Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

Revisiting the role of computational neuroimaging in the era of integrative neuroscience

Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.

The Mechanisms of Persisting Disability in Schizophrenia: Imprecise Predictive Coding via Corticostriatothalamic-Cortical Loop Dysfunction

Persisting symptoms and disability remain a problem for an appreciable proportion of people with schizophrenia despite treatment with antipsychotic medication. Improving outcomes requires an understanding of the nature and mechanisms of the pathological processes underlying persistence. Classical features of schizophrenia, which include disorganization and impoverishment of mental activity, are well-recognized early clinical features that predict poor long-term outcome. Substantial evidence indicates that these features reflect imprecise predictive coding. Predictive coding provides an overarching framework for understanding efficient functioning of the nervous system. Imprecise predictive coding also has the potential to precipitate acute psychosis characterized by reality distortion (delusions and hallucinations) at times of stress. On the other hand, substantial evidence indicates that persistent reality distortion itself gives rise to poor occupational and social function in the long term. Furthermore, abuse of psychotomimetic drugs, which exacerbate reality distortion, contributes to poor long-term outcome in schizophrenia. Neural circuits involved in modulating volitional acts are well understood to be implicated in addiction. Plastic changes in these circuits may account for the association between psychotomimetic drug abuse and poor outcomes in schizophrenia. We propose a mechanistic model according to which unbalanced inputs to the corpus striatum disturb the precision of subcortical modulation of cortical activity supporting volitional action. This model accounts for the evidence that early classical symptoms predict poor outcome, while in some circumstances, persistent reality distortion also predicts poor outcome. This model has implications for the development of novel treatments that address the risk of persisting symptoms and disabilities in schizophrenia.

Trustworthiness judgments and pupil-size in individuals with schizophrenia

Individuals with schizophrenia show aberrant processing of social cues. In the current study, we (1) compared trustworthiness ratings of faces between patients with schizophrenia and healthy controls, (2) compared pupillary reactivity between patients and controls (3) examined whether trustworthiness judgments in schizophrenia are related to pupil reactivity, (4) and examined associations between trustworthiness judgements and symptom severity, specifically paranoia. Patients with schizophrenia spectrum disorders (N = 48) and healthy controls (N = 33) completed a Trustworthiness Task, during which their pupil size was measured via an eye-tracking device. The mean baseline-corrected pupil size was calculated from 24 pictures of real neutral faces, each presented for 2500 ms. Self-reported psychotic experiences were measured by Community Assessment of Psychic Functioning (CAPE-42), and symptom severity was rated by Brief Psychiatric Rating Scale (BPRS). No group differences were found in trustworthiness ratings or pupil reactivity parameters during trustworthiness judgments. Separately, among patients, absolute difference in pupil-size change and dilation after reaching minimum size were related to more severe positive symptoms and self-reported paranoia. Our results did not show social cognitive biases in the stable outpatients with schizophrenia, or the role of pupil reactivity in trustworthiness judgments. Future studies should use longer stimuli for pupillary reactivity and control the type and dosage of utilized antipsychotic medication. Further studies are required to explore relationships in larger and more symptomatic groups of patients.

An Integrated theory of false insights and beliefs under psychedelics

Psychedelics are recognised for their potential to re-orient beliefs. We propose a model of how psychedelics can, in some cases, lead to false insights and thus false beliefs. We first review experimental work on laboratory-based false insights and false memories. We then connect this to insights and belief formation under psychedelics using the active inference framework. We propose that subjective and brain-based alterations caused by psychedelics increases the quantity and subjective intensity of insights and thence beliefs, including false ones. We offer directions for future research in minimising the risk of false and potentially harmful beliefs arising from psychedelics. Ultimately, knowing how psychedelics may facilitate false insights and beliefs is crucial if we are to optimally leverage their therapeutic potential.

The neurobiology of aesthetic chills: How bodily sensations shape emotional experiences

The phenomenon of aesthetic chills-shivers and goosebumps associated with either rewarding or threatening stimuli-offers a unique window into the brain basis of conscious reward because of their universal nature and simultaneous subjective and physical counterparts. Elucidating the neural mechanisms underlying aesthetic chills can reveal fundamental insights about emotion, consciousness, and the embodied mind. What is the precise timing and mechanism of bodily feedback in emotional experience? How are conscious feelings and motivations generated from interoceptive predictions? What is the role of uncertainty and precision signaling in shaping emotions? How does the brain distinguish and balance processing of rewards versus threats? We review neuroimaging evidence and highlight key questions for understanding how bodily sensations shape conscious feelings. This research stands to advance models of brain-body interactions shaping affect and may lead to novel nonpharmacological interventions for disorders of motivation and pleasure.

Rethinking interpersonal judgments: dopamine antagonists impact attributional dynamics

Barnby et al. investigated the effects of haloperidol, a D2/D3 dopamine antagonist, on social attributions. Using computational modeling, they demonstrate that haloperidol increases belief flexibility, reducing paranoia-like interpretations by enhancing sensitivity to social context and reducing self-relevant perspective taking, offering a mechanistic explanation for its therapeutic potential in schizophrenia.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

Surprise-minimization as a solution to the structural credit assignment problem

The structural credit assignment problem arises when the causal structure between actions and subsequent outcomes is hidden from direct observation. To solve this problem and enable goal-directed behavior, an agent has to infer structure and form a representation thereof. In the scope of this study, we investigate a possible solution in the human brain. We recorded behavioral and electrophysiological data from human participants in a novel variant of the bandit task, where multiple actions lead to multiple outcomes. Crucially, the mapping between actions and outcomes was hidden and not instructed to the participants. Human choice behavior revealed clear hallmarks of credit assignment and learning. Moreover, a computational model which formalizes action selection as the competition between multiple representations of the hidden structure was fit to account for participants data. Starting in a state of uncertainty about the correct representation, the central mechanism of this model is the arbitration of action control towards the representation which minimizes surprise about outcomes. Crucially, single-trial latent-variable analysis reveals that the neural patterns clearly support central quantitative predictions of this surprise minimization model. The results suggest that neural activity is not only related to reinforcement learning under correct as well as incorrect task representations but also reflects central mechanisms of credit assignment and behavioral arbitration.

The social transmission of empathy relies on observational reinforcement learning

Theories of moral development propose that empathy is transmitted across individuals. However, the mechanisms through which empathy is socially transmitted remain unclear. Here, we combine computational learning models and functional MRI to investigate whether, and if so, how empathic and non-empathic responses observed in others affect the empathy of female observers. The results of three independent studies showed that watching empathic or non-empathic responses generates a learning signal that respectively increases or decreases empathy ratings of the observer. A fourth study revealed that the learning-related transmission of empathy is stronger when observing human rather than computer demonstrators. Finally, we show that the social transmission of empathy alters empathy-related responses in the anterior insula, i.e., the same region that correlated with empathy baseline ratings, as well as its functional connectivity with the temporoparietal junction. Together, our findings provide a computational and neural mechanism for the social transmission of empathy that accounts for changes in individual empathic responses in empathic and non-empathic social environments.

Synthetic surprise as the foundation of the psychedelic experience

Psychedelic agents, such as LSD and psilocybin, induce marked alterations in consciousness via activation of the 5-HT2A receptor (5-HT2ARs). We hypothesize that psychedelics enforce a state of synthetic surprise through the biased activation of the 5-HTRs system. This idea is informed by recent insights into the role of 5-HT in signaling surprise. The effects on consciousness, explained by the cognitive penetrability of perception, can be described within the predictive coding framework where surprise corresponds to prediction error, the mismatch between predictions and actual sensory input. Crucially, the precision afforded to the prediction error determines its effect on priors, enabling a dynamic interaction between top-down expectations and incoming sensory data. By integrating recent findings on predictive coding circuitry and 5-HT2ARs transcriptomic data, we propose a biological implementation with emphasis on the role of inhibitory interneurons. Implications arise for the clinical use of psychedelics, which may rely primarily on their inherent capacity to induce surprise in order to disrupt maladaptive patterns.

The pulvinar as a hub of visual processing and cortical integration

The pulvinar nucleus of the thalamus is a crucial component of the visual system and plays significant roles in sensory processing and cognitive integration. The pulvinar's extensive connectivity with cortical regions allows for bidirectional communication, contributing to the integration of sensory information across the visual hierarchy. Recent findings underscore the pulvinar's involvement in attentional modulation, feature binding, and predictive coding. In this review, we highlight recent advances in clarifying the pulvinar's circuitry and function. We discuss the contributions of the pulvinar to signal modulation across the global cortical network and place these findings within theoretical frameworks of cortical processing, particularly the global neuronal workspace (GNW) theory and predictive coding.

Mental health meets computational neuroscience: A predictive Bayesian account of the relationship between interoception and multisensory bodily illusions in anorexia nervosa

Mental health disorders pose a significant challenge to society. The Bayesian perspective on the mind offers unique insights and tools that may help address a variety of mental health conditions. Psychopathological dysfunctions are often connected to altered predictive and active inference processes, in which cognitive and physiological pathogenic beliefs shape the clinical condition and its symptoms. However, there is a lack of general empirical models that integrate cognitive beliefs, physiological experience, and symptoms in healthy and clinical populations. In this study, we examined the relationship between altered predictive mechanisms, interoception, and pathological bodily distortions in healty individuals and in individuals suffering from anorexia nervosa (AN). AN patients (N=15) completed a Virtual Reality Full-Body Illusion along with interoceptive tasks twice: at hospital admission during an acute symptomatological phase (Time 1) and after a 12-week outpatient clinical weight-restoring rehabilitative program (Time 2). Results were compared to a healthy control group. Our findings indicated that higher levels of interoceptive metacognitive awareness were associated with a greater embodiment. However, unlike in healthy participants, AN patients' interoceptive metacognition was linked to embodiment even in multisensory mismatching (asynchronous) conditions. In addition, unlike in healthy participants, higher interoceptive metacognition in AN patients was related to prior abnormal bodily distortions during the acute symptomatology phase. Prediction errors in bodily estimates predicted posterior bodily estimate distortions after the illusion, but while this relationship was only significant in the synchronous condition in healthy participants, there was no significant difference between synchronous and asynchronous conditions in AN patients. Despite the success of the rehabilitation program in restoring some dysfunctional patterns in the AN group, prediction errors and posterior estimate distortions were present at hospital discharge. Our findings suggest that individuals with AN prioritize interoceptive metacognitive processes (i.e., confidence in their own perceived sensations rather than their actual perceptions), disregarding bottom-up bodily inputs in favour of their prior altered top-down beliefs. Moreover, even if the rehabilitative program partially mitigated these alterations, the pathological condition impaired the patients' ability to coherently update their prior erroneous expectations with real-time multisensory bottom-up bodily information, possibly locking the patients in the experience of a distorted prior top-down belief. These results suggest new therapeutic perspectives and introduce the framework of regenerative virtual therapy (RVT), which aims at utilizing technology-based somatic modification techniques to restructure the maladaptive priors underlying a pathological condition.

Insight and the selection of ideas

Perhaps it is no accident that insight moments accompany some of humanity's most important discoveries in science, medicine, and art. Here we propose that feelings of insight play a central role in (heuristically) selecting an idea from the stream of consciousness by capturing attention and eliciting a sense of intuitive confidence permitting fast action under uncertainty. The mechanisms underlying this Eureka heuristic are explained within an active inference framework. First, implicit restructuring via Bayesian reduction leads to a higher-order prediction error (i.e., the content of insight). Second, dopaminergic precision-weighting of the prediction error accounts for the intuitive confidence, pleasure, and attentional capture (i.e., the feeling of insight). This insight as precision account is consistent with the phenomenology, accuracy, and neural unfolding of insight, as well as its effects on belief and decision-making. We conclude by reflecting on dangers of the Eureka Heuristic, including the arising and entrenchment of false beliefs and the vulnerability of insights under psychoactive substances and misinformation.

The dorsal lateral geniculate nucleus and the pulvinar as essential partners for visual cortical functions

In most neuroscience textbooks, the thalamus is presented as a structure that relays sensory signals from visual, auditory, somatosensory, and gustatory receptors to the cerebral cortex. But the function of the thalamic nuclei goes beyond the simple transfer of information. This is especially true for the second-order nuclei, but also applies to first-order nuclei. First order thalamic nuclei receive information from the periphery, like the dorsal lateral geniculate nucleus (dLGN), which receives a direct input from the retina. In contrast, second order thalamic nuclei, like the pulvinar, receive minor or no input from the periphery, with the bulk of their input derived from cortical areas. The dLGN refines the information received from the retina by temporal decorrelation, thereby transmitting the most "relevant" signals to the visual cortex. The pulvinar is closely linked to virtually all visual cortical areas, and there is growing evidence that it is necessary for normal cortical processing and for aspects of visual cognition. In this article, we will discuss what we know and do not know about these structures and propose some thoughts based on the knowledge gained during the course of our careers. We hope that these thoughts will arouse curiosity about the visual thalamus and its important role, especially for the next generation of neuroscientists.

Temporal lobe perceptual predictions for speech are instantiated in motor cortex and reconciled by inferior frontal cortex

Humans use predictions to improve speech perception, especially in noisy environments. Here we use 7-T functional MRI (fMRI) to decode brain representations of written phonological predictions and degraded speech signals in healthy humans and people with selective frontal neurodegeneration (non-fluent variant primary progressive aphasia [nfvPPA]). Multivariate analyses of item-specific patterns of neural activation indicate dissimilar representations of verified and violated predictions in left inferior frontal gyrus, suggestive of processing by distinct neural populations. In contrast, precentral gyrus represents a combination of phonological information and weighted prediction error. In the presence of intact temporal cortex, frontal neurodegeneration results in inflexible predictions. This manifests neurally as a failure to suppress incorrect predictions in anterior superior temporal gyrus and reduced stability of phonological representations in precentral gyrus. We propose a tripartite speech perception network in which inferior frontal gyrus supports prediction reconciliation in echoic memory, and precentral gyrus invokes a motor model to instantiate and refine perceptual predictions for speech.

Computational mechanisms of belief updating in relation to psychotic-like experiences

Introduction

Psychotic-like experiences (PLEs) may occur due to changes in weighting prior beliefs and new evidence in the belief updating process. It is still unclear whether the acquisition or integration of stable beliefs is altered, and whether such alteration depends on the level of environmental and belief precision, which reflects the associated uncertainty. This motivated us to investigate uncertainty-related dynamics of belief updating in relation to PLEs using an online study design.

Methods

We selected a sample (n = 300) of participants who performed a belief updating task with sudden change points and provided self-report questionnaires for PLEs. The task required participants to observe bags dropping from a hidden helicopter, infer its position, and dynamically update their belief about the helicopter's position. Participants could optimize performance by adjusting learning rates according to inferred belief uncertainty (inverse prior precision) and the probability of environmental change points. We used a normative learning model to examine the relationship between adherence to specific model parameters and PLEs.

Results

PLEs were linked to lower accuracy in tracking the outcome (helicopter location) (β = 0.26 ± 0.11, p = 0.018) and to a smaller increase of belief precision across observations after a change point (β = -0.003 ± 0.0007, p < 0.001). PLEs were related to slower belief updating when participants encountered large prediction errors (β = -0.03 ± 0.009, p = 0.001). Computational modeling suggested that PLEs were associated with reduced overall belief updating in response to prediction errors (βPE = -1.00 ± 0.45, p = 0.028) and reduced modulation of updating at inferred environmental change points (βCPP = -0.84 ± 0.38, p = 0.023).

Discussion

We conclude that PLEs are associated with altered dynamics of belief updating. These findings support the idea that the process of balancing prior belief and new evidence, as a function of environmental uncertainty, is altered in PLEs, which may contribute to the development of delusions. Specifically, slower learning after large prediction errors in people with high PLEs may result in rigid beliefs. Disregarding environmental change points may limit the flexibility to establish new beliefs in the face of contradictory evidence. The present study fosters a deeper understanding of inferential belief updating mechanisms underlying PLEs.

Insular and Striatal Correlates of Uncertain Risky Reward Pursuit in Schizophrenia

BACKGROUND AND HYPOTHESIS

Risk-taking in specific contexts can be beneficial, leading to rewarding outcomes. Schizophrenia is associated with disadvantageous decision-making, as subjects pursue uncertain risky rewards less than controls. However, it is unclear whether this behavior is associated with more risk sensitivity or less reward incentivization. Matching on demographics and intelligence quotient (IQ), we determined whether risk-taking was more associated with brain activation in regions affiliated with risk evaluation or reward processing.

STUDY DESIGN

Subjects (30 schizophrenia/schizoaffective disorder, 30 controls) completed a modified, fMRI Balloon Analogue Risk Task. Brain activation was modeled during decisions to pursue risky rewards and parametrically modeled according to risk level.

STUDY RESULTS

The schizophrenia group exhibited less risky-reward pursuit despite previous adverse outcomes (Average Explosions; F(1,59) = 4.06, P = .048) but the comparable point at which risk-taking was volitionally discontinued (Adjusted Pumps; F(1,59) = 2.65, P = .11). Less activation was found in schizophrenia via whole brain and region of interest (ROI) analyses in the right (F(1,59) = 14.91, P < 0.001) and left (F(1,59) = 16.34, P < 0.001) nucleus accumbens (NAcc) during decisions to pursue rewards relative to riskiness. Risk-taking correlated with IQ in schizophrenia, but not controls. Path analyses of average ROI activation revealed less statistically determined influence of anterior insula upon dorsal anterior cingulate bilaterally (left: χ2 = 12.73, P < .001; right: χ2 = 9.54, P = .002) during risky reward pursuit in schizophrenia.

CONCLUSIONS

NAcc activation in schizophrenia varied less according to the relative riskiness of uncertain rewards compared to controls, suggesting aberrations in reward processing. The lack of activation differences in other regions suggests similar risk evaluation. Less insular influence on the anterior cingulate may relate to attenuated salience attribution or inability for risk-related brain region collaboration to sufficiently perceive situational risk.

Cessations of consciousness in meditation: Advancing a scientific understanding of nirodha samāpatti

Absence of consciousness can occur due to a concussion, anesthetization, intoxication, epileptic seizure, or other fainting/syncope episode caused by lack of blood flow to the brain. However, some meditation practitioners also report that it is possible to undergo a total absence of consciousness during meditation, lasting up to 7 days, and that these "cessations" can be consistently induced. One form of extended cessation (i.e., nirodha samāpatti) is thought to be different from sleep because practitioners are said to be completely impervious to external stimulation. That is, they cannot be 'woken up' from the cessation state as one might be from a dream. Cessations are also associated with the absence of any time experience or tiredness, and are said to involve a stiff rather than a relaxed body. Emergence from meditation-induced cessations is said to have profound effects on subsequent cognition and experience (e.g., resulting in a sudden sense of clarity, openness, and possibly insights). In this paper, we briefly outline the historical context for cessation events, present preliminary data from two labs, set a research agenda for their study, and provide an initial framework for understanding what meditation induced cessation may reveal about the mind and brain. We conclude by integrating these so-called nirodha and nirodha samāpatti experiences-as they are known in classical Buddhism-into current cognitive-neurocomputational and active inference frameworks of meditation.

Belief updating in psychosis, depression and anxiety disorders: A systematic review across computational modelling approaches

Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.

Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans

Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.

Modulation of Motor Vigor by Expectation of Reward Probability Trial-by-Trial Is Preserved in Healthy Ageing and Parkinson's Disease Patients

Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigor dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a reversal learning paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger [HYA; 37 (24 females)] and older adults [HOA; 37 (17 females)], and medicated Parkinson's disease (PD) patients [20 (7 females)]. We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo, commensurate with movement time, on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA, and PD, despite HOA and PD being slower than HYA. In Study 2 [HYA, 39 (29 females)], we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigor effects. Last, Study 3 [HYA, 33 (27 females)] revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigor to inferences about the action-reward mapping in aging and medicated PD.SIGNIFICANCE STATEMENT Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here, we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigor trial-by-trial in healthy younger and older adults, and in Parkinson's disease (PD) patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigor. This positive bias was not compromised by age or Parkinson's disease.

Aesthetic chills cause an emotional drift in valence and arousal

Aesthetic chills are an embodied peak emotional experience induced by stimuli such as music, films, and speeches and characterized by dopaminergic release. The emotional consequences of chills in terms of valence and arousal are still debated and the existing empirical data is conflicting. In this study, we tested the effects of ChillsDB, an open-source repository of chills-inducing stimuli, on the emotional ratings of 600+ participants. We found that participants experiencing chills reported significantly more positive valence and greater arousal during the experience, compared to participants who did not experience chills. This suggests that the embodied experience of chills may influence one's perception and affective evaluation of the context, in favor of theoretical models emphasizing the role of interoceptive signals such as chills in the process of perception and decision-making. We also found an interesting pattern in the valence ratings of participants, which tended to harmonize toward a similar mean after the experiment, though initially disparately distributed. We discuss the significance of these results for the diagnosis and treatment of dopaminergic disorders such as Parkinson's, schizophrenia, and depression.

Expectation violations enhance neuronal encoding of sensory information in mouse primary visual cortex

The response of cortical neurons to sensory stimuli is shaped both by past events (adaptation) and the expectation of future events (prediction). Here we employed a visual stimulus paradigm with different levels of predictability to characterise how expectation influences orientation selectivity in the primary visual cortex (V1) of male mice. We recorded neuronal activity using two-photon calcium imaging (GCaMP6f) while animals viewed sequences of grating stimuli which either varied randomly in their orientations or rotated predictably with occasional transitions to an unexpected orientation. For single neurons and the population, there was significant enhancement in the gain of orientation-selective responses to unexpected gratings. This gain-enhancement for unexpected stimuli was prominent in both awake and anaesthetised mice. We implemented a computational model to demonstrate how trial-to-trial variability in neuronal responses were best characterised when adaptation and expectation effects were combined.

Negative symptoms, striatal dopamine and model-free reward decision-making in schizophrenia

Negative symptoms, such as lack of motivation or social withdrawal, are highly prevalent and debilitating in patients with schizophrenia. Underlying mechanisms of negative symptoms are incompletely understood, thereby preventing the development of targeted treatments. We hypothesized that in patients with schizophrenia during psychotic remission, impaired influences of both model-based and model-free reward predictions on decision-making ('reward prediction influence', RPI) underlie negative symptoms. We focused on psychotic remission, because psychotic symptoms might confound reward-based decision-making. Moreover, we hypothesized that impaired model-based/model-free RPIs depend on alterations of both associative striatum dopamine synthesis and storage (DSS) and executive functioning. Both factors influence RPI in healthy subjects and are typically impaired in schizophrenia. Twenty-five patients with schizophrenia with pronounced negative symptoms during psychotic remission and 24 healthy controls were included in the study. Negative symptom severity was measured by the Positive and Negative Syndrome Scale negative subscale, model-based/model-free RPI by the two-stage decision task, associative striatum DSS by 18F-DOPA positron emission tomography and executive functioning by the symbol coding task. Model-free RPI was selectively reduced in patients and associated with negative symptom severity as well as with reduced associative striatum DSS (in patients only) and executive functions (both in patients and controls). In contrast, model-based RPI was not altered in patients. Results provide evidence for impaired model-free reward prediction influence as a mechanism for negative symptoms in schizophrenia as well as for reduced associative striatum dopamine and executive dysfunction as relevant factors. Data suggest potential treatment targets for patients with schizophrenia and pronounced negative symptoms.

Bayes' Theorem in Neurocritical Care: Principles and Practice

Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. Research designs and approaches based on Bayesian models have the potential to meet this need, as they are specifically designed to evolve with emerging evidence. This adaptability provides a benefit over the popular frequentist statistical approach, as it provides a way of adjusting hypotheses and trial procedures to maximize efficacy. This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders.

Memory-Based Prediction Deficits and Dorsolateral Prefrontal Dysfunction in Schizophrenia

BACKGROUND

Theories suggest that people with schizophrenia (SZ) have problems generating predictions based on past experiences. The dorsolateral prefrontal cortex (DLPFC) and hippocampus participate in memory-based prediction. We used functional magnetic resonance imaging to investigate DLPFC and hippocampal function in healthy control (HC) subjects and people with SZ during memory-based prediction.

METHODS

Prior to scanning, HC subjects (n = 54) and people with SZ (n = 31) learned 5-object sequences presented in fixed or random orders on each repetition. During scanning, participants made semantic decisions (e.g., "Can this object fit in a shoebox?") on a continuous stream of objects from fixed and random sequences. Sequence prediction was demonstrated by faster semantic decisions for objects in fixed versus random sequences because memory could be used to anticipate and more efficiently process semantic information about upcoming objects in fixed sequences. Representational similarity analyses were used to determine how each sequence type was represented in the posterior hippocampus and DLPFC.

RESULTS

Sequence predictions were reduced in individuals with SZ relative to HC subjects. Representational similarity analyses revealed stronger memory-based predictions in the DLPFC of HC subjects than people with SZ, and DLPFC representations correlated with more successful predictions in HC subjects only. For the posterior hippocampus, voxel pattern similarity was increased for fixed versus random sequences in HC subjects only, but no significant between-group differences or correlations with prediction success were observed.

CONCLUSIONS

Individuals with SZ are capable of learning temporal sequences; however, they are impaired using memory to predict upcoming events as efficiently as HC subjects. This deficit appears related to disrupted neural representation of sequence information in the DLPFC.

Belief Updating in Subclinical and Clinical Delusions

Background and Hypothesis

Current frameworks propose that delusions result from aberrant belief updating due to altered prediction error (PE) signaling and misestimation of environmental volatility. We aimed to investigate whether behavioral and neural signatures of belief updating are specifically related to the presence of delusions or generally associated with manifest schizophrenia.

Methods

Our cross-sectional design includes human participants (n[female/male] = 66[25/41]), stratified into four groups: healthy participants with minimal (n = 22) or strong delusional-like ideation (n = 18), and participants with diagnosed schizophrenia with minimal (n = 13) or strong delusions (n = 13), resulting in a 2 × 2 design, which allows to test for the effects of delusion and diagnosis. Participants performed a reversal learning task with stable and volatile task contingencies during fMRI scanning. We formalized learning with a hierarchical Gaussian filter model and conducted model-based fMRI analysis regarding beliefs of outcome uncertainty and volatility, precision-weighted PEs of the outcome- and the volatility-belief.

Results

Patients with schizophrenia as compared to healthy controls showed lower accuracy and heightened choice switching, while delusional ideation did not affect these measures. Participants with delusions showed increased precision-weighted PE-related neural activation in fronto-striatal regions. People with diagnosed schizophrenia overestimated environmental volatility and showed an attenuated neural representation of volatility in the anterior insula, medial frontal and angular gyrus.

Conclusions

Delusional beliefs are associated with altered striatal PE-signals. Juxtaposing, the potentially unsettling belief that the environment is constantly changing and weaker neural encoding of this subjective volatility seems to be associated with manifest schizophrenia, but not with the presence of delusional ideation.

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.

Adaptive coding occurs in object categorization and may not be associated with schizotypal personality traits

Processing more likely inputs with higher sensitivity (adaptive coding) enables the brain to represent the large range of inputs coming in from the world. Healthy individuals high in schizotypy show reduced adaptive coding in the reward domain but it is an open question whether these deficits extend to non-motivational domains, such as object categorization. Here, we develop a novel variant of a classic task to test range adaptation for face/house categorization in healthy participants on the psychosis spectrum. In each trial of this task, participants decide whether a presented image is a face or a house. Images vary on a face-house continuum and appear in both wide and narrow range blocks. The wide range block includes most of the face-house continuum (2.50-97.5% face), while the narrow range blocks limit inputs to a smaller section of the continuum (27.5-72.5% face). Adaptive coding corresponds to better performance for the overlapping smaller section of the continuum in the narrow range than in the wide range block. We find that participants show efficient use of the range in this task, with more accurate responses in the overlapping section for the narrow range blocks relative to the wide range blocks. However, we find little evidence that range adaptation in our object categorization task is reduced in healthy individuals scoring high on schizotypy. Thus, reduced range adaptation may not be a domain-general feature of schizotypy.

Persistence, Reward Dependence, and Sensitivity to Reward Are Associated With Unexpected Salience Response in Girls but Not in Adult Women: Implications for Psychiatric Vulnerabilities

BACKGROUND

Adolescence is a critical period for the development of not only personality but also psychopathology. These processes may be specific to sex, and brain reward circuits may have a role. Here, we studied how reward processing and temperament associations differ across adolescent and adult females.

METHODS

A total of 29 adolescent girls and 41 adult women completed temperament assessments and performed a classical taste conditioning paradigm during brain imaging. Data were analyzed for the dopamine-related prediction error response. In addition, unexpected stimulus receipt or omission and expected receipt response were also analyzed. Heat maps identified cortical-subcortical brain response associations.

RESULTS

Adolescents showed stronger prediction error and unexpected receipt and omission responses (partial η² = 0.063 to 0.166; p = .001 to .043) in insula, orbitofrontal cortex (OFC), and striatum than adults. Expected stimulus receipt response was similar between groups. In adolescents versus adults, persistence was more strongly positively related to prediction error (OFC, insula, striatum; Fisher's z = 1.704 to 3.008; p = .001 to .044) and unexpected stimulus receipt (OFC, insula; Fisher's z = 1.843 to 2.051; p = .014 to .033) and negatively with omission (OFC, insula, striatum; Fisher's z = -1.905 to -3.069; p = .001 to .028). Reward sensitivity and reward dependence correlated more positively with unexpected stimulus receipt and more negatively with stimulus omission response in adolescents. Adolescents showed significant correlations between the striatum and FC for unexpected stimulus receipt and omission that correlated with persistence but were absent in adults.

CONCLUSIONS

Associations between temperamental traits and brain reward response may provide neurotypical markers that contribute to developing adaptive or maladaptive behavior patterns when transitioning from adolescence to adulthood.

Etiopathogenic Models of Psychosis Spectrum Illnesses Must Resolve Four Key Features

Etiopathogenic models for psychosis spectrum illnesses are converging on a number of key processes, such as the influence of specific genes on the synthesis of proteins important in synaptic functioning, alterations in how neurons respond to synaptic inputs and engage in synaptic pruning, and microcircuit dysfunction that leads to more global cortical information processing vulnerabilities. Disruptions in prefrontal operations then accumulate and propagate over time, interacting with environmental factors, developmental processes, and homeostatic mechanisms, eventually resulting in symptoms of psychosis and disability. However, there are 4 key features of psychosis spectrum illnesses that are of primary clinical relevance but have been difficult to assimilate into a single model and have thus far received little direct attention: 1) the bidirectionality of the causal influences for the emergence of psychosis, 2) the catastrophic clinical threshold seen in first episodes of psychosis and why it is irreversible in some individuals, 3) observed biotypes that are neurophysiologically distinct but clinically both convergent and divergent, and 4) a reconciliation of the role of striatal dopaminergic dysfunction with models of prefrontal cortical state instability. In this selective review, we briefly describe these 4 hallmark features and we argue that theoretically driven computational perspectives making use of both algorithmic and neurophysiologic models are needed to reduce this complexity and variability of psychosis spectrum illnesses in a principled manner.

Functional neuroimaging in psychiatry and the case for failing better

Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.

From representations in predictive processing to degrees of representational features

Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success.

Uncertainty-guided learning with scaled prediction errors in the basal ganglia

To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We show that the new model has an advantage over conventional reinforcement learning models in a value tracking task, and approaches a theoretic limit of performance provided by the Kalman filter. Further, we propose a possible biological implementation of the model in the basal ganglia circuit. In the proposed network, dopaminergic neurons encode reward prediction errors scaled by standard deviation of rewards. We show that such scaling may arise if the striatal neurons learn the standard deviation of rewards and modulate the activity of dopaminergic neurons. The model is consistent with experimental findings concerning dopamine prediction error scaling relative to reward magnitude, and with many features of striatal plasticity. Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.

The human insula processes both modality-independent and pain-selective learning signals

Prediction errors (PEs) are generated when there are differences between an expected and an actual event or sensory input. The insula is a key brain region involved in pain processing, and studies have shown that the insula encodes the magnitude of an unexpected outcome (unsigned PEs). In addition to signaling this general magnitude information, PEs can give specific information on the direction of this deviation-i.e., whether an event is better or worse than expected. It is unclear whether the unsigned PE responses in the insula are selective for pain or reflective of a more general processing of aversive events irrespective of modality. It is also unknown whether the insula can process signed PEs at all. Understanding these specific mechanisms has implications for understanding how pain is processed in the brain in both health and in chronic pain conditions. In this study, 47 participants learned associations between 2 conditioned stimuli (CS) with 4 unconditioned stimuli (US; painful heat or loud sound, of one low and one high intensity each) while undergoing functional magnetic resonance imaging (fMRI) and skin conductance response (SCR) measurements. We demonstrate that activation in the anterior insula correlated with unsigned intensity PEs, irrespective of modality, indicating an unspecific aversive surprise signal. Conversely, signed intensity PE signals were modality specific, with signed PEs following pain but not sound located in the dorsal posterior insula, an area implicated in pain intensity processing. Previous studies have identified abnormal insula function and abnormal learning as potential causes of pain chronification. Our findings link these results and suggest that a misrepresentation of learning relevant PEs in the insular cortex may serve as an underlying factor in chronic pain.

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.

Linking Salience Signaling With Early Adversity and Affective Distress in Individuals at Clinical High Risk for Psychosis: Results From an Event-Related fMRI Study

Evidence suggests dysregulation of the salience network in individuals with psychosis, but few studies have examined the intersection of stress exposure and affective distress with prediction error (PE) signals among youth at clinical high-risk (CHR). Here, 26 individuals at CHR and 19 healthy volunteers (HVs) completed a monetary incentive delay task in conjunction with fMRI. We compared these groups on the amplitudes of neural responses to surprising outcomes-PEs without respect to their valence-across the whole brain and in two regions of interest, the anterior insula and amygdala. We then examined relations of these signals to the severity of depression, anxiety, and trauma histories in the CHR group. Relative to HV, youth at CHR presented with aberrant PE-evoked activation of the temporoparietal junction and weaker deactivation of the precentral gyrus, posterior insula, and associative striatum. No between-group differences were observed in the amygdala or anterior insula. Among youth at CHR, greater trauma histories were correlated with stronger PE-evoked amygdala activation. No associations were found between affective symptoms and the neural responses to PE. Our results suggest that unvalenced PE signals may provide unique information about the neurobiology of CHR syndromes and that early adversity exposure may contribute to neurobiological heterogeneity in this group. Longitudinal studies of young people with a range of risk syndromes are needed to further disentangle the contributions of distinct aspects of salience signaling to the development of psychopathology.

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.

Neural Circuitry of Salience and Reward Processing in Psychosis

The processing of salient and rewarding stimuli is integral to engaging our attention, stimulating anticipation for future events, and driving goal-directed behaviors. Widespread impairments in these processes are observed in psychosis, which may be associated with worse functional outcomes or mechanistically linked to the development of symptoms. Here, we summarize the current knowledge of behavioral and functional neuroimaging in salience, prediction error, and reward. Although each is a specific process, they are situated in multiple feedback and feedforward systems integral to decision making and cognition more generally. We argue that the origin of salience and reward processing dysfunctions may be centered in the subcortex during the earliest stages of psychosis, with cortical abnormalities being initially more spared but becoming more prominent in established psychotic illness/schizophrenia. The neural circuits underpinning salience and reward processing may provide targets for delaying or preventing progressive behavioral and neurobiological decline.

Drinking and smoking polygenic risk is associated with childhood and early-adulthood psychiatric and behavioral traits independently of substance use and psychiatric genetic risk

Alcohol drinking and tobacco smoking are hazardous behaviors associated with a wide range of adverse health outcomes. In this study, we explored the association of polygenic risk scores (PRS) related to drinks per week, age of smoking initiation, smoking initiation, cigarettes per day, and smoking cessation with 433 psychiatric and behavioral traits in 4498 children and young adults (aged 8-21) of European ancestry from the Philadelphia neurodevelopmental cohort. After applying a false discovery rate multiple testing correction accounting for the number of PRS and traits tested, we identified 36 associations related to psychotic symptoms, emotion and age recognition social competencies, verbal reasoning, anxiety-related traits, parents' education, and substance use. These associations were independent of the genetic correlations among the alcohol-drinking and tobacco-smoking traits and those with cognitive performance, educational attainment, risk-taking behaviors, and psychopathology. The removal of participants endorsing substance use did not affect the associations of each PRS with psychiatric and behavioral traits identified as significant in the discovery analyses. Gene-ontology enrichment analyses identified several neurobiological processes underlying mechanisms of the PRS associations we report. In conclusion, we provide novel insights into the genetic overlap of smoking and drinking behaviors in children and young adults, highlighting their independence from psychopathology and substance use.

Range-Adaptive Value Representation in Different Stages of Schizophrenia: A Proof of Concept Study

Amotivation is related to value representation. A comprehensive account of amotivation requires a mechanistic understanding of how the brain exploits external information to represent value. To achieve maximal value discriminability, brain valuation system will dynamically adapt its coding sensitivity to the range of values available in any given condition, so-called range adaptive coding. We administered an experimental task to 30 patients with chronic schizophrenia (C-SCZ), 30 first-episode schizophrenia (FE-SCZ), 34 individuals with high social anhedonia (HSoA), and their paired controls to assess range adaptation ability. C-SCZ patients exhibited over-adaptation and their performances were negatively correlated with avolition symptoms and positive symptoms and positively correlated with blunted-affect symptoms and self-reported consummatory interpersonal pleasure scores, though the results were non-significant. FE-SCZ patients exhibited reduced adaptation, which was significantly and negatively correlated with avolition symptoms and positively correlated with the overall proportion of choosing to exert more effort. Although HSoA participants exhibited comparable range adaptation to controls, their performances were significantly and negatively correlated with the proportion of choosing to exert more effort under the lowest value condition. Our results suggest that different stages of schizophrenia spectrum showed distinct range adaptation patterns. Range adaptation impairments may index a possible underlying mechanism for amotivation symptoms in FE-SCZ and more complicated and pervasive effects on clinical symptoms in C-SCZ.

Standard Tone Stability as a Manipulation of Precision in the Oddball Paradigm: Modulation of Prediction Error Responses to Fixed-Probability Deviants

Electrophysiological sensory deviance detection signals, such as the mismatch negativity (MMN), have been interpreted from the predictive coding framework as manifestations of prediction error (PE). From a frequentist perspective of the classic oddball paradigm, deviant stimuli are unexpected because of their low probability. However, the amount of PE elicited by a stimulus can be dissociated from its probability of occurrence: when the observer cannot make confident predictions, any event holds little surprise value, no matter how improbable. Here we tested the hypothesis that the magnitude of the neural response elicited to an improbable sound (D) would scale with the precision of the prediction derived from the repetition of another sound (S), by manipulating repetition stability. We recorded the Electroencephalogram (EEG) from 20 participants while passively listening to 4 types of isochronous pure tone sequences differing in the probability of the S tone (880 Hz) while holding constant the probability of the D tone [1,046 Hz; p(D) = 1/11]: Oddball [p(S) = 10/11]; High confidence (7/11); Low confidence (4/11); and Random (1/11). Tones of 9 different frequencies were equiprobably presented as fillers [p(S) + p(D) + p(F) = 1]. Using a mass-univariate non-parametric, cluster-based correlation analysis controlling for multiple comparisons, we found that the amplitude of the deviant-elicited ERP became more negative with increasing S probability, in a time-electrode window consistent with the MMN (ca. 120–200 ms; frontal), suggesting that the strength of a PE elicited to an improbable event indeed increases with the precision of the predictive model.

Cross-modal auditory priors drive the perception of bistable visual stimuli with reliable differences between individuals

It is a widely held assumption that the brain performs perceptual inference by combining sensory information with prior expectations, weighted by their uncertainty. A distinction can be made between higher- and lower-level priors, which can be manipulated with associative learning and sensory priming, respectively. Here, we simultaneously investigate priming and the differential effect of auditory vs. visual associative cues on visual perception, and we also examine the reliability of individual differences. Healthy individuals (N = 29) performed a perceptual inference task twice with a one-week delay. They reported the perceived direction of motion of dot pairs, which were preceded by a probabilistic visuo-acoustic cue. In 30% of the trials, motion direction was ambiguous, and in half of these trials, the auditory versus the visual cue predicted opposing directions. Cue-stimulus contingency could change every 40 trials. On ambiguous trials where the visual and the auditory cue predicted conflicting directions of motion, participants made more decisions consistent with the prediction of the acoustic cue. Increased predictive processing under stimulus uncertainty was indicated by slower responses to ambiguous (vs. non-ambiguous) stimuli. Furthermore, priming effects were also observed in that perception of ambiguous stimuli was influenced by perceptual decisions on the previous ambiguous and unambiguous trials as well. Critically, behavioural effects had substantial inter-individual variability which showed high test-retest reliability (intraclass correlation coefficient (ICC) > 0.78). Overall, higher-level priors based on auditory (vs. visual) information had greater influence on visual perception, and lower-level priors were also in action. Importantly, we observed large and stable differences in various aspects of task performance. Computational modelling combined with neuroimaging could allow testing hypotheses regarding the potential mechanisms causing these behavioral effects. The reliability of the behavioural differences implicates that such perceptual inference tasks could be valuable tools during large-scale biomarker and neuroimaging studies.

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

Alterations in the balance between prior expectations and sensory evidence may account for faulty perceptions and inferences leading to psychosis. However, uncertainties remain about the nature of altered prior expectations and the degree to which they vary with the emergence of psychosis. We explored how expectations arising at two different levels—cognitive and perceptual—influenced processing of sensory information and whether relative influences of higher- and lower-level priors differed across people with prodromal symptoms and those with psychotic illness. In two complementary auditory perception experiments, 91 participants (30 with first-episode psychosis, 29 at clinical risk for psychosis, and 32 controls) were required to decipher a phoneme within ambiguous auditory input. Expectations were generated in two ways: an accompanying visual input of lip movements observed during auditory presentation or through written presentation of a phoneme provided prior to auditory presentation. We determined how these different types of information shaped auditory perceptual experience, how this was altered across the prodromal and established phases of psychosis, and how this relates to cingulate glutamate levels assessed by magnetic resonance spectroscopy. The psychosis group relied more on high-level cognitive priors compared to both healthy controls and those at clinical risk for psychosis and relied more on low-level perceptual priors than the clinical risk group. The risk group was marginally less reliant on low-level perceptual priors than controls. The results are consistent with previous theory that influences of prior expectations in perceptions in psychosis differ according to level of prior and illness phase.

What we perceive and believe in any given moment will allow us to form expectations about what we will experience in the next. In psychosis, it is believed that the influence of these so-called perceptual and cognitive “prior” expectations on perception are altered, thereby giving rise to the symptoms seen in psychosis. However, research thus far has found mixed evidence, some suggesting an increase in the influence of priors and some finding a decrease. Here we test the hypothesis that perceptual and cognitive priors are differentially affected in individuals at risk for psychosis and individuals with a first episode of psychosis, thereby partially explaining the mixed findings in the literature. We indeed found evidence in favor of this hypothesis, finding weaker perceptual priors in individuals at risk but stronger cognitive priors in individuals with first-episode psychosis.

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.

Dopamine modulates subcortical responses to surprising sounds

Dopamine guides behavior and learning through pleasure, according to classic understanding. Dopaminergic neurons are traditionally thought to signal positive or negative prediction errors (PEs) when reward expectations are, respectively, exceeded or not matched. These signed PEs are quite different from the unsigned PEs, which report surprise during sensory processing. But mounting theoretical accounts from the predictive processing framework postulate that dopamine, as a neuromodulator, could potentially regulate the postsynaptic gain of sensory neurons, thereby scaling unsigned PEs according to their expected precision or confidence. Despite ample modeling work, the physiological effects of dopamine on the processing of surprising sensory information are yet to be addressed experimentally. In this study, we tested how dopamine modulates midbrain processing of unexpected tones. We recorded extracellular responses from the rat inferior colliculus to oddball and cascade sequences, before, during, and after the microiontophoretic application of dopamine or eticlopride (a D₂-like receptor antagonist). Results demonstrate that dopamine reduces the net neuronal responsiveness exclusively to unexpected sensory input without significantly altering the processing of expected input. We conclude that dopaminergic projections from the thalamic subparafascicular nucleus to the inferior colliculus could encode the expected precision of unsigned PEs, attenuating via D₂-like receptors the postsynaptic gain of sensory inputs forwarded by the auditory midbrain neurons. This direct dopaminergic modulation of sensory PE signaling has profound implications for both the predictive coding framework and the understanding of dopamine function.

Information about unexpected stimuli is encoded in the form of prediction error signals. The earliest prediction error signals identified in the auditory brain emerge subcortically in the inferior colliculus. This study reveals the essential role of dopamine in encoding the precision of prediction errors at the auditory midbrain.