Hierarchical prediction errors in midbrain and basal forebrain during sensory learning

Predictive coding in the human olfactory system

The human olfactory system is unusual. It deviates from the classical structure and function of other sensory cortices, and many of its basic computations remain mysterious. These idiosyncrasies have challenged the development of a clear and comprehensive theoretical framework in olfactory neuroscience. To address this challenge, we develop a theory of olfactory predictive coding that aims to unify diverse olfactory phenomena. Under this scheme, the olfactory system is not merely passively processing sensory information. Instead, it is actively issuing predictions about sensory inputs before they even arrive. We map this conceptual framework onto the micro- and macroscale neurobiology of the human olfactory system and review a variety of neurobiological, computational, and behavioral evidence in support of this scheme.

Precision-dependent modulation of social attention

Social attention, guided by cues like gaze direction, is crucial for effective social interactions. However, how dynamic environmental context modulates this process remains unclear. Integrating a hierarchical Bayesian model with fMRI, this study investigated how individuals adjusted attention based on the predictions about cue validity (CV). Thirty-three participants performed a modified Posner location-cueing task with varying CV. Behaviorally, individuals' allocation of social attention was finely tuned to the precision (inverse variance) of CV predictions, with the predictions updated by precision-weighted prediction errors (PEs) about the occurrence of target locations. Neuroimaging results revealed that the interaction between allocation of social attention and CV influenced activity in regions involved in spatial attention and/or social perception. Precision-weighted PEs about target locations specifically modulated activity in the temporoparietal junction (TPJ), superior temporal sulcus (STS), and primary visual cortex (V1), underscoring their roles in refining attentional predictions. Dynamic causal modeling (DCM) further demonstrated that enhanced absolute precision-weighted PEs about target locations strengthened the effective connectivity from V1 and STS to TPJ, emphasizing their roles in conveying residual error signals upwards to high-level critical attention areas. These findings emphasized the pivotal role of precision in attentional modulation, enhancing our understanding of context-dependent social attention.

Temporal context modulates the recovery of the attentional blink

Humans usually adjust their attentional mode to tackle the challenges posed by environmental inputs. Depending on the uncertainty level, different attentional strategies may be adopted. As people face increasingly complicated daily situations-e.g., driving a car or chatting online-where intervals between significant events do not necessarily follow certain rules but are likely random, it appears important to understand how temporal contexts with different uncertainty levels affect temporal attention allocation when processing rapid serial inputs. We pursued this issue by employing a task examining the temporal limit of attention-the attentional blink (AB). The manipulation of temporal context was achieved by presenting trials with different inter-target intervals following either a "random-walk" or a "random" sequence. The results suggest a facilitated recovery from the AB deficit in the "random" compared to "random-walk" context, without a corresponding change in AB magnitude. Such effect is likely attributed to the higher perceived uncertainty in the former, and could be attenuated by a decrease in the temporal uncertainty level. These observations suggest that observers likely adopted a more flexible temporal attention allocation in the more unpredictable "random" context; they also support non-overlapping mechanisms responsible for AB width/duration and amplitude or lag-1 sparing. The flexibility of temporal attentional control may provide an evolutionary advantage for organisms to deal with unpredictable changes and is likely to be exploited for reference in the design of human-machine interacting platforms.

Bayesian Workflow for Generative Modeling in Computational Psychiatry

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that models harnessing information from two different data streams (binary responses and continuous response times) ensure robust inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants' uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.

Thermosensory predictive coding underpins an illusion of pain

The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty. We find that these expectations directly modulate the perception of pain in the thermal grill illusion. Quantitative microstructural brain imaging further revealed that individual variability in computational parameters related to uncertainty-driven learning and decision-making is reflected in the microstructure of brain regions such as the precuneus, posterior cingulate gyrus, cerebellum, as well as basal ganglia and brainstem. These findings provide a framework to understand how the brain infers pain from innocuous thermal inputs, with important implications for the etiology of thermosensory symptoms under chronic pain conditions.

Exploring when to exploit: the cognitive underpinnings of foraging-type decisions in relation to psychopathy

Impairments in reinforcement learning (RL) might underlie the tendency of individuals with elevated psychopathic traits to behave exploitatively, as they fail to learn from their mistakes. Most studies on the topic have focused on binary choices, while everyday functioning requires us to learn the value of multiple options. In this study, we evaluated the cognitive correlates of naturalistic foraging-type decision-making and their electrophysiological signatures in a community sample (n = 108) with varying degrees of psychopathic traits. Reinforcers with different salience were included in a foraging-type decision-making task. Recruitment of various cognitive processes was estimated with a computational model and electrophysiology, and the relationships to psychopathic traits were assessed. Higher Antisocial traits were associated with a bias towards expecting more volatility in the environment when high-salience reinforcers were used. Additionally, higher levels of Interpersonal traits were associated with reduced learning from personalized rewards, as evidenced by reductions in the prediction errors (PEs) about rate of change. Higher Affective traits were associated with lower PEs and aberrant learning from painful punishments. Lastly, the PEs about rate of change were reflected in the trial-wise trajectories of Feedback-Related Negativity event-related potentials. Together, our results point to the importance of volatility processing in understanding aberrant decision-making in relation to psychopathy, demonstrate the relationships between psychopathic traits and learning through reward and punishment, and emphasise the potentially more beneficial effect of personalized rewards and punishment for improving reinforcement-based decision-making in individuals with elevated psychopathic traits.

A neurocomputational account of multi-line electronic gambling machines

Multi-line electronic gambling machines (EGMs) are strongly associated with problem gambling. Dopamine (DA) plays a central role in substance-use disorders, which share clinical and behavioral features with disordered gambling. The structural design features of multi-line EGMs likely lead to the elicitation of various dopaminergic effects within their nested anticipation-outcome structure. The present account draws an analogy between EGM gambling and latent state inference accounts of conditioning, and links maladaptive gambling-related beliefs and expectancies to a process of erroneous latent state inference that may be exacerbated by EGM design features and associated dopaminergic processes. Over the course of repeated exposure to gambling, these processes may foster the emergence of maladaptive state priors, which clinically manifest as gambling-related cognitions, beliefs, and expectancies.

How dopamine shapes trust beliefs

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

Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks

Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity, humans show a remarkable adaptability to conditions with uncertainty during goal-directed behavior. The origin of this cognitive ability, constitutive of general intelligence, remains elusive. Here, we show that moderate levels of computation noise in artificial neural networks promote zero-shot generalization for decision-making under uncertainty. Unlike networks featuring noise-free computations, but like human participants tested on similar decision problems (ranging from probabilistic reasoning to reversal learning), noisy networks exhibit behavioral hallmarks of optimal inference in uncertain conditions entirely unseen during training. Computation noise enables this cognitive ability jointly through "structural" regularization of network weights during training and "functional" regularization by shaping the stochastic dynamics of network activity after training. Together, these findings indicate that human cognition may ride on neural variability to support adaptive decisions under uncertainty without extensive experience or engineered sophistication.

Expectation to rewards modulates learning emotional words: Evidence from a hierarchical Bayesian model

In language acquisition, individuals learn the emotional value of words through external feedback. Previous studies have used emotional words as experimental materials to explore the cognitive mechanisms underlying emotional language processing, but have failed to recognize that languages are acquired in changing environments. To this end, this study aims to combine reinforcement learning with emotional word learning, using a probabilistic reversal learning task to explore how individuals acquire the valence of emotional words in a dynamically changing environment. Computational modeling on both behavioral and event-related potential (ERP) data revealed that individuals' expectations to rewards modulated the learning speed and temporal processing of emotional words, demonstrating a clear negative bias. Specifically, as the expected value increased, individuals responded faster and exhibited higher amplitudes for negative emotional words. These findings shed light on the neural mechanisms of emotional word learning in a volatile environment, highlighting the crucial role of expectations in this process and a preference for learning negative information.

Response and conflict expectations shape motor responses interactively

Efficient responses in dynamic environments rely on a combination of readiness and flexibility, regulated by anticipatory and online response control mechanisms. The latter are required when a motor response needs to be reprogrammed or when flanker stimuli induce response conflict and they are crucially modulated by anticipatory signals such as response and conflict expectations. The mutual influence and interplay of these control processes remain to be elucidated. Our behavioral study employed a novel combined response cueing/conflict task designed to test for interactive effects of response reprogramming and conflict resolution and their modulation by expectations. To this end, valid and invalid response cues were combined with congruent and incongruent target flankers. Expectations were modulated by systematically manipulating the proportions of valid versus invalid cues and congruent versus incongruent flanker stimuli in different task blocks. Reaction time and accuracy were assessed in thirty-one healthy volunteers. The results revealed response reprogramming and conflict resolution interactions for both behavioral measures, modulated by response and conflict expectations. Accuracy decreased disproportionally when invalidly cued targets with incongruent flankers were least expected. These findings support coordinated and partially overlapping anticipatory and online response control mechanisms within motor-cognitive networks.

Neurobehavioral meaning of pupil size

Pupil size is a widely used metric of brain state. It is one of the few signals originating from the brain that can be readily monitored with low-cost devices in basic science, clinical, and home settings. It is, therefore, important to investigate and generate well-defined theories related to specific interpretations of this metric. What exactly does it tell us about the brain? Pupils constrict in response to light and dilate during darkness, but the brain also controls pupil size irrespective of luminosity. Pupil size fluctuations resulting from ongoing "brain states" are used as a metric of arousal, but what is pupil-linked arousal and how should it be interpreted in neural, cognitive, and computational terms? Here, we discuss some recent findings related to these issues. We identify open questions and propose how to answer them through a combination of well-defined tasks, neurocomputational models, and neurophysiological probing of the interconnected loops of causes and consequences of pupil size.

Computational processes of simultaneous learning of stochasticity and volatility in humans

Making adaptive decisions requires predicting outcomes, and this in turn requires adapting to uncertain environments. This study explores computational challenges in distinguishing two types of noise influencing predictions: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. Dissociating these effects is challenging as both increase the variance of observations. Previous research examined these factors mostly separately, but it remains unclear whether and how humans dissociate them when they are played off against one another. In two large-scale experiments, through a behavioral prediction task and computational modeling, we report evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects.

Pain reflects the informational value of nociceptive inputs

ABSTRACT

Pain perception and its modulation are fundamental to human learning and adaptive behavior. This study investigated the hypothesis that pain perception is tied to pain's learning function. Thirty-one participants performed a threat conditioning task where certain cues were associated with a possibility of receiving a painful electric shock. The cues that signaled potential pain or safety were regularly changed, requiring participants to continually establish new associations. Using computational models, we quantified participants' pain expectations and prediction errors throughout the task and assessed their relationship with pain perception and electrophysiological responses. Our findings suggest that subjective pain perception increases with prediction error, that is, when pain was unexpected. Prediction errors were also related to physiological nociceptive responses, including the amplitude of nociceptive flexion reflex and electroencephalography markers of cortical nociceptive processing (N1-P2-evoked potential and gamma-band power). In addition, higher pain expectations were related to increased late event-related potential responses and alpha/beta decreases in amplitude during cue presentation. These results further strengthen the idea of a crucial link between pain and learning and suggest that understanding the influence of learning mechanisms in pain modulation could help us understand when and why pain perception is modulated in health and disease.

Toward a computational role for locus coeruleus/norepinephrine arousal systems

Brain and behavior undergo measurable changes in their underlying state and neuromodulators are thought to contribute to these fluctuations. Why do we undergo such changes, and what function could the underlying neuromodulatory systems perform? Here we examine theoretical answers to these questions with respect to the locus coeruleus/norepinephrine system focusing on peripheral markers for arousal, such as pupil diameter, that are thought to provide a window into brain wide noradrenergic signaling. We explore a computational role for arousal systems in facilitating internal state transitions that facilitate credit assignment and promote accurate perceptions in non-stationary environments. We summarize recent work that supports this idea and highlight open questions as well as alternative views of how arousal affects cognition.

Distributed midbrain responses signal the content of positive identity prediction errors

Recent work across species has shown that midbrain dopamine neurons signal not only errors in the prediction of reward value but also in the prediction of value-neutral sensory features. To support learning of associative structures in downstream areas, identity prediction errors (iPEs) should signal specific information about the mis-predicted outcome. Here, we used pattern-based analysis of functional magnetic resonance imaging (fMRI) data acquired during reversal learning to characterize the information content of iPE responses in the human midbrain. We find that fMRI responses to value-neutral identity errors contain information about the identity of the unexpectedly received reward (positive iPE+) but not about the identity of the omitted reward (negative iPE-). Exploratory analyses revealed representations of iPE- in the dorsomedial prefrontal cortex. These results demonstrate that ensemble midbrain responses to value-neutral identity errors convey information about the identity of unexpectedly received outcomes, which could shape the formation of novel stimulus-outcome associations that constitute cognitive maps.

Diminished pain sensitivity mediates the relationship between psychopathic traits and reduced learning from pain

Individuals with elevated psychopathic traits exhibit decision-making deficits linked to a failure to learn from negative outcomes. We investigated how reduced pain sensitivity affects reinforcement-based decision-making in individuals with varying levels of psychopathic traits, as measured by the Self-Report Psychopathy Scale-Short Form. Using computational modelling, we estimated the latent cognitive processes in a community non-offender sample (n = 111) that completed a task with choices leading to painful and non-painful outcomes. Higher psychopathic traits were associated with reduced pain sensitivity and disturbances in reinforcement learning from painful outcomes. In a Structural Equation Model, a superordinate psychopathy factor was associated with a faster return to original stimulus-outcome associations as pain tolerance increased. This provides evidence directly linking reduced pain sensitivity and learning from painful outcomes with elevated psychopathic traits. Our results offer insights into the computational mechanisms of maladaptive decision-making in psychopathy and antisocial behavior.

Crossmodal hierarchical predictive coding for audiovisual sequences in the human brain

Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.

Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder

In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.

Updating Prospective Self-Efficacy Beliefs About Cardiac Interoception in Anorexia Nervosa: An Experimental and Computational Study

Patients with anorexia nervosa (AN) typically hold altered beliefs about their body that they struggle to update, including global, prospective beliefs about their ability to know and regulate their body and particularly their interoceptive states. While clinical questionnaire studies have provided ample evidence on the role of such beliefs in the onset, maintenance, and treatment of AN, psychophysical studies have typically focused on perceptual and 'local' beliefs. Across two experiments, we examined how women at the acute AN (N = 86) and post-acute AN state (N = 87), compared to matched healthy controls (N = 180) formed and updated their self-efficacy beliefs retrospectively (Experiment 1) and prospectively (Experiment 2) about their heartbeat counting abilities in an adapted heartbeat counting task. As preregistered, while AN patients did not differ from controls in interoceptive accuracy per se, they hold and maintain 'pessimistic' interoceptive, metacognitive self-efficacy beliefs after performance. Modelling using a simplified computational Bayesian learning framework showed that neither local evidence from performance, nor retrospective beliefs following that performance (that themselves were suboptimally updated) seem to be sufficient to counter and update pessimistic, self-efficacy beliefs in AN. AN patients showed lower learning rates than controls, revealing a tendency to base their posterior beliefs more on prior beliefs rather than prediction errors in both retrospective and prospective belief updating. Further explorations showed that while these differences in both explicit beliefs, and the latent mechanisms of belief updating, were not explained by general cognitive flexibility differences, they were explained by negative mood comorbidity, even after the acute stage of illness.

Incorporating uncertainty within dynamic interoceptive learning

Introduction

Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.

Methods

Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures.

Results

Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty.

Discussion

These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

Learning capacity in early-stage Alzheimer's disease: The role of feedback during learning on memory performance

Alzheimer's disease is characterized by a decline in episodic memory and executive functioning, hampering learning ability. Insight into outcome-based learning capacity may be relevant for optimizing the learning potential of these patients. To date, mixed results have been found in studies in which cognitively impaired participants have to learn based on positive and negative outcomes. In this study, we investigated the role of negative and positive feedback on memory performance and participants' ability to adjust their behaviour accordingly in a sample of 23 early-stage AD patients and 23 matched healthy controls. We administered a novel computerized object-location memory task, in which participants were instructed to learn and memorize the locations of different everyday objects following errorless learning (EL) and trial-and-error learning (TEL). A separate probabilistic TEL task was employed in which participants had to learn how to adjust their behaviour based on positive and negative feedback. EL had a beneficial general effect on memory performance for object locations. However, this effect was not larger in early-stage AD patients compared to controls and error frequency during acquisition of object locations was unrelated to later recall performance. No group differences were found on the probabilistic learning task with respect to learning performance over time and based on positive and negative feedback. Although the error monitoring system seems intact in patients with early-stage AD, errors during learning are likely acting as a source of interference causing difficulty in storage or retrieval of object locations.

Evaluating the Influence of Musical and Monetary Rewards on Decision Making through Computational Modelling

A central question in behavioural neuroscience is how different rewards modulate learning. While the role of monetary rewards is well-studied in decision-making research, the influence of abstract rewards like music remains poorly understood. This study investigated the dissociable effects of these two reward types on decision making. Forty participants completed two decision-making tasks, each characterised by probabilistic associations between stimuli and rewards, with probabilities changing over time to reflect environmental volatility. In each task, choices were reinforced either by monetary outcomes (win/lose) or by the endings of musical melodies (consonant/dissonant). We applied the Hierarchical Gaussian Filter, a validated hierarchical Bayesian framework, to model learning under these two conditions. Bayesian statistics provided evidence for similar learning patterns across both reward types, suggesting individuals' similar adaptability. However, within the musical task, individual preferences for consonance over dissonance explained some aspects of learning. Specifically, correlation analyses indicated that participants more tolerant of dissonance behaved more stochastically in their belief-to-response mappings and were less likely to choose the response associated with the current prediction for a consonant ending, driven by higher volatility estimates. By contrast, participants averse to dissonance showed increased tonic volatility, leading to larger updates in reward tendency beliefs.

The empirical status of predictive coding and active inference

Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.

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

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

Alpha oscillations encode Bayesian belief updating underlying attentional allocation in dynamic environments

In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attention. To further investigate the neural mechanisms underlying attentional expectancies, we employed a modified Central Cue Posner Paradigm in which the probability of cues being valid (that is, accurately indicated the upcoming target location) was manipulated. Attentional deployment to the cued location (α), which was governed by precision of predictions on previous trials, was estimated using a hierarchical Bayesian model and was included as a regressor in the analyses of electrophysiological (EEG) data. Our results revealed that before the target appeared, alpha oscillations (8∼13 Hz) for high-predictability cues (88 % valid) were significantly predicted by precision-dependent attention (α). This relationship was not observed under low-predictability conditions (69 % and 50 % valid cues). After the target appeared, precision-dependent attention (α) correlated with alpha band oscillations only in the valid cue condition and not in the invalid condition. Further analysis under conditions of significant attentional modulation by precision suggested a separate effect of cue orientation. These results provide new insights on how trial-by-trial Bayesian belief updating relates to alpha band encoding of environmentally-sensitive allocation of visual spatial attention.

Prefrontal Cortex Codes Representations of Target Identity and Feature Uncertainty

Many objects in the real world have features that vary over time, creating uncertainty in how they will look in the future. This uncertainty makes statistical knowledge about the likelihood of features critical to attention demanding processes such as visual search. However, little is known about how the uncertainty of visual features is integrated into predictions about search targets in the brain. In the current study, we test the idea that regions prefrontal cortex code statistical knowledge about search targets before the onset of search. Across 20 human participants (13 female; 7 male), we observe target identity in the multivariate pattern and uncertainty in the overall activation of dorsolateral prefrontal cortex (DLPFC) and inferior frontal junction (IFJ) in advance of the search display. This indicates that the target identity (mean) and uncertainty (variance) of the target distribution are coded independently within the same regions. Furthermore, once the search display appears the univariate IFJ signal scaled with the distance of the actual target from the expected mean, but more so when expected variability was low. These results inform neural theories of attention by showing how the prefrontal cortex represents both the identity and expected variability of features in service of top-down attentional control.SIGNIFICANCE STATEMENT Theories of attention and working memory posit that when we engage in complex cognitive tasks our performance is determined by how precisely we remember task-relevant information. However, in the real world the properties of objects change over time, creating uncertainty about many aspects of the task. There is currently a gap in our understanding of how neural systems represent this uncertainty and combine it with target identity information in anticipation of attention demanding cognitive tasks. In this study, we show that the prefrontal cortex represents identity and uncertainty as unique codes before task onset. These results advance theories of attention by showing that the prefrontal cortex codes both target identity and uncertainty to implement top-down attentional control.

A multimodal cortical network of sensory expectation violation revealed by fMRI

The brain is subjected to multi-modal sensory information in an environment governed by statistical dependencies. Mismatch responses (MMRs), classically recorded with EEG, have provided valuable insights into the brain's processing of regularities and the generation of corresponding sensory predictions. Only few studies allow for comparisons of MMRs across multiple modalities in a simultaneous sensory stream and their corresponding cross-modal context sensitivity remains unknown. Here, we used a tri-modal version of the roving stimulus paradigm in fMRI to elicit MMRs in the auditory, somatosensory and visual modality. Participants (N = 29) were simultaneously presented with sequences of low and high intensity stimuli in each of the three senses while actively observing the tri-modal input stream and occasionally reporting the intensity of the previous stimulus in a prompted modality. The sequences were based on a probabilistic model, defining transition probabilities such that, for each modality, stimuli were more likely to repeat (p = .825) than change (p = .175) and stimulus intensities were equiprobable (p = .5). Moreover, each transition was conditional on the configuration of the other two modalities comprising global (cross-modal) predictive properties of the sequences. We identified a shared mismatch network of modality general inferior frontal and temporo-parietal areas as well as sensory areas, where the connectivity (psychophysiological interaction) between these regions was modulated during mismatch processing. Further, we found deviant responses within the network to be modulated by local stimulus repetition, which suggests highly comparable processing of expectation violation across modalities. Moreover, hierarchically higher regions of the mismatch network in the temporo-parietal area around the intraparietal sulcus were identified to signal cross-modal expectation violation. With the consistency of MMRs across audition, somatosensation and vision, our study provides insights into a shared cortical network of uni- and multi-modal expectation violation in response to sequence regularities.

Striatal correlates of Bayesian beliefs in self-efficacy in adolescents and their relation to mood and autonomy: a pilot study

Major depressive disorder often originates in adolescence and is associated with long-term functional impairment. Mechanistically characterizing this heterogeneous illness could provide important leads for optimizing treatment. Importantly, reward learning is known to be disrupted in depression. In this pilot fMRI study of 21 adolescents (16-20 years), we assessed how reward network disruption impacts specifically on Bayesian belief representations of self-efficacy (SE-B) and their associated uncertainty (SE-U), using a modified instrumental learning task probing activation induced by the opportunity to choose, and an optimal Hierarchical Gaussian Filter computational model. SE-U engaged caudate, nucleus accumbens (NAcc), precuneus, posterior parietal and dorsolateral prefrontal cortex (PFWE < 0.005). Sparse partial least squares analysis identified SE-U striatal activation as associating with one's sense of perceived choice and depressive symptoms, particularly anhedonia and negative feelings about oneself. As Bayesian uncertainty modulates belief flexibility and their capacity to steer future actions, this suggests that these striatal signals may be informative developmentally, longitudinally and in assessing response to treatment.

Studying the neural representations of uncertainty

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.

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

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

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.

Testing predictive coding theories of autism spectrum disorder using models of active inference

Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive use of manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. In contrast to some accounts, we found no evidence of chronic atypicalities in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Most notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that atypical encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors. Our results did not, however, support previous suggestions that autistic people perceive their environment to be persistently volatile.

Learning of irrelevant stimulus-response associations modulates cognitive control

It has been shown that manipulating the proportion of congruent to incongruent trials in conflict tasks (e.g., Stroop, Simon, and flanker tasks) can vary the size of conflict effects, however, by two different mechanisms. One theory is the control learning account (the brain learns the probability of conflict and uses it to proactively adjust the control demand for future trials). The other is the irrelevant stimulus-response learning account (the brain learns the probability of irrelevant stimulus-response associations and uses it to prepare responses). Previous fMRI studies have detected the brain regions that contribute to the control-learning-modulated conflict effects, but it is less known what neural substrates underlie the conflict effects modulated by irrelevant S-R learning. We here investigated this question with a model-based fMRI study, in which the proportion of congruent to incongruent trials changed dynamically in the Simon task and the models learned the probability of irrelevant S-R associations quantitatively. Behavioral analyses showed that the unsigned prediction errors (PEs) of responses generated by the learning models correlated with reaction times irrespective of congruent and incongruent trials, indicating that large unsigned PEs associated with slow responses. The fMRI results showed that the regions of fronto-parietal and cingulo-opercular network involved in cognitive control were significantly modulated by the unsigned PEs, also irrespective of congruent and incongruent trials, indicating that large unsigned PEs associated with transiently increased activity in these regions. These results together suggest that learning of irrelevant S-R associations modulates reactive control, which demonstrates a new way to modulate cognitive control compared to the control learning account.

Liraglutide restores impaired associative learning in individuals with obesity

Survival under selective pressure is driven by the ability of our brain to use sensory information to our advantage to control physiological needs. To that end, neural circuits receive and integrate external environmental cues and internal metabolic signals to form learned sensory associations, consequently motivating and adapting our behaviour. The dopaminergic midbrain plays a crucial role in learning adaptive behaviour and is particularly sensitive to peripheral metabolic signals, including intestinal peptides, such as glucagon-like peptide 1 (GLP-1). In a single-blinded, randomized, controlled, crossover basic human functional magnetic resonance imaging study relying on a computational model of the adaptive learning process underlying behavioural responses, we show that adaptive learning is reduced when metabolic sensing is impaired in obesity, as indexed by reduced insulin sensitivity (participants: N = 30 with normal insulin sensitivity; N = 24 with impaired insulin sensitivity). Treatment with the GLP-1 receptor agonist liraglutide normalizes impaired learning of sensory associations in men and women with obesity. Collectively, our findings reveal that GLP-1 receptor activation modulates associative learning in people with obesity via its central effects within the mesoaccumbens pathway. These findings provide evidence for how metabolic signals can act as neuromodulators to adapt our behaviour to our body's internal state and how GLP-1 receptor agonists work in clinics.

Suicide prevention and ketamine: insights from computational modeling

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.

Neural correlates of hierarchical predictive processes in autistic adults

Bayesian theories of autism spectrum disorders (ASD) suggest that atypical predictive mechanisms could underlie the autistic symptomatology, but little is known about their neural correlates. Twenty-six neurotypical (NT) and 26 autistic adults participated in an fMRI study where they performed an associative learning task in a volatile environment. By inverting a model of perceptual inference, we characterized the neural correlates of hierarchically structured predictions and prediction errors in ASD. Behaviorally, the predictive abilities of autistic adults were intact. Neurally, predictions were encoded hierarchically in both NT and ASD participants and biased their percepts. High-level predictions were following activity levels in a set of regions more closely in ASD than NT. Prediction errors yielded activation in shared regions in NT and ASD, but group differences were found in the anterior cingulate cortex and putamen. This study sheds light on the neural specificities of ASD that might underlie atypical predictive processing.

Aggressive and psychopathic traits are linked to the acquisition of stable but imprecise hostile expectations

Individuals with hostile expectations (HEX) anticipate harm from seemingly neutral or ambiguous stimuli. However, it is unclear how HEX are acquired, and whether specific components of HEX learning can predict antisocial thought, conduct, and personality. In an online sample of healthy young individuals (n = 256, 69% women), we administered a virtual shooting task and applied computational modelling of behaviour to investigate HEX learning and its constellation of correlates. HEX acquisition was best explained by a hierarchical reinforcement learning mechanism. Crucially, we found that individuals with relatively higher self-reported aggressiveness and psychopathy developed stronger and less accurate hostile beliefs as well as larger prediction errors. Moreover, aggressive and psychopathic traits were associated with more temporally stable hostility representations. Our study thus shows that aggressiveness and psychopathy are linked with the acquisition of robust yet imprecise hostile beliefs through reinforcement learning.

Pavlovian conditioning-induced hallucinations reduce MMN amplitudes for duration but not frequency deviants

The mismatch negativity (MMN) is an evoked potential that indexes auditory regularity violations. Since the 90's, a reduced amplitude of this brain activity in patients with schizophrenia has been consistently reported. Recently, this alteration has been related to the presence of auditory hallucinations (AHs) rather than the schizophrenia diagnostic per se. However, making this attribution is rather problematic due to the high heterogeneity of symptoms in schizophrenia. In an attempt to isolate the AHs influence on the MMN amplitude from other cofounding variables, we artificially induced AHs in a non-clinical population by Pavlovian conditioning. Before and after conditioning, volunteers (N = 31) participated in an oddball paradigm that elicited an MMN. Two different types of deviants were presented: a frequency and a duration deviant, as the MMN alteration seems to be especially present in schizophrenia with the latter type of deviant. Hence, this pre-post design allowed us to compare whether experiencing conditioning-induced AHs exert any influence on MMN amplitudes. Our results show that duration-deviant related MMN reductions significantly correlate with the number of AHs experienced. Moreover, we found a significant correlation between AHs proneness (measured with the Launay-Slade Hallucination Extended Scale) and the number of AHs experienced during the paradigm. In sum, our study shows that AHs can be conditioned and exert similar effects on MMN modulation in healthy participants as has been reported for patients with schizophrenia. Thus, conditioning paradigms offer the possibility to study the association between hallucinations and MMN reductions without the confounding variables present in schizophrenia patients.

Anticipating social feedback involves basal forebrain and mesolimbic functional connectivity

The mesolimbic system and basal forebrain (BF) are implicated in processing rewards and punishment, but their interplay and functional properties of subregions with respect to future social outcomes remain unclear. Therefore, this study investigated regional responses and interregional functional connectivity of the lateral (l), medial (m), and ventral (v) Substantia Nigra (SN), Nucleus Accumbens (NAcc), Nucleus basalis of Meynert (NBM), and Medial Septum/Diagonal Band (MS/DB) during reward and punishment anticipation in a social incentive delay task with neutral, positive, and negative feedback using high-resolution fMRI (1.5mm³). Neuroimaging data (n=36 healthy humans) of the anticipation phase was analyzed using mass-univariate, functional connectivity, and multivariate-pattern analysis. As expected, participants responded faster when anticipating positive and negative compared to neutral social feedback. At the neural level, anticipating social information engaged valence-related and valence-unrelated functional connectivity patterns involving the BF and mesolimbic areas. Precisely, valence-related connectivity between the lSN and NBM was associated with anticipating neutral social feedback, while connectivity between the vSN and NBM was associated with anticipating positive social feedback. A more complex pattern was observed for anticipating negative social feedback, including connectivity between the lSN and MS/DB, lSN and NAcc, as well as mSN and NAcc. To conclude, behavioral responses are modulated by the possibility to obtain positive and avoid negative social feedback. The neural processing of feedback anticipation relies on functional connectivity patterns between the BF and mesolimbic areas associated with the emotional valence of the social information. As such, our findings give novel insights into the underlying neural processes of social information processing.

On the normative advantages of dopamine and striatal opponency for learning and choice

The basal ganglia (BG) contribute to reinforcement learning (RL) and decision-making, but unlike artificial RL agents, it relies on complex circuitry and dynamic dopamine modulation of opponent striatal pathways to do so. We develop the OpAL* model to assess the normative advantages of this circuitry. In OpAL*, learning induces opponent pathways to differentially emphasize the history of positive or negative outcomes for each action. Dynamic DA modulation then amplifies the pathway most tuned for the task environment. This efficient coding mechanism avoids a vexing explore-exploit tradeoff that plagues traditional RL models in sparse reward environments. OpAL* exhibits robust advantages over alternative models, particularly in environments with sparse reward and large action spaces. These advantages depend on opponent and nonlinear Hebbian plasticity mechanisms previously thought to be pathological. Finally, OpAL* captures risky choice patterns arising from DA and environmental manipulations across species, suggesting that they result from a normative biological mechanism.

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.

Habitual daily intake of a sweet and fatty snack modulates reward processing in humans

Western diets rich in fat and sugar promote excess calorie intake and weight gain; however, the underlying mechanisms are unclear. Despite a well-documented association between obesity and altered brain dopamine function, it remains elusive whether these alterations are (1) pre-existing, increasing the individual susceptibility to weight gain, (2) secondary to obesity, or (3) directly attributable to repeated exposure to western diet. To close this gap, we performed a randomized, controlled study (NCT05574660) with normal-weight participants exposed to a high-fat/high-sugar snack or a low-fat/low-sugar snack for 8 weeks in addition to their regular diet. The high-fat/high-sugar intervention decreased the preference for low-fat food while increasing brain response to food and associative learning independent of food cues or reward. These alterations were independent of changes in body weight and metabolic parameters, indicating a direct effect of high-fat, high-sugar foods on neurobehavioral adaptations that may increase the risk for overeating and weight gain.

From fear of falling to choking under pressure: A predictive processing perspective of disrupted motor control under anxiety

Under the Predictive Processing Framework, perception is guided by internal models that map the probabilistic relationship between sensory states and their causes. Predictive processing has contributed to a new understanding of both emotional states and motor control but is yet to be fully applied to their interaction during the breakdown of motor movements under heightened anxiety or threat. We bring together literature on anxiety and motor control to propose that predictive processing provides a unifying principle for understanding motor breakdowns as a disruption to the neuromodulatory control mechanisms that regulate the interactions of top-down predictions and bottom-up sensory signals. We illustrate this account using examples from disrupted balance and gait in populations who are anxious/fearful of falling, as well as 'choking' in elite sport. This approach can explain both rigid and inflexible movement strategies, as well as highly variable and imprecise action and conscious movement processing, and may also unite the apparently opposing self-focus and distraction approaches to choking. We generate predictions to guide future work and propose practical recommendations.

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.

A characterization of the neural representation of confidence during probabilistic learning

Learning in a stochastic and changing environment is a difficult task. Models of learning typically postulate that observations that deviate from the learned predictions are surprising and used to update those predictions. Bayesian accounts further posit the existence of a confidence-weighting mechanism: learning should be modulated by the confidence level that accompanies those predictions. However, the neural bases of this confidence are much less known than the ones of surprise. Here, we used a dynamic probability learning task and high-field MRI to identify putative cortical regions involved in the representation of confidence about predictions during human learning. We devised a stringent test based on the conjunction of four criteria. We localized several regions in parietal and frontal cortices whose activity is sensitive to the confidence of an ideal observer, specifically so with respect to potential confounds (surprise and predictability), and in a way that is invariant to which item is predicted. We also tested for functionality in two ways. First, we localized regions whose activity patterns at the subject level showed an effect of both confidence and surprise in qualitative agreement with the confidence-weighting principle. Second, we found neural representations of ideal confidence that also accounted for subjective confidence. Taken together, those results identify a set of cortical regions potentially implicated in the confidence-weighting of learning.

Brain connectivity modulation by Bayesian surprise in relation to control demand drives cognitive flexibility via control engagement

Human control is characterized by its flexibility and adaptability in response to the conditional probability in the environment. Previous studies have revealed that efficient conflict control could be attained by predicting and adapting to the changing control demand. However, it is unclear whether cognitive flexibility could also be gained by predicting and adapting to the changing control demand. The present study aimed to explore this issue by combining the model-based analyses of behavioral and neuroimaging data with a probabilistic cued task switching paradigm. We demonstrated that the Bayesian surprise (i.e. unsigned precision-weighted prediction error [PE]) negatively modulated the connections among stimulus processing brain regions and control regions/networks. The effect of Bayesian surprise modulation on these connections guided control engagement as reflected by the control PE effect on behavior, which in turn facilitated cognitive flexibility. These results bridge a gap in the literature by illustrating the neural and behavioral effect of control demand prediction (or PE) on cognitive flexibility and offer novel insights into the source of switch cost and the mechanism of cognitive flexibility.

Reaction Time "Mismatch Costs" Change with the Likelihood of Stimulus-Response Compatibility

Dyadic interactions require dynamic correspondence between one's own movements and those of the other agent. This mapping is largely viewed as imitative, with the behavioural hallmark being a reaction-time cost for mismatched actions. Yet the complex motor patterns humans enact together extend beyond direct-matching, varying adaptively between imitation, complementary movements, and counter-imitation. Optimal behaviour requires an agent to predict not only what is likely to be observed but also how that observed action will relate to their own motor planning. In 28 healthy adults, we examined imitation and counter-imitation in a task that varied the likelihood of stimulus-response congruence from highly predictable, to moderately predictable, to unpredictable. To gain mechanistic insights into the statistical learning of stimulus-response compatibility, we compared two computational models of behaviour: (1) a classic fixed learning-rate model (Rescorla-Wagner reinforcement [RW]) and (2) a hierarchical model of perceptual-behavioural processes in which the learning rate adapts to the inferred environmental volatility (hierarchical Gaussian filter [HGF]). Though more complex and hence penalized by model selection, the HGF provided a more likely model of the participants' behaviour. Matching motor responses were only primed (faster) in the most experimentally volatile context. This bias was reversed so that mismatched actions were primed when beliefs about volatility were lower. Inferential statistics indicated that matching responses were only primed in unpredictable contexts when stimuli-response congruence was at 50:50 chance. Outside of these unpredictable blocks the classic stimulus-response compatibility effect was reversed: Incongruent responses were faster than congruent ones. We show that hierarchical Bayesian learning of environmental statistics may underlie response priming during dyadic interactions.

Computational psychiatry: from synapses to sentience

This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.