Modeling perception and behavior in individuals at clinical high risk for psychosis: Support for the predictive processing framework

Early Support for People Who Hear Voices: Exploratory Research on Family Medicine Physicians' Clinical Practice and Beliefs

Nowadays the phenomenon of hearing voices represents a very fertile and discussed field of research. In psychological and psychiatric fields, the phenomenon has been described as a normal phenomenon, but also as a prodromal stage and as a symptom of psychosis. Through a qualitative research methodology, the aim was to explore how family medicine physicians configure the phenomenon and its clinical and interactive implications. The present research involved 35 family medicine physicians as figures of primary importance in the approach toward people who start to hear voices. Semi-structured interviews have been used and they have been analyzed by the method of discourse analysis. The results show a remarkable difficulty in understanding the phenomenon in all its complexity and the tendency to consider it a symptom or a prodromal stage of psychopathology. Increasing the knowledge of doctors on the subject is necessary so that their evaluation and choice of intervention match the needs of each patient. We also discuss the importance of promoting the knowledge of the potential meanings taken on by the voices in the context of the personal and family background of the individual hearer, and of collaboration with other relevant professionals and services.

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.

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.

Neural Signatures of Predictive Strategies Track Individuals Along the Autism-Schizophrenia Continuum

BACKGROUND AND HYPOTHESIS

Humans develop a constellation of different representations of the external environment, even in the face of the same sensory exposure. According to the Bayesian framework, these differentiations could be grounded in a different weight assigned to prior knowledge vs. new external inputs in predictive inference. Since recent advances in computational psychiatry suggest that autism (ASD) and schizophrenia (SSD) lie on the two diametric poles of the same predictive continuum, the adoption of a specific inferential style could be routed by dispositional factors related to autistic and schizotypal traits. However, no studies have directly investigated the role of ASD-SSD dimension in shaping the neuro-behavioral markers underlying perceptual inference.

STUDY DESIGN

We used a probabilistic detection task while simultaneously recording EEG to investigate whether neurobehavioral signatures related to prior processing were diametrically shaped by ASD and SSD traits in the general population (n = 80).

RESULTS

We found that the position along the ASD-SSD continuum directed the predictive strategies adopted by the individuals in decision-making. While proximity to the positive schizotypy pole was associated with the adoption of the predictive approach associated to the hyper-weighting of prior knowledge, proximity to ASD pole was related to strategies that favored sensory evidence in decision-making.

CONCLUSIONS

These findings revealed that the weight assigned to prior knowledge is a marker of the ASD-SSD continuum, potentially useful for identifying individuals at-risk of developing mental disorders and for understanding the mechanisms contributing to the onset of symptoms observed in ASD and SSD clinical forms.

Slowed alpha oscillations and percept formation in psychotic psychopathology

Introduction

Psychosis is in part defined by disturbances in perception. Recent investigations have implicated the speed of alpha oscillations observed in brain electrical activity as reflective of a sampling rate of the visual environment and perception. Although both slowed alpha oscillations and aberrant percept formation are evident in disorders of psychotic psychopathology such as schizophrenia it is unclear whether slow alpha accounts for abnormal visual perception in these disorders.

Methods

To examine the role of the speed of alpha oscillations in perception in psychotic psychopathology we gathered resting-state magneto-encephalography data from probands with psychotic psychopathology (i.e., schizophrenia, schizoaffective disorder, and bipolar disorder with a history of psychosis), their biological siblings, and healthy controls. We appraised visual perceptual function without the confound of cognitive ability and effort through the use of a simple binocular rivalry task.

Results

We found a slowed pace of alpha oscillations in psychotic psychopathology that was associated with longer percept durations during binocular rivalry, consistent with the assertion that occipital alpha oscillations govern the rate of accumulation of visual information used to generate percepts. Alpha speed varied widely across individuals with psychotic psychopathology and was highly stable across several months indicating that it is likely a trait characteristic of neural function that is relevant to visual perception. Finally, a lower speed of alpha oscillation was associated with a lower IQ and greater disorder symptomatology implying that the effects of the endogenous neural oscillation on visual perception may have wider consequences for everyday functioning.

Discussion

Slowed alpha oscillations in individuals with psychotic psychopathology appear to reflect altered neural functions related to percept formation.

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.

Variables influencing conditioning-evoked hallucinations: overview and future applications

Hallucinations occur in the absence of sensory stimulation and result in vivid perceptual experiences of nonexistent events that manifest across a range of sensory modalities. Approaches from the field of experimental and cognitive psychology have leveraged the idea that associative learning experiences can evoke conditioning-induced hallucinations in both animals and humans. In this review, we describe classical and contemporary findings and highlight the variables eliciting these experiences. We also provide an overview of the neurobiological mechanisms, along with the associative and computational factors that may explain hallucinations that are generated by representation-mediated conditioning phenomena. Through the integration of animal and human research, significant advances into the psychobiology of hallucinations are possible, which may ultimately translate to more effective clinical applications.

Targeted effects of ketamine on perceptual expectation during mediated learning in rats

RATIONALE

While neural correlates of hallucinations are known, the mechanisms have remained elusive. Mechanistic insight is more practicable in animal models, in which causal relationships can be established. Recent work developing animal models of hallucination susceptibility has focused on the genesis of perceptual expectations and perceptual decision-making. Both processes are encompassed within mediated learning, which involves inducing a strong perceptual expectation via associative learning, retrieving that memory representation, and deciding whether this internally generated percept is predictive of an external outcome. Mediated learning in rodents is sensitive to many psychotomimetic manipulations. However, we do not know if these manipulations selectively alter learning of perceptual expectations versus their retrieval because of their presence throughout all task phases.

OBJECTIVES

Here, we used mediated learning to study the targeted effect of a psychotomimetic agent on the retrieval of perceptual expectation.

METHODS

We administered (R,S)-ketamine to rats selectively during the devaluation phase of a mediated learning task, when the representation of the expected cue is retrieved, to test the hypothesis that internally generated perceptual experiences underlie this altered mediated learning.

RESULTS

We found that ketamine increased only mediated learning at a moderate dose in rats, but impaired direct learning at the high dose.

CONCLUSIONS

These results suggest that ketamine can augment retrieval of perceptual expectations and thus this may be how it induces hallucination-like experiences in humans. More broadly, mediated learning may unite the conditioning, perceptual decision-making, and even reality monitoring accounts of psychosis in a manner that translates across species.

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.

Relating Glutamate, Conditioned, and Clinical Hallucinations via 1H-MR Spectroscopy

BACKGROUND AND HYPOTHESIS

Hallucinations may be driven by an excessive influence of prior expectations on current experience. Initial work has supported that contention and implicated the anterior insula in the weighting of prior beliefs.

STUDY DESIGN

Here we induce hallucinated tones by associating tones with the presentation of a visual cue. We find that people with schizophrenia who hear voices are more prone to the effect and using computational modeling we show they overweight their prior beliefs. In the same participants, we also measured glutamate levels in anterior insula, anterior cingulate, dorsolateral prefrontal, and auditory cortices, using magnetic resonance spectroscopy.

STUDY RESULTS

We found a negative relationship between prior-overweighting and glutamate levels in the insula that was not present for any of the other voxels or parameters.

CONCLUSIONS

Through computational psychiatry, we bridge a pathophysiological theory of psychosis (glutamate hypofunction) with a cognitive model of hallucinations (prior-overweighting) with implications for the development of new treatments for hallucinations.

Susceptibility to auditory hallucinations is associated with spontaneous but not directed modulation of top-down expectations for speech

Auditory verbal hallucinations (AVHs)-or hearing voices-occur in clinical and non-clinical populations, but their mechanisms remain unclear. Predictive processing models of psychosis have proposed that hallucinations arise from an over-weighting of prior expectations in perception. It is unknown, however, whether this reflects (i) a sensitivity to explicit modulation of prior knowledge or (ii) a pre-existing tendency to spontaneously use such knowledge in ambiguous contexts. Four experiments were conducted to examine this question in healthy participants listening to ambiguous speech stimuli. In experiments 1a (n = 60) and 1b (n = 60), participants discriminated intelligible and unintelligible sine-wave speech before and after exposure to the original language templates (i.e. a modulation of expectation). No relationship was observed between top-down modulation and two common measures of hallucination-proneness. Experiment 2 (n = 99) confirmed this pattern with a different stimulus-sine-vocoded speech (SVS)-that was designed to minimize ceiling effects in discrimination and more closely model previous top-down effects reported in psychosis. In Experiment 3 (n = 134), participants were exposed to SVS without prior knowledge that it contained speech (i.e. naïve listening). AVH-proneness significantly predicted both pre-exposure identification of speech and successful recall for words hidden in SVS, indicating that participants could actually decode the hidden signal spontaneously. Altogether, these findings support a pre-existing tendency to spontaneously draw upon prior knowledge in healthy people prone to AVH, rather than a sensitivity to temporary modulations of expectation. We propose a model of clinical and non-clinical hallucinations, across auditory and visual modalities, with testable predictions for future research.

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.

A computational lens on menopause-associated psychosis

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

Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model

The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.

Increased face detection responses on the mooney faces test in people at clinical high risk for psychosis

Identifying state-sensitive measures of perceptual and cognitive processes implicated in psychosis may allow for objective, earlier, and better monitoring of changes in mental status that are predictive of an impending psychotic episode, relative to traditional self-report-based clinical measures. To determine whether a measure of visual perception that has demonstrated sensitivity to the clinical state of schizophrenia in multiple prior studies is sensitive to features of the at-risk mental state, we examined differences between young people identified as being at clinical high risk for psychosis (CHR; n = 37) and non-psychiatric matched controls (n = 29) on the Mooney Faces Test (MFT). On each trial of the MFT, participants report whether they perceive a face in a degraded face image. The CHR group reported perceiving a greater number of faces in both upright and inverted MFT stimuli. Consistent with prior work, males reported more faces on the MFT than females in both conditions. However, the finding of greater reported face perception among CHR subjects was robustly observed in the female CHR group relative to the female control group. Among male CHR participants, greater reported face perception was related to increased perceptual abnormalities. These preliminary results are consistent with a small but growing literature suggesting that heightened perceptual sensitivity may characterize individuals at increased clinical risk for psychosis. Further studies are needed to determine the contributions of specific perceptual, cognitive, and motivational mechanisms to the findings.

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.