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Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the selection of data—either through sampling subsets of data from a large dataset or through optimising experimental design—based upon the models we have of how those data are generated. Optimising data-selection ensures we can achieve good inference with fewer data, saving on computational and experimental costs. This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. Finally, we consider how this approach to data selection could be applied to the design of (Bayes-adaptive) clinical trials. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) systems, such as large language models (LLMs), have achieved remarkable performance in various tasks such as text and image generation. We discuss the foundations of generative AI systems by comparing them with our current understanding of living organisms, when seen as active inference systems. Both generative AI and active inference are based on generative models, but they acquire and use them in fundamentally different ways. Living organisms and active inference agents learn their generative models by engaging in purposive interactions with the environment and by predicting these interactions. This provides them with a core understanding and a sense of mattering, upon which their subsequent knowledge is grounded. Future generative AI systems might follow the same (biomimetic) approach – and learn the affordances implicit in embodied engagement with the world before – or instead of – being trained passively. 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. [ABSTRACT FROM AUTHOR]

Animals are a critical component of biogeochemical cycles. While animal mediated fluxes of nutrients and energy have received considerable attention, the impacts of these fluxes on microbial community structure and function are comparatively understudied. Here, we investigated if freshwater mussel influences on biogeochemical cycling in stream sediment are accompanied by changes in sediment microbial community composition and ecoenzymatic activity, and if these relationships change under different nutrient regimes.We predicted that mussel effects on ecosystem function are reflected by modified sediment microbial communities. We hypothesized that if changes in either sediment ecoenzymatic function or microbial community composition are driven by mussel‐derived nutrient amendments, we should see muted changes in microbial community assemblages or function when a given nutrient is abundant. However, if microbial communities and function are influenced by other mussel functions, then we should see uniform changes regardless of nutrient availability.We transplanted freshwater mussels and natural river sediment to flow‐through mesocosms and monitored changes in microbial community composition over 1 week. Our results indicate that mussels always changed sediment microbial community composition, but the way communities changed was dependent on ambient nutrient concentrations.On the final day we measured the activity of ecoenzymes known to correlate to microbial function and nutrient availability. Mussels homogenized the stoichiometric ratios of ecoenzyme activities, indicating a consistent function of sediment microbes associated with freshwater mussels. Our results suggest that mussels may promote functional redundancy in sediment microbial communities and highlight the importance of animals in controlling biogeochemical transformations under changing nutrient conditions. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

In streams, unionoid mussels and fish form aggregations that exert bottom‐up and top‐down effects on food webs, but the magnitude and spatial extent of their effects are controlled by species traits. Sedentary mussels live burrowed in the sediment in patchily distributed dense aggregations (mussel beds) where they filter seston and provide a local, relatively constant nutrient subsidy. In contrast, fish move on and off mussel beds, and thus comprise a transient nutrient subsidy.We asked how overlap between fish and mussels influences nutrient recycling and resource distribution in streams. We conducted an 8‐week study in experimental streams where we created mussel beds (comprised of two species, Actinonaias ligamentina and Amblema plicata), manipulated the occurrence of a grazing minnow (Campostoma anomalum), and tracked nutrient (nitrogen and phosphorus) and resource (algae, detritus, and chironomids) abundance up and downstream of the mussel beds.In general, neither consumer had strong effects on the concentration or spatial distribution of nutrients. Water turnover time in our experimental streams may have diluted fish and mussel nutrient excretion effects, making it difficult to detect spatial patterns during a given sampling period.Fish controlled the abundance and productivity of algae. In treatments without fish, large mats of filamentous algae formed early in the experiment. These algae senesced, decomposed, and were not replaced. When fish were present, algae consisted of attached biofilms with consistent biomass and spatial distribution over time.Although previous work has shown that mussels can have strong, seasonal bottom‐up effects on both primary and secondary production, our results suggested that adding grazing mobile fishes, led to a more consistent and homogenous supply of algal resources. Because mussels rarely occur in the absence of fish, considering their combined influence on ecosystem dynamics is likely to be important. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • We review the history and future of active inference: a unifying perspective on action and perception. • We discuss the conceptual roots of active inference, its current status and promising future directions. • We highlight the importance of the brain's generative models to predict, infer, and direct action. • We critically discuss how active inference can unify extant psychological theories. [ABSTRACT FROM AUTHOR]

• New generative model for selective attention during cocktail party listening. • Computational 'lesions' in the model dissociate different errors during word report. • We model different temporal hypotheses for preparatory attention. • Temporal changes in precision are necessary to explain ERPs but not reaction times. • CNV-like responses can be explained by subjective precision rather than action. 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. [ABSTRACT FROM AUTHOR]

Traditionally, short-term memory (STM) has been assessed by asking participants to remember words, visual objects, or numbers for a short amount of time before their recall or recognition of those items is tested. However, this focus on memory for past sensory input might have obscured potential theoretical insights into the function of this cognitive faculty. Here, we suggest that STM may have an important role in predicting future sensory input. This reconceptualization of STM may provide a functional explanation for its capacity limitation. [ABSTRACT FROM AUTHOR]

There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a step back from a historical mind–body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems? [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection—even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay—based on sequential serology—that provides a (1) quantitative measure of latent immunological responses and (2) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines. [ABSTRACT FROM AUTHOR]

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. • Communication—and language in particular—is an emergent property of agents that seek evidence for generative models of their shared world. • Nested free energy minimising—evidence maximising—processes explain the emergence of language and its transmission over generations. • Reading these processes as inference integrates perspectives on communication; from generalised synchrony to cultural niche construction. [ABSTRACT FROM AUTHOR]

This study investigates the combined effects of land management and hydrology on the temporal dynamics of dissolved organic matter (DOM) quantity and composition in stream water and groundwaters in an agricultural watershed. We assessed dissolved organic carbon (DOC) concentrations, DOM UV–Vis absorbance, and DOM fluorescence in groundwater under cultivated upland, riparian grassland, and riparian woodland land covers, as well as in the stream water at the watershed outlet and livestock-impacted runoff. During one year, stream water and groundwater were monitored weekly to biweekly, complemented by sub-hourly stream sampling during seven storm events. Results showed that: (1) groundwater DOC concentration was lower in cultivated upland (6.4 ± 5.6 mg l−1) than in riparian grassland and woodland (22.4 ± 13.7 mg l−1 and 17.2 ± 9.9 mg l−1, respectively). (2) The proportion of microbially processed compounds decreased in the order upland cropland > riparian grassland > riparian woodland. (3) Principal component analysis (PCA) of groundwater DOM revealed a change in composition indicating that low-aromaticity microbially processed compounds were preferentially exported to the stream. (4) PCA of stream DOM indicated that seasonal increases in groundwater elevation expanded the contributing source areas, thereby increasing the connectivity between upland croplands and the stream, which amplified the effects of cultivation on fluvial DOM during the winter. (5) Storm events occurring after manure application in spring produced hot moments of manure-derived protein-like DOM transport to streams. Together, these results suggest that cultivated uplands in agricultural lands using animal manure as fertilizer may leach more DOM than vegetative buffers. [ABSTRACT FROM AUTHOR]

• New (hierarchical) generative model for linguistic interactions. • Builds on active inference formulations of dyadic interactions. • We simulate agents that ask and answer questions together. • Theta-gamma coupling emerges from belief updating under this framework. This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure—necessary to accommodate the rich combinatorics of language. We illustrate linguistic communication by simulating a synthetic subject who can play the 'Twenty Questions' game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of linguistic communication emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The roles mobile animals and abiotic processes play as vectors for resource transfers between ecosystems ("subsidies") are well studied, but the idea that resources from animals with limited mobility may be transported across boundaries through intermediate taxa remains unexplored. Aquatic plants ("macrophytes") are globally distributed and may mediate transfers of aquatic‐derived nutrients from aggregations of aquatic animals to terrestrial ecosystems when consumed by terrestrial herbivores. We used mesocosms (94 × 44 cm) to test whether aquatic animal‐generated biogeochemical hotspots increase growth and nutrient content in macrophytes using the macrophyte Justicia americana and freshwater mussels. Justicia americana biomass production increased and belowground biomass allocation changed with increasing mussel density. At high mussel density, water‐column phosphorus increased and carbon:phosphorus ratios in J. americana tissues decreased. We deployed motion‐sensing cameras to explore herbivory on J. americana growing along the margins of the Kiamichi River, Oklahoma, and documented feeding by large mammals (Odocoileus virginianus, Sus scrofa, and Bos taurus). Thus, biogeochemical hotspots generated by aquatic animal aggregations can promote macrophyte production that subsequently is transferred to terrestrial animals. More broadly, this suggests that reductions in aquatic animal biomass may have bottom‐up impacts that indirectly affect terrestrial ecosystems via plant–animal interactions bridging ecosystem boundaries. [ABSTRACT FROM AUTHOR]

Biomass is often used to scale the contributions of individuals and their functional traits to a community or ecosystem. However, accurate biomass measurements can require destructive sampling, which is detrimental to long-lived organisms such as unionid mussels. We amassed a database of 6684 measurements of length and soft tissue dry mass (STDM) or shell dry mass (SHDM) from 43 species of unionid mussels to reduce the need for destructive sampling. We used these data to produce regression equations that relate maximum shell length to mass (either STDM or SHDM) at 3 taxonomic levels: family, phylogenetic tribe, and species. For 2 widely-distributed unionid species, Amblema plicata and Elliptio complanata , we present length–STDM regression equations from 6 waterbodies and basins. We also used bootstrapping resampling at the family level to develop a universal regression equation for unionid mussels. We compared models within all 3 taxonomic levels to determine if multiplicative (log–log transformation) or additive (non-linear parameter estimation) error structures provided better fits for biomass prediction. Models with multiplicative errors best fit length–STDM data at the family level (STDM = 6.63 × 10−6 × L max2.89 × 1.13; r 2 = 0.94), for 83% of tribes (n = 5, average r 2 = 0.95 ± 0.03), and for 90% of species (n = 33, average r 2 = 0.93 ± 0.07). For length–SHDM, models with multiplicative errors best fit the family level (SHDM = 2.98 × 10−4 × L max2.98 × 1.32; r 2 = 0.86), all tribes (n = 5, average r 2 = 0.88 ± 0.13), and all species (n = 27, average r 2 = 0.94 ± 0.09). Models with multiplicative errors also provided the best fit (r 2 > 0.76) for our 2 wide-ranging species, A. plicata and E. complanata. Finally, we present a case study based on data collected from 19 river sites in Alabama and Oklahoma, USA, to determine the performance of our power relationships (bootstrap resampling versus length–STDM regressions). In both river systems, tribe- and species-specific equations improved the prediction of unionid STDM over the family-level regression by 2–20%. Finer taxonomic resolution equations produce more accurate mass predictions, but where accurate taxonomic identifications are lacking, our family-level regression for STDM will produce acceptable estimates, which are key parameters when estimating mussel contributions to ecosystem services. Our study provides a toolkit that will allow scientists and managers to non-destructively quantify biomass (with uncertainty) of freshwater unionid mussels for secondary production, ecosystem function, and services estimates. [ABSTRACT FROM AUTHOR]

Animals exert both direct and indirect controls over elemental cycles, linking primary producer‐based (green) and decomposer‐based (brown) food webs through top‐down trophic interactions and bottom‐up element regeneration. Where animals are aggregated at high biomass, they create hotspots of elemental cycling. The relative importance of animal control on elemental cycling depends on animal biomass, species functional traits (i.e. feeding mode and stoichiometry), and their overlap.We evaluated how animal community complexity affects the mechanisms regulating energy flow to the brown food web. We conducted a mesocosm experiment where we varied the biomass and overlap of animals with different life history and stoichiometric traits (stream fish and mussels) and measured how this influenced the quantity and fraction of labile carbon available to microbes. We used linear models and structural equation modelling to evaluate direct (excretion) and indirect (herbivory, nutrient availability, and nutrient stoichiometry) effects of animals on bioavailable dissolved organic carbon (BDOC) concentration.In experimental stream mesocosms, we found support for both direct (DOC excretion) and indirect (grazing) animal influences on BDOC concentration. Although we found that snail, fish, and mussel biomass increased nutrient concentrations, neither nutrient concentration nor stoichiometry had a significant effect on BDOC concentration. This has been due to the high background nutrient concentration context of our stream mesocosm water. Snails, probably due to their high biomass and small body size, exerted a significant positive direct control on BDOC concentration. Fish and mussels exerted a significant negative indirect control on BDOC via their effects (grazing and bioturbation) on algal biomass.Our results imply that primary consumers with different feeding strategies provide a key mechanism regulating the flow of DOC into the brown food web through direct (excretion) and indirect (grazing) controls on primary producers. This highlights that animals can provide important controls on the production of bioavailable organic energy supporting microbes in aquatic ecosystems, but the importance of these controls depends on the nutrient context and the distribution of primary producer and animal biomasses. [ABSTRACT FROM AUTHOR]

This paper considers the relationship between thermodynamics, information and inference. In particular, it explores the thermodynamic concomitants of belief updating, under a variational (free energy) principle for self-organization. In brief, any (weakly mixing) random dynamical system that possesses a Markov blanket--i.e. a separation of internal and external states--is equipped with an information geometry. This means that internal states parametrize a probability density over external states. Furthermore, at non-equilibrium steady-state, the flow of internal states can be construed as a gradient flow on a quantity known in statistics as Bayesian model evidence. In short, there is a natural Bayesian mechanics for any system that possesses a Markov blanket. Crucially, this means that there is an explicit link between the inference performed by internal states and their energetics--as characterized by their stochastic thermodynamics. [ABSTRACT FROM AUTHOR]

Nitrate retention is an important ecosystem service provided by streams which reduces nitrogen loads to downstream ecosystems. However, most inferences about nitrate retention and uptake in streams are based on measurements at sub-annual scales. Our objective was to evaluate variation in nitration retention and solute concentrations at weekly to decadal time scales. We measured nitrate and chloride concentration, nitrate flux and nitrate retention in Emmons Creek, a groundwater-dominated, nitrate-rich stream in central Wisconsin during a 10-year period. We used a two-station mass balance approach to measure nitrate retention while accounting for groundwater inputs. Surface water nitrate concentration, nitrate yield and nitrate retention exhibited strong seasonal variation. Seasonal Kendall tests revealed positive trends for surface water discharge, nitrate and chloride concentrations and groundwater chloride concentration, but not nitrate retention. Generalized additive mixed models (GAMM), accounting for serial autocorrelations, also indicated positive trends in surface water nitrate and chloride concentrations but no trend for nitrate retention. Nitrate retention averaged 370 mg NO3-N m−2 d−1 (± 316 SD) and was highest during the spring (512 mg NO3-N m−2 d−1 ± 319), followed by summer (460 ± 322), fall (340 ± 281) and winter (165 ± 212). Nitrate retention rates in Emmons Creek were relatively high compared to most published estimates. However, our results suggest that Emmons Cr. may have saturated its capacity to retain nitrate during a period when nitrate yield from its watershed increased, which is an important finding given the increases in N availability in many regions of earth. [ABSTRACT FROM AUTHOR]

Active inference is an approach to understanding behaviour that rests upon the idea that the brain uses an internal generative model to predict incoming sensory data. The fit between this model and data may be improved in two ways. The brain could optimise probabilistic beliefs about the variables in the generative model (i.e. perceptual inference). Alternatively, by acting on the world, it could change the sensory data, such that they are more consistent with the model. This implies a common objective function (variational free energy) for action and perception that scores the fit between an internal model and the world. We compare two free energy functionals for active inference in the framework of Markov decision processes. One of these is a functional of beliefs (i.e. probability distributions) about states and policies, but a function of observations, while the second is a functional of beliefs about all three. In the former (expected free energy), prior beliefs about outcomes are not part of the generative model (because they are absorbed into the prior over policies). Conversely, in the second (generalised free energy), priors over outcomes become an explicit component of the generative model. When using the free energy function, which is blind to future observations, we equip the generative model with a prior over policies that ensure preferred (i.e. priors over) outcomes are realised. In other words, if we expect to encounter a particular kind of outcome, this lends plausibility to those policies for which this outcome is a consequence. In addition, this formulation ensures that selected policies minimise uncertainty about future outcomes by minimising the free energy expected in the future. When using the free energy functional—that effectively treats future observations as hidden states—we show that policies are inferred or selected that realise prior preferences by minimising the free energy of future expectations. Interestingly, the form of posterior beliefs about policies (and associated belief updating) turns out to be identical under both formulations, but the quantities used to compute them are not. [ABSTRACT FROM AUTHOR]

To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an interesting problem: When receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning of the processes that generate sensory inputs (e.g., listening and understanding) and reproduction of those inputs (e.g., talking or singing), under multiple generative models. This is based on recent advances in theoretical neurobiology—namely, active inference and post hoc (online) Bayesian model selection. In brief, this scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability that each model could have generated the input (i.e., model evidence). The proposed scheme is demonstrated using a series of (real zebra finch) birdsongs, where each song is generated by several different birds. The scheme is implemented using physiologically plausible models of birdsong production. We show that generalized Bayesian filtering, combined with model selection, leads to successful learning across generative models, each possessing different parameters. These results highlight the utility of having multiple internal models when making inferences in social environments with multiple sources of sensory information. [ABSTRACT FROM AUTHOR]

Hallucinations, including auditory verbal hallucinations (AVH), occur in both the healthy population and in psychotic conditions such as schizophrenia (often developing after a prodromal period). In addition, hallucinations can be in-context (they can be consistent with the environment, such as when one hallucinates the end of a sentence that has been repeated many times), or out-of-context (such as the bizarre hallucinations associated with schizophrenia). In previous work, we introduced a model of hallucinations as false (positive) inferences based on a (Markov decision process) formulation of active inference. In this work, we extend this model to include content–to disclose the computational mechanisms behind in- and out-of-context hallucinations. In active inference, sensory information is used to disambiguate alternative hypotheses about the causes of sensations. Sensory information is balanced against prior beliefs, and when this balance is tipped in the favor of prior beliefs, hallucinations can occur. We show that in-context hallucinations arise when (simulated) subjects cannot use sensory information to correct prior beliefs about hearing a voice, but beliefs about content (i.e. the sequential order of a sentence) remain accurate. When hallucinating subjects also have inaccurate beliefs about state transitions, out-of-context hallucinations occur; i.e. their hallucinated speech content is disordered. Note that out-of-context hallucinations in this setting does not refer to inference about context, but rather to false perceptual inference that emerges when the confidence in–or precision of–sensory evidence is reduced. Furthermore, subjects with inaccurate beliefs about state transitions but an intact ability to use sensory information do not hallucinate and are reminiscent of prodromal patients. This work demonstrates the different computational mechanisms that may underlie the spectrum of hallucinatory experience–from the healthy population to psychotic states. [ABSTRACT FROM AUTHOR]

In this paper, we draw from recent theoretical work on active perception, which suggests that the brain makes use of an internal (i.e., generative) model to make inferences about the causes of sensations. This view treats visual sensations as consequent on action (i.e., saccades) and implies that visual percepts must be actively constructed via a sequence of eye movements. Oculomotor control calls on a distributed set of brain sources that includes the dorsal and ventral frontoparietal (attention) networks. We argue that connections from the frontal eye fields to ventral parietal sources represent the mapping from "where", fixation location to information derived from "what" representations in the ventral visual stream. During scene construction, this mapping must be learned, putatively through changes in the effective connectivity of these synapses. Here, we test the hypothesis that the coupling between the dorsal frontal cortex and the right temporoparietal cortex is modulated during saccadic interrogation of a simple visual scene. Using dynamic causal modeling for magnetoencephalography with (male and female) human participants, we assess the evidence for changes in effective connectivity by comparing models that allow for this modulation with models that do not. We find strong evidence for modulation of connections between the two attention networks; namely, a disinhibition of the ventral network by its dorsal counterpart. [ABSTRACT FROM AUTHOR]

Many physiological and pathological changes in brain function manifest in eye-movement control. As such, assessment of oculomotion is an invaluable part of a clinical examination and affords a non-invasive window on several key aspects of neuronal computation. While oculomotion is often used to detect deficits of the sort associated with vascular or neoplastic events; subtler (e.g. pharmacological) effects on neuronal processing also induce oculomotor changes. We have previously framed oculomotor control as part of active vision, namely, a process of inference comprising two distinct but related challenges. The first is inferring where to look, and the second is inferring how to implement the selected action. In this paper, we draw from recent theoretical work on the neuromodulatory control of active inference. This allows us to simulate the sort of changes we would expect in oculomotor behaviour, following pharmacological enhancement or suppression of key neuromodulators—in terms of deciding where to look and the ensuing trajectory of the eye movement itself. We focus upon the influence of cholinergic and GABAergic agents on the speed of saccades, and consider dopaminergic and noradrenergic effects on more complex, memory-guided, behaviour. In principle, a computational approach to understanding the relationship between pharmacology and oculomotor behaviour affords the opportunity to estimate the influence of a given pharmaceutical upon neuronal function, and to use this to optimise therapeutic interventions on an individual basis. [ABSTRACT FROM AUTHOR]

Living creatures must accurately infer the nature of their environments. They do this despite being confronted by stochastic and context sensitive contingencies—and so must constantly update their beliefs regarding their uncertainty about what might come next. In this work, we examine how we deal with uncertainty that evolves over time. This prospective uncertainty (or imprecision) is referred to as volatility and has previously been linked to noradrenergic signals that originate in the locus coeruleus. Using pupillary dilatation as a measure of central noradrenergic signalling, we tested the hypothesis that changes in pupil diameter reflect inferences humans make about environmental volatility. To do so, we collected pupillometry data from participants presented with a stream of numbers. We generated these numbers from a process with varying degrees of volatility. By measuring pupillary dilatation in response to these stimuli—and simulating the inferences made by an ideal Bayesian observer of the same stimuli—we demonstrate that humans update their beliefs about environmental contingencies in a Bayes optimal way. We show this by comparing general linear (convolution) models that formalised competing hypotheses about the causes of pupillary changes. We found greater evidence for models that included Bayes optimal estimates of volatility than those without. We additionally explore the interaction between different causes of pupil dilation and suggest a quantitative approach to characterising a person’s prior beliefs about volatility. [ABSTRACT FROM AUTHOR]

Animal communities are essential drivers of energy and elemental flow in ecosystems. However, few studies have investigated the functional role of animals as sources of dissolved organic matter (DOM) and the subsequent utilization of that DOM by the microbial community.In a small forested headwater stream, we tested the effects of taxonomy, feeding traits, and body size on the quality and quantity of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) excreted by aquatic insects. In addition, we conducted steady‐state solute additions to estimate instream demand for labile C and compared it to the C excreted by invertebrates.Individual excretion rates and excretion composition varied with body size, taxonomy and feeding guild. The estimated average community excretion rate was 1.31 μg DOC· per mg insect dry weight (DW)−1 hr−1 and 0.33 μg DON·mg DW−1 hr‐1, and individuals excreted DON at nearly twice the rate of NH4+.This DOM was 2–5 times more bioavailable to microbial heterotrophs than ambient stream water DOM.We estimated that the insect community, conservatively, excreted 1.62 mg of bioavailable DOC·m−2 hr−1 and through steady‐state additions measured an ambient labile C demand as 3.97 ± 0.67 mg C m−2 hr−1. This suggests that insect‐mediated transformation and excretion of labile DOC could satisfy a significant fraction (40 ± 7%) of labile C demand in this small stream.Collectively, our results suggest that animal excretion plays an essential functional role in transforming organic matter into microbially bioavailable forms and may satisfy a variable but significant portion of microbial demand for labile C and N. A plain language summary is available for this article. Plain Language Summary [ABSTRACT FROM AUTHOR]

Abstract Drinking water supplies are increasingly affected by overlapping anthropogenic global change processes. As a key currency of ecosystem function in aquatic ecosystems, dissolved organic carbon (DOC) concentration and composition is sensitive to many global change processes. However, DOC must also be removed to avoid the production of harmful disinfection byproducts as water is processed. Thus, understanding the effects of global change processes on the seasonal and long-term dynamics of DOC composition and concentration is critical for ensuring the sustainability of drinking water supplies. To understand these dynamics, we analyzed a novel 11-year time series of stream water DOC concentration and composition using Weighted Regressions on Time Discharge and Season (WRTDS) to understand the influences of co-occurring changes in climate and atmospheric deposition. We also discuss the implications for water supply provision and management. We found that, during our study period, overlapping global change processes in the watershed had the net effect of increasing the DOC aromaticity, as measured by SUVA 254 , at moderate to high discharge levels during the late spring and early summer and the autumn and early winter. However, changes in DOC concentration were more dynamic and we observed both increasing and decreasing trends depending on season and hydrologic state. During summer, at low to moderate flow levels we observed a significant (p < 0.05) increase in DOC concentration. During autumn, at moderate to high flow levels we observed a significant (p < 0.05) decrease in DOC concentration and an increase in SUVA 254. For drinking water providers, our results suggest that close monitoring of source waters must be coupled with the development of plans accounting for season- and hydrology-specific long-term changes. Graphical abstract Image 1 Highlights • Direction of DOC concentration change was seasonally and hydrologically divergent. • Changes in DOC composition were partly asynchronous with changes in concentration. • Trends in anthropogenic inputs and climate likely explain patterns of change in DOC. • Sustainable water supply requires hydrologic and seasonal understanding of DOC trends. [ABSTRACT FROM AUTHOR]

Abstract Identifying freshwater systems that are at risk from anthropogenic stressors is a pressing management problem. In particular, the detection of metal pollution is often constrained by data availability and resources. To address this challenge and develop a tool to identify susceptible systems, we tested whether land cover could be predictive of stream sensitivity to metal pollution, as determined by the biotic ligand model (BLM). We used water chemistry data from the conterminous United States to estimate metal sensitivity in streams using two BLMs (i.e., HydoQual, Bio-Met). Subsequently, we combined the sensitivity estimates with land cover and physiochemical data from the GAGES-II database to build predictive models of sensitivity to metals in streams. When combined, our predictor variables (e.g., land cover, mean annual temperature, mean annual precipitation) generally explained about half of the variation in our dataset. In each model, the percent of wetlands in a watershed was strongly correlated with reduced sensitivity to metals, likely due to increased concentrations of dissolved organic carbon associated with wetlands. To validate the utility of the models, we used them to predict metal sensitivity in sites where metal concentrations had been collected, but where the full suite of BLM parameters were unknown. We were able to classify several hundred sites which are likely at risk to metal pollution. Our work highlights the value in considering metal toxicity at the landscape-scale and describes a new approach to estimate metal sensitivity when site-specific chemical parameters are unknown. Graphical abstract Image 1 Highlights • Metal pollution is linked to landscape process. • We explain a large portion of variation in metal sensitivity using geospatial data. • Wetland area within a watershed appears to buffer streams from metal contamination. [ABSTRACT FROM AUTHOR]

To act upon the world, creatures must change continuous variables such as muscle length or chemical concentration. In contrast, decision making is an inherently discrete process, involving the selection among alternative courses of action. In this article, we consider the interface between the discrete and continuous processes that translate our decisions into movement in a Newtonian world—and how movement informs our decisions. We do so by appealing to active inference, with a special focus on the oculomotor system. Within this exemplar system, we argue that the superior colliculus is well placed to act as a discrete-continuous interface. Interestingly, when the neuronal computations within the superior colliculus are formulated in terms of active inference, we find that many aspects of its neuroanatomy emerge from the computations it must perform in this role. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • This paper offers a formalisation of 'cognitive effort' under the active inference framework. • Cognitive effort is formulated as a deviation from prior beliefs about mental (covert) action—i.e., effort is exerted to overcome a mental habit. • A computational model of the Stroop task—a characteristically effortful task—is developed to illustrate this notion of effort. • We demonstrate that it is possible to recover combinations of effort-related model parameters from simulated data. [ABSTRACT FROM AUTHOR]

This commentary focuses upon the relationship between two themes in the target article: the ways in which a Markov blanket may be defined and the role of precision and salience in mediating the interactions between what is internal and external to a system. These each rest upon the different perspectives we might take while "choosing" a Markov blanket. [ABSTRACT FROM AUTHOR]

Given that eye movement control can be framed as an inferential process, how are the requisite forces generated to produce anticipated or desired fixation? Starting from a generative model based on simple Newtonian equations of motion, we derive a variational solution to this problem and illustrate the plausibility of its implementation in the oculomotor brainstem. We show, through simulation, that the Bayesian filtering equations that implement ‘planning as inference’ can generate both saccadic and smooth pursuit eye movements. Crucially, the associated message passing maps well onto the known connectivity and neuroanatomy of the brainstem – and the changes in these messages over time are strikingly similar to single unit recordings of neurons in the corresponding nuclei. Furthermore, we show that simulated lesions to axonal pathways reproduce eye movement patterns of neurological patients with damage to these tracts. [ABSTRACT FROM AUTHOR]

What we see is fundamentally dependent on where we look. Despite this seemingly obvious statement, many accounts of the neurobiology underpinning visual perception fail to consider the active nature of how we sample our sensory world. This review offers an overview of the neurobiology of visual perception, which begins with the control of saccadic eye movements. Starting from here, we can follow the anatomy backwards, to try to understand the functional architecture of neuronal networks that support the interrogation of a visual scene. Many of the principles encountered in this exercise are equally applicable to other perceptual modalities. For example, the somatosensory system, like the visual system, requires the sampling of data through mobile receptive epithelia. Analysis of a somatosensory scene depends on what is palpated, in much the same way that visual analysis relies on what is foveated. The discussion here is structured around the anatomical systems involved in active vision and visual scene construction, but will use these systems to introduce some general theoretical considerations. We will additionally highlight points of contact between the biology and the pathophysiology that has been proposed to cause a clinical disorder of scene construction – spatial hemineglect. [ABSTRACT FROM AUTHOR]

The impacts of animals on the biogeochemical cycles of major bioelements like C, N, and P are well-studied across ecosystem types. However, more than 20 elements are necessary for life. The feedbacks between animals and the biogeochemical cycles of the other bioelements are an emerging research priority. We explored how much freshwater mussels (Bivalvia: Unionoida) were related to variability in ecosystem pools of 10 bioelements (Ca, Cu, Fe, K, Mn, Na, Mg, P, S and Zn) in streams containing a natural mussel density gradient in the US Interior Highlands. We studied the concentrations of these bioelements across the aquatic-terrestrial interface—in the porewater of riverine gravel bars, and the emergent macrophyte Justicia americana. Higher mussel density was associated with increased calcium in gravel bars and macrophytes. Mussel density also correlated with variability in iron and other redox-sensitive trace elements in gravel bars and macrophytes, although this relationship was mediated by sediment grain size. We found that two explanations for the patterns we observed are worthy of further research: (1) increased calcium availability in gravel bars near denser mussel aggregations may be a product of the buildup and dissolution of shells in the gravel bar, and (2) mussels may alter redox conditions, and thus elemental availability in gravel bars with fine sediments, either behaviorally or through physical structure provided by shell material. A better understanding of the physical and biogeochemical impacts of animals on a wide range of elemental cycles is thus necessary to conserve the societal value of freshwater ecosystems. [ABSTRACT FROM AUTHOR]

Stormwater drainage and wastewater disposal are primary pathways through which urbanization degrades streams. These technologies and management practices change over time, reshaping the urban template and affecting the environmental challenges a city will face in the following decades to centuries. Spatial and temporal asynchrony in the implementation and replacement of these technologies and the adoption of new management approaches means that the mechanisms of the urban stream syndrome will be heterogeneous. Thus, promoting 'one size fits all' global panaceas for urban streams may be less effective than local solutions based on the unique urban templates and socioeconomic factors governing streams. Understanding the cumulative effects of spatiotemporal changes in urban templates on streams is critical to protecting and managing them because: 1) existing water infrastructure in many countries (especially high-income countries) is aging and requires replacement, 2) urbanization in rapidly developing countries requires ever more infrastructure, and 3) demand for higher levels of environmental quality is transforming technological and management approaches globally. The management and technological decisions made during the current infrastructure replacement or new construction cycle will define the future urban template and select the trajectory and character of the urban stream syndrome during the coming decades. [ABSTRACT FROM AUTHOR]

Reliable and inexpensive indicators of ecosystem function are essential for accurately monitoring and describing ecosystem integrity. Currently, most state and federal assessments of aquatic ecological integrity rely on structural indicators and assume tight coupling of structure and function. We used fluorescent composition of dissolved organic matter as a metric for certain ecosystem functions and compared the resulting index of autochthonous microbial dissolved organic matter (DOM) to macroinvertebrate indicators and classifications of water quality attainment reported by the Maine Department of Environmental Protection (Maine DEP) at 142 stream sites. We observed that metrics of sensitive insect orders such as relative Plecoptera generic richness, relative Ephemeroptera abundance, and generic richness of EPT (Ephemeroptera, Plecoptera, and Trichoptera) were negatively correlated with higher values of metrics based on autochthonous microbial DOM sources. At the same time we observed an increase in the Hilsenhoff Biotic Index with increasing microbial DOM. We compared the abundance of this microbial DOM component to Maine DEP measured attainment classes and found that microbial DOM generally separated sites with high biological integrity from sites where the biotic community was highly degraded. This highlights that measures of biogeochemical ecosystem function complement measures of structure in biological assessments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Population growth in cities has resulted in the rapid expansion of urbanized land. Most research and management of stream ecosystems affected by urban expansion has focused on the maintenance and restoration of biotic communities rather than their basal resources. We examined the potential for urbanization to induce bottom-up ecosystem effects by looking at its influence on dissolved organic matter (DOM) composition and bioavailability and microbial enzyme activity. We selected 113 headwater streams across a gradient of urbanization in central and southern Maine and used elemental and optical analyses, including parallel factor analysis of excitation-emission matrices, to characterize DOM composition. Results show that fluorescent and stoichiometric DOM composition changed significantly across the rural to urban gradient. Specifically, the proportion of humic-like allochthonous DOM decreased while that of more bioavailable autochthonous DOM increased in the more urbanized streams. In laboratory incubations, increased autochthonous DOM was associated with a doubling in the decay rate of dissolved organic carbon as well as increased activity of C-acquiring enzymes. These results suggest that urbanization replaces upstream humic material with more local sources of DOM that turnover more rapidly and may drive bottom-up changes in microbial communities and affect the quality and quantity of downstream DOM delivery. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is involved in many important soil processes such as carbon sequestration and the solubility of plant nutrients and metals. Ultrahigh resolution mass spectrometry was used to determine the influence of forest vegetation type and soil depth on the molecular composition of the water-extractable organic matter (WEOM) fraction. Contrasting the upper 0-5 cm with the 25-50 cm B horizon depth increment, the relative abundance of lipids and carbohydrates significantly increased, whereas condensed aromatics and tannins significantly decreased for the deciduous stand WEOM. No significant abundance changes were found for the coniferous stand DOM. Kendrick mass defect analysis showed that the WEOM of the 25-50 cm B horizon was depleted in oxygen-rich and higher mass components as compared to the 0-5 cm B horizon WEOM, suggesting that higher mass WEOM components with oxygen-containing functionality show greater reactivity in abiotic and/or biotic reactions. Furthermore, using an inoculated 14-day laboratory incubation study and multivariate ordination methods, we identified the WEOM components with H:C > 1.2 and O:C > 0.5 as being correlated most strongly with biodegradability. Our findings highlight the importance of understanding soil depth differences for various forest types in the chemical composition of SOM and the processes governing SOM production and transformations to fully understand the ecological implications of changes in forest composition and function in a changing climate. [ABSTRACT FROM AUTHOR]

In theoretical biology, we are often interested in random dynamical systems—like the brain—that appear to model their environments. This can be formalized by appealing to the existence of a (possibly non-equilibrium) steady state, whose density preserves a conditional independence between a biological entity and its surroundings. From this perspective, the conditioning set, or Markov blanket, induces a form of vicarious synchrony between creature and world—as if one were modelling the other. However, this results in an apparent paradox. If all conditional dependencies between a system and its surroundings depend upon the blanket, how do we account for the mnemonic capacity of living systems? It might appear that any shared dependence upon past blanket states violates the independence condition, as the variables on either side of the blanket now share information not available from the current blanket state. This paper aims to resolve this paradox, and to demonstrate that conditional independence does not preclude memory. Our argument rests upon drawing a distinction between the dependencies implied by a steady state density, and the density dynamics of the system conditioned upon its configuration at a previous time. The interesting question then becomes: What determines the length of time required for a stochastic system to 'forget' its initial conditions? We explore this question for an example system, whose steady state density possesses a Markov blanket, through simple numerical analyses. We conclude with a discussion of the relevance for memory in cognitive systems like us. [ABSTRACT FROM AUTHOR]

Active inference is an increasingly prominent paradigm in theoretical biology. It frames the dynamics of living systems as if they were solving an inference problem. This rests upon their flow towards some (non-equilibrium) steady state—or equivalently, their maximisation of the Bayesian model evidence for an implicit probabilistic model. For many models, these self-evidencing dynamics manifest as messages passed among elements of a system. Such messages resemble synaptic communication at a neuronal network level but could also apply to other network structures. This paper attempts to apply the same formulation to biochemical networks. The chemical computation that occurs in regulation of metabolism relies upon sparse interactions between coupled reactions, where enzymes induce conditional dependencies between reactants. We will see that these reactions may be viewed as the movement of probability mass between alternative categorical states. When framed in this way, the master equations describing such systems can be reformulated in terms of their steady-state distribution. This distribution plays the role of a generative model, affording an inferential interpretation of the underlying biochemistry. Finally, we see that—in analogy with computational neurology and psychiatry—metabolic disorders may be characterized as false inference under aberrant prior beliefs. [ABSTRACT FROM AUTHOR]

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. Author summary: 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. [ABSTRACT FROM AUTHOR]

Active inference is a normative framework for explaining behaviour under the free energy principle—a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy—a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error—plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference. [ABSTRACT FROM AUTHOR]

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised 'modules', each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience. [ABSTRACT FROM AUTHOR]

In recent years, the "planning as inference" paradigm has become central to the study of behaviour. The advance offered by this is the formalisation of motivation as a prior belief about "how I am going to act". This paper provides an overview of the factors that contribute to this prior. These are rooted in optimal experimental design, information theory, and statistical decision making. We unpack how these factors imply a functional architecture for motivated behaviour. This raises an important question: how can we put this architecture to work in the service of understanding observed neurobiological structure? To answer this question, we draw from established techniques in experimental studies of behaviour. Typically, these examine the influence of perturbations of the nervous system—which include pathological insults or optogenetic manipulations—to see their influence on behaviour. Here, we argue that the message passing that emerges from inferring what to do can be similarly perturbed. If a given perturbation elicits the same behaviours as a focal brain lesion, this provides a functional interpretation of empirical findings and an anatomical grounding for theoretical results. We highlight examples of this approach that influence different sorts of goal-directed behaviour, active learning, and decision making. Finally, we summarise their implications for the neuroanatomy of inferring what to do (and what not to). [ABSTRACT FROM AUTHOR]