Influence of prior beliefs on perception in early psychosis: Effects of illness stage and hierarchical level of belief

Predictive Processing: A Circuit Approach to Psychosis

Predictive processing is a computational framework that aims to explain how the brain processes sensory information by making predictions about the environment and minimizing prediction errors. It can also be used to explain some of the key symptoms of psychotic disorders such as schizophrenia. In recent years, substantial advances have been made in our understanding of the neuronal circuitry that underlies predictive processing in cortex. In this review, we summarize these findings and how they might relate to psychosis and to observed cell type-specific effects of antipsychotic drugs. We argue that quantifying the effects of antipsychotic drugs on specific neuronal circuit elements is a promising approach to understanding not only the mechanism of action of antipsychotic drugs but also psychosis. Finally, we outline some of the key experiments that should be done. The aims of this review are to provide an overview of the current circuit-based approaches to psychosis and to encourage further research in this direction.

Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework

Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis, are efficacious; however, mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support for explaining symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in cognitive behavioral therapy for psychosis-based psychotherapy for persecutory delusions, which targets specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective cognitive behavioral therapy for psychosis systematically targets the way sensory data are selected, experienced, and interpreted, thus allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.

Priors and prejudice: hierarchical predictive processing in intergroup perception

Hierarchical predictive processing provides a framework outlining how prior expectations shape perception and cognition. Here, we highlight hierarchical predictive processing as a framework for explaining how social context and group-based social knowledge can directly shape intergroup perception. More specifically, we argue that hierarchical predictive processing confers a uniquely valuable toolset to explain extant findings and generate novel hypotheses for intergroup perception. We first provide an overview of hierarchical predictive processing, specifying its primary theoretical assumptions. We then review evidence showing how prior knowledge influences intergroup perception. Next, we outline how hierarchical predictive processing can account well for findings in the intergroup perception literature. We then underscore the theoretical strengths of hierarchical predictive processing compared to other frameworks in this space. We finish by outlining future directions and laying out hypotheses that test the implications of hierarchical predictive processing for intergroup perception and intergroup cognition more broadly. Taken together, hierarchical predictive processing provides explanatory value and capacity for novel hypothesis generation for intergroup perception.

The self, neuroscience and psychosis study: Testing a neurophenomenological model of the onset of psychosis

AIM

Basic self disturbance is a putative core vulnerability marker of schizophrenia spectrum disorders. The primary aims of the Self, Neuroscience and Psychosis (SNAP) study are to: (1) empirically test a previously described neurophenomenological self-disturbance model of psychosis by examining the relationship between specific clinical, neurocognitive, and neurophysiological variables in UHR patients, and (2) develop a prediction model using these neurophenomenological disturbances for persistence or deterioration of UHR symptoms at 12-month follow-up.

METHODS

SNAP is a longitudinal observational study. Participants include 400 UHR individuals, 100 clinical controls with no attenuated psychotic symptoms, and 50 healthy controls. All participants complete baseline clinical and neurocognitive assessments and electroencephalography. The UHR sample are followed up for a total of 24 months, with clinical assessment completed every 6 months.

RESULTS

This paper presents the protocol of the SNAP study, including background rationale, aims and hypotheses, design, and assessment procedures.

CONCLUSIONS

The SNAP study will test whether neurophenomenological disturbances associated with basic self-disturbance predict persistence or intensification of UHR symptomatology over a 2-year follow up period, and how specific these disturbances are to a clinical population with attenuated psychotic symptoms. This may ultimately inform clinical care and pathoaetiological models of psychosis.

Multisensory Integration and Causal Inference in Typical and Atypical Populations

Multisensory perception is critical for effective interaction with the environment, but human responses to multisensory stimuli vary across the lifespan and appear changed in some atypical populations. In this review chapter, we consider multisensory integration within a normative Bayesian framework. We begin by outlining the complex computational challenges of multisensory causal inference and reliability-weighted cue integration, and discuss whether healthy young adults behave in accordance with normative Bayesian models. We then compare their behaviour with various other human populations (children, older adults, and those with neurological or neuropsychiatric disorders). In particular, we consider whether the differences seen in these groups are due only to changes in their computational parameters (such as sensory noise or perceptual priors), or whether the fundamental computational principles (such as reliability weighting) underlying multisensory perception may also be altered. We conclude by arguing that future research should aim explicitly to differentiate between these possibilities.

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.

The Placebo Effect in Psychosis: Why It Matters and How to Measure It

Psychosis is characterized by unusual percepts and beliefs in the form of hallucinations and delusions. Antipsychotic medication, the primary treatment for psychosis, is often ineffective and accompanied by severe side effects, but research has not identified an effective alternative in several decades. One reason that clinical trials fail is that patients with psychosis tend to show a significant therapeutic response to inert control treatments, known as the placebo effect, which makes it difficult to distinguish drug effects from placebo effects. Conversely, in clinical practice, a strong placebo effect may be useful because it could enhance the overall treatment response. Identifying factors that predict large placebo effects could improve the future outlook of psychosis treatment. Biomarkers of the placebo effect have already been suggested in pain and depression, but not in psychosis. Quantifying markers of the placebo effect would have the potential to predict placebo effects in psychosis clinical trials. Furthermore, the placebo effect and psychosis may represent a shared neurocognitive mechanism in which prior beliefs are weighted against new sensory information to make inferences about reality. Examining this overlap could reveal new insights into the mechanisms underlying psychosis and indicate novel treatment targets. We provide a narrative review of the importance of the placebo effect in psychosis and propose a novel method to assess it.

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.

Mismatch negativity and clinical trajectories in psychotic disorders: Five-year stability and predictive utility

BACKGROUND

Mismatch negativity (MMN) amplitude is reduced in psychotic disorders and associated with symptoms and functioning. Due to these robust associations, it is often considered a biomarker for psychotic illness. The relationship between MMN and clinical outcomes has been examined well in early onset psychotic illness; however, its stability and predictive utility in chronic samples are not clear.

METHOD

We examined the five-year stability of MMN amplitude over two timepoints in individuals with established psychotic disorders (cases; N = 132) and never-psychotic participants (NP; N = 170), as well as longitudinal associations with clinical symptoms and functioning.

RESULTS

MMN amplitude exhibited good temporal stability (cases, r = 0.53; never-psychotic, r = 0.52). In cases, structural equation models revealed MMN amplitude to be a significant predictor of worsening auditory hallucinations (β = 0.19), everyday functioning (β = -0.13), and illness severity (β = -0.12) at follow-up. Meanwhile, initial IQ (β = -0.24), negative symptoms (β = 0.23), and illness severity (β = -0.16) were significant predictors of worsening MMN amplitude five years later.

CONCLUSIONS

These results imply that MMN measures a neural deficit that is reasonably stable up to five years. Results support disordered cognition and negative symptoms as preceding reduced MMN, which then may operate as a mechanism driving reductions in everyday functioning and the worsening of auditory hallucinations in chronic psychotic disorders. This pattern may inform models of illness course, clarifying the relationships amongst biological mechanisms of predictive processing and clinical deficits in chronic psychosis and allowing us to better understand the mechanisms driving such impairments over time.

Association between increased anterior cingulate glutamate and psychotic-like experiences, but not autistic traits in healthy volunteers

Despite many differences, autism spectrum disorder and schizophrenia spectrum disorder share environmental risk factors, genetic predispositions as well as neuronal abnormalities, and show similar cognitive deficits in working memory, perspective taking, or response inhibition. These shared abnormalities are already present in subclinical traits of these disorders. The literature proposes that changes in the inhibitory GABAergic and the excitatory glutamatergic system could explain underlying neuronal commonalities and differences. Using magnetic resonance spectroscopy (1H-MRS), we investigated the associations between glutamate concentrations in the anterior cingulate cortex (ACC), the left/right putamen, and left/right dorsolateral prefrontal cortex and psychotic-like experiences (Schizotypal Personality Questionnaire) and autistic traits (Autism Spectrum Quotient) in 53 healthy individuals (26 women). To investigate the contributions of glutamate concentrations in different cortical regions to symptom expression and their interactions, we used linear regression analyses. We found that only glutamate concentration in the ACC predicted psychotic-like experiences, but not autistic traits. Supporting this finding, a binomial logistic regression predicting median-split high and low risk groups for psychotic-like experiences revealed ACC glutamate levels as a significant predictor for group membership. Taken together, this study provides evidence that glutamate levels in the ACC are specifically linked to the expression of psychotic-like experiences, and may be a potential candidate in identifying early risk individuals prone to developing psychotic-like experiences.

Action selection in early stages of psychosis: an active inference approach

BACKGROUND

To interact successfully with their environment, humans need to build a model to make sense of noisy and ambiguous inputs. An inaccurate model, as suggested to be the case for people with psychosis, disturbs optimal action selection. Recent computational models, such as active inference, have emphasized the importance of action selection, treating it as a key part of the inferential process. Based on an active inference framework, we sought to evaluate previous knowledge and belief precision in an action-based task, given that alterations in these parameters have been linked to the development of psychotic symptoms. We further sought to determine whether task performance and modelling parameters would be suitable for classification of patients and controls.

METHODS

Twenty-three individuals with an at-risk mental state, 26 patients with first-episode psychosis and 31 controls completed a probabilistic task in which action choice (go/no-go) was dissociated from outcome valence (gain or loss). We evaluated group differences in performance and active inference model parameters and performed receiver operating characteristic (ROC) analyses to assess group classification.

RESULTS

We found reduced overall performance in patients with psychosis. Active inference modelling revealed that patients showed increased forgetting, reduced confidence in policy selection and less optimal general choice behaviour, with poorer action-state associations. Importantly, ROC analysis showed fair-to-good classification performance for all groups, when combining modelling parameters and performance measures.

LIMITATIONS

The sample size is moderate.

CONCLUSION

Active inference modelling of this task provides further explanation for dysfunctional mechanisms underlying decision-making in psychosis and may be relevant for future research on the development of biomarkers for early identification of psychosis.

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.

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.

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

IMPORTANCE

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

OBJECTIVE

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

DESIGN, SETTING, AND PARTICIPANTS

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

MAIN OUTCOMES AND MEASURES

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

RESULTS

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

CONCLUSIONS AND RELEVANCE

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

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.

Beyond imagination: Hypnotic visual hallucination induces greater lateralised brain activity than visual mental imagery

Hypnotic suggestions can produce a broad range of perceptual experiences, including hallucinations. Visual hypnotic hallucinations differ in many ways from regular mental images. For example, they are usually experienced as automatic, vivid, and real images, typically compromising the sense of reality. While both hypnotic hallucination and mental imagery are believed to mainly rely on the activation of the visual cortex via top-down mechanisms, it is unknown how they differ in the neural processes they engage. Here we used an adaptation paradigm to test and compare top-down processing between hypnotic hallucination, mental imagery, and visual perception in very highly hypnotisable individuals whose ability to hallucinate was assessed. By measuring the N170/VPP event-related complex and using multivariate decoding analysis, we found that hypnotic hallucination of faces involves greater top-down activation of sensory processing through lateralised neural mechanisms in the right hemisphere compared to mental imagery. Our findings suggest that the neural signatures that distinguish hypnotically hallucinated faces from imagined faces lie in the right brain hemisphere.

Taking others into account: combining directly experienced and indirect information in schizophrenia

An abnormality in inference, resulting in distorted internal models of the world, has been argued to be a common mechanism underlying the heterogeneous psychopathology in schizophrenia. However, findings have been mixed as to wherein the abnormality lies and have typically failed to find convincing relations to symptoms. The limited and inconsistent findings may have been due to methodological limitations of the experimental design, such as conflating other factors (e.g. comprehension) with the inferential process of interest, and a failure to adequately assess and model the key aspects of the inferential process. Here, we investigated probabilistic inference based on multiple sources of information using a new digital version of the beads task, framed in a social context. Thirty-five patients with schizophrenia or schizoaffective disorder with a wide range of symptoms and 40 matched healthy control subjects performed the task, where they guessed the colour of the next marble drawn from a jar based on a sample from the jar as well as the choices and the expressed confidence of four people, each with their own independent sample (which was hidden from participant view). We relied on theoretically motivated computational models to assess which model best captured the inferential process and investigated whether it could serve as a mechanistic model for both psychotic and negative symptoms. We found that 'circular inference' best described the inference process, where patients over-weighed and overcounted direct experience and under-weighed information from others. Crucially, overcounting of direct experience was uniquely associated with most psychotic and negative symptoms. In addition, patients with worse social cognitive function had more difficulties using others' confidence to inform their choices. This difficulty was related to worse real-world functioning. The findings could not be easily ascribed to differences in working memory, executive function, intelligence or antipsychotic medication. These results suggest hallucinations, delusions and negative symptoms could stem from a common underlying abnormality in inference, where directly experienced information is assigned an unreasonable weight and taken into account multiple times. By this, even unreliable first-hand experiences may gain disproportionate significance. The effect could lead to false perceptions (hallucinations), false beliefs (delusions) and deviant social behaviour (e.g. loss of interest in others, bizarre and inappropriate behaviour). This may be particularly problematic for patients with social cognitive deficits, as they may fail to make use of corrective information from others, ultimately leading to worse social functioning.

Innovations in the psychopathology of schizophrenia: a primer for busy clinicians

Significant developments in schizophrenia psychopathology are ready to be incorporated into clinical practice. These advances allow a way forward through the well-described challenges experienced with current diagnostic and psychopathological frameworks. This article discusses approaches that will enable clinicians to access a wider and richer spectrum of patient experience; describes process-based models of schizophrenia in the domains of both the brain and the mind; and considers how different levels of analysis might be linked via the predictive processing framework. Multiple levels of analysis provide different targets for varying modalities of treatment – dopamine blockade at the molecular level, psychological therapy at the level of the mind, and social interventions at the personal level. Psychiatry needs to align itself closer to neuroscientific research. It should move from a symptom-based understanding to a model based on process. That is – after having asked about a patient's symptoms and experience clinicians need to introduce steps involving a consideration of what might be the brain and mind processes underlying the experience.

A continuum hypothesis of psychotomimetic rapid antidepressants

Ketamine, classical psychedelics and sleep deprivation are associated with rapid effects on depression. Interestingly, these interventions also have common psychotomimetic actions, mirroring aspects of psychosis such as an altered sense of self, perceptual distortions and distorted thinking. This raises the question whether these interventions might be acute antidepressants through the same mechanisms that underlie some of their psychotomimetic effects. That is, perhaps some symptoms of depression can be understood as occupying the opposite end of a spectrum where elements of psychosis can be found on the other side. This review aims at reviewing the evidence underlying a proposed continuum hypothesis of psychotomimetic rapid antidepressants, suggesting that a range of psychotomimetic interventions are also acute antidepressants as well as trying to explain these common features in a hierarchical predictive coding framework, where we hypothesise that these interventions share a common mechanism by increasing the flexibility of prior expectations. Neurobiological mechanisms at play and the role of different neuromodulatory systems affected by these interventions and their role in controlling the precision of prior expectations and new sensory evidence will be reviewed. The proposed hypothesis will also be discussed in relation to other existing theories of antidepressants. We also suggest a number of novel experiments to test the hypothesis and highlight research areas that could provide further insights, in the hope to better understand the acute antidepressant properties of these interventions.