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Most of our movement consists of sequences of discrete actions at regular intervals-including speech, walking, playing music, or even chewing. Despite this, few models of the motor system address how the brain determines the interval at which to trigger actions. This paper offers a theoretical analysis of the problem of timing movements. We consider a scenario in which we must align an alternating movement with a regular external (auditory) stimulus. We assume that our brains employ generative world models that include internal clocks of various speeds. These allow us to associate a temporally regular sensory input with an internal clock, and actions with parts of that clock cycle. We treat this as process of inferring which clock best explains sensory input. This offers a way in which temporally discrete choices might emerge from a continuous process. This is not straightforward, particularly if each of those choices unfolds during a time that has a (possibly unknown) duration. We develop a route for translation to neurology, in the context of Parkinson's disease-a disorder that characteristically slows down movements. The effects are often elicited in clinic by alternating movements. We find that it is possible to reproduce behavioural and electrophysiological features associated with parkinsonism by disrupting specific parameters-that determine the priors for inferences made by the brain. We observe three core features of Parkinson's disease: amplitude decrement, festination, and breakdown of repetitive movements. Our simulations provide a mechanistic interpretation of how pathology and therapeutics might influence behaviour and neural activity., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A central theme of theoretical neurobiology is that most of our cognitive operations require processing of discrete sequences of items. This processing in turn emerges from continuous neuronal dynamics. Notable examples are sequences of words during linguistic communication or sequences of locations during navigation. In this perspective, we address the problem of sequential brain processing from the perspective of active inference, which inherits from a Helmholtzian view of the predictive (Bayesian) brain. Underneath the active inference lies a generative model; namely, a probabilistic description of how (observable) consequences are generated by (unobservable) causes. We show that one can account for many aspects of sequential brain processing by assuming the brain entails a generative model of the sensed world that comprises central pattern generators, narratives, or well-defined sequences. We provide examples in the domains of motor control (e.g., handwriting), perception (e.g., birdsong recognition) through to planning and understanding (e.g., language). The solutions to these problems include the use of sequences of attracting points to direct complex movements-and the move from continuous representations of auditory speech signals to the discrete words that generate those signals., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© The Author(s) 2023.)

We investigated if feeding earthworms (EW) or vermicompost (VC) to broilers improves performance and aids in coping with dietary challenges from a soluble non-starch polysaccharide (NSP)-enriched diet (negative control diet; CON-). Newly-hatched male Cobb-500 birds (N = 480) were fed either a positive (+) control diet (CON+, n = 240) or CON+ supplemented with either 1% EW (CON+EW; n = 120) or 1% VC in DM (CON+VC; n = 120) for 8 d (Period 1; P1). At the end of P1, blood and intestinal samples were taken from half the birds in each group. Half of remaining birds on CON+ stayed on CON+ for further 8 d (P2; d9-16) or switched to CON-. Birds on CON+EW and CON+VC in P1 were switched to CON- in P2 (CON-EW and CON-VC, respectively). The CON+VC improved (P < 0.05) BW and ADG in P1 through an elevated feed intake (FI) (P < 0.05) with no effect on FCR. CON+EW did not differ from the CON+ in terms of growth and FI in P1. In P2 CON- did not affect growth or DMI relative to CON+. In the end of P2, 10% of CON+ birds had pasty vent (PV). CON- increased incidence of PV and CON-VC aggravated this effect (P < 0.05), whereas CON-EW did not differ from CON+. CON- diet reduced proportion of 16S rDNA in colon digesta (P = 0.049), while CON-EW and CON-VC did not differ from CON+. Compared to CON-, CON-EW tended to decrease (P = 0.072) incidence of PV. Ceca were heavier (P < 0.05) in CON-EW than in CON+ fed birds. In conclusion, the challenge diet induced PV and reduced bacterial 16S rDNA in colon digesta, likely due to soluble NSP-induced anti-nutritive effects. VC supplementation enhanced early growth by increasing feed intake. Provision of EW did not impact performance but decreased incidence of PV and increased cecal size, suggesting that potential inoculation with beneficial microorganisms may counteract NSP effects., Competing Interests: DISCLOSURES The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move-in the ensuing schemes-is to place priors on the selection of models, based upon expected free energy. In this setting, expected free energy reduces to a constrained mutual information, where the constraints inherit from priors over outcomes (i.e., preferred outcomes). The resulting scheme is first used to perform image classification on the MNIST dataset to illustrate the basic idea, and then tested on a more challenging problem of discovering models with dynamics, using a simple sprite-based visual disentanglement paradigm and the Tower of Hanoi (cf., blocks world) problem. In these examples, generative models are constructed autodidactically to recover (i.e., disentangle) the factorial structure of latent states-and their characteristic paths or dynamics., Competing Interests: Declaration of Competing Interest The authors have no disclosures or conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one's sensory observations, through the optimisation of a generative model (of the hidden causes of one's sensory data) in the brain. One of active inference's key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident-many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action, i.e., the key processes that rely on adequate estimates of precision, from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical, "mixed" models-generative models spanning multiple levels of discrete and continuous inference. These kinds of models open up new perspectives on the unified description of hierarchical computation, and its implementation, in action. Here, we highlight how these models reflect the many roles of precision in action-from planning to execution-and the associated pathologies if precision estimation goes wrong. We also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision.

This review paper offers an overview of the history and future of active inference-a unifying perspective on action and perception. Active inference is based upon the idea that sentient behavior depends upon our brains' implicit use of internal models to predict, infer, and direct action. Our focus is upon the conceptual roots and development of this theory of (basic) sentience and does not follow a rigid chronological narrative. We trace the evolution from Helmholtzian ideas on unconscious inference, through to a contemporary understanding of action and perception. In doing so, we touch upon related perspectives, the neural underpinnings of active inference, and the opportunities for future development. Key steps in this development include the formulation of predictive coding models and related theories of neuronal message passing, the use of sequential models for planning and policy optimization, and the importance of hierarchical (temporally) deep internal (i.e., generative or world) models. Active inference has been used to account for aspects of anatomy and neurophysiology, to offer theories of psychopathology in terms of aberrant precision control, and to unify extant psychological theories. We anticipate further development in all these areas and note the exciting early work applying active inference beyond neuroscience. This suggests a future not just in biology, but in robotics, machine learning, and artificial intelligence., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI., Competing Interests: Declaration of interests K.F. holds a chief scientific adviser position at VERSES AI. The other authors declare no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bistable perception and posit that perceptual transitions between different interpretations (i.e. inferences) of the same stimulus ensue from specific eye movements that shift the focus to a different visual feature. Formally, these inferences are a consequence of precision control that determines how confident beliefs are and change the frequency with which one can perceive-and alternate between-two distinct percepts. We hypothesized that there are multiple, but distinct, ways in which precision modulation can interact to give rise to a similar frequency of bistable perception. We validated this using numerical simulations of the Necker cube paradigm and demonstrate the multiple routes that underwrite the frequency of perceptual alternation. Our results provide an (enactive) computational account of the intricate precision balance underwriting bistable perception. Importantly, these precision parameters can be considered the computational homologs of particular neurotransmitters-i.e. acetylcholine, noradrenaline, dopamine-that have been previously implicated in controlling bistable perception, providing a computational link between the neurochemistry and perception., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Background: Skeletal muscle development during embryogenesis depends on proliferation of myoblasts followed by differentiation into myotubes/multinucleated myofibers. Vitamin D (VD) has been shown to affect these processes, but there is conflicting evidence within the current literature on the exact nature of these effects due to a lack of time course data. With 20%-40% of pregnant women worldwide being VD deficient, it is crucial that a clearer understanding of the impact of VD on myogenesis is gained. Methods: A detailed 8-day differentiation time course was used where C2C12 cells were differentiated in control media (2% horse serum) or with different concentrations of active VD, 1,25 (OH) 2 D 3 (10 -13  M, 10 -11  M, 10 -9  M or 10 -7  M), and measurements were taken at 6 time points. DNA, creatine kinase and protein assays were carried out as well as quantitative PCR to determine expression of Myf5, MyoD, myogenin, MHC I, and MHC neonatal, MHC embryonic, MHC IIa, MHC IIx, and MHC IIb mRNAs. Transfections were carried out using one vector containing the myogenin promoter and another containing the same promoter with a 3 base mutation within a putative vitamin D response element (VDRE) to determine effects of 1,25 (OH) 2 D 3 on myogenin transcription. Finally, a ChIP assay was performed to determine whether the VD receptor (VDR) binds to the putative VDRE. Results: 1,25(OH) 2 D 3 caused an inhibition of proliferation and an increase in differentiation in C2C12 cells. Myf5, myogenin, MHC I, and MHC neonatal, MHC embryonic, MHC IIa, MHC IIx, and MHC IIb expression were all increased by 1,25(OH) 2 D 3 . Myotube size was also increased by VD. When the putative VDRE on the myogenin promoter was mutated, the increase in expression by VD was lost. ChIP analysis revealed that the VDR does bind to the putative VDRE on the myogenin promoter. Conclusion: Active VD directly increases myogenin transcription via a functional VDRE on the myogenin promoter, resulting in increased myogenic differentiation, increased expression of both the early and late MHC isoforms, and also increased myotube size. These results highlight the importance of VD status during pregnancy for normal myogenesis to occur, but further in vivo work is needed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Alliband, Parr, Jethwa and Brameld.)

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time. The main results are a method or principle of least action that can be used to emulate the behaviour of particles in open exchange with their external milieu. Particles are defined by a particular partition, in which internal states are individuated from external states by active and sensory blanket states. The variational principle at hand allows one to interpret internal dynamics-of certain kinds of particles-as inferring external states that are hidden behind blanket states. We consider different kinds of particles, and to what extent they can be imbued with an elementary form of inference or sentience. Specifically, we consider the distinction between dissipative and conservative particles, inert and active particles and, finally, ordinary and strange particles. Strange particles can be described as inferring their own actions, endowing them with apparent autonomy or agency. In short-of the kinds of particles afforded by a particular partition-strange kinds may be apt for describing sentient behaviour., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Despite decades of research, we do not definitively know how people sometimes see things that are not there. Eight models of complex visual hallucinations have been published since 2000, including Deafferentation, Reality Monitoring, Perception and Attention Deficit, Activation, Input, and Modulation, Hodological, Attentional Networks, Active Inference, and Thalamocortical Dysrhythmia Default Mode Network Decoupling. Each was derived from different understandings of brain organisation. To reduce this variability, representatives from each research group agreed an integrated Visual Hallucination Framework that is consistent with current theories of veridical and hallucinatory vision. The Framework delineates cognitive systems relevant to hallucinations. It allows a systematic, consistent, investigation of relationships between the phenomenology of visual hallucinations and changes in underpinning cognitive structures. The episodic nature of hallucinations highlights separate factors associated with the onset, persistence, and end of specific hallucinations suggesting a complex relationship between state and trait markers of hallucination risk. In addition to a harmonised interpretation of existing evidence, the Framework highlights new avenues of research, and potentially, new approaches to treating distressing hallucinations., Competing Interests: Conflicts of interest The following authors report no competing interests: James Barnes, Robert Dudley, Nico Diederich, Dominic ffytche, Karl Friston, Simon Lewis, Shigetoshi Nara, Claire O’Callaghan, Javier Pagonabarraga, James M Shine, Ichiro Tsuda. Daniel Collerton has received royalty payments from Wiley publishers. Christopher C Goetz has received faculty stipends from the International Parkinson and Movement Disorder Society, Guest professorship honoraria provided by University of Chicago and Illinois State Neurological Society, and a stipend as Volume Editor from Elsevier Publishers. He has also received royalty payments from Elsevier Publishers and Wolters Kluwer Publishers. Jennifer G Goldman has received grant/research support from Acadia Pharmaceuticals and honoraria from Medscape. Renaud Jardri has been invited to scientific meetings and expert boards by Lundbeck, Janssen and Otsuka. Jaime Kulisevsky has received fees for presentations or advisory boards from: Teva, UCB, Roche, Abbvie, Zambon, Bial, Sanofii and Neuroderm. Marco Onofrj has served on the scientific advisory boards of GlaxoSmithKline, Novartis, Lundbeck, Eisai, Valeant, Medtronic, and Newron; has received speaker honoraria from Zambon, the World Parkinson Congress, the Movement Disorder Society, and the Atypical Dementias congress; publishing royalties from Springer; was an invited guest and lecturer for the Mental Disorders in Parkinson Disease Congress; serves on the editorial board of Medicine (Baltimore) and Frontiers in Neuroscience; has been employed as a speaker for Boehringer Ingelheim, GlaxoSmithKline, UCB, and Zambon; and has received research support from the Italian Ministry of Health and the Italian Ministry of Education. Glenn Stebbins received compensation for consulting and advisory board membership from Acadia Pharmaceuticals, Adamas Pharmaceuticals, Biogen, Ceregene, CHDI Management, Neurocrine Biosciences, Pfizer, Tools-4-Patients, Ultragenyx and the Sunshine Care Foundation. John-Paul Taylor has received speaker fees from GE Healthcare. He has consulted for Kirin Kyowa and Sosei-Heptares. Rimona S Weil has received speaking honoraria from GE Healthcare and writing honoraria from Britannia., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This paper introduces a variational formulation of natural selection, paying special attention to the nature of 'things' and the way that different 'kinds' of 'things' are individuated from-and influence-each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain-and are constrained by-fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses-and identify points of contact with related mathematical formulations of evolution.

This paper aims to integrate some key constructs in the cognitive neuroscience of cognitive control and executive function by formalising the notion of cognitive (or mental) effort in terms of active inference. To do so, we call upon a task used in neuropsychology to assess impulse inhibition-a Stroop task. In this task, participants must suppress the impulse to read a colour word and instead report the colour of the text of the word. The Stroop task is characteristically effortful, and we unpack a theory of mental effort in which, to perform this task accurately, participants must overcome prior beliefs about how they would normally act. However, our interest here is not in overt action, but in covert (mental) action. Mental actions change our beliefs but have no (direct) effect on the outside world-much like deploying covert attention. This account of effort as mental action lets us generate multimodal (choice, reaction time, and electrophysiological) data of the sort we might expect from a human participant engaging in this task. We analyse how parameters determining cognitive effort influence simulated responses and demonstrate that-when provided only with performance data-these parameters can be recovered, provided they are within a certain range., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Active inference is a probabilistic framework for modeling the behavior of biological and artificial agents, which derives from the principle of minimizing free energy. In recent years, this framework has been applied successfully to a variety of situations where the goal was to maximize reward, often offering comparable and sometimes superior performance to alternative approaches. In this article, we clarify the connection between reward maximization and active inference by demonstrating how and when active inference agents execute actions that are optimal for maximizing reward. Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation, a formulation that underlies several approaches to model-based reinforcement learning and control. On partially observed Markov decision processes, the standard active inference scheme can produce Bellman optimal actions for planning horizons of 1 but not beyond. In contrast, a recently developed recursive active inference scheme (sophisticated inference) can produce Bellman optimal actions on any finite temporal horizon. We append the analysis with a discussion of the broader relationship between active inference and reinforcement learning., (© 2023 Massachusetts Institute of Technology.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

When compared to research centered on the executive function development of white, middle-class children, relatively little is known about their non-white, low socioeconomic status peers. In an effort to harmonize how executive functions are measured within under-represented contexts, the present study addresses gaps in the evaluation of everyday executive functioning to better understand whether behavior rating scales completed by teachers (BASC2 EF - BASC executive function scale, 2nd edition; BASC3 EF - BASC executive function scale, 3rd edition) capture distinctions between performance-based measures. This study includes two large samples of older, ethnic minority children from high-poverty backgrounds (Sample 1. N = 243; M age  = 9.28 years, SD age  = 0.80; n female  = 125; n AfricanAmerican  = 216, n LatinAmerican  = 15, n AsianAmerican  = 6; Sample 2. N = 229; M age  = 10.02 years, SD age  = 1.01; n female  = 120; n AfricanAmerican  = 132, n LatinAmerican  = 92, n White  = 3, n PacificIslander  = 1). Based on structural equation models testing the links between computerized performance-based measures and the teacher rating scales, the results indicate that BASC2 EF in its original form might be a good fit for some populations but there is not a strong factor structure for the current high-poverty samples. In addition, post-hoc analyses suggest that only including BASC2 EF items also in BASC3 EF or using BASC3 EF is best practice for high-poverty populations. BASC3 EF seems better able to capture different components of performance-driven tasks, whereas BASC2 EF captures overall executive functioning better than individual tasks. These findings encourage continued questioning surrounding metrics used to assess everyday executive functions in older children from diverse backgrounds. HIGHLIGHTS: This study explores whether teacher ratings of children's everyday executive functioning (using standardized behavior rating scales) capture distinctions between performance-based measures. Results indicate that BASC2 EF teacher rating scale (Karr & Garcia-Barrera, 2017) is not a good representation of everyday executive function behaviors by children from schools in high-poverty communities. The findings suggest that restricting BASC2 EF analyses to only items included in BASC3 EF (Reynolds & Kamphaus, 2015) or using BASC3 EF for high-poverty populations. BASC3 EF seems better able to capture the different components of performance-driven tasks, whereas BASC2 EF captures overall executive functioning better than individual tasks., (© 2022 The Authors. Developmental Science published by John Wiley & Sons Ltd.)

This commentary suggests that, although Markov blankets may have different interpretations in different systems, these distinctions rest not upon the type of blanket, but upon the model that determines the blanket. As an example, the conditions for a model in which the Markov blanket may be interpretable as a physical (spatial) boundary are considered.

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., Competing Interests: The authors have declared that no competing interests exist.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action. In other words, they do not consider where to sample next, given current beliefs. Here, we reclaim salience as an active inference process that relies on two basic principles: uncertainty minimization and rhythmic scheduling. For this, we make a distinction between attention and salience. Briefly, we associate attention with precision control, i.e., the confidence with which beliefs can be updated given sampled sensory data, and salience with uncertainty minimization that underwrites the selection of future sensory data. Using this, we propose a new account of attention based on rhythmic precision-modulation and discuss its potential in robotics, providing numerical experiments that showcase its advantages for state and noise estimation, system identification and action selection for informative path planning., (Copyright © 2022 Anil Meera, Novicky, Parr, Friston, Lanillos and Sajid.)

Delusions are, by popular definition, false beliefs that are held with certainty and resistant to contradictory evidence. They seem at odds with the notion that the brain at least approximates Bayesian inference. This is especially the case in schizophrenia, a disorder thought to relate to decreased - rather than increased - certainty in the brain's model of the world. We use an active inference Markov decision process model (a Bayes-optimal decision-making agent) to perform a simple task involving social and non-social inferences. We show that even moderate changes in some model parameters - decreasing confidence in sensory input and increasing confidence in states implied by its own (especially habitual) actions - can lead to delusions as defined above. Incorporating affect in the model increases delusions, specifically in the social domain. The model also reproduces some classic psychological effects, including choice-induced preference change, and an optimism bias in inferences about oneself. A key observation is that no change in a single parameter is both necessary and sufficient for delusions; rather, delusions arise due to conditional dependencies that create 'basins of attraction' which trap Bayesian beliefs. Simulating the effects of antidopaminergic antipsychotics - by reducing the model's confidence in its actions - demonstrates that the model can escape from these attractors, through this synthetic pharmacotherapy., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Under the Bayesian brain hypothesis, behavioral variations can be attributed to different priors over generative model parameters. This provides a formal explanation for why individuals exhibit inconsistent behavioral preferences when confronted with similar choices. For example, greedy preferences are a consequence of confident (or precise) beliefs over certain outcomes. Here, we offer an alternative account of behavioral variability using Rényi divergences and their associated variational bounds. Rényi bounds are analogous to the variational free energy (or evidence lower bound) and can be derived under the same assumptions. Importantly, these bounds provide a formal way to establish behavioral differences through an α parameter, given fixed priors. This rests on changes in α that alter the bound (on a continuous scale), inducing different posterior estimates and consequent variations in behavior. Thus, it looks as if individuals have different priors and have reached different conclusions. More specifically, α→0+ optimization constrains the variational posterior to be positive whenever the true posterior is positive. This leads to mass-covering variational estimates and increased variability in choice behavior. Furthermore, α→+∞ optimization constrains the variational posterior to be zero whenever the true posterior is zero. This leads to mass-seeking variational posteriors and greedy preferences. We exemplify this formulation through simulations of the multiarmed bandit task. We note that these α parameterizations may be especially relevant (i.e., shape preferences) when the true posterior is not in the same family of distributions as the assumed (simpler) approximate density, which may be the case in many real-world scenarios. The ensuing departure from vanilla variational inference provides a potentially useful explanation for differences in behavioral preferences of biological (or artificial) agents under the assumption that the brain performs variational Bayesian inference., (© 2022 Massachusetts Institute of Technology.)

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'.