Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Electroencephalography Alpha Traveling Waves as Early Predictors of Treatment Response in Major Depressive Episodes: Insights from Intermittent Photic Stimulation

Background: Early evaluation of treatment efficacy in adolescents and young adults with major depressive episodes (MDEs) remains a clinical challenge, often delaying timely therapeutic adjustments. Electroencephalography (EEG) alpha traveling waves, particularly those elicited by intermittent photic stimulation (IPS), may serve as biomarkers reflecting neural dynamics. This study aimed to investigate whether IPS-induced alpha traveling waves could predict early treatment outcomes in transitional-aged youth with MDEs. Methods: We recorded EEG signals from 119 patients aged 16-24 years at admission, prior to a standardized two-week treatment regimen. IPS was applied using multiple stimulus frequencies, and alpha traveling waves were analyzed in terms of directionality (forward vs. backward) and hemispheric lateralization. Results: Alpha traveling wave amplitudes varied across individuals, depending on stimulus frequency and hemisphere. Notably, a higher amplitude of backward alpha traveling waves at 10 Hz IPS in the left hemisphere significantly predicted positive early treatment response. In contrast, forward waves and right hemisphere responses did not show predictive value. Conclusions: IPS-induced backward alpha traveling waves in the left hemisphere may represent promising EEG biomarkers for early prediction of treatment efficacy in youth with MDEs. These findings offer a potential neurophysiological tool to support personalized treatment strategies and inform future clinical applications in adolescent and young adult depression.

Qualia as query act, the phenomenology of predictive error coding

This paper explores the intersection of phenomenology and neuroscience to address foundational questions about consciousness, particularly the nature of qualia-subjective, ineffable contents of experience. Drawing on Thomas Nagel's seminal inquiry into subjective experience and Husserlian phenomenology, we propose that phenomenological "What is it like?" questions can be integrated with neuroscientific models through predictive error coding (PEC). PEC reconceptualizes the brain as an active inference system, continuously generating and updating predictions about sensory inputs. We introduce the concept of "query acts" to describe the brain's interrogative engagement with sensory information, linking intentionality in phenomenology with predictive mechanisms in neuroscience. By framing qualia as dynamic processes arising from query acts rather than static entities, we bridge the explanatory gap between subjective experience and objective inquiry. This interdisciplinary framework highlights structural parallels between noetic processes in phenomenology and PEC in neuroscience, providing a novel perspective on the emergence of conscious experience. Additionally, we explore the implications of query acts for clinical interventions in psychiatric disorders and the development of context-sensitive artificial intelligence systems. This synthesis fosters deeper integration of philosophical and scientific approaches to understanding the nature of consciousness.

Relationships and representations of brain structures, connectivity, dynamics and functions

The review explores the complex interplay between brain structures and their associated functions, presenting a diversity of hierarchical models that enhances our understanding of these relationships. Central to this approach are structure-function flow diagrams, which offer a visual representation of how specific neuroanatomical structures are linked to their functional roles. These diagrams are instrumental in mapping the intricate connections between different brain regions, providing a clearer understanding of how functions emerge from the underlying neural architecture. The study details innovative attempts to develop new functional hierarchies that integrate structural and functional data. These efforts leverage recent advancements in neuroimaging techniques such as fMRI, EEG, MEG, and PET, as well as computational models that simulate neural dynamics. By combining these approaches, the study seeks to create a more refined and dynamic hierarchy that can accommodate the brain's complexity, including its capacity for plasticity and adaptation. A significant focus is placed on the overlap of structures and functions within the brain. The manuscript acknowledges that many brain regions are multifunctional, contributing to different cognitive and behavioral processes depending on the context. This overlap highlights the need for a flexible, non-linear hierarchy that can capture the brain's intricate functional landscape. Moreover, the study examines the interdependence of these functions, emphasizing how the loss or impairment of one function can impact others. Another crucial aspect discussed is the brain's ability to compensate for functional deficits following neurological diseases or injuries. The investigation explores how the brain reorganizes itself, often through the recruitment of alternative neural pathways or the enhancement of existing ones, to maintain functionality despite structural damage. This compensatory mechanism underscores the brain's remarkable plasticity, demonstrating its ability to adapt and reconfigure itself in response to injury, thereby ensuring the continuation of essential functions. In conclusion, the study presents a system of brain functions that integrates structural, functional, and dynamic perspectives. It offers a robust framework for understanding how the brain's complex network of structures supports a wide range of cognitive and behavioral functions, with significant implications for both basic neuroscience and clinical applications.

Atypical pupil-linked arousal induced by low-risk probabilistic choices, and intolerance of uncertainty in adults with ASD

Adults with autism spectrum disorder (ASD) experience stress when operating in a probabilistic environment, even if it is familiar, but the underlying mechanisms remain unclear. Their decision-making may be affected by the uncertainty aversion implicated in ASD and associated with increased autonomic arousal. Previous studies have shown that in neurotypical (NT) people, decisions with predictably better outcomes are less stressful and elicit smaller pupil-linked arousal than those involving exploration. Here, in a sample of 46 high-functioning ASD and NT participants, using mixed-effects model analysis, we explored pupil-linked arousal and behavioral performance in a probabilistic reward learning task with a stable advantage of one choice option over the other. We found that subjects with ASD learned and preferred advantageous probabilistic choices at the same rate and to the same extent as NT participants, both in terms of choice ratio and response time. Although both groups exhibited similar predictive behaviors, learning to favor advantageous choices led to increased pupillary arousal for these choices in the ASD group, while it caused a decrease in pupillary arousal in the NT group. Moreover, greater pupil-linked arousal during decisions with higher expected value correlated with greater degree of self-reported intolerance of uncertainty in everyday life. Our results suggest that in a nonvolatile probabilistic environment, objectively good predictive abilities in people with ASD are coupled with elevated physiological stress and subjective uncertainty regarding the decisions with the best possible but still uncertain outcome that contributes to their intolerance of uncertainty.

Further N-Frame networking dynamics of conscious observer-self agents via a functional contextual interface: predictive coding, double-slit quantum mechanical experiment, and decision-making fallacy modeling as applied to the measurement problem in humans and AI

Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-based architecture. These advancements have sparked growing calls to develop tests not only for intelligence but also for consciousness. However, existing benchmarks assess reasoning abilities across various domains but fail to directly address consciousness. To bridge this gap, this paper introduces the functional contextual N-Frame model, a novel framework integrating predictive coding, quantum Bayesian (QBism), and evolutionary dynamics. This comprehensive model explicates how conscious observers, whether human or artificial, should update beliefs and interact within a quantum cognitive system. It provides a dynamic account of belief evolution through the interplay of internal observer states and external stimuli. By modeling decision-making fallacies such as the conjunction fallacy and conscious intent collapse experiments within this quantum probabilistic framework, the N-Frame model establishes structural and functional equivalence between cognitive processes identified within these experiments and traditional quantum mechanics (QM). It is hypothesized that consciousness serves as an active participant in wavefunction collapse (or actualization of the physical definite states we see), bridging quantum potentiality and classical outcomes via internal observer states and contextual interactions via a self-referential loop. This framework formalizes decision-making processes within a Hilbert space, mapping cognitive states to quantum operators and contextual dependencies, and demonstrates structural and functional equivalence between cognitive and quantum systems in order to address the measurement problem. Furthermore, the model extends to testable predictions about AI consciousness by specifying informational boundaries, contextual parameters, and a conscious-time dimension derived from Anti-de Sitter/Conformal Field Theory correspondence (AdS/CFT). This paper theorizes that human cognitive biases reflect adaptive, evolutionarily stable strategies that optimize predictive accuracy (i.e., evolved quantum heuristic strategies rather than errors relative to classical rationality) under uncertainty within a quantum framework, challenging the classical interpretation of irrationality. The N-Frame model offers a unified account of consciousness, decision-making, behavior, and quantum mechanics, incorporating the idea of finding truth without proof (thus overcoming Gödelian uncertainty), insights from quantum probability theory (such as the Linda cognitive bias findings), and the possibility that consciousness can cause waveform collapse (or perturbation) accounting for the measurement problem. It proposes a process for conscious time and branching worldlines to explain subjective experiences of time flow and conscious free will. These theoretical advancements provide a foundation for interdisciplinary exploration into consciousness, cognition, and quantum systems, offering a path toward developing tests for AI consciousness and addressing the limitations of classical computation in representing conscious agency.

Thoughts and thinkers: On the complementarity between objects and processes

We argue that "processes versus objects" is not a useful dichotomy. There is, instead, substantial theoretical utility in viewing "objects" and "processes" as complementary ways of describing persistence through time, and hence the possibility of observation and manipulation. This way of thinking highlights the role of memory as an essential resource for observation, and makes it clear that "memory" and "time" are also mutually inter-defined, complementary concepts. We formulate our approach in terms of the Free Energy Principle (FEP) of Friston and colleagues and the fundamental idea from quantum theory that physical interactions can be represented by linear operators. Following Levin (2024) [30], we emphasize that memory is, first and foremost, an interpretative function, from which the idea of memory as a record, at some level of accuracy, of past events is derivative. We conclude that the distinction between objects and processes is always contrived, and always misleading, and that science would be better served by abandoning it entirely.

Training the embodied self in its impermanence: meditators evidence neurophysiological markers of death acceptance

BACKGROUND

Human predictive capacity underlies its adaptive strength but also the potential for existential terror. Grounded in the predictive processing framework of brain function, we recently showed using a magnetoencephalogram visual mismatch-response (vMMR) paradigm that prediction-based self-specific neural mechanisms shield the self from existential threat-at the level of perception-by attributing death to the 'other' (nonself). Here we test the preregistered hypothesis that insight meditation grounded on mindful awareness is associated with a reduction in the brain's defensiveness toward mortality. In addition, we examine whether these neurophysiological markers of death-denial are associated with the phenomenology of meditative self-dissolution (embodied training in impermanence).

METHODS

Thirty-eight meditators pooled from a previous project investigating self-dissolution neurophenomenology underwent the vMMR task, as well as self-report measures of mental health, and afterlife beliefs. Results were associated with the previously-reported phenomenological dimensions of self-dissolution.

RESULTS

Meditators' brains responded to the coupling of death and self-stimuli in a manner indicating acceptance rather than denial, corresponding to increased self-reported well-being. Additionally, degree of death acceptance predicted positively valenced meditation-induced self-dissolution experiences, thus shedding light on possible mechanisms underlying wholesome vs. pathological disruptions to self-consciousness.

CONCLUSIONS

The findings provide empirical support for the hypothesis that the neural mechanisms underlying the human tendency to avoid death are not hard-wired but are amenable to mental training, one which is linked with meditating on the experience of the embodied self's impermanence. The results also highlight the importance of assessing and addressing mortality concerns when implementing psychopharmacological or contemplative interventions with the potential of inducing radical disruptions to self-consciousness.

Computational Approaches for Uncovering Interoceptive Mechanisms in Psychiatric Disorders and Their Biological Basis

Interoception, the process of detecting, perceiving, and interpreting signals from within the body, is essential for physiological regulation and adaptive behavior. A growing body of research underscores important potential links between interoceptive dysfunction and psychiatric disorders. Parallel advancements in the field of computational psychiatry have led to the development of biologically plausible models of information processing in the brain. This review surveys the current state of traditional and computational research approaches to study interoceptive processes in psychiatry. We also provide a foundational description of predominant computational approaches and theoretical models of interoception. Finally, we discuss the potential molecular foundations of interoceptive computation and consider future directions for incorporating computational models to enhance clinical insights and inform personalized treatments. We conclude that combining interoception and computational modeling approaches holds considerable promise in moving the field forward, both in addressing unresolved mechanistic questions and identifying novel potential therapeutic targets.

Towards an expanded neurocognitive account of ketamine's rapid antidepressant effects

Ketamine is an N-methyl-D-aspartate receptor antagonist that has shown effectiveness as a rapidly acting treatment for depression. Although advances have been made in understanding ketamine's antidepressant pharmacological and molecular mechanisms of action, the large-scale neurocognitive mechanisms driving its therapeutic effects are less clearly understood. To help provide such a framework, we provide a synthesis of current evidence linking ketamine treatment to the modulation of brain systems supporting reward processing, interoception, and self-related cognition. We suggest that ketamine's antidepressant effects are, at least in part, driven by dynamic multi-level influences across these key functional domains.

Auditory MMN is associated with the volume of thalamic higher order nuclei in individuals with psychotic disorders and healthy controls

OBJECTIVE

Predictive coding is a theoretical framework that integrates models of brain dysconnectivity and psychopathology in psychosis. Thalamocortical dysconnectivity as well as reduced thalamic volumes have been reported in psychotic disorders. However, the role of the thalamus in predictive coding is not clear. We examined the relationship between magnetic resonance imaging (MRI)- based thalamic nuclei volumes and mismatch negativity (MMN), a purported index of prediction error signaling known to be impaired in psychosis.

METHODS

We obtained MRI and MMN using a roving paradigm from individuals with SCZ spectrum disorder (SSD, n = 60) or bipolar disorder (BD, n = 69) and HC (n = 252). We segmented volumes of 25 thalamic nuclei bilaterally and tested their associations with MMN amplitude using linear models while covarying for age, sex, diagnosis, and intracranial volumes (ICV).

RESULTS

We did not find group differences in thalamic volumes that could account for differences in MMN, neither did we find significant volume × diagnosis interactions on MMN for any of the 25 nuclei examined. Across the whole sample, significant positive associations were found between MMN amplitude and the volumes of several higher-order thalamic nuclei, including the mediodorsal medial and lateral nuclei, anterior and medial pulvinar, nucleus reuniens, as well as the lateral geniculate nucleus.

CONCLUSION

The results demonstrate a positive association between MMN amplitude and volumes of thalamic association nuclei in patients with psychotic disorders and HC. These findings may suggest a modulatory role of the thalamus in prediction error signaling.

Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models

We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python. ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive and behavioural modelling, as it is used in computational psychiatry, cognitive science and neuroscience. This means that POMDP active inference models can now be easily fit to empirically observed behaviour using sampling, as well as variational methods. In this article, we show how ActiveInference.jl makes building POMDP active inference models straightforward, and how it enables researchers to use them for simulation, as well as fitting them to data or performing a model comparison.

Different abnormalities of mismatch negativity in schizophrenia and depression as assessed with magnetoencephalography

Mismatch negativity (MMN) is widely considered a candidate diagnostic biomarker for schizophrenia (SCZ). Although blunted MMN responses have been reliably observed in psychosis, the evidence for MMN deficits in other disorders, such as major depressive disorder (MDD), is mixed. This study explores whether MMN alterations in amplitude or latency are unique to SCZ or extend to non-psychotic MDD patients. Seventeen patients diagnosed with a first MDD episode, 18 with recurrent MDD, 17 with first episode of SCZ spectrum disorder, and 18 with chronic SCZ, along with two groups of age- and sex-matched neurotypical controls (NC, 17 and 18), participated in a passive auditory MMN task during magnetoencephalography (MEG) recording. We examined the magnetic MMN (mMMN) amplitude and latency, exploring potential links between observed MMN alterations and psychotropic medication treatments. The mMMN amplitudes were significantly attenuated in SCZ compared to NC. Although, on average, mMMN amplitudes also appeared to be small in MDD, there was no significant difference between MDD and SCZ or NC. Notably, MDD patients had longer mMMN latencies compared to SCZ and NC, especially those with recurrent MDD. These results remained consistent after controlling for mood stabilizers, antidepressants, or benzodiazepines. These findings show that mMMN amplitude reductions may be more pronounced in psychotic disorders than in depressive disorders, whereas abnormal mMMN latencies may be more specific to MDD, suggesting differential mMMN alterations in SCZ and MDD. Caution is advised regarding mMMN amplitude as a diagnostic biomarker for SCZ, as small reductions also occur in MDD.

Structurally informed models of directed brain connectivity

Understanding how one brain region exerts influence over another in vivo is profoundly constrained by models used to infer or predict directed connectivity. Although such neural interactions rely on the anatomy of the brain, it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints on models of directed connectivity. Here, we review the current state of research on this question, highlighting a key distinction between inference-based effective connectivity and prediction-based directed functional connectivity. We explore the methods via which structural connectivity has been integrated into directed connectivity models: through prior distributions, fixed parameters in state-space models and inputs to structure learning algorithms. Although the evidence suggests that integrating structural connectivity substantially improves directed connectivity models, assessments of reliability and out-of-sample validity are lacking. We conclude this Review with a strategy for future research that addresses current challenges and identifies opportunities for advancing the integration of structural and directed connectivity to ultimately improve understanding of the brain in health and disease.

Are mental dysfunctions autonomous from brain dysfunctions? A perspective from the personal/subpersonal distinction

Despite many authors in psychiatry endorsing a naturalist view of the mind, many still consider that mental dysfunctions cannot be reduced to brain dysfunctions. This paper investigates the main reasons for this view. Some arguments rely on the analogy that the mind is like software while the brain is like hardware. The analogy suggests that just as software can malfunction independently of hardware malfunctions, similarly the mind can malfunction independently of any brain malfunction. This view has been critically examined in recent literature. However, other less discussed reasons suggest that mental dysfunctions cannot be reduced to brain dysfunctions because mental dysfunctions are appropriately ascribed at the level of intentional mental states, while brain dysfunctions are solely related to abnormalities in anatomy and physiological processes. This paper questions why such a view would be upheld. The discussion is framed within the interface problem in the philosophy of cognitive science, which concerns the relationship between personal and subpersonal levels of explanation. The paper examines the view that an autonomist perspective on the personal/subpersonal distinction could justify the separation of mental dysfunctions, described in intentional terms, from brain dysfunctions, described in anatomical or physiological terms. Ultimately, the paper argues that the autonomist view cannot be upheld in psychiatry and, consequently, does not provide a principled justification for rejecting the reduction of mental dysfunctions to brain dysfunctions.

Nature heals: An informational entropy account of self-organization and change in field psychotherapy

This paper reviews biophysical models of psychotherapeutic change based on synergetics and the free energy principle. These models suggest that introducing sensory surprise into the patient-therapist system can lead to self-organization and the formation of new attractor states, disrupting entrenched patterns of thoughts, emotions, and behaviours. We propose that the therapist can facilitate this process by cultivating epistemic trust and modulating embodied attention to allow surprising affective states to enter shared awareness. Transient increases in free energy enable the update of generative models, expanding the range of experiences available within the patient-therapist phenomenal field. We hypothesize that patterns of disorganization at behavioural and physiological levels, indexed by increased entropy, complexity, and lower determinism, are key markers and predictors of psychotherapeutic gains. Future research should investigate how the therapist's openness to novelty shapes therapeutic outcomes.

Intensity dependence of auditory evoked potentials distinguish participants with unmedicated depression from non-depressed controls

Depression is a heterogeneous syndrome that impacts an individual's emotional, social, cognitive and bodily functioning. Depression is associated with biases in emotional processing, but alterations in basic sensory processing have received less attention in depression research. Here, we measured event-related potentials (ERPs) in response to changes in the intensity of auditory stimuli and the location of somatosensory stimuli in participants with depression and in non-depressed control participants. We tested whether auditory mismatch negativity, P3a or N1 intensity dependence response or somatosensory mismatch response, P3a, P50 or N80 can dissociate depressed participants and non-depressed controls, and we also analysed the effects of depression medication and age in this sample. N1 intensity dependence response was increased in unmedicated depressed participants relative to non-depressed controls. When age was controlled for in the analysis, the effect of depression was only at a trend level. N1 intensity dependence response correlated with depression severity at the whole sample level. We did not observe any depression-related alterations in auditory mismatch negativity or P3a or somatosensory ERPs. Our results may reflect an association between the N1 intensity dependence response and altered neurotransmitter activity in depression, but this should be confirmed in future studies.

RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain

In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.

Exploring the Frontiers of Mathematical Neuroscience: A Comprehensive Bibliometric Analysis

Mathematical neuroscience is the branch of interdisciplinarity between mathematical modeling and neuroscience through computational techniques to study the structure, function, and dynamics of the brain. The objective of this paper is to undertake a comprehensive review of research trends in mathematical neuroscience and important developments in the period from 1973 to 2024. From this source of bibliographic data, Scopus alone returns 727 retrieved documents, consisting of journals, book chapters, and conference papers. The analysis showed an annual growth rate of 6.51% in this field and significant contributions from authors of 1957 sources. Specific tools were used in this review for in-depth analysis of publication patterns, co-authorship networks, keyword co-occurrences, and thematic evolution within this discipline. The most influential authors, dominant publication sources, and most active countries in the field were identified. The survey also underlines various other emerging trends, of which the highly increasing approach to the integration of machine learning and artificial intelligence (AI) with mathematical neuroscience is certainly the most interesting. Results highlight the dynamic and collaborative features of research in this area and provide insight into the intellectual landscape for research in this field, along with its future directions.

Active Inference in Psychology and Psychiatry: Progress to Date?

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible 'process theories' of cognition and behaviour, which fall under the banner of 'active inference'. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology's subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry.

Reconceptualizing complex posttraumatic stress disorder: A predictive processing framework for mechanisms and intervention

In this article, we introduce a framework for interpreting Complex Posttraumatic Stress Disorder (C-PTSD) through predictive processing, a neuroscience concept explaining the brain's interpretation and prediction of sensory information. While closely related to PTSD, C-PTSD encompasses additional symptom clusters marked by disturbances in self-organization (DSO), such as negative self-concept, affect dysregulation, and relational difficulties, typically resulting from prolonged traumatic stressors. Our model leverages advances in computational psychiatry and neuroscience, offering a mechanistic explanation for these symptoms by illustrating how prolonged trauma disrupts the brain's predictive processing. Specifically, altered predictive mechanisms contribute to C-PTSD's symptomatology, focusing on DSO: (1) Negative self-concept emerges from maladaptive priors that bias perception towards self-criticism, misaligning expected and actual interoceptive states; (2) Misalignment between predicted and actual interoceptive signals leads to affect dysregulation, with sensitivity to bodily cues; and (3) Relationship challenges arise from skewed social prediction errors, fostering mistrust and withdrawal. This precision-focused approach sheds light on the dynamics underpinning C-PTSD and highlights potential intervention targets aimed at recalibrating the predictive processing system.

Disembodiment and Affective Resonances in Esketamine Treatment of Depersonalized Depression Subtype: Two Case Studies

INTRODUCTION

Dissociative experiences are considered undesirable ketamine's adverse events. However, they might be crucial for ketamine's antidepressant effects, at least in some depression subtypes. Current understandings of ketamine's therapeutic potentials converge on the so-called "relaxed prior hypothesis," suggesting that glutamatergic blockage up-weights bottom-up surprising somatosensory/affective states. As a result, ketamine improves short-term plasticity in depression by enhancing sensitivity to interoceptive signals.

METHODS

We selected 2 case studies for their paradigmatic description of "depersonalized depression" (Entfremdungsdepression) symptoms. Patients were included in a 6-month-long esketamine program for treatment resistant depression, during which we collected their spontaneous experience with esketamine. According to a neurophenomenological approach, we combined subjective reports from unstructured clinical interviews and the review of previous objective neuroimaging results and neurocomputational models to unveil the relation between esketamine antidepressant effects and interoceptive sensitivity.

RESULTS

According to our clinical observations, esketamine-induced dissociation might be particularly effective in the depersonalized depression subtype, in which interoceptive awareness and interaffectivity are particularly compromised. Ketamine and esketamine's dissociative effects and particularly disembodiment might suspend previously acquired patterns of feeling, sensing, and behaving.

CONCLUSIONS

Coherently with previous research, we suggest that esketamine-induced disembodiment allows for a transient window of psychological plasticity and enhanced sensitivity, where the body recovers its permeability to affective affordances.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

The future of psychological treatments: The Marburg Declaration

Although psychological treatments are broadly recognized as evidence-based interventions for various mental disorders, challenges remain. For example, a substantial proportion of patients receiving such treatments do not fully recover, and many obstacles hinder the dissemination, implementation, and training of psychological treatments. These problems require those in our field to rethink some of our basic models of mental disorders and their treatments, and question how research and practice in clinical psychology should progress. To answer these questions, a group of experts of clinical psychology convened at a Think-Tank in Marburg, Germany, in August 2022 to review the evidence and analyze barriers for current and future developments. After this event, an overview of the current state-of-the-art was drafted and suggestions for improvements and specific recommendations for research and practice were integrated. Recommendations arising from our meeting cover further improving psychological interventions through translational approaches, improving clinical research methodology, bridging the gap between more nomothetic (group-oriented) studies and idiographic (person-centered) decisions, using network approaches in addition to selecting single mechanisms to embrace the complexity of clinical reality, making use of scalable digital options for assessments and interventions, improving the training and education of future psychotherapists, and accepting the societal responsibilities that clinical psychology has in improving national and global health care. The objective of the Marburg Declaration is to stimulate a significant change regarding our understanding of mental disorders and their treatments, with the aim to trigger a new era of evidence-based psychological interventions.

Metaverse in Mental Health: The Beginning of a Long History

PURPOSE OF REVIEW

We review the first pilot studies applying metaverse-related technologies in psychiatric patients and discuss the rationale for using this complex federation of technologies to treat mental diseases. Concerning previous virtual-reality applications in medical care, metaverse technologies provide the unique opportunity to define, control, and shape virtual scenarios shared by multi-users to exploit the "synchronized brains" potential exacerbated by social interactions.

RECENT FINDINGS

The application of an avatar-based sexual therapy program conducted on a metaverse platform has been demonstrated to be more effective concerning traditional sexual coaching for treating female orgasm disorders. Again, a metaverse-based social skills training program has been tested on children with autism spectrum disorders, demonstrating a significant impact on social interaction abilities. Metaverse-related technologies could enable us to develop new reliable approaches for treating diseases where behavioral symptoms can be addressed using socio-attentive tasks and social-interaction strategies.

Perceptual Dysfunction in Eating Disorders

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

Collective behavior from surprise minimization

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.

A new predictive coding model for a more comprehensive account of delusions

Attempts to understand psychosis-the experience of profoundly altered perceptions and beliefs-raise questions about how the brain models the world. Standard predictive coding approaches suggest that it does so by minimising mismatches between incoming sensory evidence and predictions. By adjusting predictions, we converge iteratively on a best guess of the nature of the reality. Recent arguments have shown that a modified version of this framework-hybrid predictive coding-provides a better model of how healthy agents make inferences about external reality. We suggest that this more comprehensive model gives us a richer understanding of psychosis compared with standard predictive coding accounts. In this Personal View, we briefly describe the hybrid predictive coding model and show how it offers a more comprehensive account of the phenomenology of delusions, thereby providing a potentially powerful new framework for computational psychiatric approaches to psychosis. We also make suggestions for future work that could be important in formalising this novel perspective.

Reduced stereotypicality and spared use of facial expression predictions for social evaluation in autism

Background/Objective

Autism has been investigated through traditional emotion recognition paradigms, merely investigating accuracy, thereby constraining how potential differences across autistic and control individuals may be observed, identified, and described. Moreover, the use of emotional facial expression information for social functioning in autism is of relevance to provide a deeper understanding of the condition.

Method

Adult autistic individuals (n = 34) and adult control individuals (n = 34) were assessed with a social perception behavioral paradigm exploring facial expression predictions and their impact on social evaluation.

Results

Autistic individuals held less stereotypical predictions than controls. Importantly, despite such differences in predictions, the use of such predictions for social evaluation did not differ significantly between groups, as autistic individuals relied on their predictions to evaluate others to the same extent as controls.

Conclusions

These results help to understand how autistic individuals perceive social stimuli and evaluate others, revealing a deviation from stereotypicality beyond which social evaluation strategies may be intact.

Object-Centric Scene Representations Using Active Inference

Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment. In this letter, we propose a novel approach for scene understanding, leveraging an object-centric generative model that enables an agent to infer object category and pose in an allocentric reference frame using active inference, a neuro-inspired framework for action and perception. For evaluating the behavior of an active vision agent, we also propose a new benchmark where, given a target viewpoint of a particular object, the agent needs to find the best matching viewpoint given a workspace with randomly positioned objects in 3D. We demonstrate that our active inference agent is able to balance epistemic foraging and goal-driven behavior, and quantitatively outperforms both supervised and reinforcement learning baselines by more than a factor of two in terms of success rate.

Digital Being: social media and the predictive mind

Social media is implicated today in an array of mental health concerns. While concerns around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience, however, are now poised to shed light on this matter. In this paper, we approach the phenomenon of social media addiction through the lens of the active inference framework. According to this framework, predictive agents like us use a 'generative model' of the world to predict our own incoming sense data and act to minimize any discrepancy between the prediction and incoming signal (prediction error). In order to live well and be able to act effectively to minimize prediction error, it is vital that agents like us have a generative model, which not only accurately reflects the regularities of our complex environment but is also flexible and dynamic and able to stay accurate in volatile and turbulent circumstances. In this paper, we propose that some social media platforms are a spectacularly effective way of warping an agent's generative model and of arresting the model's ability to flexibly track and adapt to changes in the environment. We go on to investigate cases of digital tech, which do not have these adverse effects and suggest-based on the active inference framework-some ways to understand why some forms of digital technology pose these risks, while others do not.

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

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.

Intrinsic timescales and predictive allostatic interoception in brain health and disease

The cognitive neuroscience of brain diseases faces challenges in understanding the complex relationship between brain structure and function, the heterogeneity of brain phenotypes, and the lack of dimensional and transnosological explanations. This perspective offers a framework combining the predictive coding theory of allostatic interoceptive overload (PAIO) and the intrinsic neural timescales (INT) theory to provide a more dynamic understanding of brain health in psychiatry and neurology. PAIO integrates allostasis and interoception to assess the interaction between internal patterns and environmental stressors, while INT shows that different brain regions operate on different intrinsic timescales. The allostatic overload can be understood as a failure of INT, which involves a breakdown of proper temporal integration and segregation. This can lead to dimensional disbalances between exteroceptive/interoceptive inputs across brain and whole-body levels (cardiometabolic, cardiovascular, inflammatory, immune). This approach offers new insights, presenting novel perspectives on brain spatiotemporal hierarchies and interactions. By integrating these theories, the paper opens innovative paths for studying brain health dynamics, which can inform future research in brain health and disease.

To be or not to be: The active inference of suicide

Suicide presents an apparent paradox as a behavior whose motivation is not obvious since its outcome is non-existence and cannot be experienced. To address this paradox, we propose to frame suicide in the integrated theory of stress and active inference. We present an active inference-based cognitive model of suicide as a type of stress response hanging in cognitive balance between predicting self-preservation and self-destruction. In it, self-efficacy emerges as a meta-cognitive regulator that can bias the model toward either survival or suicide. The model suggests conditions under which cognitive homeostasis can override physiological homeostasis in motivating self-destruction. We also present a model proto-suicidal behavior, programmed cell death (apoptosis), in active inference terms to illustrate how an active inference model of self-destruction can be embodied in molecular mechanisms and to offer a hypothesis on another puzzle of suicide: why only humans among brain-endowed animals are known to practice it.

Individual prediction tendencies do not generalize across modalities

Predictive processing theories, which model the brain as a "prediction machine", explain a wide range of cognitive functions, including learning, perception and action. Furthermore, it is increasingly accepted that aberrant prediction tendencies play a crucial role in psychiatric disorders. Given this explanatory value for clinical psychiatry, prediction tendencies are often implicitly conceptualized as individual traits or as tendencies that generalize across situations. As this has not yet explicitly been shown, in the current study, we quantify to what extent the individual tendency to anticipate sensory features of high probability generalizes across modalities. Using magnetoencephalography (MEG), we recorded brain activity while participants were presented with a sequence of four different (either visual or auditory) stimuli, which changed according to predefined transitional probabilities of two entropy levels: ordered vs. random. Our results show that, on a group-level, under conditions of low entropy, stimulus features of high probability are preactivated in the auditory but not in the visual modality. Crucially, the magnitude of the individual tendency to predict sensory events seems not to correlate between the two modalities. Furthermore, reliability statistics indicate poor internal consistency, suggesting that the measures from the different modalities are unlikely to reflect a single, common cognitive process. In sum, our findings suggest that quantification and interpretation of individual prediction tendencies cannot be generalized across modalities.

Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models

Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.

Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks

Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.

Emotion processes and perceptual control of action choice

This editorial introduces an invited article by Andreas Eder on a new perceptual control theory of action choice, based on the comparison of real and simulated interoceptive signals generated by action alternatives. Eder extends the cognitive action-control framework, postulating a bi-directional connection between outcomes and actions by introducing "emotional feelings", defined as valued interoceptive signals from the body. An invited commentary by Agnes Moors compares this theory with her own goal-directed theory of action control. While agreeing on the central role of a control cycle and the goal-directed nature of emotional actions, Moors disagrees on the content of the representations involved in the control cycle and the nature of the feelings involved. A second commentary by Bob Bramson and Karin Roelofs discusses the issues of the distinction between perception control vs. action control, the need for biologically plausible implementation alternatives, and potential implications for psychopathology and clinical intervention. Finally, the potential relevance of predictive coding theory and the role of appraisal processes in emotion generation with respect to their bearing on action comparison and choice are discussed.

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

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

Mental health meets computational neuroscience: A predictive Bayesian account of the relationship between interoception and multisensory bodily illusions in anorexia nervosa

Mental health disorders pose a significant challenge to society. The Bayesian perspective on the mind offers unique insights and tools that may help address a variety of mental health conditions. Psychopathological dysfunctions are often connected to altered predictive and active inference processes, in which cognitive and physiological pathogenic beliefs shape the clinical condition and its symptoms. However, there is a lack of general empirical models that integrate cognitive beliefs, physiological experience, and symptoms in healthy and clinical populations. In this study, we examined the relationship between altered predictive mechanisms, interoception, and pathological bodily distortions in healty individuals and in individuals suffering from anorexia nervosa (AN). AN patients (N=15) completed a Virtual Reality Full-Body Illusion along with interoceptive tasks twice: at hospital admission during an acute symptomatological phase (Time 1) and after a 12-week outpatient clinical weight-restoring rehabilitative program (Time 2). Results were compared to a healthy control group. Our findings indicated that higher levels of interoceptive metacognitive awareness were associated with a greater embodiment. However, unlike in healthy participants, AN patients' interoceptive metacognition was linked to embodiment even in multisensory mismatching (asynchronous) conditions. In addition, unlike in healthy participants, higher interoceptive metacognition in AN patients was related to prior abnormal bodily distortions during the acute symptomatology phase. Prediction errors in bodily estimates predicted posterior bodily estimate distortions after the illusion, but while this relationship was only significant in the synchronous condition in healthy participants, there was no significant difference between synchronous and asynchronous conditions in AN patients. Despite the success of the rehabilitation program in restoring some dysfunctional patterns in the AN group, prediction errors and posterior estimate distortions were present at hospital discharge. Our findings suggest that individuals with AN prioritize interoceptive metacognitive processes (i.e., confidence in their own perceived sensations rather than their actual perceptions), disregarding bottom-up bodily inputs in favour of their prior altered top-down beliefs. Moreover, even if the rehabilitative program partially mitigated these alterations, the pathological condition impaired the patients' ability to coherently update their prior erroneous expectations with real-time multisensory bottom-up bodily information, possibly locking the patients in the experience of a distorted prior top-down belief. These results suggest new therapeutic perspectives and introduce the framework of regenerative virtual therapy (RVT), which aims at utilizing technology-based somatic modification techniques to restructure the maladaptive priors underlying a pathological condition.

Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations

Breathing plays a crucial role in shaping perceptual and cognitive processes by regulating the strength and synchronisation of neural oscillations. Numerous studies have demonstrated that respiratory rhythms govern a wide range of behavioural effects across cognitive, affective, and perceptual domains. Additionally, respiratory-modulated brain oscillations have been observed in various mammalian models and across diverse frequency spectra. However, a comprehensive framework to elucidate these disparate phenomena remains elusive. In this review, we synthesise existing findings to propose a neural gradient of respiratory-modulated brain oscillations and examine recent computational models of neural oscillations to map this gradient onto a hierarchical cascade of precision-weighted prediction errors. By deciphering the computational mechanisms underlying respiratory control of these processes, we can potentially uncover new pathways for understanding the link between respiratory-brain coupling and psychiatric disorders.

Signal processing in the vagus nerve: Hypotheses based on new genetic and anatomical evidence

Each organism must regulate its internal state in a metabolically efficient way as it interacts in space and time with an ever-changing and only partly predictable world. Success in this endeavor is largely determined by the ongoing communication between brain and body, and the vagus nerve is a crucial structure in that dialogue. In this review, we introduce the novel hypothesis that the afferent vagus nerve is engaged in signal processing rather than just signal relay. New genetic and structural evidence of vagal afferent fiber anatomy motivates two hypotheses: (1) that sensory signals informing on the physiological state of the body compute both spatial and temporal viscerosensory features as they ascend the vagus nerve, following patterns found in other sensory architectures, such as the visual and olfactory systems; and (2) that ascending and descending signals modulate one another, calling into question the strict segregation of sensory and motor signals, respectively. Finally, we discuss several implications of our two hypotheses for understanding the role of viscerosensory signal processing in predictive energy regulation (i.e., allostasis) as well as the role of metabolic signals in memory and in disorders of prediction (e.g., mood disorders).

Regulative development as a model for origin of life and artificial life studies

Using the formal framework of the Free Energy Principle, we show how generic thermodynamic requirements on bidirectional information exchange between a system and its environment can generate complexity. This leads to the emergence of hierarchical computational architectures in systems that operate sufficiently far from thermal equilibrium. In this setting, the environment of any system increases its ability to predict system behavior by "engineering" the system towards increased morphological complexity and hence larger-scale, more macroscopic behaviors. When seen in this light, regulative development becomes an environmentally-driven process in which "parts" are assembled to produce a system with predictable behavior. We suggest on this basis that life is thermodynamically favorable and that, when designing artificial living systems, human engineers are acting like a generic "environment".

Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus

Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function.

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.

Hierarchical predictive coding in distributed pain circuits

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

Therapeutic touch and therapeutic alliance in pediatric care and neonatology: An active inference framework

Therapeutic affective touch has been recognized as essential for survival, nurturing supportive interpersonal interactions, accelerating recovery-including reducing hospitalisations, and promoting overall health and building robust therapeutic alliances. Through the lens of active inference, we present an integrative model, combining therapeutic touch and communication, to achieve biobehavioural synchrony. This model speaks to how the brain develops a generative model required for recovery, developing successful therapeutic alliances, and regulating allostasis within paediatric manual therapy. We apply active inference to explain the neurophysiological and behavioural mechanisms that underwrite the development and maintenance of synchronous relationships through touch. This paper foregrounds the crucial role of therapeutic touch in developing a solid therapeutic alliance, the clinical effectiveness of paediatric care, and triadic synchrony between health care practitioner, caregiver, and infant in a variety of clinical situations. We start by providing a brief overview of the significance and clinical role of touch in the development of social interactions in infants; facilitating a positive therapeutic alliance and restoring homeostasis through touch to allow a more efficient process of allostatic regulation. Moreover, we explain the role of CT tactile afferents in achieving positive clinical outcomes and updating prior beliefs. We then discuss how touch is implemented in treatment sessions to promote cooperative interactions in the clinic and facilitate theory of mind. This underwrites biobehavioural synchrony, epistemic trust, empathy, and the resolution of uncertainty. The ensuing framework is underpinned by a critical application of the active inference framework to the fields of pediatrics and neonatology.

Interpersonal attunement in social interactions: from collective psychophysiology to inter-personalized psychiatry and beyond

In this article, we analyse social interactions, drawing on diverse points of views, ranging from dialectics, second-person neuroscience and enactivism to dynamical systems, active inference and machine learning. To this end, we define interpersonal attunement as a set of multi-scale processes of building up and materializing social expectations-put simply, anticipating and interacting with others and ourselves. While cultivating and negotiating common ground, via communication and culture-building activities, are indispensable for the survival of the individual, the relevant multi-scale mechanisms have been largely considered in isolation. Here, collective psychophysiology, we argue, can lend itself to the fine-tuned analysis of social interactions, without neglecting the individual. On the other hand, an interpersonal mismatch of expectations can lead to a breakdown of communication and social isolation known to negatively affect mental health. In this regard, we review psychopathology in terms of interpersonal misattunement, conceptualizing psychiatric disorders as disorders of social interaction, to describe how individual mental health is inextricably linked to social interaction. By doing so, we foresee avenues for an inter-personalized psychiatry, which moves from a static spectrum of disorders to a dynamic relational space, focusing on how the multi-faceted processes of social interaction can help to promote mental health. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.