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

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.

Enhanced top-down sensorimotor processing in somatic anxiety

Functional neuroimaging research on anxiety has traditionally focused on brain networks associated with the psychological aspects of anxiety. Here, instead, we target the somatic aspects of anxiety. Motivated by the growing appreciation that top-down cortical processing plays a crucial role in perception and action, we used resting-state functional MRI data from the Human Connectome Project and Dynamic Causal Modeling (DCM) to characterize effective connectivity among hierarchically organized regions in the exteroceptive, interoceptive, and motor cortices. In people with high (fear-related) somatic arousal, top-down effective connectivity was enhanced in all three networks: an observation that corroborates well with the phenomenology of anxiety. The anxiety-associated changes in connectivity were sufficiently reliable to predict whether a new participant has mild or severe somatic anxiety. Interestingly, the increase in top-down connections to sensorimotor cortex were not associated with fear affect scores, thus establishing the (relative) dissociation between somatic and cognitive dimensions of anxiety. Overall, enhanced top-down effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of trait somatic anxiety.

Active inference unifies intentional and conflict-resolution imperatives of motor control

The field of motor control has long focused on the achievement of external goals through action (e.g., reaching and grasping objects). However, recent studies in conditions of multisensory conflict, such as when a subject experiences the rubber hand illusion or embodies an avatar in virtual reality, reveal the presence of unconscious movements that are not goal-directed, but rather aim at resolving multisensory conflicts; for example, by aligning the position of a person’s arm with that of an embodied avatar. This second, conflict-resolution imperative of movement control did not emerge in classical studies of motor adaptation and online corrections, which did not allow movements to reduce the conflicts; and has been largely ignored so far in formal theories. Here, we propose a model of movement control grounded in the theory of active inference that integrates intentional and conflict-resolution imperatives. We present three simulations showing that the active inference model is able to characterize movements guided by the intention to achieve an external goal, by the necessity to resolve multisensory conflict, or both. Furthermore, our simulations reveal a fundamental difference between the (active) inference underlying intentional and conflict-resolution imperatives by showing that it is driven by two different (model and sensory) kinds of prediction errors. Finally, our simulations show that when movement is only guided by conflict resolution, the model incorrectly infers that is velocity is zero, as if it was not moving. This result suggests a novel speculative explanation for the fact that people are unaware of their subtle compensatory movements to avoid multisensory conflict. Furthermore, it can potentially help shed light on deficits of motor awareness that arise in psychopathological conditions.

Interoceptive pathways to understand and treat mental health conditions

An increasing recognition that brain and body are dynamically coupled has enriched our scientific understanding of mental health conditions. Peripheral signals interact centrally to influence how we think and feel, generating our sense of the internal condition of the body, a process known as interoception. Disruptions to this interoceptive system may contribute to clinical conditions, including anxiety, depression, and psychosis. After reviewing the nature of interoceptive disturbances in mental health conditions, this review focuses on interoceptive pathways of existing and putative mental health treatments. Emerging clinical interventions may target novel peripheral treatment mechanisms. Future treatment development requires forward- and back-translation to uncover and target specific interoceptive processes in mental health to elucidate their efficacy relative to interventions targeting other factors.

An Active Inference Account of Skilled Anticipation in Sport: Using Computational Models to Formalise Theory and Generate New Hypotheses

Optimal performance in time-constrained and dynamically changing environments depends on making reliable predictions about future outcomes. In sporting tasks, performers have been found to employ multiple information sources to maximise the accuracy of their predictions, but questions remain about how different information sources are weighted and integrated to guide anticipation. In this paper, we outline how predictive processing approaches, and active inference in particular, provide a unifying account of perception and action that explains many of the prominent findings in the sports anticipation literature. Active inference proposes that perception and action are underpinned by the organism’s need to remain within certain stable states. To this end, decision making approximates Bayesian inference and actions are used to minimise future prediction errors during brain–body–environment interactions. Using a series of Bayesian neurocomputational models based on a partially observable Markov process, we demonstrate that key findings from the literature can be recreated from the first principles of active inference. In doing so, we formulate a number of novel and empirically falsifiable hypotheses about human anticipation capabilities that could guide future investigations in the field.

A step-by-step tutorial on active inference and its application to empirical data

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.

From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.

Higher emotional awareness is associated with greater domain-general reflective tendencies

The tendency to reflect on the emotions of self and others is a key aspect of emotional awareness (EA)—a trait widely recognized as relevant to mental health. However, the degree to which EA draws on general reflective cognition vs. specialized socio-emotional mechanisms remains unclear. Based on a synthesis of work in neuroscience and psychology, we recently proposed that EA is best understood as a learned application of domain-general cognitive processes to socio-emotional information. In this paper, we report a study in which we tested this hypothesis in 448 (125 male) individuals who completed measures of EA and both general reflective cognition and socio-emotional performance. As predicted, we observed a significant relationship between EA measures and both general reflectiveness and socio-emotional measures, with the strongest contribution from measures of the general tendency to engage in effortful, reflective cognition. This is consistent with the hypothesis that EA corresponds to the application of general reflective cognitive processes to socio-emotional signals.

Beyond Description and Deficits: How Computational Psychiatry Can Enhance an Understanding of Decision-Making in Anorexia Nervosa

PURPOSE OF REVIEW

Despite decades of research, knowledge of the mechanisms maintaining anorexia nervosa (AN) remains incomplete and clearly effective treatments elusive. Novel theoretical frameworks are needed to advance mechanistic and treatment research for this disorder. Here, we argue the utility of engaging a novel lens that differs from existing perspectives in psychiatry. Specifically, we argue the necessity of expanding beyond two historically common perspectives: (1) the descriptive perspective: the tendency to define mechanisms on the basis of surface characteristics and (2) the deficit perspective: the tendency to search for mechanisms associated with under-functioning of decision-making abilities and related circuity, rather than problems of over-functioning, in psychiatric disorders.

RECENT FINDINGS

Computational psychiatry can provide a novel framework for understanding AN because this approach emphasizes the role of computational misalignments (rather than absolute deficits or excesses) between decision-making strategies and environmental demands as the key factors promoting psychiatric illnesses. Informed by this approach, we argue that AN can be understood as a disorder of excess goal pursuit, maintained by over-engagement, rather than disengagement, of executive functioning strategies and circuits. Emerging evidence suggests that this same computational imbalance may constitute an under-investigated phenotype presenting transdiagnostically across psychiatric disorders. A variety of computational models can be used to further elucidate excess goal pursuit in AN. Most traditional psychiatric treatments do not target excess goal pursuit or associated neurocognitive mechanisms. Thus, targeting at the level of computational dysfunction may provide a new avenue for enhancing treatment for AN and related disorders.

Interoception as modeling, allostasis as control

The brain regulates the body by anticipating its needs and attempting to meet them before they arise - a process called allostasis. Allostasis requires a model of the changing sensory conditions within the body, a process called interoception. In this paper, we examine how interoception may provide performance feedback for allostasis. We suggest studying allostasis in terms of control theory, reviewing control theory's applications to related issues in physiology, motor control, and decision making. We synthesize these by relating them to the important properties of allostatic regulation as a control problem. We then sketch a novel formalism for how the brain might perform allostatic control of the viscera by analogy to skeletomotor control, including a mathematical view on how interoception acts as performance feedback for allostasis. Finally, we suggest ways to test implications of our hypotheses.

Alcohol use and interoception - A narrative review

Interoception, defined as the ability to perceive and interpret body signals, may play an important role in alcohol use disorder (AUD). Earlier studies suggested an association between interoception impairment and known risk factors for AUD (e.g., alexithymia, emotion dysregulation, impulsivity, pain). Neurobiological studies show that the neurotoxicity of alcohol affects various elements of the interoceptive system (especially the insula) at structural and functional levels, with differential short/long term impacts. Conversely, primary interoceptive impairments may promote alcohol consumption and foster the evolution towards addiction. Despite convincing evidence demonstrating that interoception impairment may be an important contributor to the development and course of AUD, only a few studies directly evaluated interoceptive abilities in AUD. The research shows that interoceptive accuracy, the objective component of interoception, is lower in AUD individuals, and is correlated with craving and emotion dysregulation. Interoceptive sensibility is in turn higher in AUD individuals compared to healthy controls. Moreover, there is evidence that therapy focused on improving the ability to sense signals from the body in addiction treatment is effective. However, important methodological limitations in interoceptive measures persist, and it is therefore necessary to further investigate the associations between interoception and AUD.

Interoception of breathing and its relationship with anxiety

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. Although interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modeling and high-field (7 T) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low versus moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multi-modal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

The heart rate discrimination task: a psychophysical method to estimate the accuracy and precision of interoceptive beliefs

Interoception - the physiological sense of our inner bodies - has risen to the forefront of psychological and psychiatric research. Much of this research utilizes tasks that attempt to measure the ability to accurately detect cardiac signals. Unfortunately, these approaches are confounded by well-known issues limiting their validity and interpretation. At the core of this controversy is the role of subjective beliefs about the heart rate in confounding measures of interoceptive accuracy. Here, we recast these beliefs as an important part of the causal machinery of interoception, and offer a novel psychophysical "heart rate discrimination" method to estimate their accuracy and precision. By applying this task in 223 healthy participants, we demonstrate that cardiac interoceptive beliefs are more biased, less precise, and are associated with poorer metacognitive insight relative to an exteroceptive control condition. Our task, provided as an open-source python package, offers a robust approach to quantifying cardiac beliefs.

Alpha-to-beta- and gamma-band activity reflect predictive coding in affective visual processing

Processing of negative affective pictures typically leads to desynchronization of alpha-to-beta frequencies (ERD) and synchronization of gamma frequencies (ERS). Given that in predictive coding higher frequencies have been associated with prediction errors, while lower frequencies have been linked to expectations, we tested the hypothesis that alpha-to-beta ERD and gamma ERS induced by aversive pictures are associated with expectations and prediction errors, respectively. We recorded EEG while volunteers were involved in a probabilistically cued affective picture task using three different negative valences to produce expectations and prediction errors. Our data show that alpha-to-beta band activity after stimulus presentation was related to the expected valence of the stimulus as predicted by a cue. The absolute mismatch of the expected and actual valence, which denotes an absolute prediction error was related to increases in alpha, beta and gamma band activity. This demonstrates that top-down predictions and bottom-up prediction errors are represented in typical spectral patterns associated with affective picture processing. This study provides direct experimental evidence that negative affective picture processing can be described by neuronal predictive coding computations.

The Role of Interoceptive Sensibility and Emotional Conceptualization for the Experience of Emotions

The theory of constructed emotions suggests that different psychological components, including core affect (mental and neural representations of bodily changes), and conceptualization (meaning-making based on prior experiences and semantic knowledge), are involved in the formation of emotions. However, little is known about their role in experiencing emotions. In the current study, we investigated how individual differences in interoceptive sensibility and emotional conceptualization (as potential correlates of these components) interact to moderate three important aspects of emotional experiences: emotional intensity (strength of emotion felt), arousal (degree of activation), and granularity (ability to differentiate emotions with precision). To this end, participants completed a series of questionnaires assessing interoceptive sensibility and emotional conceptualization and underwent two emotion experience tasks, which included standardized material (emotion differentiation task; ED task) and self-experienced episodes (day reconstruction method; DRM). Correlational analysis showed that individual differences in interoceptive sensibility and emotional conceptualization were related to each other. Principal Component Analysis (PCA) revealed two independent factors that were referred to as sensibility and monitoring. The Sensibility factor, interpreted as beliefs about the accuracy of an individual in detecting internal physiological and emotional states, predicted higher granularity for negative words. The Monitoring factor, interpreted as the tendency to focus on the internal states of an individual, was negatively related to emotional granularity and intensity. Additionally, Sensibility scores were more strongly associated with greater well-being and adaptability measures than Monitoring scores. Our results indicate that independent processes underlying individual differences in interoceptive sensibility and emotional conceptualization contribute to emotion experiencing.

Sensitivity to changes in rate of heartbeats as a measure of interoceptive ability

Individuals vary in their ability to perceive, as conscious sensations, signals like the beating of the heart. Tests of such interoceptive ability are, however, constrained in nature and reliability. Performance of the heartbeat tracking task, a widely used test of cardiac interoception, often corresponds well with individual differences in emotion and cognition, yet is susceptible to reporting bias and influenced by higher-order knowledge, e.g., of expected heart rate. The present study introduces a new way of assessing cardiac interoceptive ability, focusing on sensitivity to short-term, naturalistic changes in frequency of heartbeats. At rest, such heart rate variability typically reflects the dominant influence of respiration on vagus parasympathetic control of the sinoatrial pacemaker. We observed an overall tendency of healthy participants to report feeling fewer heartbeats during increases in heart rate, which we speculate reflects a reduction in heartbeat strength and salience during inspiratory periods when heart rate typically increases to maintain a stable cardiac output. Within-participant performance was more variable on this measure of cardiac interoceptive sensitivity relative to the "classic" heartbeat tracking task. Our findings indicate that cardiac interoceptive ability, rather than reflecting the veridical monitoring of subtle variations in physiology, appears to involve more interpolation wherein interoceptive decisions are informed by dynamic working estimates derived from the integration of afferent signaling and higher-order predictions.NEW & NOTEWORTHY This study presents a new method for evaluating cardiac interoceptive ability, measuring sensitivity to naturalistic changes in the number of heartbeats over time periods. Results show participants have an overall tendency toward sensing fewer heartbeats during higher heart rates. This likely reflects the influence of changing heartbeat strength on cardiac interoception at rest, which should be taken into account when evaluating cardiac interoceptive ability and its relationship to anxiety and psychosomatic conditions.

Osteopathy and Mental Health: An Embodied, Predictive, and Interoceptive Framework

Globally, mental and musculoskeletal disorders present with high prevalence, disease burden, and comorbidity. In order to improve the quality of care for patients with persistent physical and comorbid mental health conditions, person-centered care approaches addressing psychosocial factors are currently advocated. Central to successful person-centered care is a multidisciplinary collaboration between mental health and musculoskeletal specialists underpinned by a robust therapeutic alliance. Such a collaborative approach might be found in osteopathy, which is typically utilized to treat patients with musculoskeletal disorders but may arguably also benefit mental health outcomes. However, research and practice exploring the reputed effect of osteopathy on patients with mental health problems lack a robust framework. In this hypothesis and theory article, we build upon research from embodied cognition, predictive coding, interoception, and osteopathy to propose an embodied, predictive and interoceptive framework that underpins osteopathic person-centered care for individuals with persistent physical and comorbid mental health problems. Based on the premise that, for example, chronic pain and comorbid depression are underlined by overly precise predictions or imprecise sensory information, we hypothesize that osteopathic treatment may generate strong interoceptive prediction errors that update the generative model underpinning the experience of pain and depression. Thus, physical and mental symptoms may be reduced through active and perceptual inference. We discuss how these theoretical perspectives can inform future research into osteopathy and mental health to reduce the burden of comorbid psychological factors in patients with persistent physical symptoms and support person-centered multidisciplinary care in mental health.

Predictive eye movements are adjusted in a Bayes-optimal fashion in response to unexpectedly changing environmental probabilities

This study examined the application of active inference to dynamic visuomotor control. Active inference proposes that actions are dynamically planned according to uncertainty about sensory information, prior expectations, and the environment, with motor adjustments serving to minimise future prediction errors. We investigated whether predictive gaze behaviours are indeed adjusted in this Bayes-optimal fashion during a virtual racquetball task. In this task, participants intercepted bouncing balls with varying levels of elasticity, under conditions of higher or lower environmental volatility. Participants' gaze patterns differed between stable and volatile conditions in a manner consistent with generative models of Bayes-optimal behaviour. Partially observable Markov models also revealed an increased rate of associative learning in response to unpredictable shifts in environmental probabilities, although there was no overall effect of volatility on this parameter. Findings extend active inference frameworks into complex and unconstrained visuomotor tasks and present important implications for a neurocomputational understanding of the visual guidance of action.

Inference on homeostatic belief precision

Interoception and homeostatic/allostatic control are intertwined branches of closed-loop brain-body interactions (BBI). Given their importance in mental and psychosomatic disorders, establishing computational assays of BBI represents a clinically important but methodologically challenging endeavor. This technical note presents a novel approach, derived from a generic computational model of homeostatic/allostatic control that underpins (meta)cognitive theories of affective and psychosomatic disorders. This model views homeostatic setpoints as probability distributions ("homeostatic beliefs") whose parameters determine regulatory efforts and change dynamically under allostatic predictions. In particular, changes in homeostatic belief precision, triggered by anticipated threats to homeostasis, are thought to alter cerebral regulation of bodily states. Here, we present statistical procedures for inferring homeostatic belief precision from measured bodily states and/or regulatory (action) signals. We analyze the inference problem, derive two alternative estimators of homeostatic belief precision, and apply our method to simulated data. Our proposed approach may prove useful for assessing BBI in individual subjects.

Gut inference: A computational modelling approach

Neurocomputational theories have hypothesized that Bayesian inference underlies interoception, which has become a topic of recent experimental work in heartbeat perception. To extend this approach beyond cardiac interoception, we describe the application of a Bayesian computational model to a recently developed gastrointestinal interoception task completed by 40 healthy individuals undergoing simultaneous electroencephalogram (EEG) and peripheral physiological recording. We first present results that support the validity of this modelling approach. Second, we provide a test of, and confirmatory evidence supporting, the neural process theory associated with a particular Bayesian framework (active inference) that predicts specific relationships between computational parameters and event-related potentials in EEG. We also offer some exploratory evidence suggesting that computational parameters may influence the regulation of peripheral physiological states. We conclude that this computational approach offers promise as a tool for studying individual differences in gastrointestinal interoception.

Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing

Active inference is a normative framework for explaining behaviour under the free energy principle-a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy-a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error-plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.

Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

This study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.

Harnessing the Spatial Foundation of Mind in Breaking Vicious Cycles in Anxiety, Insomnia, and Depression: The Future of Virtual Reality Therapy Applications

Mental Illnesses, particularly anxiety, insomnia, and depression often involve vicious cycles which are self-perpetuating and can trap one into a more chronic state. For example in the case of insomnia, sympathetic overactivity, intrusive thoughts, and emotional instability due to sleep loss can perpetuate further sleep loss the next night and so on. In this article, we put forward a perspective on breaking these vicious cycles based on preeminent theories in global and spatial cognition, that the foundation of the conscious mind is a spatial coordinate system. Based on this we discuss the potential and future of virtual reality therapeutic applications which utilize massive virtual spaces along with biofeedback designed to help break perpetual cycles in depression, anxiety, and insomnia. "Massive spaces" are those which are truly expansive such as when looking to the clear night sky. These virtual realities may take the form of a night sky, fantastical cosmic scenes, or other scenes such as mountain tops. We also hope to inspire research into such a spatial foundation of mind, use of perceived massive spaces for therapy, and the integration of biofeedback into virtual therapies.

Heartbeat evoked potentials in patients with post-traumatic stress disorder: an unaltered neurobiological regulation system?

BACKGROUND

Early life maltreatment is a risk factor for psychiatric disorders, including post-traumatic stress disorder (PTSD). Post-traumatic stress disorder is a severe and heterogeneous disorder with fluctuating states of emotional over- and undermodulation, including hypervigilance, dissociation, and emotion regulation deficits. The perception and regulation of emotions have been linked to interoception, the cortical representation and sensing of inner bodily processes. Although first therapeutic approaches targeting bodily sensations have been found effective in patients with PTSD, and deficits in interoceptive signal representation have been reported in other trauma-related disorders, such as borderline personality disorder (BPD), the role of interoception remains largely unexplored for PTSD.

OBJECTIVE

The objective was to investigate the cortical representation of cardiac interoceptive signals in patients with PTSD and its associations with early life maltreatment, trait dissociation, and emotion dysregulation.

METHODS

Twenty-four medication-free patients with PTSD and 31 healthy controls (HC) completed a 5-min resting electrocardiogram (ECG) with parallel electroencephalogram (EEG). Heartbeat evoked potential (HEP) amplitudes as a measure for cortical representation of cardiac interoceptive signals were compared between groups and correlated with self-report questionnaires.

RESULTS

We did not find significantly different mean HEP amplitudes in patients with PTSD compared to HC, although HEPs of patients with PTSD were descriptively higher. No significant associations between mean HEP amplitudes and early life maltreatment, trait dissociation or emotion dysregulation were obtained within the groups.

CONCLUSION

The current finding does not indicate deficits in interoceptive signal representation at rest in individuals with PTSD. Whether patients with PTSD show altered HEP modulations during emotion regulation tasks and might benefit from therapeutic approaches aiming at changing the perception of bodily signals, needs to be investigated in future studies.