The graphical brain: Belief propagation and active inference

The Emperor Is Naked: Replies to commentaries on the target article

The 35 commentaries cover a wide range of topics and take many different stances on the issues explored by the target article. We have organised our response to the commentaries around three central questions: Are Friston blankets just Pearl blankets? What ontological and metaphysical commitments are implied by the use of Friston blankets? What kind of explanatory work are Friston blankets capable of? We conclude our reply with a short critical reflection on the indiscriminate use of both Markov blankets and the free energy principle.

A Bayesian model for chronic pain

The perceiving mind constructs our coherent and embodied experience of the world from noisy, ambiguous and multi-modal sensory information. In this paper, we adopt the perspective that the experience of pain may similarly be the result of a probabilistic, inferential process. Prior beliefs about pain, learned from past experiences, are combined with incoming sensory information in a Bayesian manner to give rise to pain perception. Chronic pain emerges when prior beliefs and likelihoods are biased towards inferring pain from a wide range of sensory data that would otherwise be perceived as harmless. We present a computational model of interoceptive inference and pain experience. It is based on a Bayesian graphical network which comprises a hidden layer, representing the inferred pain state; and an observable layer, representing current sensory information. Within the hidden layer, pain states are inferred from a combination of priors p(pain), transition probabilities between hidden states p(paint+1∣paint) and likelihoods of certain observations p(sensation∣pain). Using variational inference and free-energy minimization, the model is able to learn from observations over time. By systematically manipulating parameter settings, we demonstrate that the model is capable of reproducing key features of both healthy- and chronic pain experience. Drawing on mathematical concepts, we finally simulate treatment resistant chronic pain and discuss mathematically informed treatment options.

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

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

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

Functional neurological disorder and placebo and nocebo effects: shared mechanisms

Functional neurological disorder (FND) is characterized by neurological symptoms that cannot be explained by a structural neurological cause. Among the different aetiological models that have been proposed for FND, of note is the Bayesian predictive coding model, which posits that perception relies on top-down cortical predictions (priors) to infer the source of incoming sensory information. This model can also apply to non-pathological experiences, such as placebo and nocebo effects, wherein sensory information is shaped by prior expectations and learning. To date, most studies of the relationship between placebo and nocebo effects and FND have focused on the use of placebos for diagnosis and treatment of FND. Here, we propose that this relationship might go beyond diagnosis and therapy. We develop a framework in which shared cognitive, personality and neuroanatomical factors justify the consideration of a deeper link between FND and placebo and nocebo effects. This new perspective might offer guidance for clarification of the pathogenesis of FND and for the identification of potential biomarkers and therapeutic targets.

Branching Time Active Inference with Bayesian Filtering

Branching time active inference is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in active inference, a neuroscientific framework widely used for brain modeling, as well as in Monte Carlo tree search, a method broadly applied in the reinforcement learning literature. Up to now, the inference of the latent variables was carried out by taking advantage of the flexibility offered by variational message passing, an iterative process that can be understood as sending messages along the edges of a factor graph. In this letter, we harness the efficiency of an alternative method for inference, Bayesian filtering, which does not require the iteration of the update equations until convergence of the variational free energy. Instead, this scheme alternates between two phases: integration of evidence and prediction of future states. Both phases can be performed efficiently, and this provides a forty times speedup over the state of the art.

Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence

What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference-affective self-modelling-are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to "tune" organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.

Believing processes during the COVID-19 pandemic in individuals with bipolar disorder: An exploratory study

BACKGROUND

Believing or “credition” refers to psychological processes that integrate the cognitions and emotions that influence our behavior. In the credition model by Angel and Seitz, four parameters are postulated: proposition, certainty, emotion and mightiness. It is assumed that believing processes are influenced by both the individual as well as socio-cultural factors and external circumstances. External or environmental circumstances can include threatening situations such as the ongoing pandemic. It has been hypothesized that believing processes related to the pandemic differ between individuals with bipolar disorder (BD) and healthy controls (HC).

AIM

To investigate credition in individuals with BD during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

Psychiatrically stable individuals with BD (n = 52) and age- and sex matched HC (n = 52) participated in an online survey during the first lockdown of the COVID-19 pandemic. The survey took place between April 9^th and June 4^th, 2020, in Austria. Participants completed the Brief Symptom Inventory-18, the Beck Depression Inventory-II, the Altman Self-Rating Mania Scale, the Pittsburgh Sleep Quality Index and a dedicated Believing Questionnaire assessing four parameters of credition (proposition, certainty, emotion and mightiness). The MAXQDA software was used to analyze the qualitative data. Statistical analyses included analyses of variance, a multivariate analysis of variance and a multivariate analysis of co-variance.

RESULTS

Individuals with BD reported significantly more negative propositions [F (1,102) = 8.89, P = 0.004, η²_p = 0.08] and negative emotions [Welch´s F (1,82.46) = 18.23, P < 0.001, η²_p = 0.18], while HC showed significantly more positive propositions [F (1,102) = 7.78, P = 0.006, η²_p = 0.07] and emotions [F (1,102) = 14.31, P < 0.001, η²_p = 0.12]. In addition, individuals with BD showed a higher incongruence between their propositions and their emotions [F (1,102) = 9.42, P = 0.003, η²_p = 0.08] and showed strong correlations between the parameters of the Believing Questionnaire and their psychiatric symptoms (r = 0.51-0.77, all P < 0.001). Positive as well as negative emotions and propositions were associated with scores measuring symptoms of depression, anxiety and sleep quality.

CONCLUSION

Believing parameters were associated with psychiatric symptoms in BD during the pandemic. Findings broaden knowledge about the susceptibility of believing processes for ambient challenges in individuals with BD.

Horizontal connectivity in V1: Prediction of coherence in contour and motion integration

This study demonstrates the functional importance of the Surround context relayed laterally in V1 by the horizontal connectivity, in controlling the latency and the gain of the cortical response to the feedforward visual drive. We report here four main findings: 1) a centripetal apparent motion sequence results in a shortening of the spiking latency of V1 cells, when the orientation of the local inducer and the global motion axis are both co-aligned with the RF orientation preference; 2) this contextual effects grows with visual flow speed, peaking at 150–250°/s when it matches the propagation speed of horizontal connectivity (0.15–0.25 mm/ms); 3) For this speed range, the axial sensitivity of V1 cells is tilted by 90° to become co-aligned with the orientation preference axis; 4) the strength of modulation by the surround context correlates with the spatiotemporal coherence of the apparent motion flow. Our results suggest an internally-generated binding process, linking local (orientation /position) and global (motion/direction) features as early as V1. This long-range diffusion process constitutes a plausible substrate in V1 of the human psychophysical bias in speed estimation for collinear motion. Since it is demonstrated in the anesthetized cat, this novel form of contextual control of the cortical gain and phase is a built-in property in V1, whose expression does not require behavioral attention and top-down control from higher cortical areas. We propose that horizontal connectivity participates in the propagation of an internal “prediction” wave, shaped by visual experience, which links contour co-alignment and global axial motion at an apparent speed in the range of saccade-like eye movements.

Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes

Schizophrenia is a severe mental disorder whose neural basis remains difficult to ascertain. Among the available pathophysiological theories, recent work has pointed towards subtle perturbations in the excitation-inhibition (E/I) balance within different neural circuits. Computational approaches have suggested interesting mechanisms that can account for both E/I imbalances and psychotic symptoms. Based on hierarchical neural networks propagating information through a message-passing algorithm, it was hypothesized that changes in the E/I ratio could cause a "circular belief propagation" in which bottom-up and top-down information reverberate. This circular inference (CI) was proposed to account for the clinical features of schizophrenia. Under this assumption, this paper examined the impact of CI on network dynamics in light of brain imaging findings related to psychosis. Using brain-inspired graphical models, we show that CI causes overconfidence and overactivation most specifically at the level of connector hubs (e.g., nodes with many connections allowing integration across networks). By also measuring functional connectivity in these graphs, we provide evidence that CI is able to predict specific changes in modularity known to be associated with schizophrenia. Altogether, these findings suggest that the CI framework may facilitate behavioral and neural research on the multifaceted nature of psychosis.

Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility

Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicated these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian and frequentist analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); and (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 ≤ ICCs ≤ .54). Exploratory analyses also suggested that learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 ≤ rs ≤ .43). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.

WSN-driven Posture Recognition and Correction towards Basketball Exercise

In order to enhance the daily training for basketball sport, this paper establishes a human posture estimation framework by using monocular camera and wireless sensor network. First, the daily basketball training images are collected by monocular camera and transmitted through wireless sensor network. Second, the collected images are processed by an observation and reasoning model which is based on component and graph reasoning. The basketball player’s posture is depicted by the rotation invariant features of edge field. The extracted features are used to learn a boosting classifier as the observation model. The experimental results show that the posture recognition rate can achieve more than 88% for basketball player’s action.

Branching Time Active Inference: The theory and its generality

Over the last 10 to 15 years, active inference has helped to explain various brain mechanisms from habit formation to dopaminergic discharge and even modelling curiosity. However, the current implementations suffer from an exponential (space and time) complexity class when computing the prior over all the possible policies up to the time-horizon. Fountas et al. (2020) used Monte Carlo tree search to address this problem, leading to impressive results in two different tasks. In this paper, we present an alternative framework that aims to unify tree search and active inference by casting planning as a structure learning problem. Two tree search algorithms are then presented. The first propagates the expected free energy forward in time (i.e., towards the leaves), while the second propagates it backward (i.e., towards the root). Then, we demonstrate that forward and backward propagations are related to active inference and sophisticated inference, respectively, thereby clarifying the differences between those two planning strategies.

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

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

Flights and Perchings of the BrainMind: A Temporospatial Approach to Psychotherapy

This article introduces a process-oriented approach for improving present moment conceptualization in psychotherapy that is in alignment with neuroscience: the Temporospatial movements of mind (TSMM) model. We elaborate on seven temporal movements that describe the moment-to-moment morphogenesis of emotional feelings and thoughts from inception to maturity. Temporal refers to the passage of time through which feelings and thoughts develop, and electromagnetic activity, that among other responsibilities, bind information across time. Spatial dynamics extend from an undifferentiated to three dimensional experiences of emotional and cognitive processes. Neurophysiologically, spatial refers to structures within the brain and their varying interactions with one another. This article culminates in the development of an atheoretical temporospatial grid that may help clinicians conceptualize where patients are in their cognitive and emotional development to further guide technique.

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

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

How Active Inference Could Help Revolutionise Robotics

Recent advances in neuroscience have characterised brain function using mathematical formalisms and first principles that may be usefully applied elsewhere. In this paper, we explain how active inference-a well-known description of sentient behaviour from neuroscience-can be exploited in robotics. In short, active inference leverages the processes thought to underwrite human behaviour to build effective autonomous systems. These systems show state-of-the-art performance in several robotics settings; we highlight these and explain how this framework may be used to advance robotics.

Linking Brain Structure, Activity, and Cognitive Function through Computation

Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.

The Free Energy Principle for Perception and Action: A Deep Learning Perspective

The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i.e., they minimize their free energy. Under this principle, biological agents learn a generative model of the world and plan actions in the future that will maintain the agent in an homeostatic state that satisfies its preferences. This framework lends itself to being realized in silico, as it comprehends important aspects that make it computationally affordable, such as variational inference and amortized planning. In this work, we investigate the tool of deep learning to design and realize artificial agents based on active inference, presenting a deep-learning oriented presentation of the free energy principle, surveying works that are relevant in both machine learning and active inference areas, and discussing the design choices that are involved in the implementation process. This manuscript probes newer perspectives for the active inference framework, grounding its theoretical aspects into more pragmatic affairs, offering a practical guide to active inference newcomers and a starting point for deep learning practitioners that would like to investigate implementations of the free energy principle.

Bayesian mechanics for stationary processes

This paper develops a Bayesian mechanics for adaptive systems. Firstly, we model the interface between a system and its environment with a Markov blanket. This affords conditions under which states internal to the blanket encode information about external states. Second, we introduce dynamics and represent adaptive systems as Markov blankets at steady state. This allows us to identify a wide class of systems whose internal states appear to infer external states, consistent with variational inference in Bayesian statistics and theoretical neuroscience. Finally, we partition the blanket into sensory and active states. It follows that active states can be seen as performing active inference and well-known forms of stochastic control (such as PID control), which are prominent formulations of adaptive behaviour in theoretical biology and engineering.

The evolution of brain architectures for predictive coding and active inference

This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors-and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising-about predictive processing-with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Synthetic Spatial Foraging With Active Inference in a Geocaching Task

Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we present a deep Active Inference model of goal-directed behavior, and the accompanying belief updating. Active Inference rests upon optimizing Bayesian beliefs to maximize model evidence or marginal likelihood. Bayesian beliefs are probability distributions over the causes of observable outcomes. These causes include an agent's actions, which enables one to treat planning as inference. We use simulations of a geocaching task to elucidate the belief updating-that underwrites spatial foraging-and the associated behavioral and neurophysiological responses. In a geocaching task, the aim is to find hidden objects in the environment using spatial coordinates. Here, synthetic agents learn about the environment via inference and learning (e.g., learning about the likelihoods of outcomes given latent states) to reach a target location, and then forage locally to discover the hidden object that offers clues for the next location.

On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances

In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.

Rhythm and Music-Based Interventions in Motor Rehabilitation: Current Evidence and Future Perspectives

Research in basic and clinical neuroscience of music conducted over the past decades has begun to uncover music’s high potential as a tool for rehabilitation. Advances in our understanding of how music engages parallel brain networks underpinning sensory and motor processes, arousal, reward, and affective regulation, have laid a sound neuroscientific foundation for the development of theory-driven music interventions that have been systematically tested in clinical settings. Of particular significance in the context of motor rehabilitation is the notion that musical rhythms can entrain movement patterns in patients with movement-related disorders, serving as a continuous time reference that can help regulate movement timing and pace. To date, a significant number of clinical and experimental studies have tested the application of rhythm- and music-based interventions to improve motor functions following central nervous injury and/or degeneration. The goal of this review is to appraise the current state of knowledge on the effectiveness of music and rhythm to modulate movement spatiotemporal patterns and restore motor function. By organizing and providing a critical appraisal of a large body of research, we hope to provide a revised framework for future research on the effectiveness of rhythm- and music-based interventions to restore and (re)train motor function.

Canonical neural networks perform active inference

This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function-and plasticity is modulated with a certain delay. We show that such neural networks implicitly perform active inference and learning to minimise the risk associated with future outcomes. Mathematical analyses demonstrate that this biological optimisation can be cast as maximisation of model evidence, or equivalently minimisation of variational free energy, under the well-known form of a partially observed Markov decision process model. This equivalence indicates that the delayed modulation of Hebbian plasticity-accompanied with adaptation of firing thresholds-is a sufficient neuronal substrate to attain Bayes optimal inference and control. We corroborated this proposition using numerical analyses of maze tasks. This theory offers a universal characterisation of canonical neural networks in terms of Bayesian belief updating and provides insight into the neuronal mechanisms underlying planning and adaptive behavioural control.

Predictive Neuronal Adaptation as a Basis for Consciousness

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation. Similarly at a single neuron level, predicting future activity and adapting synaptic inputs accordingly was shown to be the best strategy to maximize the metabolic energy for a neuron. Following on these ideas, here we examined if surprise minimization by single neurons could be a basis for consciousness. First, we showed in simulations that as a neural network learns a new task, then the surprise within neurons (defined as the difference between actual and expected activity) changes similarly to the consciousness of skills in humans. Moreover, implementing adaptation of neuronal activity to minimize surprise at fast time scales (tens of milliseconds) resulted in improved network performance. This improvement is likely because adapting activity based on the internal predictive model allows each neuron to make a more "educated" response to stimuli. Based on those results, we propose that the neuronal predictive adaptation to minimize surprise could be a basic building block of conscious processing. Such adaptation allows neurons to exchange information about own predictions and thus to build more complex predictive models. To be precise, we provide an equation to quantify consciousness as the amount of surprise minus the size of the adaptation error. Since neuronal adaptation can be studied experimentally, this can allow testing directly our hypothesis. Specifically, we postulate that any substance affecting neuronal adaptation will also affect consciousness. Interestingly, our predictive adaptation hypothesis is consistent with multiple ideas presented previously in diverse theories of consciousness, such as global workspace theory, integrated information, attention schema theory, and predictive processing framework. In summary, we present a theoretical, computational, and experimental support for the hypothesis that neuronal adaptation is a possible biological mechanism of conscious processing, and we discuss how this could provide a step toward a unified theory of consciousness.

Believing processes around COVID-19 vaccination: An exploratory study investigating workers in the health sector

BACKGROUND

The processes underlying believing have been labeled "creditions", which are important brain functions between emotion and cognition. Creditions are influenced by both internal and external factors, one of which is the coronavirus disease 2019 (COVID-19) pandemic and the vaccination against the disease.

METHODS

To investigate believing processes shortly before the implementation of a mandatory vaccination in Austria, both vaccinated and unvaccinated workers in the health sector (WHS) were surveyed in December 2021/January 2022. In total, 1,062 vaccinated and 97 unvaccinated WHS (920 females) completed the online survey. Beliefs were assessed using the parameters of the credition model (narrative, certainty, emotion, and mightiness) with regard to (1) the COVID-19 pandemic in general, and (2) the vaccination. Type of emotion and narrative were divided into positive, negative, and indifferent. Moreover, the congruence between emotion and narrative was calculated.

RESULTS

The vaccination rate of the sample was 91.6%, with a significantly higher percentage of men being in the group of vaccinated (21.1%) as compared to unvaccinated individuals (12.4%). Pertaining beliefs about the COVID-19 vaccination, unvaccinated WHS reported more negative and less positive emotions as well as content of narrative than vaccinated WHS. In addition, they showed higher levels of certainty as well as mightiness while believing and felt less sufficiently informed about governmental and workplace-related COVID-19 measures. The groups did not differ in the type of emotion or content of narrative in their beliefs about the pandemic in general.

CONCLUSION

In conclusion, unvaccinated WHS had more negative and less positive emotions and thoughts than vaccinated WHS in their beliefs about the COVID-19 vaccination and their motivations for not having received it. They were more certain about their beliefs and felt stronger negative emotions in their beliefs compared to vaccinated individuals. Providing unvaccinated WHS with adequate information might be helpful in reducing their mental burden.

Active inference leads to Bayesian neurophysiology

The neuronal substrates that implement the free-energy principle and ensuing active inference at the neuron and synapse level have not been fully elucidated. This Review considers possible neuronal substrates underlying the principle. First, the foundations of the free-energy principle are introduced, and then its ability to empirically explain various brain functions and psychological and biological phenomena in terms of Bayesian inference is described. Mathematically, the dynamics of neural activity and plasticity that minimise a cost function can be cast as performing Bayesian inference that minimises variational free energy. This equivalence licenses the adoption of the free-energy principle as a universal characterisation of neural networks. Further, the neural network structure itself represents a generative model under which an agent operates. A virtue of this perspective is that it enables the formal association of neural network properties with prior beliefs that regulate inference and learning. The possible neuronal substrates that implement prior beliefs and how to empirically examine the theory are discussed. This perspective renders brain activity explainable, leading to a deeper understanding of the neuronal mechanisms underlying basic psychology and psychiatric disorders in terms of an implicit generative model.

'Consciousnessoids': clues and insights from human cerebral organoids for the study of consciousness

Human cerebral organoids (HCOs) are an in vitro three-dimensional model of early neural development, aimed at modelling and understanding brain development and neurological disorders. In just a few years, there has been a rapid and considerable progress in the attempt to create a brain model capable of showcasing the structure and functions of the human brain. There are still strong limitations to address, including the absence of vascularization that makes it difficult to feed the central layers of organoids. Nevertheless, some important features of the nervous system have recently been observed: HCOs manifest electrical activity, are sensitive to light stimulation and are able to connect to a spinal cord by sending impulses that make a muscle contract. Recent data show that cortical organoid network development at 10 months resembles some preterm babies' electroencephalography (EEG) patterns. In the light of the fast pace of research in this field, one might consider the hypothesis that HCOs might become a living laboratory for studying the emergence of consciousness and investigating its mechanisms and neural correlates. HCOs could be also a benchmark for different neuroscientific theories of consciousness. In this paper, I propose some potential lines of research and offer some clues and insights so as to use HCOs in trying to unveil some puzzles concerning our conscious states. Finally, I consider some relevant ethical issues regarding this specific experimentation on HCOs and conclude that some of them could require strict regulation in this field.

Active inference, selective attention, and the cocktail party problem

In this paper, we introduce a new generative model for an active inference account of preparatory and selective attention, in the context of a classic 'cocktail party' paradigm. In this setup, pairs of words are presented simultaneously to the left and right ears and an instructive spatial cue directs attention to the left or right. We use this generative model to test competing hypotheses about the way that human listeners direct preparatory and selective attention. We show that assigning low precision to words at attended-relative to unattended-locations can explain why a listener reports words from a competing sentence. Under this model, temporal changes in sensory precision were not needed to account for faster reaction times with longer cue-target intervals, but were necessary to explain ramping effects on event-related potentials (ERPs)-resembling the contingent negative variation (CNV)-during the preparatory interval. These simulations reveal that different processes are likely to underlie the improvement in reaction times and the ramping of ERPs that are associated with spatial cueing.

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

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

"Project for a Spatiotemporal Neuroscience" - Brain and Psyche Share Their Topography and Dynamic

What kind of neuroscience does psychoanalysis require? At his time, Freud in his "Project for a Scientific Psychology" searched for a model of the brain that could relate to incorporate the psyche's topography and dynamic. Current neuropsychoanalysis builds on specific functions as investigated in Affective and Cognitive (and Social) Neuroscience including embodied approaches. The brain's various functions are often converged with prediction as operationalized in predictive coding (PC) and free energy principle (FEP) which, recently, have been conceived as core for a "New Project for Scientific Psychology." We propose to search for a yet more comprehensive and holistic neuroscience that focuses primarily on its topography and dynamic analogous to Freud's model of the psyche. This leads us to what we describe as "Spatiotemporal Neuroscience" that focuses on the spatial topography and temporal dynamic of the brain's neural activity including how they shape affective, cognitive, and social functions including PC and FEP (first part). That is illustrated by the temporally and spatially nested neural hierarchy of the self in the brain's neural activity (second and third part). This sets the ground for developing our proposed "Project for a Spatiotemporal Neuroscience," which complements and extends both Freud's and Solms' projects (fourth part) and also carries major practical implications as it lays the ground for a novel form of neuroscientifically informed psychotherapy, namely, "Spatiotemporal Psychotherapy." In conclusion, "Spatiotemporal Neuroscience" provides an intimate link of brain and psyche by showing topography and dynamic as their shared features, that is, "common currency."

Bayesian theories of consciousness: a review in search for a minimal unifying model

The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties appear to be the minimal necessary components for the emergence of conscious experience-a Minimal Unifying Model of consciousness.

Trust as Extended Control: Human-Machine Interactions as Active Inference

In order to interact seamlessly with robots, users must infer the causes of a robot's behavior-and be confident about that inference (and its predictions). Hence, trust is a necessary condition for human-robot collaboration (HRC). However, and despite its crucial role, it is still largely unknown how trust emerges, develops, and supports human relationship to technological systems. In the following paper we review the literature on trust, human-robot interaction, HRC, and human interaction at large. Early models of trust suggest that it is a trade-off between benevolence and competence; while studies of human to human interaction emphasize the role of shared behavior and mutual knowledge in the gradual building of trust. We go on to introduce a model of trust as an agent' best explanation for reliable sensory exchange with an extended motor plant or partner. This model is based on the cognitive neuroscience of active inference and suggests that, in the context of HRC, trust can be casted in terms of virtual control over an artificial agent. Interactive feedback is a necessary condition to the extension of the trustor's perception-action cycle. This model has important implications for understanding human-robot interaction and collaboration-as it allows the traditional determinants of human trust, such as the benevolence and competence attributed to the trustee, to be defined in terms of hierarchical active inference, while vulnerability can be described in terms of information exchange and empowerment. Furthermore, this model emphasizes the role of user feedback during HRC and suggests that boredom and surprise may be used in personalized interactions as markers for under and over-reliance on the system. The description of trust as a sense of virtual control offers a crucial step toward grounding human factors in cognitive neuroscience and improving the design of human-centered technology. Furthermore, we examine the role of shared behavior in the genesis of trust, especially in the context of dyadic collaboration, suggesting important consequences for the acceptability and design of human-robot collaborative systems.

World model learning and inference

Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world.

Active inference through whiskers

Rodents use whisking to probe actively their environment and to locate objects in space, hence providing a paradigmatic biological example of active sensing. Numerous studies show that the control of whisking has anticipatory aspects. For example, rodents target their whisker protraction to the distance at which they expect objects, rather than just reacting fast to contacts with unexpected objects. Here we characterize the anticipatory control of whisking in rodents as an active inference process. In this perspective, the rodent is endowed with a prior belief that it will touch something at the end of the whisker protraction, and it continuously modulates its whisking amplitude to minimize (proprioceptive and somatosensory) prediction errors arising from an unexpected whisker-object contact, or from a lack of an expected contact. We will use the model to qualitatively reproduce key empirical findings about the ways rodents modulate their whisker amplitude during exploration and the scanning of (expected or unexpected) objects. Furthermore, we will discuss how the components of active inference model can in principle map to the neurobiological circuits of rodent whisking.

Gut inference: A computational modelling approach

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

Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference

Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data.

Sophisticated Inference

Active inference offers a first principle account of sentient behavior, from which special and important cases-for example, reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design-can be derived. Active inference finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on the same footing as reward or value. In brief, active inference replaces value functions with functionals of (Bayesian) beliefs, in the form of an expected (variational) free energy. In this letter, we consider a sophisticated kind of active inference using a recursive form of expected free energy. Sophistication describes the degree to which an agent has beliefs about beliefs. We consider agents with beliefs about the counterfactual consequences of action for states of affairs and beliefs about those latent states. In other words, we move from simply considering beliefs about "what would happen if I did that" to "what I would believe about what would happen if I did that." The recursive form of the free energy functional effectively implements a deep tree search over actions and outcomes in the future. Crucially, this search is over sequences of belief states as opposed to states per se. We illustrate the competence of this scheme using numerical simulations of deep decision problems.

Some Interesting Observations on the Free Energy Principle

Biehl et al. (2021) present some interesting observations on an early formulation of the free energy principle. We use these observations to scaffold a discussion of the technical arguments that underwrite the free energy principle. This discussion focuses on solenoidal coupling between various (subsets of) states in sparsely coupled systems that possess a Markov blanket-and the distinction between exact and approximate Bayesian inference, implied by the ensuing Bayesian mechanics.

Seven computations of the social brain

The social environment presents the human brain with the most complex information processing demands. The computations that the brain must perform occur in parallel, combine social and nonsocial cues, produce verbal and nonverbal signals and involve multiple cognitive systems, including memory, attention, emotion and learning. This occurs dynamically and at timescales ranging from milliseconds to years. Here, we propose that during social interactions, seven core operations interact to underwrite coherent social functioning; these operations accumulate evidence efficiently-from multiple modalities-when inferring what to do next. We deconstruct the social brain and outline the key components entailed for successful human-social interaction. These include (i) social perception; (ii) social inferences, such as mentalizing; (iii) social learning; (iv) social signaling through verbal and nonverbal cues; (v) social drives (e.g. how to increase one's status); (vi) determining the social identity of agents, including oneself and (vii) minimizing uncertainty within the current social context by integrating sensory signals and inferences. We argue that while it is important to examine these distinct aspects of social inference, to understand the true nature of the human social brain, we must also explain how the brain integrates information from the social world.

The computational neurology of movement under active inference

We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology by simulating a clinical examination of the motor system using an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles.

A Variational Approach to Scripts

This paper proposes a formal reconstruction of the script construct by leveraging the active inference framework, a behavioral modeling framework that casts action, perception, emotions, and attention as processes of (Bayesian or variational) inference. We propose a first principles account of the script construct that integrates its different uses in the behavioral and social sciences. We begin by reviewing the recent literature that uses the script construct. We then examine the main mathematical and computational features of active inference. Finally, we leverage the resources of active inference to offer a formal model of scripts. Our integrative model accounts for the dual nature of scripts (as internal, psychological schema used by agents to make sense of event types and as constitutive behavioral categories that make up the social order) and also for the stronger and weaker conceptions of the construct (which do and do not relate to explicit action sequences, respectively).

Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker

Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of brain function. It is also proposed as a framework for planning in AI. Unfortunately, the complex mathematics required to create new models can impede application of active inference in neuroscience and AI research. This letter addresses this problem by providing a complete mathematical treatment of the active inference framework in discrete time and state spaces and the derivation of the update equations for any new model. We leverage the theoretical connection between active inference and variational message passing as described by John Winn and Christopher M. Bishop in 2005. Since variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this letter opens the door to advanced generative models for active inference. We show that using a fully factorized variational distribution simplifies the expected free energy, which furnishes priors over policies so that agents seek unambiguous states. Finally, we consider future extensions that support deep tree searches for sequential policy optimization based on structure learning and belief propagation.

Chance-Constrained Active Inference

Active inference (ActInf) is an emerging theory that explains perception and action in biological agents in terms of minimizing a free energy bound on Bayesian surprise. Goal-directed behavior is elicited by introducing prior beliefs on the underlying generative model. In contrast to prior beliefs, which constrain all realizations of a random variable, we propose an alternative approach through chance constraints, which allow for a (typically small) probability of constraint violation, and demonstrate how such constraints can be used as intrinsic drivers for goal-directed behavior in ActInf. We illustrate how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing, for example, for a trade-off between robust control and empirical chance constraint violation. Second, we interpret the proposed solution within a message passing framework. Interestingly, the message passing interpretation is not only relevant to the context of ActInf, but also provides a general-purpose approach that can account for chance constraints on graphical models. The chance constraint message updates can then be readily combined with other prederived message update rules without the need for custom derivations. The proposed chance-constrained message passing framework thus accelerates the search for workable models in general and can be used to complement message-passing formulations on generative neural models.

Prefrontal Computation as Active Inference

The prefrontal cortex is vital for a range of cognitive processes, including working memory, attention, and decision-making. Notably, its absence impairs the performance of tasks requiring the maintenance of information through a delay period. In this paper, we formulate a rodent task—which requires maintenance of delay-period activity—as a Markov decision process and treat optimal task performance as an (active) inference problem. We simulate the behavior of a Bayes optimal mouse presented with 1 of 2 cues that instructs the selection of concurrent visual and auditory targets on a trial-by-trial basis. Formulating inference as message passing, we reproduce features of neuronal coupling within and between prefrontal regions engaged by this task. We focus on the micro-circuitry that underwrites delay-period activity and relate it to functional specialization within the prefrontal cortex in primates. Finally, we simulate the electrophysiological correlates of inference and demonstrate the consequences of lesions to each part of our in silico prefrontal cortex. In brief, this formulation suggests that recurrent excitatory connections—which support persistent neuronal activity—encode beliefs about transition probabilities over time. We argue that attentional modulation can be understood as the contextualization of sensory input by these persistent beliefs.

Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.