Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework

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.

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.

The utility of a latent-cause framework for understanding addiction phenomena

Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent "latent causes". We demonstrate that applying this framework to Pavlovian and instrumental settings can help account for defining features of craving and relapse such as outcome-specificity, generalization, and cyclical dynamics. Finally, we argue that this framework can bridge model-free and model-based formulations, and account for individual variability in phenomenology by accommodating the memories, beliefs, and goals of those living with addiction, motivating a centering of the individual, subjective experience of addiction and recovery.

Nicotine-related beliefs induce dose-dependent responses in the human brain

Beliefs have a powerful influence on our behavior, yet their neural mechanisms remain elusive. Here we investigate whether beliefs could impact brain activities in a way akin to pharmacological dose-dependent effects. Nicotine-dependent humans were told that nicotine strength in an electronic cigarette was either 'low', 'medium' or 'high', while nicotine content was held constant. After vaping, participants underwent functional neuroimaging and performed a decision-making task known to engage neural circuits affected by nicotine. Beliefs about nicotine strength induced dose-dependent responses in the thalamus, a key binding site for nicotine, but not in other brain regions such as the striatum. Nicotine-related beliefs also parametrically modulated the connectivity between the thalamus and ventromedial prefrontal cortex, a region important for decision-making. These findings reveal a high level of precision in the way beliefs influence the brain, offering mechanistic insights into humans' heterogeneous responses to drugs and a pivotal role of beliefs in addiction.

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

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.

Longing to act: Bayesian inference as a framework for craving in behavioral addiction

Traditionally, craving is considered a defining feature of drug addiction. Accumulating evidence suggests that craving can also exist in behavioral addictions (e.g., gambling disorder) without drug-induced effects. However, the degree to which mechanisms of craving overlap between classic substance use disorders and behavioral addictions remains unclear. There is, therefore, an urgent need to develop an overarching theory of craving that conceptually integrates findings across behavioral and drug addictions. In this review, we will first synthesize existing theories and empirical findings related to craving in both drug-dependent and -independent addictive disorders. Building on the Bayesian brain hypothesis and previous work on interoceptive inference, we will then propose a computational theory for craving in behavioral addiction, where the target of craving is execution of an action (e.g., gambling) rather than a drug. Specifically, we conceptualize craving in behavioral addiction as a subjective belief about physiological states of the body associated with action completion and is updated based on both a prior belief ("I need to act to feel good") and sensory evidence ("I cannot act"). We conclude by briefly discussing the therapeutic implications of this framework. In summary, this unified Bayesian computational framework for craving generalizes across addictive disorders, provides explanatory power for ostensibly conflicting empirical findings, and generates strong hypotheses for future empirical studies. The disambiguation of the computational components underlying domain-general craving using this framework will lead to a deeper understanding of, and effective treatment targets for, behavioral and drug addictions.

The neuroanatomy of social trust predicts depression vulnerability

Trust attitude is a social personality trait linked with the estimation of others’ trustworthiness. Trusting others, however, can have substantial negative effects on mental health, such as the development of depression. Despite significant progress in understanding the neurobiology of trust, whether the neuroanatomy of trust is linked with depression vulnerability remains unknown. To investigate a link between the neuroanatomy of trust and depression vulnerability, we assessed trust and depressive symptoms and employed neuroimaging to acquire brain structure data of healthy participants. A high depressive symptom score was used as an indicator of depression vulnerability. The neuroanatomical results observed with the healthy sample were validated in a sample of clinically diagnosed depressive patients. We found significantly higher depressive symptoms among low trusters than among high trusters. Neuroanatomically, low trusters and depressive patients showed similar volume reduction in brain regions implicated in social cognition, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial PFC, posterior cingulate, precuneus, and angular gyrus. Furthermore, the reduced volume of the DLPFC and precuneus mediated the relationship between trust and depressive symptoms. These findings contribute to understanding social- and neural-markers of depression vulnerability and may inform the development of social interventions to prevent pathological depression.

The Cardiac Timing Toolbox (CaTT): Testing for physiologically plausible effects of cardiac timing on behaviour

There is a long history of, and renewed interest in, cardiac timing effects on behaviour and cognition. Cardiac timing effects may be identified by expressing events as a function of their location in the cardiac cycle, and applying circular (i.e. directional) statistics to test cardiac time-behaviour associations. Typically this approach 'stretches' all points in the cardiac cycle equally, but this is not necessarily physiologically valid. Moreover, many tests impose distributional assumptions that are not met by such data. We present a set of statistical techniques robust to this, instantiated within our new Cardiac Timing Toolbox (CaTT) for MATLAB: A physiologically-motivated method of wrapping behaviour to the cardiac cycle; and a set of non-parametric statistical tests that control for common confounds and distributional characteristics of these data. Using a reanalysis of previously published data, we guide readers through analyses using CaTT, aiding researchers in identifying physiologically plausible associations between heart-timing and cognition.

How Expectations Shape the Formation of Intrusive Memories: An Experimental Study Using the Trauma Film Paradigm

Background

Although intrusions are the hallmark symptom of posttraumatic stress disorder, there is still limited knowledge about the processes that contribute to the development of intrusions. Here, we used the well-established trauma film paradigm (TFP) to investigate how expectations about the intensity and controllability of intrusions influence their occurrence.

Methods

90 healthy participants underwent the TFP before they were randomized to one of three conditions manipulating their expectations about intrusions: positive expectations group; negative expectations group; control group. The primary outcome was the frequency and severity of intrusive memories as assessed with an intrusion diary over seven days.

Results

The TFP was well implemented, as indicated by significant post-film anxiety and a substantial number of intrusions reported for the subsequent week. The three groups did not differ in their expectations about intrusions and, relatedly, in their experience of intrusions. A mediation analysis revealed that the influence of post-film anxiety on intrusive memories was fully mediated by expectations.

Conclusions

Despite the failure of the expectation manipulation, the results of the mediation analysis support the hypothesis that post-film expectations influence the formation of intrusive memories, suggesting that intrusions may result from maladaptive dynamics between emotional and cognitive processes following trauma(like) experiences.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.

Bodily Information and Top-Down Affective Priming Jointly Affect the Processing of Fearful Faces

According to embodied theories, the processing of emotions such as happiness or fear is grounded in emotion-specific perceptual, bodily, and physiological processes. Under these views, perceiving an emotional stimulus (e.g., a fearful face) re-enacts interoceptive and bodily states congruent with that emotion (e.g., increases heart rate); and in turn, interoceptive and bodily changes (e.g., increases of heart rate) influence the processing of congruent emotional content. A previous study by Pezzulo et al. (2018) provided evidence for this embodied congruence, reporting that experimentally increasing heart rate with physical exercise facilitated the processing of facial expressions congruent with that interoception (fear), but not those conveying incongruent states (disgust or neutrality). Here, we investigated whether the above (bottom-up) interoceptive manipulation and the (top-down) priming of affective content may jointly influence the processing of happy and fearful faces. The fact that happiness and fear are both associated with high heart rate but have different (positive and negative) valence permits testing the hypothesis that their processing might be facilitated by the same interoceptive manipulation (the increase of heart rate) but two opposite (positive and negative) affective primes. To test this hypothesis, we asked participants to perform a gender-categorization task of happy, fearful, and neutral faces, which were preceded by positive, negative, and neutral primes. Participants performed the same task in two sessions (after rest, with normal heart rate, or exercise, with faster heart rate) and we recorded their response times and mouse movements during the choices. We replicated the finding that when participants were in the exercise condition, they processed fearful faces faster than when they were in the rest condition. However, we did not find the same reduction in response time for happy (or neutral) faces. Furthermore, we found that when participants were in the exercise condition, they processed fearful faces faster in the presence of negative compared to positive or neutral primes; but we found no equivalent facilitation of positive (or neutral) primes during the processing of happy (or neutral) faces. While the asymmetries between the processing of fearful and happy faces require further investigation, our findings promisingly indicate that the processing of fearful faces is jointly influenced by both bottom-up interoceptive states and top-down affective primes that are congruent with the emotion.

Be still my heart: Cardiac regulation as a mode of uncertainty reduction

Decreased heart rate (HR) and variability (HRV) are well-established correlates of attention; however, the functional significance of these dynamics remains unclear. Here, we investigate whether attention-related cardiac modulation is sensitive to different varieties of uncertainty. Thirty-nine adults performed a binocular rivalry-replay task in which changes in visual perception were driven either internally (in response to constant, conflicting stimuli; rivalry) or externally (in response to physically alternating stimuli; replay). Tonic HR and high-frequency HRV linearly decreased as participants progressed from resting-state baseline (minimal visual uncertainty) through replay (temporal uncertainty) to rivalry (temporal uncertainty and ambiguity). Time-resolved frequency estimates revealed that cardiac deceleration was sustained throughout the trial period and modulated by ambiguity, novelty, and switch rate. These findings suggest cardiac regulation during active attention may play an instrumental role in uncertainty reduction.

Vagus Nerve Stimulation as a Gateway to Interoception

The last two decades have seen a growing interest in the study of interoception. Interoception can be understood as a hierarchical phenomenon, referring to the body-to-brain communication of internal signals, their sensing, encoding, and representation in the brain, influence on other cognitive and affective processes, and their conscious perception. Interoceptive signals have been notoriously challenging to manipulate in experimental settings. Here, we propose that this can be achieved through electrical stimulation of the vagus nerve (either in an invasive or non-invasive fashion). The vagus nerve is the main pathway for conveying information about the internal condition of the body to the brain. Despite its intrinsic involvement in interoception, surprisingly little research in the field has used Vagus Nerve Stimulation to explicitly modulate bodily signals. Here, we review a range of cognitive, affective and clinical research using Vagus Nerve Stimulation, showing that it can be applied to the study of interoception at each level of its hierarchy. This could have considerable implications for our understanding of the interoceptive dimension of cognition and affect in both health and disease, and lead to development of new therapeutic tools.

Rethinking post-traumatic stress disorder - A predictive processing perspective

Predictive processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the predictive processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a predictive processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.

Understanding persistent physical symptoms: Conceptual integration of psychological expectation models and predictive processing accounts

Persistent physical symptoms (PPS) are distressing, difficult to treat, and pose a major challenge to health care providers and systems. In this article, we review two disparate bodies of literature on PPS to provide a novel integrative model of this elusive condition. First, we draw on the clinical-psychological literature on the role of expectations to suggest that people with PPS develop dysfunctional expectations about health and disease that become increasingly immune to disconfirmatory information (such as medical reassurance) through cognitive reappraisal. Second, we invoke neuroscientific predictive processing accounts and propose that the psychological process of 'cognitive immunization' against disconfirmatory evidence corresponds, at the neurobiological and computational level, to too much confidence (i.e. precision) afforded to prior predictions. This can lead to an attenuation of disconfirming sensory information so that strong priors override benign bodily signals and make people believe that something serious is wrong with the body. Combining these distinct accounts provides a unifying framework for persistent physical symptoms and shifts the focus away from their causes to the sustaining mechanisms that prevent symptoms from subsiding spontaneously. Based on this integrative model, we derive new avenues for future research and discuss implications for treating people with PPS in clinical practice.