Predicting green: really radical (plant) predictive processing

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

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

Future medicine: from molecular pathways to the collective intelligence of the body

The remarkable anatomical homeostasis exhibited by complex living organisms suggests that they are inherently reprogrammable information-processing systems that offer numerous interfaces to their physiological and anatomical problem-solving capacities. We briefly review data suggesting that the multiscale competency of living forms affords a new path for biomedicine that exploits the innate collective intelligence of tissues and organs. The concept of tissue-level allostatic goal-directedness is already bearing fruit in clinical practice. We sketch a roadmap towards 'somatic psychiatry' by using advances in bioelectricity and behavioral neuroscience to design methods that induce self-repair of structure and function. Relaxing the assumption that cellular control mechanisms are static, exploiting powerful concepts from cybernetics, behavioral science, and developmental biology may spark definitive solutions to current biomedical challenges.

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

Integrating nanotechnology with plant microbiome for next-generation crop health

Nanotechnology has enormous potential for sustainable agriculture, such as improving nutrient use efficiency, plant health, and food production. Nanoscale modulation of the plant-associated microbiota offers an additional valuable opportunity to increase global crop production and ensure future food and nutrient security. Nanomaterials (NMs) applied to agricultural crops can impact plant and soil microbiota, which offers valuable services to host plants, including the acquisition of nutrients, abiotic stress tolerance, and disease suppression. Dissecting the complex interactions between NMs and plants by integrating multi-omic approaches is providing new insights into how NMs can activate host responses and functionality as well as influence native microbial communities. Such nexus and moving beyond descriptive microbiome studies to hypothesis-driven research will foster microbiome engineering and open up opportunities for the development of synthetic microbial communities to provide agronomic solutions. Herein, we first summarize the significant role of NMs and the plant microbiome in crop productivity and then focus on NMs effects on plant-associated microbiota. We outline three urgent priority research areas and call for a transdisciplinary collaborative approach, involving plant scientists, soil scientists, environmental scientists, ecologists, microbiologists, taxonomists, chemists, physicists, and stakeholders, to advance nano-microbiome research. Detailed understanding of the nanomaterial-plant-microbiome interactions and the mechanisms underlying NMs-mediated shifts in the microbiome assembly and functions may help to exploit the services of both nano-objects and microbiota for next-generation crop health.

From dark to darkness, negative phototropism influences the support-tree location of the massive woody climber Hydrangea serratifolia (Hydrangeaceae) in a Chilean temperate rainforest

Climbing plants rely on suitable support to provide the light conditions they require in the canopy. Negative phototropism is a directional search behavior proposed to detect a support-tree, which indicates growth or movement away from light, based on light attenuation. In a Chilean temperate rainforest, we addressed whether the massive woody climber Hydrangea serratifolia (H. et A.) F. Phil (Hydrangeaceae) presents a support-tree location pattern influenced by light availability. We analyzed direction and light received in two groups of juvenile shoots: searching shoots (SS), with plagiotropic (creeping) growth vs. ascending shoots (AS), with orthotropic growth. We found that, in accordance with light attenuation, SS and AS used directional orientation to search and then ascend host trees. The light available to H. serratifolia searching shoots was less than that of the general forest understory; the directional orientation in both groups showed a significant deviation from a random distribution, with no circular statistical difference between them. Circular-linear regression indicated a relationship between directional orientations and light availability. Negative phototropism encodes the light environment's heterogeneous spatial and temporal information, guiding the shoot apex to the most shaded part of the support-tree base, the climbing start point.

The seductive allure of cargo cult computationalism

Bruineberg and colleagues report a striking confusion, in which the formal Bayesian notion of a "Markov blanket" has been frequently misunderstood and misapplied to phenomena of mind and life. I argue that misappropriation of formal concepts is pervasive in the "predictive processing" literature, and echo Richard Feynman in suggesting how we might resist the allure of cargo cult computationalism.

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

Enacting Plant-Inspired Robotics

Plants offer a source of bioinspiration for soft robotics. Nevertheless, a gap remains in designing robots based on the fundamental principles of plant intelligence, rooted in a non-centralized, modular architecture and a highly plastic phenotype. We contend that a holistic approach to plant bioinspiration—one that draws more fully on the features of plant intelligence and behavior—evidences the value of an enactivist perspective. This is because enactivism emphasizes not only features of embodiment such as material composition and morphology, but also autonomy as an important aspect of plant intelligence and behavior. The enactivist sense of autonomy concerns the dynamics of self-producing systems (such as plants) that create a distinction between themselves and a domain of interactions that bear on the conditions of viability of the system. This contrasts with the widespread, but diluted notion of autonomy that merely indicates the independent operability of a system for an arbitrary period. Different notions of autonomy are relevant for soft roboticists, for instance, when evaluating limitations on existing growing robots (“growbots”) that take bioinspiration from plants, but depend on a fixed source of energy and material provided by an external agent. More generally, plant-inspired robots serve as a case study for an enactivist approach to intelligence, while, correspondingly, enactivism calls attention to the possibility of non-zoological forms of intelligence embodied in a self-organizing, autonomous system.

Synchronous Generative Development amidst Situated Entropy

The Sustainable Development Goals have been criticized for not providing sufficient balance between human well-being and environmental well-being. By contrast, joint agent-environment systems theory is focused on reciprocal synchronous generative development. The purpose of this paper is to extend this theory towards practical application in sustainable development projects. This purpose is fulfilled through three interrelated contributions. First, a practitioner description of the theory is provided. Then, the theory is extended through reference to research concerned with multilevel pragmatics, competing signals, commitment processes, technological mediation, and psychomotor functioning. In addition, the theory is related to human-driven biosocial-technical innovation through the example of digital twins for agroecological urban farming. Digital twins being digital models that mirror physical processes; that are connected to physical processes through, for example, sensors and actuators; and which carry out analyses of physical processes in order to improve their performance. Together, these contributions extend extant theory towards application for synchronous generative development that balances human well-being and environmental well-being. However, the practical examples in the paper indicate that counterproductive complexity can arise from situated entropy amidst biosocial-technical innovations: even when those innovations are compatible with synchronous generative development.

How Psychedelic-Assisted Treatment Works in the Bayesian Brain

Psychedelics are experiencing a renaissance in clinical research. In recent years, an increasing number of studies on psychedelic-assisted treatment have been conducted. So far, the results are promising, suggesting that this new (or rather, rediscovered) form of therapy has great potential. One particular reason for that appears to be the synergistic combination of the pharmacological and psychotherapeutic interventions in psychedelic-assisted treatment. But how exactly do these two interventions complement each other? This paper provides the first account of the interaction between pharmacological and psychological effects in psychedelic-assisted treatment. Building on the relaxed beliefs under psychedelics (REBUS) hypothesis of Carhart-Harris and Friston and the contextual model of Wampold, it argues that psychedelics amplify the common factors and thereby the remedial effects of psychotherapy. More precisely, psychedelics are assumed to attenuate the precision of high-level predictions, making them more revisable by bottom-up input. Psychotherapy constitutes an important source of such input. At best, it signalizes a safe and supportive environment (cf. setting) and induces remedial expectations (cf. set). During treatment, these signals should become incorporated when high-level predictions are revised: a process that is hypothesized to occur as a matter of course in psychotherapy but to get reinforced and accelerated under psychedelics. Ultimately, these revisions should lead to a relief of symptoms.

Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

The fields of developmental biology, biomedicine, and artificial life are being revolutionized by advances in synthetic morphology. The next phase of synthetic biology and bioengineering is resulting in the construction of novel organisms (biobots), which exhibit not only morphogenesis and physiology but functional behavior. It is now essential to begin to characterize the behavioral capacity of novel living constructs in terms of their ability to make decisions, form memories, learn from experience, and anticipate future stimuli. These synthetic organisms are highly diverse, and often do not resemble familiar model systems used in behavioral science. Thus, they represent an important context in which to begin to unify and standardize vocabulary and techniques across developmental biology, behavioral ecology, and neuroscience. To facilitate the study of behavior in novel living systems, we present a primer on techniques from the behaviorist tradition that can be used to probe the functions of any organism – natural, chimeric, or synthetic – regardless of the details of their construction or origin. These techniques provide a rich toolkit for advancing the fields of synthetic bioengineering, evolutionary developmental biology, basal cognition, exobiology, and robotics.

Our sisters the plants? notes from phylogenetics and botany on plant kinship blindness

Before the upheaval brought about by phylogenetic classification, classical taxonomy separated living beings into two distinct kingdoms, animals and plants. Rooted in 'naturalist' cosmology, Western science has built its theoretical apparatus on this dichotomy mostly based on ancient Aristotelian ideas. Nowadays, despite the adoption of the Darwinian paradigm that unifies living organisms as a kinship, the concept of the "scale of beings" continues to structure our analysis and understanding of living species. Our aim is to combine developments in phylogeny, recent advances in biology, and renewed interest in plant agency to craft an interdisciplinary stance on the living realm. The lines at the origin of plant or animal have a common evolutionary history dating back to about 3.9 Ga, separating only 1.6 Ga ago. From a phylogenetic perspective of living species history, plants and animals belong to sister groups. With recent data related to the field of Plant Neurobiology, our aim is to discuss some socio-cultural obstacles, mainly in Western naturalist epistemology, that have prevented the integration of living organisms as relatives, while suggesting a few avenues inspired by practices principally from other ontologies that could help overcome these obstacles and build bridges between different ways of connecting to life.

How particular is the physics of the free energy principle?

The free energy principle (FEP) states that any dynamical system can be interpreted as performing Bayesian inference upon its surrounding environment. Although, in theory, the FEP applies to a wide variety of systems, there has been almost no direct exploration or demonstration of the principle in concrete systems. In this work, we examine in depth the assumptions required to derive the FEP in the simplest possible set of systems – weakly-coupled non-equilibrium linear stochastic systems. Specifically, we explore (i) how general the requirements imposed on the statistical structure of a system are and (ii) how informative the FEP is about the behaviour of such systems. We discover that two requirements of the FEP – the Markov blanket condition (i.e. a statistical boundary precluding direct coupling between internal and external states) and stringent restrictions on its solenoidal flows (i.e. tendencies driving a system out of equilibrium) – are only valid for a very narrow space of parameters. Suitable systems require an absence of perception-action asymmetries that is highly unusual for living systems interacting with an environment. More importantly, we observe that a mathematically central step in the argument, connecting the behaviour of a system to variational inference, relies on an implicit equivalence between the dynamics of the average states of a system with the average of the dynamics of those states. This equivalence does not hold in general even for linear stochastic systems, since it requires an effective decoupling from the system's history of interactions. These observations are critical for evaluating the generality and applicability of the FEP and indicate the existence of significant problems of the theory in its current form. These issues make the FEP, as it stands, not straightforwardly applicable to the simple linear systems studied here and suggest that more development is needed before the theory could be applied to the kind of complex systems that describe living and cognitive processes.

•

We review the free energy principle (FEP) in a family of analytically tractable non-equilibrium linear systems.

•

We study how general and how informative the FEP is in this class of systems.

•

We find that crucial assumptions of the FEP restrict its applicability to a very narrow space of the studied linear systems.

•

Secondly, a core step relies on an equivalence between the dynamics of the average and the average of the dynamics.

•

As a result, the FEP does not result in a good description of the (history-dependent) behaviour of a system.

Consciousness and cognition in plants

Unlike animal behavior, behavior in plants is traditionally assumed to be completely determined either genetically or environmentally. Under this assumption, plants are usually considered to be noncognitive organisms. This view nonetheless clashes with a growing body of empirical research that shows that many sophisticated cognitive capabilities traditionally assumed to be exclusive to animals are exhibited by plants too. Yet, if plants can be considered cognitive, even in a minimal sense, can they also be considered conscious? Some authors defend that the quest for plant consciousness is worth pursuing, under the premise that sentience can play a role in facilitating plant's sophisticated behavior. The goal of this article is not to provide a positive argument for plant cognition and consciousness, but to invite a constructive, empirically informed debate about it. After reviewing the empirical literature concerning plant cognition, we introduce the reader to the emerging field of plant neurobiology. Research on plant electrical and chemical signaling can help shed light into the biological bases for plant sentience. To conclude, we shall present a series of approaches to scientifically investigate plant consciousness. In sum, we invite the reader to consider the idea that if consciousness boils down to some form of biological adaptation, we should not exclude a priori the possibility that plants have evolved their own phenomenal experience of the world. This article is categorized under: Cognitive Biology > Evolutionary Roots of Cognition Philosophy > Consciousness Neuroscience > Cognition.

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by 'subjective valuation'-a deep inference about the precision or 'predictability' of the self-evidencing ('fitness-promoting') outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of 'selflessness'; in particular the 'totally selfless' states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology.

Enabling sustainable agriculture through understanding and enhancement of microbiomes

Harnessing plant-associated microbiomes offers an invaluable strategy to help agricultural production become more sustainable while also meeting growing demands for food, feed and fiber. A plethora of interconnected interactions among the host, environment and microbes, occurring both above and below ground, drive recognition, recruitment and colonization of plant-associated microbes, resulting in activation of downstream host responses and functionality. Dissecting these complex interactions by integrating multiomic approaches, high-throughput culturing, and computational and synthetic biology advances is providing deeper understanding of the structure and function of native microbial communities. Such insights are paving the way towards development of microbial products as well as microbiomes engineered with synthetic microbial communities capable of delivering agronomic solutions. While there is a growing market for microbial-based solutions to improve crop productivity, challenges with commercialization of these products remain. The continued translation of plant-associated microbiome knowledge into real-world scenarios will require concerted transdisciplinary research, cross-training of a next generation of scientists, and targeted educational efforts to prime growers and the general public for successful adoption of these innovative technologies.

Debunking a myth: plant consciousness

Claims that plants have conscious experiences have increased in recent years and have received wide coverage, from the popular media to scientific journals. Such claims are misleading and have the potential to misdirect funding and governmental policy decisions. After defining basic, primary consciousness, we provide new arguments against 12 core claims made by the proponents of plant consciousness. Three important new conclusions of our study are (1) plants have not been shown to perform the proactive, anticipatory behaviors associated with consciousness, but only to sense and follow stimulus trails reactively; (2) electrophysiological signaling in plants serves immediate physiological functions rather than integrative-information processing as in nervous systems of animals, giving no indication of plant consciousness; (3) the controversial claim of classical Pavlovian learning in plants, even if correct, is irrelevant because this type of learning does not require consciousness. Finally, we present our own hypothesis, based on two logical assumptions, concerning which organisms possess consciousness. Our first assumption is that affective (emotional) consciousness is marked by an advanced capacity for operant learning about rewards and punishments. Our second assumption is that image-based conscious experience is marked by demonstrably mapped representations of the external environment within the body. Certain animals fit both of these criteria, but plants fit neither. We conclude that claims for plant consciousness are highly speculative and lack sound scientific support.

Debunking a myth: plant consciousness

Claims that plants have conscious experiences have increased in recent years and have received wide coverage, from the popular media to scientific journals. Such claims are misleading and have the potential to misdirect funding and governmental policy decisions. After defining basic, primary consciousness, we provide new arguments against 12 core claims made by the proponents of plant consciousness. Three important new conclusions of our study are (1) plants have not been shown to perform the proactive, anticipatory behaviors associated with consciousness, but only to sense and follow stimulus trails reactively; (2) electrophysiological signaling in plants serves immediate physiological functions rather than integrative-information processing as in nervous systems of animals, giving no indication of plant consciousness; (3) the controversial claim of classical Pavlovian learning in plants, even if correct, is irrelevant because this type of learning does not require consciousness. Finally, we present our own hypothesis, based on two logical assumptions, concerning which organisms possess consciousness. Our first assumption is that affective (emotional) consciousness is marked by an advanced capacity for operant learning about rewards and punishments. Our second assumption is that image-based conscious experience is marked by demonstrably mapped representations of the external environment within the body. Certain animals fit both of these criteria, but plants fit neither. We conclude that claims for plant consciousness are highly speculative and lack sound scientific support.

Multiscale Free Energy Analysis of Human Ecosystem Engineering

Unlike ecosystem engineering by other living things, which brings a relatively limited range of sensations that are connected to a few enduring survival preferences, human ecosystem engineering brings an increasing variety and frequency of novel sensations. Many of these novel sensations can quickly become preferences as they indicate that human life will be less strenuous and more stimulating. Furthermore, they can soon become addictive. By contrast, unwanted surprise from these novel sensations may become apparent decades later. This recognition can come after the survival of millions of humans and other species has been undermined. In this paper, it is explained that, while multiscale free energy provides a useful hypothesis for framing human ecosystem engineering, disconnects between preferences and survival from human ecosystem engineering limit the application of current assumptions that underlie continuous state-space and discrete state-space modelling of active inference.

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

Toward an Embodied, Embedded Predictive Processing Account

In this paper, I argue for an embodied, embedded approach to predictive processing and thus align the framework with situated cognition. The recent popularity of theories conceiving of the brain as a predictive organ has given rise to two broad camps in the literature that I call free energy enactivism and cognitivist predictive processing. The two approaches vary in scope and methodology. The scope of cognitivist predictive processing is narrow and restricts cognition to brain processes and structures; it does not consider the body-beyond-brain and the environment as constituents of cognitive processes. Free energy enactivism, on the other hand, includes all self-organizing systems that minimize free energy (including non-living systems) and thus does not offer any unique explanations for more complex cognitive phenomena that are unique to human cognition. Furthermore, because of its strong commitment to the mind-life continuity thesis, it does not provide an explanation of what distinguishes more sophisticated cognitive systems from simple systems. The account that I develop in this paper rejects both of these radical extremes. Instead, I propose a compromise that highlights the necessary components of predictive processing by making use of a mechanistic methodology of explanation. The starting point of the argument in this paper is that despite the interchangeable use of the terms, prediction error minimization and the free energy principle are not identical. But this distinction does not need to disrupt the status quo of the literature if we consider an alternative approach: Embodied, Embedded Predictive Processing (EEPP). EEPP accommodates the free energy principle, as argued for by free energy enactivism, but it also allows for mental representations in its explanation of cognition. Furthermore, EEPP explains how prediction error minimization is realized but, unlike cognitivist PP, it allocates a constitutive role to the body in cognition. Despite highlighting concerns regarding cognitivist PP, I do not wish to discredit the role of the neural domain or representations as free energy enactivism does. Neural structures and processes undeniably contribute to the minimization of prediction error but the role of the body is equally important. On my account, prediction error minimization and free energy minimization are deeply dependent on the body of an agent, such that the body-beyond-brain plays a constitutive role in cognitive processing. I suggest that the body plays three constitutive roles in prediction error minimization: The body regulates cognitive activity, ensuring that cognition and action are intricately linked. The body acts as distributor in the sense that it carries some of the cognitive load by fulfilling the function of minimizing prediction error. Finally, the body serves to constrain the information that is processed by an agent. In fulfilling these three roles, the agent and environment enter into a bidirectional relation through influencing and modeling the structure of the other. This connects EEPP to the free energy principle because the whole embodied agent minimizes free energy in virtue of being a model of its econiche. This grants the body a constitutive role as part of the collection of mechanisms that minimize prediction error and free energy. The body can only fulfill its role when embedded in an environment, of which it is a model. In this sense, EEPP offers the most promising alternative to cognitivist predictive processing and free energy enactivism.

How higher goals are constructed and collapse under stress: A hierarchical Bayesian control systems perspective

In this paper, we show that organisms can be modeled as hierarchical Bayesian control systems with small world and information bottleneck (bow-tie) network structure. Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control. We argue that hierarchical Bayesian control systems produce deep hierarchies of goal states, from which it follows that organisms must have some form of 'highest goals'. For all organisms, these involve internal (self) models, external (social) models and overarching (normative) models. We show that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for tipping points (disease or death of the organism). In humans, learning goal hierarchies corresponds to personality development (maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits. A top-down collapse of such hierarchies under stress is identified as a common factor in all forms of episodic mental disorders (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically.

Nonhuman rationality: a predictive coding perspective

How can we rethink 'rationality' in the wake of animal and artificial intelligence studies? Can nonhuman systems be rational in any nontrivial sense? In this paper, we propose that all organisms, under certain circumstances, exhibit rationality to a diverse degree and aspect in the sense of the standard picture (SP): Their inferential processes conform to logic and probability rules. We first show that according to Calvo and Friston (J R Soc Interface 14(131):20170096, 2017) and Orlandi (2018), all biological systems must embody a top-down process (active inference) to minimize free energy. Next, based on Maddy's (Second philosophy, Oxford University Press, Oxford, 2007; The logical must: Wittgenstein on logic, Oxford University Press, Oxford, 2014) analysis, we argue that this inferential process conforms to logic and probability rules; thus, it satisfies the SP, which explains the rudimentary logic and arithmetic (e.g., categorizing and numbering) found among pigeons and mice. We also hold that the mammalian brain is only one among many ways of implementing rationality. Finally, we discuss data from microorganisms to support this view.

Whence the Expected Free Energy?

The expected free energy (EFE) is a central quantity in the theory of active inference. It is the quantity that all active inference agents are mandated to minimize through action, and its decomposition into extrinsic and intrinsic value terms is key to the balance of exploration and exploitation that active inference agents evince. Despite its importance, the mathematical origins of this quantity and its relation to the variational free energy (VFE) remain unclear. In this letter, we investigate the origins of the EFE in detail and show that it is not simply "the free energy in the future." We present a functional that we argue is the natural extension of the VFE but actively discourages exploratory behavior, thus demonstrating that exploration does not directly follow from free energy minimization into the future. We then develop a novel objective, the free energy of the expected future (FEEF), which possesses both the epistemic component of the EFE and an intuitive mathematical grounding as the divergence between predicted and desired futures.

Neural and phenotypic representation under the free-energy principle

The aim of this paper is to leverage the free-energy principle and its corollary process theory, active inference, to develop a generic, generalizable model of the representational capacities of living creatures; that is, a theory of phenotypic representation. Given their ubiquity, we are concerned with distributed forms of representation (e.g., population codes), whereby patterns of ensemble activity in living tissue come to represent the causes of sensory input or data. The active inference framework rests on the Markov blanket formalism, which allows us to partition systems of interest, such as biological systems, into internal states, external states, and the blanket (active and sensory) states that render internal and external states conditionally independent of each other. In this framework, the representational capacity of living creatures emerges as a consequence of their Markovian structure and nonequilibrium dynamics, which together entail a dual-aspect information geometry. This entails a modest representational capacity: internal states have an intrinsic information geometry that describes their trajectory over time in state space, as well as an extrinsic information geometry that allows internal states to encode (the parameters of) probabilistic beliefs about (fictive) external states. Building on this, we describe here how, in an automatic and emergent manner, information about stimuli can come to be encoded by groups of neurons bound by a Markov blanket; what is known as the neuronal packet hypothesis. As a concrete demonstration of this type of emergent representation, we present numerical simulations showing that self-organizing ensembles of active inference agents sharing the right kind of probabilistic generative model are able to encode recoverable information about a stimulus array.

Beyond planetary-scale feedback self-regulation: Gaia as an autopoietic system

The Gaia hypothesis states that the Earth is an instance of life. However, appraisals of it tend to focus on the claim that life is a feedback self-regulator that controls Earth's chemistry and climate dynamics, yet, self-regulation by feedbacks is not a definitive characteristic of living systems. Here, we consider the characterization of biological systems as autopoietic systems (causally organized to self-produce through metabolic efficient closure) and then ask whether the Gaia hypothesis is a tractable question from this standpoint. A proof-of-concept based on Chemical Organization Theory (COT) and the Zero Deficiency Theorem (ZDT) applied on a simple but representative Earth's molecular reaction network supports the thesis of Gaia as an autopoietic system. We identify the formation of self-producing organizations within the reaction network, corresponding to recognizable scenarios of Earth's history. These results provide further opportunities to discuss how the instantiation of autopoiesis at the planetary scale could manifests central features of biological phenomenon, such as autonomy and anticipation, and what this implies for the further development of the Gaia theory, Earth's climate modelling and geoengineering.

The dynamics of plant nutation

In this article we advance a cutting-edge methodology for the study of the dynamics of plant movements of nutation. Our approach, unlike customary kinematic analyses of shape, period, or amplitude, is based on three typical signatures of adaptively controlled processes and motions, as reported in the biological and behavioral dynamics literature: harmonicity, predictability, and complexity. We illustrate the application of a dynamical methodology to the bending movements of shoots of common beans (Phaseolus vulgaris L.) in two conditions: with and without a support to climb onto. The results herewith reported support the hypothesis that patterns of nutation are influenced by the presence of a support to climb in their vicinity. The methodology is in principle applicable to a whole range of plant movements.

Future climates: Markov blankets and active inference in the biosphere

We formalize the Gaia hypothesis about the Earth climate system using advances in theoretical biology based on the minimization of variational free energy. This amounts to the claim that non-equilibrium steady-state dynamics-that underwrite our climate-depend on the Earth system possessing a Markov blanket. Our formalization rests on how the metabolic rates of the biosphere (understood as Markov blanket's internal states) change with respect to solar radiation at the Earth's surface (i.e. external states), through the changes in greenhouse and albedo effects (i.e. active states) and ocean-driven global temperature changes (i.e. sensory states). Describing the interaction between the metabolic rates and solar radiation as climatic states-in a Markov blanket-amounts to describing the dynamics of the internal states as actively inferring external states. This underwrites climatic non-equilibrium steady-state through free energy minimization and thus a form of planetary autopoiesis.

Speed-accuracy trade-off in plants

Speed-accuracy trade-off (SAT) is the tendency for decision speed to covary with decision accuracy. SAT is an inescapable property of aimed movements being present in a wide range of species, from insects to primates. An aspect that remains unsolved is whether SAT extends to plants' movement. Here, we tested this possibility by examining the swaying in circles of the tips of shoots exhibited by climbing plants (Pisum sativum L.) as they approach to grasp a potential support. In particular, by means of three-dimensional kinematical analysis, we investigated whether climbing plants scale movement velocity as a function of the difficulty to coil a support. Results showed that plants are able to process the properties of the support before contact and, similarly to animal species, strategically modulate movement velocity according to task difficulty.

Cognition in some surprising places

The most widely accepted view in the biopsychological sciences is that the cognitive functions that are diagnostic of mental operations, sentience or, more commonly, consciousness emerged fairly late in evolution, most likely in the Cambrian period. Our position dovetails with James's below - subjectivity, feeling, consciousness has a much longer evolutionary history, one that goes back to the first appearance of life. The Cellular Basis of Consciousness (CBC) model is founded on the presumption that sentience and life are coterminous; that all organisms, based on inherent cellular activities via processes that take place in excitable membranes of their cells, are sentient, have subjective experiences and feelings. These, in turn, guide the context-relevant behaviors essential for their survival in often hostile environments in constant flux. The CBC framework is reductionistic, mechanistic, and calls for bottom-up research programs into the evolutionary origin of biological consciousness.

Is the Free-Energy Principle a Formal Theory of Semantics? From Variational Density Dynamics to Neural and Phenotypic Representations

The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference; and (2) if so, to assess which philosophical stance—in relation to the ontological and epistemological status of representations—is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account—an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the ‘aboutness’ or intentionality of cognitive systems; our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors.

Plant Bioinspired Ecological Robotics

Plants are movers, but the nature of their movement differs dramatically from that of creatures that move their whole body from point A to point B. Plants grow to where they are going. Bio-inspired robotics sometimes emulates plants' growth-based movement; but growing is part of a broader system of movement guidance and control. We argue that ecological psychology's conception of "information" and "control" can simultaneously make sense of what it means for a plant to navigate its environment and provide a control scheme for the design of ecological plant-inspired robotics. In this effort, we will outline several control laws and give special consideration to the class of control laws identified by tau theory, such as time to contact.

Plants are intelligent, here's how

HYPOTHESES

The drive to survive is a biological universal. Intelligent behaviour is usually recognized when individual organisms including plants, in the face of fiercely competitive or adverse, real-world circumstances, change their behaviour to improve their probability of survival.

SCOPE

This article explains the potential relationship of intelligence to adaptability and emphasizes the need to recognize individual variation in intelligence showing it to be goal directed and thus being purposeful. Intelligent behaviour in single cells and microbes is frequently reported. Individual variation might be underpinned by a novel learning mechanism, described here in detail. The requirements for real-world circumstances are outlined, and the relationship to organic selection is indicated together with niche construction as a good example of intentional behaviour that should improve survival. Adaptability is important in crop development but the term may be complex incorporating numerous behavioural traits some of which are indicated.

CONCLUSION

There is real biological benefit to regarding plants as intelligent both from the fundamental issue of understanding plant life but also from providing a direction for fundamental future research and in crop breeding.

Temporal Junctures in the Mind

Humans can imagine what happened in the past and what will happen in the future, but also what did not happen and what might happen. We reflect on envisioned events from alternative timelines, while knowing that we only ever live on one timeline. Considering alternative timelines rests on representations of temporal junctures, or points in time at which possible versions of reality diverge. These representations become increasingly sophisticated over childhood, first enabling preparation for mutually exclusive future possibilities and later the experience of counterfactual emotions like regret. By contrast, it remains unclear whether non-human animals represent temporal junctures at all. The emergence of these representations may have been a prime mover in human evolution.

The hierarchically mechanistic mind: A free-energy formulation of the human psyche

This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour.

Are plants cognitive? A reply to Adams

According to F. Adams [this journal, vol. 68, 2018] cognition cannot be realized in plants or bacteria. In his view, plants and bacteria respond to the here-and-now in a hardwired, inflexible manner, and are therefore incapable of cognitive activity. This article takes issue with the pursuit of plant cognition from the perspective of an empirically informed philosophy of plant neurobiology. As we argue, empirical evidence shows, contra Adams, that plant behavior is in many ways analogous to animal behavior. This renders plants suitable to be described as cognitive agents in a non-metaphorical way. Sections two to four review the arguments offered by Adams in light of scientific evidence on plant adaptive behavior, decision-making, anticipation, as well as learning and memory. Section five introduces the 'phyto-nervous' system of plants. To conclude, section six resituates the quest for plant cognition into a broader approach in cognitive science, as represented by enactive and ecological schools of thought. Overall, we aim to motivate the idea that plants may be considered genuine cognitive agents. Our hope is to help propel public awareness and discussion of plant intelligence once appropriately stripped of anthropocentric preconceptions of the sort that Adams' position appears to exemplify.

A roadmap towards a functional paradigm for learning & memory in plants

In plants, the acquisition, processing and storage of empirical information can result in the modification of their behavior according to the nature of the stimulus, and yet this area of research remained relatively understudied until recently. As the body of evidence supporting the inclusion of plants among the higher organisms demonstrating the adaptations to accomplish these tasks keeps increasing, the resistance by traditional botanists and agricultural scientists, who were at first cautious in allowing the application of animal models onto plant physiology and development, subsides. However, the debate retains much of its heat, a good part of it originating from the controversial use of nervous system terms to describe plant processes. By focusing on the latest findings on the cellular and molecular mechanisms underlying the well established processes of Learning and Memory, recognizing what has been accomplished and what remains to be explored, and without seeking to bootstrap neuronal characteristics where none are to be found, a roadmap guiding towards a comprehensive paradigm for Learning and Memory in plants begins to emerge. Meanwhile the applications of the new field of Plant Gnosophysiology look as promising as ever.

Anticipatory Behavior of the Clonal Plant Fragaria vesca

Active foraging for patchy resources is a crucial feature of many clonal plant species. It has been recently shown that plants' foraging for resources can be facilitated by anticipatory behavior via association of resource position with other environmental cues. We therefore tested whether clones of Fragaria vesca are able to associate and memorize positions of soil nutrients with particular light intensity, which will consequently enable them anticipating nutrients in new environment. We trained clones of F. vesca for nutrients to occur either in shade or in light. Consequently, we tested their growth response to differing light intensity in the absence of soil nutrients. We also manipulated epigenetic status of a subset of the clones to test the role of DNA methylation in the anticipatory behavior. Clones of F. vesca were able to associate presence of nutrients with particular light intensity, which enabled them to anticipate nutrient positions in the new environment based on its light intensity. Clones that had been trained for nutrients to occur in shade increased placement of ramets to shade whereas clones trained for nutrients to occur in light increased biomass of ramets in light. Our study clearly shows that the clonal plant F. vesca is able to relate two environmental factors, light and soil nutrients, and use this connection in anticipatory behavior. We conclude that anticipatory behavior can substantially improve the ability of clonal plants to utilize scarce and unevenly distributed resources.

Anaesthetics stop diverse plant organ movements, affect endocytic vesicle recycling and ROS homeostasis, and block action potentials in Venus flytraps

Background and Aims

Anaesthesia for medical purposes was introduced in the 19th century. However, the physiological mode of anaesthetic drug actions on the nervous system remains unclear. One of the remaining questions is how these different compounds, with no structural similarities and even chemically inert elements such as the noble gas xenon, act as anaesthetic agents inducing loss of consciousness. The main goal here was to determine if anaesthetics affect the same or similar processes in plants as in animals and humans.

Methods

A single-lens reflex camera was used to follow organ movements in plants before, during and after recovery from exposure to diverse anaesthetics. Confocal microscopy was used to analyse endocytic vesicle trafficking. Electrical signals were recorded using a surface AgCl electrode.

Key Results

Mimosa leaves, pea tendrils, Venus flytraps and sundew traps all lost both their autonomous and touch-induced movements after exposure to anaesthetics. In Venus flytrap, this was shown to be due to the loss of action potentials under diethyl ether anaesthesia. The same concentration of diethyl ether immobilized pea tendrils. Anaesthetics also impeded seed germination and chlorophyll accumulation in cress seedlings. Endocytic vesicle recycling and reactive oxygen species (ROS) balance, as observed in intact Arabidopsis root apex cells, were also affected by all anaesthetics tested.

Conclusions

Plants are sensitive to several anaesthetics that have no structural similarities. As in animals and humans, anaesthetics used at appropriate concentrations block action potentials and immobilize organs via effects on action potentials, endocytic vesicle recycling and ROS homeostasis. Plants emerge as ideal model objects to study general questions related to anaesthesia, as well as to serve as a suitable test system for human anaesthesia.

The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework (AIF) makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness.

Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?

The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system's behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics.

Are plants sentient?

Feelings in humans are mental states representing groups of physiological functions that usually have defined behavioural purposes. Feelings, being evolutionarily ancient, are thought to be coordinated in the brain stem of animals. One function of the brain is to prioritise between competing mental states and, thus, groups of physiological functions and in turn behaviour. Plants use groups of coordinated physiological activities to deal with defined environmental situations but currently have no known mental state to prioritise any order of response. Plants do have a nervous system based on action potentials transmitted along phloem conduits but which in addition, through anastomoses and other cross-links, forms a complex network. The emergent potential for this excitable network to form a mental state is unknown, but it might be used to distinguish between different and even contradictory signals to the individual plant and thus determine a priority of response. This plant nervous system stretches throughout the whole plant providing the potential for assessment in all parts and commensurate with its self-organising, phenotypically plastic behaviour. Plasticity may, in turn, depend heavily on the instructive capabilities of local bioelectric fields enabling both a degree of behavioural independence but influenced by the condition of the whole plant.