A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders

Interoception in anxiety, depression, and psychosis: a review

Research has examined the relationship between interoception and anxiety, depression, and psychosis; however, it is unclear which aspects of interoception have been systematically examined, what the combined findings are, and which areas require further research. To answer these questions, we systematically searched and narratively synthesised relevant reviews, meta-analyses, and theory papers (total n = 34). Existing systematic reviews and meta-analyses (anxiety n = 2; depression n = 2; psychosis n = 0), focus on cardiac interoceptive accuracy (heartbeat perception), and indicate that heartbeat perception is not systematically impaired in anxiety or depression. Heartbeat perception might be poorer in people with psychosis, but further evidence is needed. Other aspects of interoception, such as different body systems and processing levels, have been studied but not systematically reviewed. We highlight studies examining these alternative bodily domains and levels, review the efficacy of interoception-based psychological interventions, and make suggestions for future research.

Funding

Wellcome Trust UK.

The role of interoceptive awareness in shaping the relationship between desire thinking and cigarette consumption

Interoception, the ability to sense and interpret bodily sensations, has recently emerged as a crucial factor in substance use disorders, including smoking. However, the role of interoceptive awareness in tobacco use remains poorly understood. The relationship between interoceptive ability and addictive behavior is complex, and attempting to conceptualize it as a linear association is unlikely to fully capture the complexity of the mechanisms underlying cravings and urges. We hypothesized that the role played by interoceptive awareness in tobacco use is deeply linked to desire thinking, that is, the conscious and voluntary cognitive process orienting to prefigure images, information, and memories about positive target-related experiences. Desire thinking is typically observed in addiction, where it may contribute to interpreting specific bodily sensations, such as the perceived need for a cigarette. From this perspective, the physiological impact and inclination toward desire thinking contribute to a higher daily cigarette consumption, particularly in situations of low interoceptive awareness. To test this hypothesis, we assessed the physiological activation, the tendency toward desire thinking about smoking, cigarette consumption, and the interoceptive abilities of smoking volunteers. Through a moderation analysis, we showed that desire thinking about smoking predicts a higher number of cigarettes per day in individuals with lower interoceptive awareness (p < .05). These findings suggest that the relationship between desire thinking and interoceptive awareness is a fundamental component of tobacco use, highlighting the importance of taking into account the bodily feedback deriving from the cognitive representation of smoking in addiction research and therapy.

The fear-avoidance model as an embodied prediction of threat

The fear-avoidance model is a well-established framework in the understanding of persistent pain. It proposes a dichotomous path: either the context is interpreted as safe; there is no fear reaction and, therefore, the individual engages in active (positive) coping; or the context is interpreted as threatening, leading to a self-reinforcing vicious circle of fear and (negative) avoidance. We propose an embodied interpretation of this phenomenon employing the joint framework of predictive coding and active inference. The key idea is that multisensory integration of exteroceptive, proprioceptive, and interoceptive sensory inputs can lead to dysfunctional experiences of threat in nonthreatening situations. Threat inference can promote fear responses, maladaptive strategies (i.e., avoidance) and self-provides evidence for threat in associated or future contexts, or both. Under this treatment, the prediction of nonrealized threat becomes self-evidencing and context-invariant, and hence self-perpetuating. Safety cues are unable to attenuate the interpretation of the negative context as the dominant inference of the context is threatful and gains more precision and becomes resistant over time. Our model provides an explanation for the emergence of a dysfunctional fear response in the clinical setting despite apparent safety based on modern concepts from theoretical (computational) neuroscience.

Priors and prejudice: hierarchical predictive processing in intergroup perception

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

Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication and extension of previous results

Recent Bayesian theories of interoception suggest that perception of bodily states rests upon a precision-weighted integration of afferent signals and prior beliefs. In a previous study, we fit a computational model of perception to behavior on a heartbeat tapping task to test whether aberrant precision-weighting could explain misestimation of cardiac states in psychopathology. We found that, during an interoceptive perturbation designed to amplify afferent signal precision (inspiratory breath-holding), healthy individuals increased the precision-weighting assigned to ascending cardiac signals (relative to resting conditions), while individuals with anxiety, depression, substance use disorders, and/or eating disorders did not. In this pre-registered study, we aimed to replicate and extend our prior findings in a new transdiagnostic patient sample (N = 285) similar to the one in the original study. As expected, patients in this new sample were also unable to adjust beliefs about the precision of cardiac signals - preventing the ability to accurately perceive changes in their cardiac state. Follow-up analyses combining samples from the previous and current study (N = 719) also afforded power to identify group differences between narrower diagnostic categories, and to examine predictive accuracy when logistic regression models were trained on one sample and tested on the other. With this confirmatory evidence in place, future studies should examine the utility of interceptive precision measures in predicting treatment outcomes and test whether these computational mechanisms might represent novel therapeutic targets.

Brain-body states embody complex temporal dynamics

We propose a computational framework for high-dimensional brain-body states as transient embodiments of nested internal and external dynamics governed by interoception. Unifying recent theoretical work, we suggest ways to reduce arbitrary state complexity to an observable number of features in order to accurately predict and intervene in pathological trajectories.

Modality-specific effects of threat on self-motion perception

BACKGROUND

Threat and individual differences in threat-processing bias perception of stimuli in the environment. Yet, their effect on perception of one's own (body-based) self-motion in space is unknown. Here, we tested the effects of threat on self-motion perception using a multisensory motion simulator with concurrent threatening or neutral auditory stimuli.

RESULTS

Strikingly, threat had opposite effects on vestibular and visual self-motion perception, leading to overestimation of vestibular, but underestimation of visual self-motions. Trait anxiety tended to be associated with an enhanced effect of threat on estimates of self-motion for both modalities.

CONCLUSIONS

Enhanced vestibular perception under threat might stem from shared neural substrates with emotional processing, whereas diminished visual self-motion perception may indicate that a threatening stimulus diverts attention away from optic flow integration. Thus, threat induces modality-specific biases in everyday experiences of self-motion.

Hemispheric divergence of interoceptive processing across psychiatric disorders

Interactions between top-down attention and bottom-up visceral inputs are assumed to produce conscious perceptions of interoceptive states, and while each process has been independently associated with aberrant interoceptive symptomatology in psychiatric disorders, the neural substrates of this interface are unknown. We conducted a preregistered functional neuroimaging study of 46 individuals with anxiety, depression, and/or eating disorders (ADE) and 46 propensity-matched healthy comparisons (HC), comparing their neural activity across two interoceptive tasks differentially recruiting top-down or bottom-up processing within the same scan session. During an interoceptive attention task, top-down attention was voluntarily directed towards cardiorespiratory or visual signals, whereas during an interoceptive perturbation task, intravenous infusions of isoproterenol (a peripherally-acting beta-adrenergic receptor agonist) were administered in a double-blinded and placebo-controlled fashion to drive bottom-up cardiorespiratory sensations. Across both tasks, neural activation converged upon the insular cortex, localizing within the granular and ventral dysgranular subregions bilaterally. However, contrasting hemispheric differences emerged, with the ADE group exhibiting (relative to HCs) an asymmetric pattern of overlap in the left insula, with increased or decreased proportions of co-activated voxels within the left or right dysgranular insula, respectively. The ADE group also showed less agranular anterior insula activation during periods of bodily uncertainty (i.e., when anticipating possible isoproterenol-induced changes that never arrived). Finally, post-task changes in insula functional connectivity were associated with anxiety and depression severity. These findings confirm the dysgranular mid-insula as a key cortical interface where attention and prediction meet real-time bodily inputs, especially during heightened awareness of interoceptive states. Further, the dysgranular mid-insula may indeed be a "locus of disruption" for psychiatric disorders.

Evaluating the predictions of an interoceptive inference model of bulimia nervosa

OBJECTIVE

Bulimia nervosa (BN) is associated with loss-of-control (LOC) eating episodes that frequently occur in response to negative emotions. According to recent neurocomputational models, this link could be explained by a failure to accurately update beliefs about the body in states of high arousal. Specifically, these interoceptive inference models suggest that under-relying on signals from one's body about sensory experience ("low sensory precision") and/or over-relying on previously held beliefs ("excessively precise priors") lead to inaccurate perception and maladaptive behaviors. We conducted an initial test of these core predictions of the interoceptive inference model in BN using self-report measures.

METHODS

We compared women with BN (n = 30) and age-, BMI-, and full-scale IQ-matched controls (n = 31) on trust in sensory information from the body and two types of beliefs about what can be done to regulate high negative affect. Within the BN group, we tested interrelations among these measures and explored their associations with LOC eating frequency.

RESULTS

Compared with healthy controls, the BN group reported lower levels of trust in sensory information and stronger beliefs that once upset, there is little one can do, apart from eating, to self-regulate. These beliefs were associated with each other and with lower body trust. Beliefs about the uncontrollability of emotion were associated with more frequent subjective binge-eating episodes.

CONCLUSIONS

Findings provide initial support for the core predictions of an interoceptive inference account of BN: low trust in sensory information ("sensory precision") may promote an overreliance on maladaptive "prior beliefs" about the effects of eating on negative emotions, ultimately interfering with accurate updating of beliefs about other strategies that could regulate emotions and maintain LOC eating. Low body trust, strong expectations about emotions, and their neurocomputational underpinnings could be promising combined treatment targets for BN.

Perceptual Dysfunction in Eating Disorders

Eating disorders (EDs) are characterized by abnormal responses to food and weight-related stimuli and are associated with significant distress, impairment, and poor outcomes. Because many of the cardinal symptoms of EDs involve disturbances in perception of one's body or abnormal affective or cognitive reactions to food intake and how that affects one's size, there has been longstanding interest in characterizing alterations in sensory perception among differing ED diagnostic groups. Within the current review, we aimed to critically assess the existing research on exteroceptive and interoceptive perception and how sensory perception may influence ED behavior. Overall, existing research is most consistent regarding alterations in taste, visual, tactile, and gastric-specific interoceptive processing in EDs, with emerging work indicating elevated respiratory and cardiovascular sensitivity. However, this work is far from conclusive, with most studies unable to speak to the precise etiology of observed perceptual differences in these domains and disentangle these effects from affective and cognitive processes observed within EDs. Further, existing knowledge regarding perceptual disturbances in EDs is limited by heterogeneity in methodology, lack of multimodal assessment protocols, and inconsistent attention to different ED diagnoses. We propose several new avenues for improving neurobiology-informed research on sensory processing to generate actionable knowledge that can inform the development of innovative interventions for these serious disorders.

Chronic pain as an emergent property of a complex system and the potential roles of psychedelic therapies

Despite research advances and urgent calls by national and global health organizations, clinical outcomes for millions of people suffering with chronic pain remain poor. We suggest bringing the lens of complexity science to this problem, conceptualizing chronic pain as an emergent property of a complex biopsychosocial system. We frame pain-related physiology, neuroscience, developmental psychology, learning, and epigenetics as components and mini-systems that interact together and with changing socioenvironmental conditions, as an overarching complex system that gives rise to the emergent phenomenon of chronic pain. We postulate that the behavior of complex systems may help to explain persistence of chronic pain despite current treatments. From this perspective, chronic pain may benefit from therapies that can be both disruptive and adaptive at higher orders within the complex system. We explore psychedelic-assisted therapies and how these may overlap with and complement mindfulness-based approaches to this end. Both mindfulness and psychedelic therapies have been shown to have transdiagnostic value, due in part to disruptive effects on rigid cognitive, emotional, and behavioral patterns as well their ability to promote neuroplasticity. Psychedelic therapies may hold unique promise for the management of chronic pain.

A computationally informed distinction of interoception and exteroception

While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.

A social inference model of idealization and devaluation

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neural representation in active inference: Using generative models to interact with-and understand-the lived world

This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action-perception cycles to those that underwrite planning and imagination; namely, from explicit models that entail variables for predicting concurrent sensations, like objects, faces, or people-to action-oriented models that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities-and the gradual transition from pragmatic to detached neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation.

Arousal coherence, uncertainty, and well-being: an active inference account

Here we build on recent findings which show that greater alignment between our subjective experiences (how we feel) and physiological states (measurable changes in our body) plays a pivotal role in the overall psychological well-being. Specifically, we propose that the alignment or 'coherence' between affective arousal (e.g. how excited we 'feel') and autonomic arousal (e.g. heart rate or pupil dilation) may be key for maintaining up-to-date uncertainty representations in dynamic environments. Drawing on recent advances in interoceptive and affective inference, we also propose that arousal coherence reflects interoceptive integration, facilitates adaptive belief updating, and impacts our capacity to adapt to changes in uncertainty, with downstream consequences to well-being. We also highlight the role of meta-awareness of arousal, a third level of inference, which may permit conscious awareness, learning about, and intentional regulation of lower-order sources of arousal. Practices emphasizing meta-awareness of arousal (like meditation) may therefore elicit some of their known benefits via improved arousal coherence. We suggest that arousal coherence is also likely to be associated with markers of adaptive functioning (like emotional awareness and self-regulatory capacities) and discuss mind-body practices that may increase coherence.

Effect of non-invasive spinal cord stimulation in unmedicated adults with major depressive disorder: a pilot randomized controlled trial and induced current flow pattern

Converging theoretical frameworks suggest a role and a therapeutic potential for spinal interoceptive pathways in major depressive disorder (MDD). Here, we aimed to evaluate the antidepressant effects and tolerability of transcutaneous spinal direct current stimulation (tsDCS) in MDD. This was a double-blind, randomized, sham-controlled, parallel group, pilot clinical trial in unmedicated adults with moderate MDD. Twenty participants were randomly allocated (1:1 ratio) to receive "active" 2.5 mA or "sham" anodal tsDCS sessions with a thoracic (anode; T10)/right shoulder (cathode) electrode montage 3 times/week for 8 weeks. Change in depression severity (MADRS) scores (prespecified primary outcome) and secondary clinical outcomes were analyzed with ANOVA models. An E-Field model was generated using the active tsDCS parameters. Compared to sham (n = 9), the active tsDCS group (n = 10) showed a greater baseline to endpoint decrease in MADRS score with a large effect size (-14.6 ± 2.5 vs. -21.7 ± 2.3, p = 0.040, d = 0.86). Additionally, compared to sham, active tsDCS induced a greater decrease in MADRS "reported sadness" item (-1.8 ± 0.4 vs. -3.2 ± 0.4, p = 0.012), and a greater cumulative decrease in pre/post tsDCS session diastolic blood pressure change from baseline to endpoint (group difference: 7.9 ± 3.7 mmHg, p = 0.039). Statistical trends in the same direction were observed for MADRS "pessimistic thoughts" item and week-8 CGI-I scores. No group differences were observed in adverse events (AEs) and no serious AEs occurred. The current flow simulation showed electric field at strength within the neuromodulation range (max. ~0.45 V/m) reaching the thoracic spinal gray matter. The results from this pilot study suggest that tsDCS is feasible, well-tolerated, and shows therapeutic potential in MDD. This work also provides the initial framework for the cautious exploration of non-invasive spinal cord neuromodulation in the context of mental health research and therapeutics. The underlying mechanisms warrant further investigation. Clinicaltrials.gov registration: NCT03433339 URL: https://clinicaltrials.gov/ct2/show/NCT03433339 .

Heart activity perception: narrative review on the measures of the cardiac perceptual ability

Measures of cardiac perceptual ability (also called cardiac accuracy) are methods of cardiac interoception, the perception of bodily sensation related to heart activity. This narrative review aims to provide an overview of these methods. We differentiate between three main measurement types: (1) change detection, i.e., when the task is to notice the change in the heart rate; also called: heart rate perception tasks, (2) discrimination tasks, i.e., when the task is to compare the internal sensations with external signal(s); also called: heartbeat detection tasks and (3) tracking tasks, i.e., when the task is to follow and report heartbeats via tapping or counting. We describe some of the new methods under "mixed methods," as they share features with more than one of the large measurement types described above. Specific measures differ in various aspects, such as their focus (heart rhythm vs. single beats), their sensitivity to non-conscious sensations and the calculated indices (e.g., whether significance level by hypothesis test is provided). When a measure of cardiac perceptual ability is chosen, it is advisable to take its characteristics into consideration in light of the planned research.

Bayesianism and wishful thinking are compatible

Bayesian principles show up across many domains of human cognition, but wishful thinking-where beliefs are updated in the direction of desired outcomes rather than what the evidence implies-seems to threaten the universality of Bayesian approaches to the mind. In this Article, we show that Bayesian optimality and wishful thinking are, despite first appearances, compatible. The setting of opposing goals can cause two groups of people with identical prior beliefs to reach opposite conclusions about the same evidence through fully Bayesian calculations. We show that this is possible because, when people set goals, they receive privileged information in the form of affective experiences, and this information systematically supports goal-consistent conclusions. We ground this idea in a formal, Bayesian model in which affective prediction errors drive wishful thinking. We obtain empirical support for our model across five studies.

Transcutaneous auricular vagus nerve stimulation modulates the processing of interoceptive prediction error signals and their role in allostatic regulation

It has recently been suggested that predictive processing principles may apply to interoception, defined as the processing of hormonal, autonomic, visceral, and immunological signals. In the current study, we aimed at providing empirical evidence for the role of cardiac interoceptive prediction errors signals on allostatic adjustments, using transcutaneous auricular vagus nerve stimulation (taVNS) as a tool to modulate the processing of interoceptive afferents. In a within-subject design, participants performed a cardiac-related interoceptive task (heartbeat counting task) under taVNS and sham stimulation, spaced 1-week apart. We observed that taVNS, in contrast to sham stimulation, facilitated the maintenance of interoceptive accuracy levels over time (from the initial, stimulation-free, baseline block to subsequent stimulation blocks), suggesting that vagus nerve stimulation may have helped to maintain engagement to cardiac afferent signals. During the interoceptive task, taVNS compared to sham, produced higher heart-evoked potentials (HEP) amplitudes, a potential readout measure of cardiac-related prediction error processing. Further analyses revealed that the positive relation between interoceptive accuracy and allostatic adjustments-as measured by heart rate variability (HRV)-was mediated by HEP amplitudes. Providing initial support for predictive processing accounts of interoception, our results suggest that the stimulation of the vagus nerve may increase the precision with which interoceptive signals are processed, favoring their influence on allostatic adjustments.

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.

Interoception: Where do we go from here?

In recent years, there has been a significant rise in interest in interoception, the processing of internal bodily signals. This interest has been coupled by increased concerns regarding the measurement and conceptualisation of interoception. Focusing on cardiac interoceptive accuracy, I outline what I believe to be the most pressing issues in the field of interoception-specifically the continued reliance on the heartbeat counting task. I then provide an overview of what I believe to be more general limitations concerning how we measure and conceptualise individual differences in interoception and suggestions for a way forward. Specifically, I believe that by moving beyond single measurements, establishing optimal levels of interoceptive accuracy, and refocusing from accuracy to propensity, we may be able to uncover the real-life relevance of interoceptive abilities.

Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation

Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, μg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 μg isoproterenol infusion, the GAD group (n=24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the HC group (n=24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than HC during saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 μg isoproterenol infusion. Using analyzable blood oxygenation level dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing according to prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.

Modeling and controlling the body in maladaptive ways: an active inference perspective on non-suicidal self-injury behaviors

A significant number of persons engage in paradoxical behaviors, such as extreme food restriction (up to starvation) and non-suicidal self-injuries, especially during periods of rapid changes, such as adolescence. Here, we contextualize these and related paradoxical behavior within an active inference view of brain functions, which assumes that the brain forms predictive models of bodily variables, emotional experiences, and the embodied self and continuously strives to reduce the uncertainty of such models. We propose that not only in conditions of excessive or prolonged uncertainty, such as in clinical conditions, but also during pivotal periods of developmental transition, paradoxical behaviors might emerge as maladaptive strategies to reduce uncertainty-by "acting on the body"- soliciting salient perceptual and interoceptive sensations, such as pain or excessive levels of hunger. Although such strategies are maladaptive and run against our basic homeostatic imperatives, they might be functional not only to provide some short-term reward (e.g. relief from emotional distress)-as previously proposed-but also to reduce uncertainty and possibly to restore a coherent model of one's bodily experience and the self, affording greater confidence in who we are and what course of actions we should pursue.

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

BACKGROUND

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

METHODS

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

RESULTS

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (partial η2=.02). Previously observed differences in action precision were not confirmed. Learning rates for positive outcomes were also similar between groups. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

CONCLUSIONS

These results provide robust evidence that individuals with SUDs adjust behavior more slowly in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

Back to the future: Progressing memory research in eating disorders

OBJECTIVE

Human behaviors, thoughts, and emotions are guided by memories of the past. Thus, there can be little doubt that memory plays a fundamental role in the behaviors (e.g., binging), thoughts (e.g., body-image concerns), and emotions (e.g., guilt) that characterize eating disorders (EDs). Although a growing body of research has begun to investigate the role of memory in EDs, this literature is limited in numerous ways and has yet to be integrated into an overarching framework.

METHODS

In the present article, we provide an operational framework for characterizing different domains of memory, briefly review existing ED memory research within this framework, and highlight crucial gaps in the literature.

RESULTS

We distinguish between three domains of memory-episodic, procedural, and working-which differ based on functional attributes and underlying neural systems. Most recent ED memory research has focused on procedural memory broadly defined (e.g., reinforcement learning), and findings within all three memory domains are highly mixed. Further, few studies have attempted to assess these different domains simultaneously, though most behavior is achieved through coordination and competition between memory systems. We, therefore, offer recommendations for how to move ED research forward within each domain of memory and how to study the interactions between memory systems, using illustrative examples from other areas of basic and clinical research.

DISCUSSION

A stronger and more integrated understanding of the mechanisms that connect memory of past experiences to present ED behavior may yield more comprehensive theoretical models of EDs that guide novel treatment approaches.

PUBLIC SIGNIFICANCE

Memories of previous eating-related experiences may contribute to the onset and maintenance of eating disorders (EDs). However, research on the role of memory in EDs is limited, and distinct domains of ED memory research are rarely connected. We, therefore, offer a framework for organizing, progressing, and integrating ED memory research, to provide a better foundation for improving ED treatment and intervention going forward.

An Introduction to Predictive Processing Models of Perception and Decision-Making

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.

Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication study

Recent computational theories of interoception suggest that perception of bodily states rests upon an expected reliability- or precision-weighted integration of afferent signals and prior beliefs. The computational psychiatry framework further suggests that aberrant precision-weighting may lead to misestimation of bodily states, potentially hindering effective visceral regulation and promoting psychopathology. In a previous study, we fit a Bayesian computational model of perception to behavior on a heartbeat tapping task to test whether aberrant precision-weighting was associated with misestimation of bodily states. We found that, during an interoceptive perturbation designed to amplify afferent signal precision (inspiratory breath-holding), healthy individuals increased the precision-weighting assigned to ascending cardiac signals (relative to resting conditions), while individuals with symptoms of anxiety, depression, substance use disorders, and/or eating disorders did not. A second study also replicated the pattern observed in healthy participants. In this pre-registered study, we aimed to replicate our prior findings in a new transdiagnostic patient sample (N=285) similar to the one in the original study. These new results successfully replicated those found in our previous study, indicating that, transdiagnostically, patients were unable to adjust beliefs about the reliability of interoceptive signals - preventing the ability to accurately perceive changes in their bodily state. Follow-up analyses combining samples from the previous and current study (N=719) also afforded the power to identify group differences within narrower diagnostic groups and to examine predictive accuracy when logistic regression models were trained on one sample and tested on the other. Given the increased confidence in the generalizability of these effects, future studies should examine the utility of interceptive precision measures in predicting treatment outcomes or identify whether these computational mechanisms might represent novel therapeutic targets for improving visceral regulation.

The interpersonal computational psychiatry of social coordination in schizophrenia

Impairments in social coordination form a core dimension of various psychiatric disorders, including schizophrenia. Advances in interpersonal and computational psychiatry support a major change in studying social coordination in schizophrenia. Although these developments provided novel perspectives to study how interpersonal activities shape coordination and to examine computational mechanisms, direct attempts to integrate the two methodologies have been sparse. Here, we propose an interpersonal computational framework that (1) leverages the active inference framework to model aberrant social coordination processes in schizophrenia and (2) incorporates dynamical system models to dissect intrapersonal and interpersonal synchronisation to inform a statistical model based on active inference. We discuss how this interpersonal computational psychiatry framework can elucidate the aberrant processes leading to psychopathology, with schizophrenia as an example, and highlight how it might aid clinical intervention and practice. Finally, we discuss challenges and opportunities for using the framework in studying social coordination impairments.

The new measures of interoceptive accuracy: A systematic review and assessment

Conscious interoception, the perception of internal bodily states, is thought to contribute to fundamental human abilities (e.g., decision-making and emotional regulation). One of its most studied dimensions is interoceptive accuracy: the objective capacity to detect internal bodily signals. In the past few years, several labs across the world have started developing new tasks aimed at overcoming limitations inherent in classical measures of interoceptive accuracy. In this systematic review, we identified these tasks (since 2015) for the cardiac, respiratory, and gastrointestinal domains. For each identified task, we discuss their strengths and weaknesses, and make constructive suggestions for further improvement. In the general discussion, we discuss the (potentially elusive) possibility of reaching high validity in the measurement of interoceptive accuracy. We also point out that interoceptive accuracy may not be the most critical dimension for informing current theories, and we encourage researchers to investigate other dimensions of conscious interoception.

Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain

Placebo analgesia is a replicable and well-studied phenomenon, yet it remains unclear to what degree it includes modulation of nociceptive processes. Some studies find effects consistent with nociceptive effects, but meta-analyses show that these effects are often small. We analyzed placebo analgesia in a large fMRI study (N = 392), including placebo effects on brain responses to noxious stimuli. Placebo treatment caused robust analgesia in both conditioned thermal and unconditioned mechanical pain. Placebo did not decrease fMRI activity in nociceptive pain regions, including the Neurologic Pain Signature (NPS) and pre-registered spinothalamic pathway regions, with strong support from Bayes Factor analyses. However, placebo treatment affected activity in pre-registered analyses of a second neuromarker, the Stimulus Intensity Independent Pain Signature (SIIPS), and several associated a priori brain regions related to motivation and value, in both thermal and mechanical pain. Individual differences in behavioral analgesia were correlated with neural changes in both thermal and mechanical pain. Our results indicate that processes related to affective and cognitive aspects of pain primarily drive placebo analgesia.

Lifeworlds in pain: a principled method for investigation and intervention

The experience of pain spans biological, psychological and sociocultural realms, both basic and complex, it is by turns necessary and devastating. Despite an extensive knowledge of the constituents of pain, the ability to translate this into effective intervention remains limited. It is suggested that current, multiscale, medical approaches, largely informed by the biopsychosocial (BPS) model, attempt to integrate knowledge but are undermined by an epistemological obligation, one that necessitates a prior isolation of the constituent parts. To overcome this impasse, we propose that an anthropological stance needs to be taken, underpinned by a Bayesian apparatus situated in computational psychiatry. Here, pain is presented within the context of lifeworlds, where attention is shifted away from the constituents of experience (e.g. nociception, reward processing and fear-avoidance), towards the dynamic affiliation that occurs between these processes over time. We argue that one can derive a principled method of investigation and intervention for pain from modelling approaches in computational psychiatry. We suggest that these modelling methods provide the necessary apparatus to navigate multiscale ontology and epistemology of pain. Finally, a unified approach to the experience of pain is presented, where the relational, inter-subjective phenomenology of pain is brought into contact with a principled method of translation; in so doing, revealing the conditions and possibilities of lifeworlds in pain.

Body as First Teacher: The Role of Rhythmic Visceral Dynamics in Early Cognitive Development

Embodied cognition-the idea that mental states and processes should be understood in relation to one's bodily constitution and interactions with the world-remains a controversial topic within cognitive science. Recently, however, increasing interest in predictive processing theories among proponents and critics of embodiment alike has raised hopes of a reconciliation. This article sets out to appraise the unificatory potential of predictive processing, focusing in particular on embodied formulations of active inference. Our analysis suggests that most active-inference accounts invoke weak, potentially trivial conceptions of embodiment; those making stronger claims do so independently of the theoretical commitments of the active-inference framework. We argue that a more compelling version of embodied active inference can be motivated by adopting a diachronic perspective on the way rhythmic physiological activity shapes neural development in utero. According to this visceral afferent training hypothesis, early-emerging physiological processes are essential not only for supporting the biophysical development of neural structures but also for configuring the cognitive architecture those structures entail. Focusing in particular on the cardiovascular system, we propose three candidate mechanisms through which visceral afferent training might operate: (a) activity-dependent neuronal development, (b) periodic signal modeling, and (c) oscillatory network coordination.

An active inference model of hierarchical action understanding, learning and imitation

We advance a novel active inference model of the cognitive processing that underlies the acquisition of a hierarchical action repertoire and its use for observation, understanding and imitation. We illustrate the model in four simulations of a tennis learner who observes a teacher performing tennis shots, forms hierarchical representations of the observed actions, and imitates them. Our simulations show that the agent's oculomotor activity implements an active information sampling strategy that permits inferring the kinematic aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions: proximal goals and intentions. Finally, the inferred action representations can steer imitative responses, but interfere with the execution of different actions. Our simulations show that hierarchical active inference provides a unified account of action observation, understanding, learning and imitation and helps explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms.

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.

Beyond simple laboratory studies: Developing sophisticated models to study rich behavior

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage.

Discrepancies in the Definition and Measurement of Human Interoception: A Comprehensive Discussion and Suggested Ways Forward

Interoception has been the subject of renewed interest over the past 2 decades. The involvement of interoception in a variety of fundamental human abilities (e.g., decision-making and emotional regulation) has led to the hypothesis that interoception is a central transdiagnostic process that causes and maintains mental disorders and physical diseases. However, interoception has been inconsistently defined and conceptualized. In the first part of this article, we argue that the widespread practice of defining interoception as the processing of signals originating from within the body and limiting it to specific physiological pathways (lamina I spinothalamic afferents) is problematic. This is because, in humans, the processing of internal states is underpinned by other physiological pathways generally assigned to the somatosensory system. In the second part, we explain that the consensual dimensions of interoception are empirically detached from existing measures, the latter of which capture loosely related phenomena. This is detrimental to the replicability of findings across measures and the validity of interpretations. In the general discussion, we discuss the main insights of the current analysis and suggest a more refined way to define interoception in humans and conceptualize its underlying dimensions.

Parieto-occipital ERP indicators of gut mechanosensation in humans

Understanding the neural processes governing the human gut-brain connection has been challenging due to the inaccessibility of the body's interior. Here, we investigated neural responses to gastrointestinal sensation using a minimally invasive mechanosensory probe by quantifying brain, stomach, and perceptual responses following the ingestion of a vibrating capsule. Participants successfully perceived capsule stimulation under two vibration conditions (normal and enhanced), as evidenced by above chance accuracy scores. Perceptual accuracy improved significantly during the enhanced relative to normal stimulation, which was associated with faster stimulation detection and reduced reaction time variability. Capsule stimulation induced late neural responses in parieto-occipital electrodes near the midline. Moreover, these 'gastric evoked potentials' showed intensity-dependent increases in amplitude and were significantly correlated with perceptual accuracy. Our results replicated in a separate experiment, and abdominal X-ray imaging localized most capsule stimulations to the gastroduodenal segments. Combined with our prior observation that a Bayesian model is capable of estimating computational parameters of gut-brain mechanosensation, these findings highlight a unique form of enterically-focused sensory monitoring within the human brain, with implications for understanding gut feelings and gut-brain interactions in healthy and clinical populations.

Multidimensional assessment of anticipated and experienced interoceptive states

As the sense of the body's internal state, interoception represents the afferent component of the brain-body feedback loop essential for linking internal sensation with body regulation, thereby minimizing erroneous feedback and maintaining homeostasis. The anticipation of potential future interoceptive states enables organisms to take regulatory actions to meet demands before they arise, and alterations of anticipation have been implicated in the pathophysiology of medical and psychiatric conditions. However, laboratory approaches operationalizing the anticipation of interoceptive states are missing. Therefore, we developed two interoceptive awareness paradigms, the Accuracy of Interoceptive Anticipation paradigm, and the Interoceptive Discrepancy paradigm, which we tested in 52 healthy participants on two sensory modalities: nociception and respiroception. Ten participants took part in a retest. The Accuracy of Interoceptive Anticipation paradigm focused on assessing how individuals anticipate and experience interoceptive stimuli of varying strengths. The Interoceptive Discrepancy paradigm extended this measure by manipulating previously learned expectations to induce discrepancies between anticipated and experienced stimuli. We found that anticipation and experience ratings successfully related to stimulus strength in both paradigms and modalities and were stable between test-retest. Furthermore, the Interoceptive Discrepancy paradigm successfully induced the expected discrepancies between anticipation and experience conditions, and discrepancy values were correlated across sensory modalities. Thus, both paradigms are valid and reliable tools for assessing the anticipation of future interoceptive states, and the Interoceptive Discrepancy paradigm is additionally suited to evaluate discrepancy awareness.

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

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

Invasive Computational Psychiatry

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results

Background

Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences.

Methods

Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders.

Results

Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (η 2 =.02). Previously observed differences in action precision were not confirmed. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other.

Conclusions

These results provide robust evidence that individuals with SUDs have more difficulty adjusting behavior in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.

Expectation focused and frequency enhanced cognitive behavioural therapy for patients with major depression (EFFECT): a study protocol of a randomised active-control trial

INTRODUCTION

The effectiveness of psychotherapy in depression is subject of an ongoing debate. The mechanisms of change are still underexplored. Research tries to find influencing factors fostering the effect of psychotherapy. In that context, the dose-response relationship should receive more attention. Increasing the frequency from one to two sessions per week seems to be a promising start. Moreover, the concept of expectations and its influence in depression can be another auspicious approach. Dysfunctional expectations and the lack of their modification are central in symptom maintenance. Expectation focused psychological interventions (EFPI) have been investigated, primarily in the field of depression. The aim of this study is to compare cognitive behavioural therapy (CBT) once a week with an intensified version of CBT (two times a week) in depression as well as to include a third proof-of-principle intervention group receiving a condensed expectation focused CBT.

METHODS AND ANALYSIS

Participants are recruited through an outpatient clinic in Germany. A current major depressive episode, diagnosed via structured clinical interviews should present as the main diagnosis. The planned randomised-controlled trial will allow comparisons between the following treatment conditions: CBT (one session/week), condensed CBT (two sessions/week) and EFPI (two sessions/week). All treatment arms include a total dose of 24 sessions. Depression severity applies as the outcome variable (Beck Depression Inventory II, Montgomery Asberg Depression Rating Scale). A sample size of n=150 is intended.

ETHICS AND DISSEMINATION

The local ethics committee of the Department of Psychology, Philipps-University Marburg approved the study (reference number 2020-68 v). The final research article including the study results is intended to be published in international peer-reviewed journals.

TRIAL REGISTRATION NUMBER

German Clinical Trials Registry (DRKS00023203).

A Bayesian computational model to investigate expert anticipation of a seemingly unpredictable ball bounce

During dynamic and time-constrained sporting tasks performers rely on both online perceptual information and prior contextual knowledge to make effective anticipatory judgments. It has been suggested that performers may integrate these sources of information in an approximately Bayesian fashion, by weighting available information sources according to their expected precision. In the present work, we extended Bayesian brain approaches to anticipation by using formal computational models to estimate how performers weighted different information sources when anticipating the bounce direction of a rugby ball. Both recreational (novice) and professional (expert) rugby players (n = 58) were asked to predict the bounce height of an oncoming rugby ball in a temporal occlusion paradigm. A computational model, based on a partially observable Markov decision process, was fitted to observed responses to estimate participants' weighting of online sensory cues and prior beliefs about ball bounce height. The results showed that experts were more sensitive to online sensory information, but that neither experts nor novices relied heavily on prior beliefs about ball trajectories in this task. Experts, but not novices, were observed to down-weight priors in their anticipatory decisions as later and more precise visual cues emerged, as predicted by Bayesian and active inference accounts of perception.

Mapping the mental space of emotional concepts through kinematic measures of decision uncertainty

Emotional concepts and their mental representations have been extensively studied. Yet, some ecologically relevant aspects, such as how they are processed in ambiguous contexts (e.g., in relation to other emotional stimuli that share similar characteristics), are incompletely known. We employed a similarity judgement of emotional concepts and manipulated the contextual congruency of the responses along the two main affective dimensions of hedonic valence and physiological activation, respectively. Behavioural and kinematics (mouse-tracking) measures were combined to gather a novel 'similarity index' between emotional concepts, to derive topographical maps of their mental representations. Self-report (interoceptive sensibility, positive-negative affectivity, depression) and physiological measures (heart rate variability, HRV) have been collected to explore their possible association with emotional conceptual representation. Results indicate that emotional concepts typically associated with low arousal profit by contextual congruency, with faster responses and reduced uncertainty when contextual ambiguity decreases. The emotional maps recreate two almost orthogonal axes of valence and arousal, and the similarity measure captures the smooth boundaries between emotions. The emotional map of a subgroup of individuals with low positive affectivity reveals a narrower conceptual distribution, with variations in positive emotions and in individuals with reduced arousal (such as those with reduced HRV). Our work introduces a novel methodology to study emotional conceptual representations, bringing the behavioural dynamics of decision-making processes and choice uncertainty into the affective domain. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Two-stage reinforcement learning task predicts psychological traits

External sources of information influence human actions. However, psychological traits (PTs), considered internal variables, also play a crucial role in decision making. PTs are stable across time and contexts and define the set of behavioral repertoires that individuals express. Here, we explored how multiple metrics of adaptive behavior under uncertainty related to several PTs. Participants solved a reversal-learning task with volatile contingencies, from which we characterized a detailed behavioral profile based on their response sequences. We then tested the relationship between this multimetric behavioral profile and scores obtained from self-report psychological questionnaires. The PT measurements were based on the Hierarchical Taxonomy Of Psychopathology (HiTOP) model. By using multiple linear regression models (MLRMs), we found that the learning curves predicted important differences in the PTs and task response times. We confirmed the significance of these relationships by using random permutations of the predictors of the MLRM. Therefore, the behavioral profile configurations predicted the PTs and served as a "fingerprint" to identify participants with a high certainty level. We discuss briefly how this characterization and approach could contribute to better nosological classifications.

The unique face of comorbid anxiety and depression: increased interoceptive fearfulness and reactivity

Anxiety and depression commonly co-occur, yet the underlying brain and behavioral processes are poorly understood. Here we examined the hypothesis that individuals with comorbid anxiety and depression would show increased fearful reactivity to an aversive interoceptive perturbation relative to depressed-only individuals. One-hundred and eighty anxious and/or depressed participants from the Tulsa 1000 study completed multi-level behavioral or functional magnetic resonance imaging assessments of interoception and nociception including breath-hold and cold-pressor challenges, and heartbeat perception and interoceptive attention tasks. One-hundred and four individuals with comorbid depression and anxiety disorders (Dep+Anx) were propensity matched with 52 individuals with depression-only (Dep). Data were analyzed using mixed-effects linear regression. The Dep+Anx group showed significantly greater self-reported fear of suffocation during breath holding (Wilcoxon r = 0.23) and reduced cold pain tolerance (R ² = 0.027) signified by hand removal during immersion. However, these groups did not differ with respect to neutrally-valenced behavioral indices of heartbeat perception or neural indices of interoceptive attention. Individuals with comorbid depression and anxiety, vs. those with only depression, show increased respiratory fearfulness and nociceptive reactivity during perturbations of these signals, whilst showing similar interoceptive awareness in the absence of perturbation. Our findings suggest that individuals with comorbid anxiety and depression process aversive interoceptive and nociceptive signals differently than those with depression alone, providing support for a process model of increased threat sensitivity and hyperarousal in anxious depression.

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.

Cognition as Morphological/Morphogenetic Embodied Computation In Vivo

Cognition, historically considered uniquely human capacity, has been recently found to be the ability of all living organisms, from single cells and up. This study approaches cognition from an info-computational stance, in which structures in nature are seen as information, and processes (information dynamics) are seen as computation, from the perspective of a cognizing agent. Cognition is understood as a network of concurrent morphological/morphogenetic computations unfolding as a result of self-assembly, self-organization, and autopoiesis of physical, chemical, and biological agents. The present-day human-centric view of cognition still prevailing in major encyclopedias has a variety of open problems. This article considers recent research about morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, free energy principle, cognition as Bayesian learning, active inference, and related topics, offering new theoretical and practical perspectives on problems inherent to the old computationalist cognitive models which were based on abstract symbol processing, and unaware of actual physical constraints and affordances of the embodiment of cognizing agents. A better understanding of cognition is centrally important for future artificial intelligence, robotics, medicine, and related fields.

Active inference and the two-step task

Sequential decision problems distill important challenges frequently faced by humans. Through repeated interactions with an uncertain world, unknown statistics need to be learned while balancing exploration and exploitation. Reinforcement learning is a prominent method for modeling such behaviour, with a prevalent application being the two-step task. However, recent studies indicate that the standard reinforcement learning model sometimes describes features of human task behaviour inaccurately and incompletely. We investigated whether active inference, a framework proposing a trade-off to the exploration-exploitation dilemma, could better describe human behaviour. Therefore, we re-analysed four publicly available datasets of the two-step task, performed Bayesian model selection, and compared behavioural model predictions. Two datasets, which revealed more model-based inference and behaviour indicative of directed exploration, were better described by active inference, while the models scored similarly for the remaining datasets. Learning using probability distributions appears to contribute to the improved model fits. Further, approximately half of all participants showed sensitivity to information gain as formulated under active inference, although behavioural exploration effects were not fully captured. These results contribute to the empirical validation of active inference as a model of human behaviour and the study of alternative models for the influential two-step task.

In the Body’s Eye: The computational anatomy of interoceptive inference

A growing body of evidence highlights the intricate linkage of exteroceptive perception to the rhythmic activity of the visceral body. In parallel, interoceptive inference theories of affective perception and self-consciousness are on the rise in cognitive science. However, thus far no formal theory has emerged to integrate these twin domains; instead, most extant work is conceptual in nature. Here, we introduce a formal model of cardiac active inference, which explains how ascending cardiac signals entrain exteroceptive sensory perception and uncertainty. Through simulated psychophysics, we reproduce the defensive startle reflex and commonly reported effects linking the cardiac cycle to affective behaviour. We further show that simulated ‘interoceptive lesions’ blunt affective expectations, induce psychosomatic hallucinations, and exacerbate biases in perceptual uncertainty. Through synthetic heart-rate variability analyses, we illustrate how the balance of arousal-priors and visceral prediction errors produces idiosyncratic patterns of physiological reactivity. Our model thus offers a roadmap for computationally phenotyping disordered brain-body interaction.

Understanding interactions between the brain and the body has become a topic of increased interest in computational neuroscience and psychiatry. A particular question here concerns how visceral, homeostatic rhythms such as the heart beat influence sensory, affective, and cognitive processing. To better understand these and other oscillatory brain-body interactions, we here introduce a novel computational model of interoceptive inference in which a synthetic agent’s perceptual beliefs are coupled to the rhythm of the heart. Our model both helps to explain emerging empirical data indicating that perceptual inference depends upon beat-to-beat heart rhythms, and can be used to better quantify intra- and inter-individual differences in heart-brain coupling. Using proof-of-principle simulations, we demonstrate how future empirical works could utilize our model to better understand and stratify disorders of interoception and brain-body interaction.