Active inference, stressors, and psychological trauma: A neuroethological model of (mal)adaptive explore-exploit dynamics in ecological context

Narrative as active inference: an integrative account of cognitive and social functions in adaptation

While the ubiquity and importance of narratives for human adaptation is widely recognized, there is no integrative framework for understanding the roles of narrative in human adaptation. Research has identified several cognitive and social functions of narratives that are conducive to well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the cognitive and social functions of narratives in terms of active inference, to support the claim that one of the main adaptive functions of narrative is to generate more useful (i.e., accurate, parsimonious) predictions for the individual, as well as to coordinate group action (over multiple timescales) through shared predictions about collective behavior. Active inference is a theory that depicts the fundamental tendency of living organisms to adapt by proactively inferring the causes of their sensations (including their own actions). We review narrative research on identity, event segmentation, episodic memory, future projections, storytelling practices, enculturation, and master narratives. We show how this research dovetails with the active inference framework and propose an account of the cognitive and social functions of narrative that emphasizes that narratives are for the future-even when they are focused on recollecting or recounting the past. Understanding narratives as cognitive and cultural tools for mutual prediction in social contexts can guide research on narrative in adaptive behavior and psychopathology, based on a parsimonious mechanistic model of some of the basic adaptive functions of narrative.

Enhancing Agency in Posttraumatic Stress Disorder Therapies through Sensorimotor technologies

UNSTRUCTURED

Posttraumatic stress disorder (PTSD) is a significant public health concern, with only a third of patients recovering within a year of treatment. While PTSD often disrupts the sense of body ownership and the sense of agency (SA), attention to SA in trauma has been lacking. This perspective article explores the loss of SA in PTSD and its relevance in the development of symptoms. Trauma is viewed as a breakdown of SA, related to a freeze response, with peritraumatic dissociation increasing the risk of PTSD. Drawing from embodied cognition, we propose an "enactive" perspective of PTSD, suggesting therapies that restore the SA through direct engagement with the body and environment. We discuss the potential of agency-based therapies and innovative technologies like gesture sonification (GS), which translates body movements into sounds to enhance the SA. GS offers a screen-free, non-invasive approach that could complement existing trauma-focused therapies. We emphasize the need for interdisciplinary collaboration and clinical research to further explore these approaches in preventing and treating PTSD.

A computational account of the development and evolution of psychotic symptoms

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

Cellular mechanisms of cooperative context-sensitive predictive inference

We argue that prediction success maximization is a basic objective of cognition and cortex, that it is compatible with but distinct from prediction error minimization, that neither objective requires subtractive coding, that there is clear neurobiological evidence for the amplification of predicted signals, and that we are unconvinced by evidence proposed in support of subtractive coding. We outline recent discoveries showing that pyramidal cells on which our cognitive capabilities depend usually transmit information about input to their basal dendrites and amplify that transmission when input to their distal apical dendrites provides a context that agrees with the feedforward basal input in that both are depolarizing, i.e., both are excitatory rather than inhibitory. Though these intracellular discoveries require a level of technical expertise that is beyond the current abilities of most neuroscience labs, they are not controversial and acclaimed as groundbreaking. We note that this cellular cooperative context-sensitivity greatly enhances the cognitive capabilities of the mammalian neocortex, and that much remains to be discovered concerning its evolution, development, and pathology.

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.

Metacognitive Feelings: A Predictive-Processing Perspective

Metacognitive feelings are affective experiences that concern the subject's mental processes and capacities. Paradigmatic examples include the feeling of familiarity, the feeling of confidence, or the tip-of-the-tongue experience. In this article, we advance an account of metacognitive feelings based on the predictive-processing framework. The core tenet of predictive processing is that the brain is a hierarchical hypothesis-testing mechanism, predicting sensory input on the basis of prior experience and updating predictions on the basis of the incoming prediction error. According to the proposed account, metacognitive feelings arise out of a process in which visceral changes serve as cues to predict the error dynamics relating to a particular mental process. The expected rate of prediction-error reduction corresponds to the valence at the core of the emerging metacognitive feeling. Metacognitive feelings use prediction dynamics to model the agent's situation in a way that is both descriptive and directive. Thus, metacognitive feelings are not only an appraisal of ongoing cognitive performance but also a set of action policies. These action policies span predictive trajectories across bodily action, mental action, and interoceptive changes, which together transform the epistemic landscape within which metacognitive feelings unfold.

Brain evolution and the meaning of catatonia - An update

Back in 2004, in a chapter titled "Brain Evolution and the Meaning of Catatonia", a case was made that the syndrome's core meaning is embedded in millions of years of vertebrate brain evolution. (Fricchione, 2004) In this update, advances over the last almost 20 years, in catatonia theory and research in particular, and pertinent neuropsychiatry in general, will be applied to this question of meaning. The approach will rely heavily on a number of thought leaders, including Nicos Tinbergen, Paul MacLean, John Bowlby, M. Marsel Mesulam, Bruce McEwen and Karl Friston. Their guidance will be supplemented with a selected survey of 21sty century neuropsychiatry, neurophysiology, molecular biology, neuroimaging and neurotherapeutics as applied to the catatonic syndrome. In an attempt to address the question of the meaning of the catatonic syndrome in human life, we will employ two conceptual networks representing the intersubjectivity of the quantitative conceptual network of physical terms and the subjectivity of the qualitative conceptual network of mental and spiritual terms. In the process, a common referent providing extensional identity may emerge (Goodman, 1991). The goal of this exercise is to enhance our attunement with the experience of patients suffering with catatonia. A deeper understanding of catatonia's origins in brain evolution and of the challenges of individual epigenetic development in the setting of environmental events coupled with appreciation of what has been described as the most painful mammalian condition, that of separation, has the potential to foster greater efforts on the part of clinicians to diagnose and treat patients who present with catatonia. In addition, in this ancient and extreme tactic, evolved to provide safety from extreme survival threat, one can speculate what is at the core of human fear and the challenge it presents to all of us. And when the biology, psychology and sociology of catatonia are examined, the nature of solutions to the challenge may emerge.

Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications

Interoception-the perception of internal bodily signals-has emerged as an area of interest due to its implications in emotion and the prevalence of dysfunctional interoceptive processes across psychopathological conditions. Despite the importance of interoception in cognitive neuroscience and psychiatry, its experimental manipulation remains technically challenging. This is due to the invasive nature of existing methods, the limitation of self-report and unimodal measures of interoception, and the absence of standardized approaches across disparate fields. This article integrates diverse research efforts from psychology, physiology, psychiatry, and engineering to address this oversight. Following a general introduction to the neurophysiology of interoception as hierarchical predictive processing, we review the existing paradigms for manipulating interoception (e.g., interoceptive modulation), their underlying mechanisms (e.g., interoceptive conditioning), and clinical applications (e.g., interoceptive exposure). We suggest a classification for interoceptive technologies and discuss their potential for diagnosing and treating mental health disorders. Despite promising results, considerable work is still needed to develop standardized, validated measures of interoceptive function across domains and before these technologies can translate safely and effectively to clinical settings.

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.

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.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

The neurobiology of functional neurological disorders characterised by impaired awareness

We review the neurobiology of Functional Neurological Disorders (FND), i.e., neurological disorders not explained by currently identifiable histopathological processes, in order to focus on those characterised by impaired awareness (functionally impaired awareness disorders, FIAD), and especially, on the paradigmatic case of Resignation Syndrome (RS). We thus provide an improved more integrated theory of FIAD, able to guide both research priorities and the diagnostic formulation of FIAD. We systematically address the diverse spectrum of clinical presentations of FND with impaired awareness, and offer a new framework for understanding FIAD. We find that unraveling the historical development of neurobiological theory of FIAD is of paramount importance for its current understanding. Then, we integrate contemporary clinical material in order to contextualise the neurobiology of FIAD within social, cultural, and psychological perspectives. We thus review neuro-computational insights in FND in general, to arrive at a more coherent account of FIAD. FIAD may be based on maladaptive predictive coding, shaped by stress, attention, uncertainty, and, ultimately, neurally encoded beliefs and their updates. We also critically appraise arguments in support of and against such Bayesian models. Finally, we discuss implications of our theoretical account and provide pointers towards an improved clinical diagnostic formulation of FIAD. We suggest directions for future research towards a more unified theory on which future interventions and management strategies could be based, as effective treatments and clinical trial evidence remain limited.

The Active Inference Model of Coherence Therapy

Coherence Therapy is an empirically derived experiential psychotherapy based on Psychological Constructivism. Symptoms are viewed as necessary output from an implicit model of the world. The therapist curates experiences and directs attention toward discovering the model. Rendered explicit, the model is juxtaposed with contradictory knowledge driving memory re-consolidation with resolution of the symptom. The Bayesian Brain views perception and action as inferential processes. Prior beliefs are combined in a generative model to explain the hidden causes of sensations through a process of Active Inference. Prior beliefs that are poor fits to the real world are suboptimal. Suboptimal priors with optimal inference produce Bayes Optimal Pathology with behavioral symptoms. The Active Inference Model of Coherence Therapy posits that Coherence Therapy is a dyadic act of therapist guided Active Inference that renders the (probable) hidden causes of a client's behavior conscious. The therapist's sustained attention on the goal of inference helps to overcome memory control bias against retrieval of the affectively charged suboptimal prior. Serial experiences cue memory retrieval and re-instantiation of the physiological/affective state that necessitates production of the symptom in a particular context. As this process continues there is a break in modularity with assimilation into broader networks of experience. Typically, the symptom produced by optimal inference with the suboptimal prior is experienced as unnecessary/inappropriate when taken out of the particular context. The implicit construct has been re-represented and rendered consciously accessible, by a more complex but more accurate model in which the symptom is necessary in some contexts but not others. There is an experience of agency and control in symptom creation, accompanied by the spontaneous production of context appropriate behavior. The capacity for inference has been restored. The Active Inference Model of Coherence Therapy provides a framework for Coherence Therapy as a computational process which can serve as the basis for new therapeutic interventions and experimental designs integrating biological, cognitive, behavioral, and environmental factors.

Foraging for the self: Environment selection for agency inference

Sometimes agents choose to occupy environments that are neither traditionally rewarding nor worth exploring, but which rather promise to help minimise uncertainty related to what they can control. Selecting environments that afford inferences about agency seems a foundational aspect of environment selection dynamics - if an agent can't form reliable beliefs about what they can and can't control, then they can't act efficiently to achieve rewards. This relatively neglected aspect of environment selection is important to study so that we can better understand why agents occupy certain environments over others - something that may also be relevant for mental and developmental conditions, such as autism. This online experiment investigates the impact of uncertainty about agency on the way participants choose to freely move between two environments, one that has greater irreducible variability and one that is more complex to model. We hypothesise that increasingly erroneous predictions about the expected outcome of agency-exploring actions can be a driver of switching environments, and we explore which type of environment agents prefer. Results show that participants actively switch between the two environments following increases in prediction error, and that the tolerance for prediction error before switching is modulated by individuals' autism traits. Further, we find that participants more frequently occupy the variable environment, which is predicted by greater accuracy and higher confidence than the complex environment. This is the first online study to investigate relatively unconstrained ongoing foraging dynamics in support of judgements of agency, and in doing so represents a significant methodological advance.

How childhood maltreatment alters perception and cognition - the predictive processing account of borderline personality disorder

Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others - leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it.

Oversampled and undersolved: Depressive rumination from an active inference perspective

Rumination is a widely recognized cognitive deviation in depression. Despite the recognition, researchers have struggled to explain why patients cannot disengage from the process, although it depresses their mood and fails to lead to effective problem-solving. We rethink rumination as repetitive but unsuccessful problem-solving attempts. Appealing to an active inference account, we suggest that adaptive problem-solving is based on the generation, evaluation, and performance of candidate policies that increase an organism's knowledge of its environment. We argue that the problem-solving process is distorted during rumination. Specifically, rumination is understood as engaging in excessive yet unsuccessful oversampling of policy candidates that do not resolve uncertainty. Because candidates are sampled from policies that were selected in states resembling one's current state, "bad" starting points (e.g., depressed mood, physical inactivity) make the problem-solving process vulnerable for generating a ruminative "halting problem". This problem leads to high opportunity costs, learned helplessness and diminished overt behavior. Besides reviewing evidence for the conceptual paths of this model, we discuss its neurophysiological correlates and point towards clinical implications.

Effect of Restraint Stress on Pain Sensitivity, Spinal Trigeminal Nucleus Neurons, and Astrocytes in the Masseter Area of Rats

To explore the changes of pain sensitivity (PS) in the masseter area (MA) in the rat model of psychological stress and the mechanism of action between spinal nucleus neurons and astrocytes in the trigeminal ganglion. The 40 Sprague-Dawley rats were randomly divided into control group (no treatment), group A (restraint stress (RS) 1 d), group B (RS 7 d), and group C (RS 14 d). The body weight growth rates (WGR) of rats in each group were compared and the difference of CORT and ACTH in serum was analyzed by ELISA. The open field test and the elevated “cross” maze test were adopted to detect the behavioral changes of rats. Finally, pain threshold of the MA in rats, the activation amount of brain tissue medulla oblongata parts astrocytes markers Glial fibrillary acidic protein (GFAP), and the protein expression of IL-1β and IL-1RI were detected. The results showed the WGR at 7 d and 14 d was greatly lower than control group (P < 0.01). In addition, the activity level and serum CORT and ACTH levels AND mean pain threshold in the MA of groups B and C were greatly lower than control group (P < 0.05). The activation rate of GFRP in group C (P < 0.01) and the protein expression of IL-1β and IL-1RI (P < 0.05) in rat trigeminal ganglion astrocytes of groups B and C was greatly higher than control group, indicating the increase of RS time, the release of IL-1β and IL-1RI can activate neurons and astrocytes in spinal trigeminal nucleus (STN) nerve and increase the PS of the MA.

How Stress Can Change Our Deepest Preferences: Stress Habituation Explained Using the Free Energy Principle

People who habituate to stress show a repetition-induced response attenuation—neuroendocrine, cardiovascular, neuroenergetic, and emotional—when exposed to a threatening environment. But the exact dynamics underlying stress habituation remain obscure. The free energy principle offers a unifying account of self-organising systems such as the human brain. In this paper, we elaborate on how stress habituation can be explained and modelled using the free energy principle. We introduce habituation priors that encode the agent’s tendency for stress habituation and incorporate them in the agent’s decision-making process. Using differently shaped goal priors—that encode the agent’s goal preferences—we illustrate, in two examples, the optimising (and thus habituating) behaviour of agents. We show that habituation minimises free energy by reducing the precision (inverse variance) of goal preferences. Reducing the precision of goal priors means that the agent accepts adverse (previously unconscionable) states (e.g., lower social status and poverty). Acceptance or tolerance of adverse outcomes may explain why habituation causes people to exhibit an attenuation of the stress response. Given that stress habituation occurs in brain regions where goal priors are encoded, i.e., in the ventromedial prefrontal cortex and that these priors are encoded as sufficient statistics of probability distributions, our approach seems plausible from an anatomical-functional and neuro-statistical point of view. The ensuing formal and generalisable account—based on the free energy principle—further motivate our novel treatment of stress habituation. Our analysis suggests that stress habituation has far-reaching consequences, protecting against the harmful effects of toxic stress, but on the other hand making the acceptability of precarious living conditions and the development of the obese type 2 diabetes mellitus phenotype more likely.

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.

Useful misrepresentation: perception as embodied proactive inference

According to the predictive processing framework, perception is geared to represent the environment in terms of embodied action opportunities as opposed to objective truth. Here, we argue that such an optimisation is reflected by biases in expectations (i.e., prior predictive information) that facilitate 'useful' inferences of external sensory causes. To support this, we highlight a body of literature suggesting that perception is systematically biased away from accurate estimates under conditions where utility and accuracy conflict with one another. We interpret this to reflect the brain's attempt to adjudicate between conflicting sources of prediction error, as external accuracy is sacrificed to facilitate actions that proactively avoid physiologically surprising outcomes. This carries important theoretical implications and offers new insights into psychopathology.

Tackling hyperarousal: an integrative multimodal approach

Introduction: The effectiveness of treatment for PTSD is limited, which is especially true for war veterans, of whom 30-50% do not respond to therapy. Hyperarousal is central to the maintenance of trauma pathology. The mainstream trauma-focused therapies traditionally target the cognitive processing of traumatic experience. In this article, we propose that these therapies may be enhanced by the inclusion of interventions specifically targeting hyperarousal.Method: We review an earlier formulated model of trauma supporting our proposal. This model is based on a theory of trauma that integrates the concept of allostasis with the predictive processing framework. In this view, trauma is considered a maladaptive stress response guided by false inference.Results: The reviewed model is in agreement with the central role of hyperarousal in the maintenance of trauma-induced disorders. It also demonstrates the importance of targeting hyperarousal at the same time as maladaptive cognitions and behaviours associated with trauma. A treatment for PTSD is proposed that combines exposure to trauma-related cues with neurofeedback-mediated regulation of arousal.Conclusions: Our analysis argues for the integration of hyperarousal-targeting interventions into existing therapies. Accordingly, we offer methodological considerations based on the nested hierarchy principle that can guide such integration.

Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model-an internal estimate of overall model fitness ("subjective fitness"). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

The two kinds of free energy and the Bayesian revolution

The concept of free energy has its origins in 19th century thermodynamics, but has recently found its way into the behavioral and neural sciences, where it has been promoted for its wide applicability and has even been suggested as a fundamental principle of understanding intelligent behavior and brain function. We argue that there are essentially two different notions of free energy in current models of intelligent agency, that can both be considered as applications of Bayesian inference to the problem of action selection: one that appears when trading off accuracy and uncertainty based on a general maximum entropy principle, and one that formulates action selection in terms of minimizing an error measure that quantifies deviations of beliefs and policies from given reference models. The first approach provides a normative rule for action selection in the face of model uncertainty or when information processing capabilities are limited. The second approach directly aims to formulate the action selection problem as an inference problem in the context of Bayesian brain theories, also known as Active Inference in the literature. We elucidate the main ideas and discuss critical technical and conceptual issues revolving around these two notions of free energy that both claim to apply at all levels of decision-making, from the high-level deliberation of reasoning down to the low-level information processing of perception.

Losing Ourselves: Active Inference, Depersonalization, and Meditation

Disruptions in the ordinary sense of selfhood underpin both pathological and "enlightened" states of consciousness. People suffering from depersonalization can experience the loss of a sense of self as devastating, often accompanied by intense feelings of alienation, fear, and hopelessness. However, for meditative contemplatives from various traditions, "selfless" experiences are highly sought after, being associated with enduring peace and joy. Little is understood about how these contrasting dysphoric and euphoric experiences should be conceptualized. In this paper, we propose a unified account of these selfless experiences within the active inference framework. Building on our recent active inference research, we propose an account of the experiences of selfhood as emerging from a temporally deep generative model. We go on to develop a view of the self as playing a central role in structuring ordinary experience by "tuning" agents to the counterfactually rich possibilities for action. Finally, we explore how depersonalization may result from an inferred loss of allostatic control and contrast this phenomenology with selfless experiences reported by meditation practitioners. We will show how, by beginning with a conception of self-modeling within an active inference framework, we have available to us a new way of conceptualizing the striking experiential similarities and important differences between these selfless experiences within a unifying theoretical framework. We will explore the implications for understanding and treating dissociative disorders, as well as elucidate both the therapeutic potential, and possible dangers, of meditation.

Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma

BACKGROUND

Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.

METHODS

We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.

RESULTS

Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen's d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.

CONCLUSIONS

Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.

Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma

BACKGROUND

Substance use disorders (SUDs) are a major public health risk. However, mechanisms accounting for continued patterns of poor choices in the face of negative life consequences remain poorly understood.

METHODS

We use a computational (active inference) modeling approach, combined with multiple regression and hierarchical Bayesian group analyses, to examine how treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 147) and healthy controls (HCs; N = 54) make choices to resolve uncertainty within a gambling task. A subset of SUDs (N = 49) and HCs (N = 51) propensity-matched on age, sex, and verbal IQ were also compared to replicate larger group findings.

RESULTS

Results indicate that: (a) SUDs show poorer task performance than HCs (p = 0.03, Cohen's d = 0.33), with model estimates revealing less precise action selection mechanisms (p = 0.004, d = 0.43), a lower learning rate from losses (p = 0.02, d = 0.36), and a greater learning rate from gains (p = 0.04, d = 0.31); and (b) groups do not differ significantly in goal-directed information seeking.

CONCLUSIONS

Findings suggest a pattern of inconsistent behavior in response to positive outcomes in SUDs combined with a tendency to attribute negative outcomes to chance. Specifically, individuals with SUDs fail to settle on a behavior strategy despite sufficient evidence of its success. These learning impairments could help account for difficulties in adjusting behavior and maintaining optimal decision-making during and after treatment.

More Than Words Can Say: A Multi-Disciplinary Consideration of the Psychotherapeutic Evaluation and Treatment of Alexithymia

Alexithymia is a disorder that stands at the border of mind and body, with psychological/affective and physiological/experiential disturbances. The purpose of this article is to propose a new clinical access point for the evaluation and treatment of the deficits in emotional awareness demonstrated in alexithymia. This will be based on insights from recent neuroscientific research, which is adding to the psychodynamic understanding of alexithymia, regarding clinical presentation and etiology. Following a brief review of definitions, forms of measurement, and potential etiologic elements of alexithymia, current neuroscientific theory and research into "predictive processing" approaches to brain function will be outlined, including how "interoception" and "interoceptive inference" underpins emotion and emotional awareness. From this synergistic perspective, I will outline how interoceptive inference provides a key to the link between: problems in early life relational experiences and the patient's long held, but suboptimal models of their inner and outer world. This is reflected in the deficits in affective experiencing and emotional awareness described in alexithymia. Three clinical cases will be presented to illustrate this nuanced consideration of alexithymic etiology and treatment. The implications of the historical, psychological, and somatic aspects of experience will be considered, regarding the patients' diminished ability to: experience and represent emotional experience as distinct feeling states; signify the relevant meaning of affective experience; and incorporate such with cognitions to adaptively guide behavior. These will be addressed using psychometric, psychological, neuro-cognitive, and neurocomputational approaches. Elements from current theory, research, and treatment of alexithymia, will be highlighted that are salient to the clinician, in order to support their understanding of patients against the backdrop of current psychodynamic and neuroscientific research, which will thereby increase treatment options and benefits. The focus, and conclusion, of this article is the role that attention to interoception can play (within the safety of the therapeutic relationship and within any therapeutic process) in allowing updating of the patient's strongly held but dysfunctional beliefs.