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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]

• 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]

• 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 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]

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]

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]

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]

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]

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]

This paper offers a formal account of emotional inference and stress-related behaviour, using the notion of active inference. We formulate responses to stressful scenarios in terms of Bayesian belief-updating and subsequent policy selection; namely, planning as (active) inference. Using a minimal model of how creatures or subjects account for their sensations (and subsequent action), we deconstruct the sequences of belief updating and behaviour that underwrite stress-related responses – and simulate the aberrant responses of the sort seen in post-traumatic stress disorder (PTSD). Crucially, the model used for belief-updating generates predictions in multiple (exteroceptive, proprioceptive and interoceptive) modalities, to provide an integrated account of evidence accumulation and multimodal integration that has consequences for both motor and autonomic responses. The ensuing phenomenology speaks to many constructs in the ecological and clinical literature on stress, which we unpack with reference to simulated inference processes and accompanying neuronal responses. A key insight afforded by this formal approach rests on the trade-off between the epistemic affordance of certain cues (that resolve uncertainty about states of affairs in the environment) and the consequences of epistemic foraging (that may be in conflict with the instrumental or pragmatic value of 'fleeing' or 'freezing'). Starting from first principles, we show how this trade-off is nuanced by prior (subpersonal) beliefs about the outcomes of behaviour – beliefs that, when held with unduly high precision, can lead to (Bayes optimal) responses that closely resemble PTSD. [ABSTRACT FROM AUTHOR]

• Low emotional awareness (EA) is associated with multiple clinical conditions. • The neurocomputational processes underlying EA are poorly understood. • We present a deep active inference model of EA that can simulate these processes. • This model illustrates 7 distinct mechanisms whereby aberrant processing produces low EA. • This may offer distinct targets that could inform individualized treatment selection. Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena – such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity – characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals – and provide a principled basis for personalized precision medicine. [ABSTRACT FROM AUTHOR]

How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively. [ABSTRACT FROM AUTHOR]

How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively. [ABSTRACT FROM AUTHOR]

Infections caused by antibiotic-resistant pathogens, particularly Gram-negative bacteria, represent significant treatment challenges for physicians resulting in high rates of morbidity and mortality. The outer membrane of Gram-negative bacteria acts as a permeability barrier to many compounds that would otherwise be effective antibacterial agents, including those effective against Gram-positive pathogens. Potentiator molecules disrupt this barrier allowing entry of otherwise impermeant molecules, thus providing a strategy to render multi-drug resistant pathogens susceptible to a broader range of antibiotics. Potentiator molecules are cationic and the mechanism of disruption involves interaction with the negatively charged outer membrane. This physical attribute, along with an often high degree of lipophilicity typically endears these molecules with unacceptable toxicity. Presented herein are examples of advanced potentiator molecules being evaluated for use in combination therapy for the treatment of resistant Gram-negative infections. [ABSTRACT FROM AUTHOR]

• We leverage the idea of 'Markov blanket' as a statistical boundary to provide an analysis of partitions in neuronal systems. • We show this partition is applicable to multiple scales, from single neurons, brain regions, and brain-wide networks. • Based on the canonical micro-circuitry, our treatment has practical applications for effective connectivity. • Our proposed partition highlights the limitations of 'modular' proposals considering a single level of description. Recent characterisations of self-organising systems depend upon the presence of a 'Markov blanket': a statistical boundary that mediates the interactions between the inside and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. The notion of effective connectivity depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level of analysis. The nuance afforded by partitioning neural systems in this way highlights certain limitations of 'modular' perspectives of brain function that only consider a single level of description. [ABSTRACT FROM AUTHOR]

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. • We provide a new Visual Hallucination Framework which integrates eight current models. • All models suggest a key role for visual input. • Variations in factors such as attention or memory may relate to differences in the phenomenology of specific hallucinations. • That hallucinations are episodic suggests different traits are relevant in the onset, persistence and end of hallucinations. [ABSTRACT FROM AUTHOR]

Abstract: As therapeutic agents of choice in the treatment of complicated infections, glycopeptide antibiotics are often preferentially used in cases of osteomyelitis, an infection located in bone and notoriously difficult to successfully manage. Yet frequent and heavy doses of these systemically administered antibiotics are conventionally prescribed to obtain higher antibiotic levels in the bone and reduce the high recurrence rates. Targeting antibiotics to the bone after systemic administration would present at least three potential advantages: (i) greater efficacy, by concentrating the therapeutic agent in bone; (ii) greater convenience, through a reduction in the frequency of administration; and (iii) greater safety, by reducing the levels of systemic drug exposure. We present here the design, synthesis and in vitro evaluation of eight prodrugs of the glycopeptide antibacterial agents vancomycin and oritavancin taking advantage of the affinity of the bisphosphonate group for bone for delivery to osseous tissues. [Copyright &y& Elsevier]

Abstract: Human serum albumin (HSA) did not affect oritavancin MICs against non–vancomycin-intermediate Staphylococcus aureus (non-VISA) strains. In time–kill assays, oritavancin bactericidal activity in the presence of HSA was significantly more rapid than comparators against non-VISA strains. HSA increased oritavancin MICs by 4-fold for VISA strains, reflective of reduced oritavancin activity in time–kill assays with HSA. [Copyright &y& Elsevier]

Abstract: Osteomyelitis is a difficult to treat bacterial infection of the bone. Delivering antibacterial agents to the bone may overcome the difficulties in treating this illness by effectively concentrating the antibiotic at the site of infection and by limiting the toxicity that may result from systemic exposure to the large doses conventionally used. Using bisphosphonates as osteophilic functional groups, different forms of fluoroquinolone esters were synthesized and evaluated for their ability to bind bone and to release the parent antibacterial agent. Bisphosphonated glycolamide fluoroquinolone esters were found to present a profile consistent with effective and rapid bone binding and efficient release of the active drug moiety. They were assessed for their ability to prevent bone infection in vivo and were found to be effective when the free fluoroquinolones were not. [Copyright &y& Elsevier]

Abstract: A 9-laboratory M23-A2 quality control (QC) study was performed to evaluate reproducibility of oritavancin MICs against reference strains of Staphylococcus aureus, Enterococcus faecalis, and Streptococcus pneumoniae using broth microdilution assays in the presence of polysorbate 80. Polysorbate 80 has previously been shown to be required for accurate measurement of oritavancin broth microdilution MICs. Greater than 95% of replicate results (n = 270/organism) fell within the following QC ranges (in micrograms per milliliter): S. aureus ATCC 29213, 0.015 to 0.12; E. faecalis ATCC 29212, 0.008 to 0.03; and S. pneumoniae ATCC 49619, 0.001 to 0.004. Oritavancin MIC QC ranges were, thus, narrow and reproducible. Parameters affecting testing results in the presence of polysorbate 80 were also evaluated. Oritavancin MICs were equivalent to or within 1 doubling dilution of those obtained under standard Clinical and Laboratory Standards Institute testing conditions, regardless of incubation time (18, 24, or 48 h), Ca2+ concentration, pH, or frozen panel storage time (up to 6 months). [Copyright &y& Elsevier]

Abstract: The RNA polymerase holoenzyme is a proven target for antibacterial agents. A high-throughput screening program based on this enzyme from Staphylococcus aureus had previously identified a 2-ureidothiophene-3-carboxylate as a low micromolar inhibitor. An investigation of the relationships between the structures of this class of compounds and their inhibitory- and antibacterial activities is described here, leading to a set of potent RNA polymerase inhibitors with antibacterial activity. Characterization of this bioactivity, including studies of the mechanism of action, is provided, highlighting the power of the reverse chemical genetics approach in providing tools to inhibit the bacterial RNA polymerase. [Copyright &y& Elsevier]

Abstract: Screening of a chemical library in a DNA helicase assay involving the Pseudomonas aeruginosa DnaB helicase provided a triaminotriazine inhibitor with good antibacterial activity but associated cytotoxicity toward mammalian cells. Synthesis of analogs provided a few inhibitors that retained antibacterial activity and demonstrated a significant reduction in cytotoxicity. The impact of serum and initial investigations toward a mode of action highlight several features of this class of compounds as antibacterials. [Copyright &y& Elsevier]

Organic matter sequestration is influenced by both biotic and abiotic factors, which determine the rate of decomposition and result into distinguish soil forming processes and different humus types. Previous observations indicated that podzol soils and mor humus form typically develop under spruce forest, while under beech forest the higher mineralization rate leads to moder humus and the development of dystric cambisol. This study was designed to observe the forming of the different microbial communities participating in diverse decomposition processes reflecting the soil types developed under naturally occurring beech and artificially planted spruce forests. A litterbag experiment was performed with beech and spruce litter placed respectively to beech and spruce forests. In the beech forest, the observed litter decomposition rate reached the exponential faze after 15 months, while in the spruce forest a steeper decrease was noted only 29 months after the litter burial. Thus, the study focused on the period between 15 and 29 months to observe the exponential stages of recalcitrant organic matter transformation. In this period, the chemical composition of two litters was distinguished by higher contents of Mn and Ca in beech and higher content of Fe, S, N and P in spruce. On top of that, the beech litter released higher amount of dissolved organic carbon and its associated bacterial community was enriched with r-selected taxa that correlated positively with organic acids and cations. In contrast, the more acidic spruce litter was dominated by K-selected acidophilic community and its turnover rate was slower, resulting in increased carbon sequestration. The higher pH and humus quality of beech cambisol also correlates with its observed higher resilience to disturbances by acidification, pests or climate change in mountainous environments. [Display omitted] • Spruce and beech litter differ by chemistry and microbial community structure but not abundance at a temperate forest site. • Beech litter inhabited enriched in DOC, oxalate and K decomposed faster than spruce litter enriched in P. • Spruce but not beech biochemical characteristics promoted soil podzolization process. [ABSTRACT FROM AUTHOR]

This paper introduces active listening, as a unified framework for synthesising and recognising speech. The notion of active listening inherits from active inference, which considers perception and action under one universal imperative: to maximise the evidence for our (generative) models of the world. First, we describe a generative model of spoken words that simulates (i) how discrete lexical, prosodic, and speaker attributes give rise to continuous acoustic signals; and conversely (ii) how continuous acoustic signals are recognised as words. The 'active' aspect involves (covertly) segmenting spoken sentences and borrows ideas from active vision. It casts speech segmentation as the selection of internal actions, corresponding to the placement of word boundaries. Practically, word boundaries are selected that maximise the evidence for an internal model of how individual words are generated. We establish face validity by simulating speech recognition and showing how the inferred content of a sentence depends on prior beliefs and background noise. Finally, we consider predictive validity by associating neuronal or physiological responses, such as the mismatch negativity and P300, with belief updating under active listening, which is greatest in the absence of accurate prior beliefs about what will be heard next. • Describes a generative model for synthesising and recognising speech. • Considers speech segmentation (placing word boundaries) as an active process. • Treats speech segmentation and lexical inferences as complementary. • Associates neural mismatch responses (e.g., MMN) with belief updating. [ABSTRACT FROM AUTHOR]