for a two-factor model comprising a general timing factor spanning the full interval range and a second factor capturing the regression to the mean of presented stimulus intervals (central tendency bias). We observed a low proportion of detected changepoints, further supporting the limited evidence for a hypothesized discontinuity between distinct underlying systems, while also finding that changepoint detection patterns were predicted by latent factor scores. These results suggest that the central tendency bias should be considered when investigating potential discontinuities in interval timing systems. Our work contributes to the integration of factor analytic and computational modelling approaches in the study of time perception and has implications for the measurement and interpretation of interval timing in a range of contexts.

Emergence and dynamics of delusions and hallucinations across stages in early psychosis

BACKGROUND

Hallucinations and delusions are often grouped together within the positive symptoms of psychosis. However, recent evidence suggests they may be driven by distinct computational and neural mechanisms. Examining the time course of their emergence may provide insights into the relationship between these underlying mechanisms.

METHODS

Participants from the second (N = 719) and third (N = 699) iterations of the North American Prodrome Longitudinal Study (NAPLS 2,3) were assessed for timing of CHR-P-level delusion and hallucination onset. Pre-onset symptom patterns in first-episode psychosis patients (FEP) from the Prevention and Early Intervention Program for Psychosis (PEPP-Montréal; N = 694) were also assessed. Symptom onset was determined at baseline assessment and the evolution of symptom patterns examined over 24 months.

RESULTS

In all three samples, participants were more likely to report the onset of attenuated/subthreshold delusions prior to attenuated/subthreshold hallucinations (odds ratios (OR): NAPLS 2=4.09; NAPLS 3=4.14; PEPP, Z=7.01, P < 0.001) and to present with only attenuated/subthreshold delusions compared to only attenuated/subthreshold hallucinations (OR: NAPLS 2=5.6; NAPLS 3=11.11; PEPP=42.75). Re-emergence of attenuated/subthreshold delusions after remission was also more common than re-emergence of attenuated/subthreshold hallucinations (Ps < 0.05), which more often resolved first (Ps < 0.001). In both CHR-P samples, ratings of delusional ideation fell with the onset of attenuated hallucinations (P = 0.007).

CONCLUSIONS

Attenuated/subthreshold delusions tend to emerge before attenuated/subthreshold hallucinations and may play a role in their development. Further work should examine the relationship between the mechanisms driving these symptoms and its utility for diagnosis and treatment.

Predictive coding in neuropsychiatric disorders: A systematic transdiagnostic review

The predictive coding framework postulates that the human brain continuously generates predictions about the environment, maximizing successes and minimizing failures based on prior experiences and beliefs. This PRISMA-compliant systematic review aims to comprehensively and transdiagnostically examine the differences in predictive coding between individuals with neuropsychiatric disorders and healthy controls. We included 72 articles including case-control studies investigating predictive coding as the primary outcome and reporting behavioral, neuroimaging, or electrophysiological findings. Thirty-three studies investigated predictive coding in the schizophrenia spectrum, 33 in neurodevelopmental disorders, 5 in mood disorders, 4 in neurocognitive disorders, 1 in post-traumatic stress disorder, and 1 in substance use disorders. Oddball and oddball-like paradigms were most frequently used to quantify predictive coding performance. Evidence showed heterogeneous impairments in the predictive coding abilities of the brain across neuropsychiatric disorders, particularly in schizophrenia and autism. Patients within the schizophrenia spectrum showed a consistent pattern of impaired non-social predictive coding. Conversely, predictive coding deficits were more selective for social cues in the autism spectrum. Predictive coding impairments were correlated with clinical symptom severity. These findings underscore the potential utility of predictive coding as a framework for understanding cognitive dysfunctions in the neuropsychiatric population, even though more evidence is needed on underexplored conditions, also considering potential confounders such as medication use and sex/gender. The potential role of predictive coding as a determinant of treatment response may also be considered to tailor personalized interventions.

A Computational Account of the Development and Evolution of Psychotic Symptoms

The mechanisms of psychotic symptoms such as hallucinations and delusions are often investigated in fully formed illness, well after symptoms emerge. These investigations have yielded key insights but are not well positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing a compensatory relative overreliance on prior beliefs. This overreliance on priors predisposes to hallucinations and covaries with hallucination severity. An overreliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptoms as a point of equilibrium among competing biological forces.

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.

Differing Pattern of Mismatch Negativity Responses in Clinical and Nonclinical Voice Hearers Challenge Predictive Coding Accounts of Psychosis

Background

Among people with schizophrenia (PSZ), reduced mismatch negativity (MMN) is conceptualized as evidence of disrupted prediction error signaling that underlies positive symptoms. However, this conceptualization has been challenged by observations that MMN and positive symptoms are often uncorrelated. In the current study, we tested the hypothesis that reduced MMN is associated with the presence of hallucinations and delusions specifically rather than the presence of a psychiatric illness. A second aim was to determine whether the strength of the association with positive symptoms increases for indices that reflect predictions at higher levels of abstraction.

Methods

Fifty-six PSZ, 34 nonclinical voice hearers, and 48 healthy comparison subjects (HCs) completed 2 MMN paradigms: one with a simple duration deviant type, and one with a higher-level, pattern-violation deviant type. We also measured the repetition positivity, which reflects the formation of auditory memory traces.

Results

We observed that although PSZ exhibited the expected pattern of significantly reduced duration MMN and reduced pattern-violation MMN at the trend level compared with HCs, nonclinical voice hearers exhibited a pattern of duration MMN and pattern-violation MMN amplitude that was statistically similar to that of HCs (ps > .64). Similarly, PSZ exhibited a significantly reduced repetition positivity slope compared with HCs in the duration condition and a trend-level reduction compared with HCs in the pattern-violation condition. Nonclinical voice hearers did not differ from either group in repetition positivity slope in either condition.

Conclusions

These results indicate that the MMN as a prediction error signal does not reflect processes relevant for the manifestation of hallucinations and delusions.

Free-water imaging in subcortical gray matter in schizophrenia patients with persistent auditory verbal hallucinations

Subcortical gray matter (SGM) is increasingly linked to the pathophysiology of schizophrenia, specifically auditory verbal hallucinations (AVHs). However, few studies have been conducted on the role of extracellular free-water (FW) and white matter microstructural abnormalities in AVHs within the SGM using the free water elimination technique. We conducted a comprehensive investigation of macroscopic volume, FW, and white matter microstructure in the SGM of 60 schizophrenia patients with persistent AVHs (p-AVH), 36 patients no AVH history (n-AVH), and 43 healthy control participants (HC). No macroscopic volume abnormalities were found in the p-AVH or n-AVH groups. However, abnormalities in both FW and microstructures were detected in multiple SGM structures of individuals with schizophrenia. Importantly, unlike the n-AVH group, the p-AVH group exhibited FW and microstructure abnormalities in the bilateral caudate that correlated with AVH severity. Our findings suggest that regardless of the presence of AVHs, FW and microstructure abnormalities in the SGM are more pronounced than macroscopic volume abnormalities in patients with schizophrenia. The bilateral caudate may be a key factor in the mechanisms underlying AVHs. Additionally, normal FW and microstructure in the bilateral caudate may be a notable characteristic of n-AVH patients.

Oscillatory Traveling Waves Provide Evidence for Predictive Coding Abnormalities in Schizophrenia

BACKGROUND

The computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that patients with schizophrenia have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms.

METHODS

Here, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding. We analyzed an electroencephalography dataset comprising 146 patients with schizophrenia and 96 age-matched healthy control participants during resting states and a visual backward masking task.

RESULTS

We found that patients with schizophrenia had stronger top-down alpha-band traveling waves compared with healthy control participants during resting state, supposedly reflecting overly precise priors at higher levels of the predictive processing hierarchy. We also found stronger bottom-up alpha-band waves in patients with schizophrenia during a visual task, consistent with the notion of enhanced signaling of sensory precision errors.

CONCLUSIONS

Our results yield a novel spatial-based characterization of oscillatory dynamics in schizophrenia, considering brain rhythms as traveling waves and providing a unique framework to study the different components involved in a predictive coding scheme. All together, our findings significantly advance our understanding of the mechanisms involved in fundamental pathophysiological aspects of schizophrenia, promoting a more comprehensive and hypothesis-driven approach to psychiatric disorders.

The Many Roles of Precision in Action

Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one's sensory observations, through the optimisation of a generative model (of the hidden causes of one's sensory data) in the brain. One of active inference's key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident-many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action, i.e., the key processes that rely on adequate estimates of precision, from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical, "mixed" models-generative models spanning multiple levels of discrete and continuous inference. These kinds of models open up new perspectives on the unified description of hierarchical computation, and its implementation, in action. Here, we highlight how these models reflect the many roles of precision in action-from planning to execution-and the associated pathologies if precision estimation goes wrong. We also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision.

Multisensory perceptual and causal inference is largely preserved in medicated post-acute individuals with schizophrenia

Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs. We combined psychophysics, Bayesian modeling, and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in response to audiovisual flash-beep sequences in medicated individuals with schizophrenia who exhibited limited psychotic symptoms. Seventeen participants with schizophrenia and 23 healthy controls reported either the number of flashes or the number of beeps of audiovisual sequences that varied in their audiovisual numeric disparity across trials. Both groups balanced sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both also showed comparable weighting of prior information regarding the signals' causal structure, although the schizophrenia group slightly overweighted prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of independent estimates of the flash and beep counts, followed by estimates that flexibly combine audiovisual inputs. Our results demonstrate that the core neurocomputational mechanisms for audiovisual perceptual and causal inference in number estimation tasks are largely preserved in our limited sample of medicated post-acute individuals with schizophrenia. Future research should explore whether these findings generalize to unmedicated patients with acute psychotic symptoms.

Contributions of Basal Ganglia Circuits to Perception, Attention, and Consciousness

Research into ascending sensory pathways and cortical networks has generated detailed models of perception. These same cortical regions are strongly connected to subcortical structures, such as the basal ganglia (BG), which have been conceptualized as playing key roles in reinforcement learning and action selection. However, because the BG amasses experiential evidence from higher and lower levels of cortical hierarchies, as well as higher-order thalamus, it is well positioned to dynamically influence perception. Here, we review anatomical, functional, and clinical evidence to demonstrate how the BG can influence perceptual processing and conscious states. This depends on the integrative relationship between cortex, BG, and thalamus, which allows contributions to sensory gating, predictive processing, selective attention, and representation of the temporal structure of events.

Aberrant heartbeat-evoked potential in schizophrenia

Self-disturbance is considered a core feature underlying the psychopathology of schizophrenia. Interoception has an important role in the development of a sense of self, leading to increased interest in the potential contribution of abnormal interoception to self-disturbances in schizophrenia. Several neuropsychological studies have demonstrated aberrant interoception in schizophrenia. However, cortical interoceptive processing has not yet been thoroughly investigated. Thus, we sought to examine resting-state heartbeat-evoked potential (HEP) in this population. We hypothesized that patients with schizophrenia would exhibit significant alterations in HEP compared to healthy controls (HCs). In this cross-sectional electroencephalogram (EEG) study, we compared the HEPs between age- and sex-matched groups of patients with schizophrenia and HCs. A 10-min resting-state EEG with eyes closed and an electrocardiogram (ECG) were recorded and analyzed for the time window of 450 ms to 500 ms after an ECG R peak. A positive HEP shift was observed in the frontal-central regions (F [1, 82] = 7.402, p = 0.008, partial η2 = 0.009) in patients with schizophrenia (n = 61) when compared with HCs (n = 31) after adjusting for confounders such as age, sex, and heart rate. A cluster-based correction analysis revealed that the HEP around the right frontal area (Fp2, F4, and F8) showed the most significant group differences (F [1, 82] = 10.079, p = 0.002, partial η2 = 0.021), with a peak at the F4 electrode site (F [1, 82] = 12.646, p < 0.001, partial η2 = 0.069). We observed no correlation between HEP and symptoms in patients with schizophrenia. A positive shift of HEP during the late component could reflect a trait abnormality in schizophrenia. Further research is required to determine the association between the altered cortical interoceptive processing indexed with HEP and self-disturbances in schizophrenia.

Phantom perception as a Bayesian inference problem: a pilot study

Tinnitus is the perception of a continuous sound in the absence of an external source. Although the role of the auditory system is well investigated, there is a gap in how multisensory signals are integrated to produce a single percept in tinnitus. Here, we train participants to learn a new sensory environment by associating a cue with a target signal that varies in perceptual threshold. In the test phase, we present only the cue to see whether the person perceives an illusion of the target signal. We perform two separate experiments to observe the behavioral and electrophysiological responses to the learning and test phases in 1) healthy young adults and 2) people with continuous subjective tinnitus and matched control subjects. We observed that in both parts of the study the percentage of false alarms was negatively correlated with the 75% detection threshold. Additionally, the perception of an illusion goes together with increased evoked response potential in frontal regions of the brain. Furthermore, in patients with tinnitus, we observe no significant difference in behavioral or evoked response in the auditory paradigm, whereas patients with tinnitus were more likely to report false alarms along with increased evoked activity during the learning and test phases in the visual paradigm. This emphasizes the importance of integrity of sensory pathways in multisensory integration and how this process may be disrupted in people with tinnitus. Furthermore, the present study also presents preliminary data supporting evidence that tinnitus patients may be building stronger perceptual models, which needs future studies with a larger population to provide concrete evidence on.NEW & NOTEWORTHY Tinnitus is the continuous phantom perception of a ringing in the ears. Recently, it has been suggested that tinnitus may be a maladaptive inference of the brain to auditory anomalies, whether they are detected or undetected by an audiogram. The present study presents empirical evidence for this hypothesis by inducing an illusion in a sensory domain that is damaged (auditory) and one that is intact (visual). It also presents novel information about how people with tinnitus process multisensory stimuli in the audio-visual domain.

Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision

Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin-targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top-down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.

A computational account of the development and evolution of psychotic symptoms

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

Evidence for Reduced Sensory Precision and Increased Reliance on Priors in Hallucination-Prone Individuals in a General Population Sample

BACKGROUND

There is increasing evidence that people with hallucinations overweight perceptual beliefs relative to incoming sensory evidence. Past work demonstrating prior overweighting has used simple, nonlinguistic stimuli. However, auditory hallucinations in psychosis are often complex and linguistic. There may be an interaction between the type of auditory information being processed and its perceived quality in engendering hallucinations.

STUDY DESIGN

We administered a linguistic version of the conditioned hallucinations (CH) task to an online sample of 88 general population participants. Metrics related to hallucination-proneness, hallucination severity, stimulus thresholds, and stimulus detection rates were collected. Data were used to fit parameters of a Hierarchical Gaussian Filter (HGF) model of perceptual inference to determine how latent perceptual states influenced task behavior.

STUDY RESULTS

Replicating past results, higher CH rates were observed both in those with recent hallucinatory experiences as well as participants with high hallucination-proneness; CH rates were positively correlated with increased prior weighting; and increased prior weighting was related to hallucination severity. Unlike past results, participants with recent hallucinatory experiences as well as those with higher hallucination-proneness had higher stimulus thresholds, lower sensitivity to stimuli presented at the highest threshold, and had lower response confidence, consistent with lower precision of sensory evidence.

CONCLUSIONS

We replicate the finding that increased CH rates and recent hallucinations correlate with increased prior weighting using a linguistic version of the CH task. Results support a role for reduced sensory precision in the interplay between prior weighting and hallucination-proneness.

Robotically-induced auditory-verbal hallucinations: combining self-monitoring and strong perceptual priors

BACKGROUND

Inducing hallucinations under controlled experimental conditions in non-hallucinating individuals represents a novel research avenue oriented toward understanding complex hallucinatory phenomena, avoiding confounds observed in patients. Auditory-verbal hallucinations (AVH) are one of the most common and distressing psychotic symptoms, whose etiology remains largely unknown. Two prominent accounts portray AVH either as a deficit in auditory-verbal self-monitoring, or as a result of overly strong perceptual priors.

METHODS

In order to test both theoretical models and evaluate their potential integration, we developed a robotic procedure able to induce self-monitoring perturbations (consisting of sensorimotor conflicts between poking movements and corresponding tactile feedback) and a perceptual prior associated with otherness sensations (i.e. feeling the presence of a non-existing another person).

RESULTS

Here, in two independent studies, we show that this robotic procedure led to AVH-like phenomena in healthy individuals, quantified as an increase in false alarm rate in a voice detection task. Robotically-induced AVH-like sensations were further associated with delusional ideation and to both AVH accounts. Specifically, a condition with stronger sensorimotor conflicts induced more AVH-like sensations (self-monitoring), while, in the otherness-related experimental condition, there were more AVH-like sensations when participants were detecting other-voice stimuli, compared to detecting self-voice stimuli (strong-priors).

CONCLUSIONS

By demonstrating an experimental procedure able to induce AVH-like sensations in non-hallucinating individuals, we shed new light on AVH phenomenology, thereby integrating self-monitoring and strong-priors accounts.

Development, Insults and Predisposing Factors of the Brain's Predictive Coding System to Chronic Perceptual Disorders-A Life-Course Examination

The predictive coding theory is currently widely accepted as the theoretical basis of perception and chronic perceptual disorders are explained as the maladaptive compensation of the brain to a prediction error. Although this gives us a general framework to work with, it is still not clear who may be more susceptible and/or vulnerable to aberrations in this system. In this paper, we study changes in predictive coding through the lens of tinnitus and pain. We take a step back to understand how the predictive coding system develops from infancy, what are the different neural and bio markers that characterise this system in the acute, transition and chronic phases and what may be the factors that pose a risk to the aberration of this system. Through this paper, we aim to identify people who may be at a higher risk of developing chronic perceptual disorders as a reflection of aberrant predictive coding, thereby giving future studies more facets to incorporate in their investigation of early markers of tinnitus, pain and other disorders of predictive coding. We therefore view this paper to encourage the thinking behind the development of preclinical biomarkers to maladaptive predictive coding.

Explanatory power by vagueness. Challenges to the strong prior hypothesis on hallucinations exemplified by the Charles-Bonnet-Syndrome

Predictive processing models are often ascribed a certain generality in conceptually unifying the relationships between perception, action, and cognition or the potential to posit a 'grand unified theory' of the mind. The limitations of this unification can be seen when these models are applied to specific cognitive phenomena or phenomenal consciousness. Our article discusses these shortcomings for predictive processing models of hallucinations by the example of the Charles-Bonnet-Syndrome. This case study shows that the current predictive processing account omits essential characteristics of stimulus-independent perception in general, which has critical phenomenological implications. We argue that the most popular predictive processing model of hallucinatory conditions - the strong prior hypothesis - fails to fully account for the characteristics of nonveridical perceptual experiences associated with Charles-Bonnet-Syndrome. To fill this explanatory gap, we propose that the strong prior hypothesis needs to include reality monitoring to apply to more than just veridical percepts.

Hallucination-Proneness is Associated With a Decrease in Robust Averaging of Perceptual Evidence

BACKGROUND AND HYPOTHESIS

Hallucinations are characterized by disturbances in perceptual decision-making about environmental stimuli. When integrating across multiple stimuli to form a perceptual decision, typical observers engage in "robust averaging" by down-weighting extreme perceptual evidence, akin to a statistician excluding outlying data. Furthermore, observers adapt to contexts with more unreliable evidence by increasing this down-weighting strategy. Here, we test the hypothesis that hallucination-prone individuals (n = 38 high vs n = 91 low) would show a decrease in this robust averaging and diminished sensitivity to changes in evidence variance.

STUDY DESIGN

We used a multielement perceptual averaging task to elicit dichotomous judgments about the "average color" (red/blue) of an array of stimuli in trials with varied strength (mean) and reliability (variance) of decision-relevant perceptual evidence. We fitted computational models to task behavior, with a focus on a log-posterior-ratio (LPR) model which integrates evidence as a function of the log odds of each perceptual option and produces a robust averaging effect.

STUDY RESULTS

Hallucination-prone individuals demonstrated less robust averaging, seeming to weigh inlying and outlying extreme or untrustworthy evidence more equally. Furthermore, the model that integrated evidence as a function of the LPR of the two perceptual options and produced robust averaging showed poorer fit for the group prone to hallucinations. Finally, the weighting strategy in hallucination-prone individuals remained insensitive to evidence variance.

CONCLUSIONS

Our findings provide empirical support for theoretical proposals regarding evidence integration aberrations in psychosis and alterations in the perceptual systems that track statistical regularities in environmental stimuli.

Biomarkers of Auditory-Verbal Hallucinations

Auditory-verbal hallucinations (AVH) are debilitating symptoms experienced by those diagnosed with psychosis as well as many other neurological and psychiatric disorders. Critical to supporting individuals with AVH is identifying biomarkers that serve to track changes in brain states that put individuals at risk for developing or worsening of symptoms. There has been substantial literature identifying neural areas to track over time that may prove to be effective clinical tools. The efficacy of these tools has been bolstered when considering them under mechanistic accounts of AVH. In this chapter, we explore the literature that connects mechanistic theories and structurally based models of AVH and the potential biomarkers derived from this research.

Expectation Cues and False Percepts Generate Stimulus-Specific Activity in Distinct Layers of the Early Visual Cortex

Perception has been proposed to result from the integration of feedforward sensory signals with internally generated feedback signals. Feedback signals are believed to play an important role in driving false percepts, that is, seeing things that are not actually there. Feedforward and feedback influences on perception can be studied using layer-specific fMRI, which we used here to interrogate neural activity underlying high-confidence false percepts while healthy human participants (N = 25, male and female) performed a perceptual orientation discrimination task. Auditory cues implicitly signaled the most likely upcoming orientation (referred to here as expectations). These expectations induced orientation-specific templates in the deep and superficial layers of V2, without affecting perception. In contrast, the orientation of falsely perceived stimuli with high confidence was reflected in the middle input layers of V2, suggesting a feedforward signal contributing to false percepts. The prevalence of high-confidence false percepts was related to everyday hallucination severity in a separate online sample (N = 100), suggesting a possible link with abnormal perceptual experiences. These results reveal a potential feedforward mechanism underlying false percepts, reflected by spontaneous stimulus-like activity in the input layers of the visual cortex, independent of top-down signals reflecting cued orientations.SIGNIFICANCE STATEMENT False percepts have been suggested to arise through excessive feedback signals. However, feedforward contributions to false percepts have remained largely understudied. Laminar fMRI has been shown to be useful in distinguishing feedforward from feedback activity as it allows the imaging of different cortical layers. In the present study we demonstrate that although cued orientations are encoded in the feedback layers of the visual cortex, the content of the false percepts are encoded in the feedforward layers and did not rely on these cued orientations. This shows that false percepts can in principle emerge from random feedforward signals in the visual cortex, with possible implications for disorders hallmarked by hallucinations like schizophrenia and Parkinson's disease.

Bayesian accounts of perceptual decisions in the nonclinical continuum of psychosis: Greater imprecision in both top-down and bottom-up processes

Neurocomputational accounts of psychosis propose mechanisms for how information is integrated into a predictive model of the world, in attempts to understand the occurrence of altered perceptual experiences. Conflicting Bayesian theories postulate aberrations in either top-down or bottom-up processing. The top-down theory predicts an overreliance on prior beliefs or expectations resulting in aberrant perceptual experiences, whereas the bottom-up theory predicts an overreliance on current sensory information, as aberrant salience is directed towards objectively uninformative stimuli. This study empirically adjudicates between these models. We use a perceptual decision-making task in a neurotypical population with varying degrees of psychotic-like experiences. Bayesian modelling was used to compute individuals' reliance on prior relative to sensory information. Across two datasets (discovery dataset n = 363; independent replication in validation dataset n = 782) we showed that psychotic-like experiences were associated with an overweighting of sensory information relative to prior expectations, which seem to be driven by decreased precision afforded to prior information. However, when prior information was more uncertain, participants with greater psychotic-like experiences encoded sensory information with greater noise. Greater psychotic-like experiences were associated with aberrant precision in the encoding both prior and likelihood information, which we suggest may be related to generally heightened perceptions of task instability. Our study lends empirical support to notions of both weaker bottom-up and weaker (rather than stronger) top-down perceptual processes, as well as aberrancies in belief updating that extend into the non-clinical continuum of psychosis.

The effect of multisensory semantic congruency on unisensory object recognition in schizophrenia

Multisensory, as opposed to unisensory processing of stimuli, has been found to enhance the performance (e.g., reaction time, accuracy, and discrimination) of healthy individuals across various tasks. However, this enhancement is not as pronounced in patients with schizophrenia (SZ), indicating impaired multisensory integration (MSI) in these individuals. To the best of our knowledge, no study has yet investigated the impact of MSI deficits in the context of working memory, a domain highly reliant on multisensory processing and substantially impaired in schizophrenia. To address this research gap, we employed two adopted versions of the continuous object recognition task to investigate the effect of single-trail multisensory encoding on subsequent object recognition in 21 schizophrenia patients and 21 healthy controls (HC). Participants were tasked with discriminating between initial and repeated presentations. For the initial presentations, half of the stimuli were audiovisual pairings, while the other half were presented unimodal. The task-relevant stimuli were then presented a second time in a unisensory manner (either auditory stimuli in the auditory task or visual stimuli in the visual task). To explore the impact of semantic context on multisensory encoding, half of the audiovisual pairings were selected to be semantically congruent, while the remaining pairs were not semantically related to each other. Consistent with prior studies, our findings demonstrated that the impact of single-trial multisensory presentation during encoding remains discernible during subsequent object recognition. This influence could be distinguished based on the semantic congruity between the auditory and visual stimuli presented during the encoding. This effect was more robust in the auditory task. In the auditory task, when congruent multisensory pairings were encoded, both participant groups demonstrated a multisensory facilitation effect. This effect resulted in improved accuracy and RT performance. Regarding incongruent audiovisual encoding, as expected, HC did not demonstrate an evident multisensory facilitation effect on memory performance. In contrast, SZs exhibited an atypically accelerated reaction time during the subsequent auditory object recognition. Based on the predictive coding model we propose that this observed deviations indicate a reduced semantic modulatory effect and anomalous predictive errors signaling, particularly in the context of conflicting cross-modal sensory inputs in SZ.

Hypnotic suggestion versus sensory modulation of bodily awareness

Bodily awareness arises from somatosensory, vestibular, and visual inputs but cannot be reduced to these incoming sensory signals. Cognitive factors are known to also impact bodily awareness, though their specific influence is poorly understood. Here we systematically compared the effects of sensory (bottom-up) and cognitive (top-down) manipulations on the estimated size of body parts. Toward this end, in a repeated-measures design, we sought to induce the illusion that the right index finger was elongating by vibrating the biceps tendon of the left arm whilst participants grasped the tip of their right index finger (Lackner illusion; bottom-up) and separately by hypnotic suggestion (top-down), with a sham version of the Lackner illusion as an active control condition. The effects of these manipulations were assessed with perceptual and motor tasks to capture different components of the representation of body size. We found that hypnotic suggestion significantly induced the illusion in both tasks relative to the sham condition. The magnitudes of these effects were stronger than those in the Lackner illusion condition, which only produced a significantly stronger illusion than the sham condition in the perceptual task. We further observed that illusion magnitude significantly correlated across tasks and conditions, suggesting partly shared mechanisms. These results are in line with theories of separate but interacting representational processes for perception and action and highlight the influence of cognitive factors on low-level body representations.

Substantia nigral dopamine transporter uptake in dementia with Lewy bodies

Nigrostriatal dopaminergic degeneration is a pathological hallmark of dementia with Lewy bodies (DLB). To identify the subregional dopamine transporter (DAT) uptake patterns that improve the diagnostic accuracy of DLB, we analyzed N-(3-[18F] fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl)-nortropane (FP-CIT) PET in 51 patients with DLB, in 36 patients with mild cognitive impairment with Lewy body (MCI-LB), and in 40 healthy controls (HCs). In addition to a high affinity for DAT, FP-CIT show a modest affinity to serotonin or norepinephrine transporters. Specific binding ratios (SBRs) of the nigrostriatal subregions were transformed to age-adjusted z-scores (zSBR) based on HCs. The diagnostic accuracy of subregional zSBRs were tested using receiver operating characteristic (ROC) curve analyses separately for MCI-LB and DLB versus HCs. Then, the effect of subregional zSBRs on the presence of clinical features and gray matter (GM) density were evaluated in all patients with MCI-LB or DLB as a group. ROC curve analyses showed that the diagnostic accuracy of DLB based on the zSBR of substantia nigra (area under the curve [AUC], 0.90) or those for MCI-LB (AUC, 0.87) were significantly higher than that based on the zSBR of posterior putamen for DLB (AUC, 0.72) or MCI-LB (AUC, 0.65). Lower zSBRs in nigrostriatal regions were associated with visual hallucination, severe parkinsonism, and cognitive dysfunction, while lower zSBR of substantia nigra was associated with widespread GM atrophy in DLB and MCI-LB patients. Taken together, our results suggest that evaluation of nigral DAT uptake may increase the diagnostic accuracy of DLB and MCI-LB than other striatal regions.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Barriers and solutions to the adoption of translational tools for computational psychiatry

Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity to incorporate computational psychiatry methodologies into large- scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as, eventually, clinical practice.

Learning to Discern the Voices of Gods, Spirits, Tulpas, and the Dead

There are communities in which hearing voices frequently is common and expected, and in which participants are not expected to have a need for care. This paper compares the ideas and practices of these communities. We observe that these communities utilize cultural models to identify and to explain voice-like events-and that there are some common features to these models across communities. All communities teach participants to "discern," or identify accurately, the legitimate voice of the spirit or being who speaks. We also observe that there are roughly two methods taught to participants to enable them to experience spirits (or other invisible beings): trained attention to inner experience, and repeated speech to the invisible other. We also observe that all of these communities model a learning process in which the ability to hear spirit (or invisible others) becomes more skilled with practice, and in which what they hear becomes clearer over time. Practice-including the practice of discernment-is presumed to change experience. We also note that despite these shared cultural ideas and practices, there is considerable individual variation in experience-some of which may reflect psychotic process, and some perhaps not. We suggest that voice-like events in this context may be shaped by cognitive expectation and trained practice as well as an experiential pathway. We also suggest that researchers could explore these common features both as a way to help those struggling with psychosis, and to consider the possibility that expectations and practice may affect the voice-hearing experience.

Digitally embodied lifespan neurocognitive development and Tactile Internet: Transdisciplinary challenges and opportunities

Mechanisms underlying perceptual processing and inference undergo substantial changes across the lifespan. If utilized properly, technologies could support and buffer the relatively more limited neurocognitive functions in the still developing or aging brains. Over the past decade, a new type of digital communication infrastructure, known as the "Tactile Internet (TI)," is emerging in the fields of telecommunication, sensor and actuator technologies and machine learning. A key aim of the TI is to enable humans to experience and interact with remote and virtual environments through digitalized multimodal sensory signals that also include the haptic (tactile and kinesthetic) sense. Besides their applied focus, such technologies may offer new opportunities for the research tapping into mechanisms of digitally embodied perception and cognition as well as how they may differ across age cohorts. However, there are challenges in translating empirical findings and theories about neurocognitive mechanisms of perception and lifespan development into the day-to-day practices of engineering research and technological development. On the one hand, the capacity and efficiency of digital communication are affected by signal transmission noise according to Shannon's (1949) Information Theory. On the other hand, neurotransmitters, which have been postulated as means that regulate the signal-to-noise ratio of neural information processing (e.g., Servan-Schreiber et al., 1990), decline substantially during aging. Thus, here we highlight neuronal gain control of perceptual processing and perceptual inference to illustrate potential interfaces for developing age-adjusted technologies to enable plausible multisensory digital embodiments for perceptual and cognitive interactions in remote or virtual environments.

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.

Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia

BACKGROUND AND HYPOTHESIS

Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology.

STUDY DESIGN

Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis.

STUDY RESULTS

Compared to HCs, patients showed significantly increased average controllability (PFDR = .023) and decreased modal controllability (PFDR = .023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks.

CONCLUSIONS

Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia.

Conditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility

BACKGROUND

Recent advances in computational psychiatry have identified latent cognitive and perceptual states that predispose to psychotic symptoms. Behavioral data fit to Bayesian models have demonstrated an overreliance on priors (i.e., prior overweighting) during perception in select samples of individuals with hallucinations, corresponding to increased precision of prior expectations over incoming sensory evidence. However, the clinical utility of this observation depends on the extent to which it reflects static symptom risk or current symptom state.

METHODS

To determine whether task performance and estimated prior weighting relate to specific elements of symptom expression, a large, heterogeneous, and deeply phenotyped sample of hallucinators (n = 249) and nonhallucinators (n = 209) performed the conditioned hallucination (CH) task.

RESULTS

We found that CH rates predicted stable measures of hallucination status (i.e., peak frequency). However, CH rates were more sensitive to hallucination state (i.e., recent frequency), significantly correlating with recent hallucination severity and driven by heightened reliance on past experiences (priors). To further test the sensitivity of CH rate and prior weighting to symptom severity, a subset of participants with hallucinations (n = 40) performed a repeated-measures version of the CH task. Changes in both CH frequency and prior weighting varied with changes in auditory hallucination frequency on follow-up.

CONCLUSIONS

These results indicate that CH rate and prior overweighting are state markers of hallucination status, potentially useful in tracking disease development and treatment response.

Catecholaminergic cell type-specific expression of Cre recombinase in knock-in transgenic rats generated by the Combi-CRISPR technology

BACKGROUND

Cell groups containing catecholamines provide a useful model to study the molecular and cellular mechanisms underlying the morphogenesis, physiology, and pathology of the central nervous system. For this purpose, it is necessary to establish a system to induce catecholaminergic group-specific expression of Cre recombinase. Recently, we introduced a gene cassette encoding 2A peptide fused to Cre recombinase into the site between the C-terminus and translational termination codons of the rat tyrosine hydroxylase (TH) open reading frame by the Combi-CRISPR technology, which is a genomic editing method to enable an efficient knock-in (KI) of long DNA sequence into a target site. However, the expression patterns of the transgene and its function as well as the effect of the mutation on the biochemical and behavioral phenotypes in the KI strains have not been characterized yet.

NEW METHOD

We aimed to evaluate the usefulness of TH-Cre KI rats as an experimental model for investigating the structure and function of catecholaminergic neurons in the brain.

RESULTS

We detected cell type-specific expression of Cre recombinase and site-specific recombination activity in the representative catecholaminergic groups in the TH-Cre KI rat strains. In addition, we measured TH protein levels and catecholamine accumulation in the brain regions, as well as motor, reward-related, and anxiety-like behaviors, indicating that catecholamine metabolism and general behavior are apparently normal in these KI rats.

CONCLUSIONS

TH-Cre KI rat strains produced by the Combi-CRISPR system offer a beneficial model to study the molecular and cellular mechanics for the morphogenesis, physiology, and pathology of catecholamine-containing neurons in the brain.

Psychedelics and schizophrenia: Distinct alterations to Bayesian inference

Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.

The temporal context in bayesian models of interval timing: Recent advances and future directions

Sensory perception, motor control, and cognition necessitate reliable timing in the range of milliseconds to seconds, which implies the existence of a highly accurate timing system. Yet, partly owing to the fact that temporal processing is modulated by contextual factors, perceived time is not isomorphic to physical time. Temporal estimates exhibit regression to the mean of an interval distribution (global context) and are also affected by preceding trials (local context). Recent Bayesian models of interval timing have provided important insights regarding these observations, but questions remain as to how exposure to past intervals shapes perceived time. In this article, we provide a brief overview of Bayesian models of interval timing and their contribution to current understanding of context effects. We then proceed to highlight recent developments in the field concerning precision weighting of Bayesian evidence in both healthy timing and disease and the neurophysiological and neurochemical signatures of timing prediction errors. We further aim to bring attention to current outstanding questions for Bayesian models of interval timing, such as the likelihood conceptualization. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Perceptual inference, accuracy, and precision in temporal reproduction in schizophrenia

Accumulating evidence suggests that deficits in perceptual inference account for symptoms of schizophrenia. One manifestation of perceptual inference is the central bias, i.e., the tendency to put emphasis on prior experiences over actual events in perceiving incoming sensory stimuli. Using an interval reproduction task, this study aimed to determine whether patients with schizophrenia show a stronger central bias than participants without schizophrenia. In the interval reproduction task, participants were shown a cross on a screen. The cross was replaced with a Gaussian patch for a predetermined time interval, and participants were required to reproduce the interval duration by pressing and releasing the space key. We manipulated the uncertainty of prior information using different interval distributions. We found no difference in the influence of prior information on interval reproduction between patients and controls. However, patients with SZ showed a stronger central bias than healthy participants in the intermediate interval range (approximately 450 ms to 900 ms). It is possible that the patients in SZ have non-uniform deficits associated with interval range or uncertainty of prior information in perceptual inference. Further, the severity of avolition and alogia was correlated with the strength of central bias in SZ. This study provides some insights into the mechanisms underlying the association between schizophrenic symptoms and perceptual inference.

A network approach to relationships between cannabis use characteristics and psychopathology in the general population

Cannabis use characteristics, such as earlier initiation and frequent use, have been associated with an increased risk for developing psychotic experiences and psychotic disorders. However, little is known how these characteristics relate to specific aspects of sub-clinical psychopathology in the general population. Here, we explore the relationships between cannabis use characteristics and psychopathology in a large general population sample (N = 2,544, mean age 29.2 years, 47% women) by employing a network approach. This allows for the identification of unique associations between two cannabis use characteristics (lifetime cumulative frequency of cannabis use, age of cannabis use initiation), and specific psychotic experiences and affective symptoms, while controlling for early risk factors (childhood trauma, urban upbringing). We found particularly pronounced unique positive associations between frequency of cannabis use and specific delusional experiences (persecutory delusions and thought broadcasting). Age of cannabis use initiation was negatively related to visual hallucinatory experiences and irritability, implying that these experiences become more likely the earlier use is initiated. Earlier initiation, but not lifetime frequency of cannabis use, was related to early risk factors. These findings suggest that cannabis use characteristics may contribute differentially to risk for specific psychotic experiences and affective symptoms in the general population.

A proxy measure of striatal dopamine predicts individual differences in temporal precision

The perception of time is characterized by pronounced variability across individuals, with implications for a diverse array of psychological functions. The neurocognitive sources of this variability are poorly understood, but accumulating evidence suggests a role for inter-individual differences in striatal dopamine levels. Here we present a pre-registered study that tested the predictions that spontaneous eyeblink rates, which provide a proxy measure of striatal dopamine availability, would be associated with aberrant interval timing (lower temporal precision or overestimation bias). Neurotypical adults (N = 69) underwent resting state eye tracking and completed visual psychophysical interval timing and control tasks. Elevated spontaneous eyeblink rates were associated with poorer temporal precision but not with inter-individual differences in perceived duration or performance on the control task. These results signify a role for striatal dopamine in variability in human time perception and can help explain deficient temporal precision in psychiatric populations characterized by elevated dopamine levels.

Arts-based methods for hallucination research

INTRODUCTION

Neurocognitive models of hallucinations posit theories of misattribution and deficits in the monitoring of mental or perceptual phenomena but cannot yet account for the subjective experience of hallucinations across individuals and diagnostic categories. Arts-based research methods (ABRM) have potential for advancing research, as art depicts experiences which cognitive neuropsychiatry seeks to explain.

METHODS

To examine how incorporating ABRM may advance hallucination research and theories, we explore data on the lived experiences of hallucinations in psychiatric and neurological populations. We present a multiple case study of two empirical ABRM studies, which used participant-generated artwork and artist collaborations alongside interviews.

RESULTS

ABRM combined with interviews illustrated that hallucinations were infused with sensory features, characterised by embodiment, and situated within lived circumstances. These findings advance neurocognitive models of hallucinations by nuancing their multimodal nature, illustrating their embodied feelings, and exploring their content and themes. The process of generating artworks aided in disclosing difficult to discuss hallucinations, promoted participant self-reflection, and clarified multimodal details that may have been misconstrued through interview alone. ABRM were relevant and acceptable for participants and researchers.

CONCLUSION

ABRM may contribute to the development of neurocognitive models of hallucinations by making hallucination experiences more visible, tangible, and accessible.

Modeling dopamine dysfunction in autism spectrum disorder: From invertebrates to vertebrates

Autism Spectrum Disorder (ASD) is a highly heterogeneous neurodevelopmental disorder characterized by deficits in social communication and by patterns of restricted interests and/or repetitive behaviors. The Simons Foundation Autism Research Initiative's Human Gene and CNV Modules now list over 1000 genes implicated in ASD and over 2000 copy number variant loci reported in individuals with ASD. Given this ever-growing list of genetic changes associated with ASD, it has become evident that there is likely not a single genetic cause of this disorder nor a single neurobiological basis of this disorder. Instead, it is likely that many different neurobiological perturbations (which may represent subtypes of ASD) can result in the set of behavioral symptoms that we called ASD. One such of possible subtype of ASD may be associated with dopamine dysfunction. Precise regulation of synaptic dopamine (DA) is required for reward processing and behavioral learning, behaviors which are disrupted in ASD. Here we review evidence for DA dysfunction in ASD and in animal models of ASD. Further, we propose that these studies provide a scaffold for scientists and clinicians to consider subcategorizing the ASD diagnosis based on the genetic changes, neurobiological difference, and behavioral features identified in individuals with ASD.