Grounding cognition: heterarchical control mechanisms in biology

Brain and cognition: The need for a broader biological perspective to overcome old biases

Even with accumulating knowledge, no consensus regarding the understanding of intelligence or cognition exists, and the universal brain bases for these functions remain unclear. Traditionally, our understanding of cognition is based on self-evident principles that appear indisputable when looking only at our species; however, this can distance us from understanding its essence (anthropocentrism, corticocentrism, intellectocentrism, neurocentrism, and idea of orthogenesis of brain evolution). Herein, we use several examples from biology to demonstrate the usefulness of comparative ways of thinking in relativizing these biases. We discuss the relationship between the number of neurons and cognition and draw attention to the highly developed cognitive performance of animals with small brains, to some "tricks" of evolution, to how animals cope with small hardware, to some animals with high-quality brains with an alternative architecture to vertebrates, and to surprising basal cognitive skills in aneural, unicellular organisms. Cognition can be supplemented by the idea of a multicellular organism as a continuum, with many levels of cognitive function, including the possible basal learning in single cells.

Centering cognitive neuroscience on task demands and generalization

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Controlling the input: How one-year-old infants sustain visual attention

Traditionally, the exogenous control of gaze by external saliencies and the endogenous control of gaze by knowledge and context have been viewed as competing systems, with late infancy seen as a period of strengthening top-down control over the vagaries of the input. Here we found that one-year-old infants control sustained attention through head movements that increase the visibility of the attended object. Freely moving one-year-old infants (n = 45) wore head-mounted eye trackers and head motion sensors while exploring sets of toys of the same physical size. The visual size of the objects, a well-documented salience, varied naturally with the infant's moment-to-moment posture and head movements. Sustained attention to an object was characterized by the tight control of head movements that created and then stabilized a visual size advantage for the attended object for sustained attention. The findings show collaboration between exogenous and endogenous attentional systems and suggest new hypotheses about the development of sustained visual attention.

Motor cognition in plants: from thought to real experiments

Motor cognition involves the process of planning and executing goal-directed movements and recognizing, anticipating, and interpreting others' actions. Motor cognitive functions are generally associated with the presence of a brain and are ascribed only to humans and other animal species. A growing body of evidence suggests that aneural organisms, like climbing plants, exhibit behaviors driven by the intention to achieve goals, challenging our understanding of cognition. Here, we propose an inclusive perspective under motor cognition to explain climbing plants' behavior. We will first review our empirical research based on kinematical analysis to understand movement in pea plants. Then, we situate this empirical research within the current theoretical debate aimed at extending the principles of cognition to aneural organisms. A novel comparative perspective that considers the perception-action cycle, involving transforming perceived environmental elements into intended movement patterns, is provided.

Understanding brain functional architecture through robotics

Robotics is increasingly seen as a useful test bed for computational models of the brain functional architecture underlying animal behavior. We provide an overview of past and current work, focusing on probabilistic and dynamical models, including approaches premised on the free energy principle, situating this endeavor in relation to evidence that the brain constitutes a layered control system. We argue that future neurorobotic models should integrate multiple neurobiological constraints and be hybrid in nature.

The Role of Information in Evolutionary Biology

The Modern Synthesis has received criticism for its purported gene-centrism. That criticism relies on a concept of the gene as a unit of instructional information. In this paper I discuss information concepts and endorse one, developed from Floridi, that sees information as a functional relationship between data and context. I use this concept to inspect developmental criticisms of the Modern Synthesis and argue that the instructional gene arose as an idealization practice when evolutionary biologists made comment on development. However, a closer inspection of key claims shows that at least some associated with the Modern Synthesis were in fact adopting the data led definition I favour and made clear arguments for the role of developmental processes beyond genetic input. There was no instructional gene.

Decision Making in Plants: A Rooted Perspective

This article discusses the possibility of plant decision making. We contend that recent work on bacteria provides a pertinent perspective for thinking about whether plants make choices. Specifically, the analogy between certain patterns of plant behaviour and apparent decision making in bacteria provides principled grounds for attributing decision making to the former. Though decision making is our focus, the discussion has implications for the wider issue of whether and why plants (and non-neural organisms more generally) are appropriate targets for cognitive abilities. Moreover, decision making is especially relevant to the issue of plant intelligence as it is commonly taken to be characteristic of cognition.

Entropy and Cross-Level Orderliness in Light of the Interconnection between the Neural System and Consciousness

Despite recent advances, the origin and utility of consciousness remains under debate. Using an evolutionary perspective on the origin of consciousness, this review elaborates on the promising theoretical background suggested in the temporospatial theory of consciousness, which outlines world-brain alignment as a critical predisposition for controlling behavior and adaptation. Such a system can be evolutionarily effective only if it can provide instant cohesion between the subsystems, which is possible only if it performs an intrinsic activity modified in light of the incoming stimulation. One can assume that the world-brain interaction results in a particular interference pattern predetermined by connectome complexity. This is what organisms experience as their exclusive subjective state, allowing the anticipation of regularities in the environment. Thus, an anticipative system can emerge only in a regular environment, which guides natural selection by reinforcing corresponding reactions and decreasing the system entropy. Subsequent evolution requires complicated, layered structures and can be traced from simple organisms to human consciousness and society. This allows us to consider the mode of entropy as a subject of natural evolution rather than an individual entity.

Mind the matter: Active matter, soft robotics, and the making of bio-inspired artificial intelligence

Philosophical and theoretical debates on the multiple realisability of the cognitive have historically influenced discussions of the possible systems capable of instantiating complex functions like memory, learning, goal-directedness, and decision-making. These debates have had the corollary of undermining, if not altogether neglecting, the materiality and corporeality of cognition-treating material, living processes as "hardware" problems that can be abstracted out and, in principle, implemented in a variety of materials-in particular on digital computers and in the form of state-of-the-art neural networks. In sum, the matter in se has been taken not to matter for cognition. However, in this paper, we argue that the materiality of cognition-and the living, self-organizing processes that it enables-requires a more detailed assessment when understanding the nature of cognition and recreating it in the field of embodied robotics. Or, in slogan form, that the matter matters for cognitive form and function. We pull from the fields of Active Matter Physics, Soft Robotics, and Basal Cognition literature to suggest that the imbrication between material and cognitive processes is closer than standard accounts of multiple realisability suggest. In light of this, we propose upgrading the notion of multiple realisability from the standard version-what we call 1.0-to a more nuanced conception 2.0 to better reflect the recent empirical advancements, while at the same time averting many of the problems that have been raised for it. These fields are actively reshaping the terrain in which we understand materiality and how it enables, mediates, and constrains cognition. We propose that taking the materiality of our embodied, precarious nature seriously furnishes an important research avenue for the development of embodied robots that autonomously value, engage, and interact with the environment in a goal-directed manner, in response to existential needs of survival, persistence, and, ultimately, reproduction. Thus, we argue that by placing further emphasis on the soft, active, and plastic nature of the materials that constitute cognitive embodiment, we can move further in the direction of autonomous embodied robots and Artificial Intelligence.

Health and environment from adaptation to adaptivity: a situated relational account

The definitions and conceptualizations of health, and the management of healthcare have been challenged by the current global scenarios (e.g., new diseases, new geographical distribution of diseases, effects of climate change on health, etc.) and by the ongoing scholarship in humanities and science. In this paper we question the mainstream definition of health adopted by the WHO-'a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity' (WHO in Preamble to the constitution of the World Health Organization as adopted by the international health conference, The World Health Organization, 1948)-and its role in providing tools to understand what health is in the contemporary context. More specifically, we argue that this context requires to take into account the role of the environment both in medical theory and in the healthcare practice. To do so, we analyse WHO documents dated 1984 and 1986 which define health as 'coping with the environment'. We develop the idea of 'coping with the environment', by focusing on two cardinal concepts: adaptation in public health and adaptivity in philosophy of biology. We argue that the notions of adaptation and adaptivity can be of major benefit for the characterization of health, and have practical implications. We explore some of these implications by discussing two recent case studies of adaptivity in public health, which can be valuable to further develop adaptive strategies in the current pandemic scenario: community-centred care and microbiologically healthier buildings.

Artifacts and levels of abstraction

The purpose of this article is to show how the comparison or analogy with artifacts (i.e., systems engineered by humans) is foundational for the idea that complex neuro-cognitive systems are amenable to explanation at distinct levels, which is a central simplifying strategy for modeling the brain. The most salient source of analogy is of course the digital computer, but I will discuss how some more general comparisons with the processes of design and engineering also play a significant role. I will show how the analogies, and the subsequent notion of a distinct computational level, have engendered common ideas about how safely to abstract away from the complexity of concrete neural systems, yielding explanations of how neural processes give rise to cognitive functions. I also raise worries about the limitations of these explanations, due to neglected differences between the human-made devices and biological organs.

Organization needs organization: Understanding integrated control in living organisms

Organization figures centrally in the understanding of biological systems advanced by both new mechanists and proponents of the autonomy framework. The new mechanists focus on how components of mechanisms are organized to produce a phenomenon and emphasize productive continuity between these components. The autonomy framework focuses on how the components of a biological system are organized in such a way that they contribute to the maintenance of the organisms that produce them. In this paper we analyze and compare these two accounts of organization and argue that understanding biological organisms as cohesively integrated systems benefits from insights from both. To bring together the two accounts, we focus on the notions of control and regulation as bridge concepts. We start from a characterization of biological mechanisms in terms of constraints and focus on a specific type of mechanism, control mechanisms, that operate on other mechanisms on the basis of measurements of variables in the system and its environment. Control mechanisms are characterized by their own set of constraints that enable them to sense conditions, convey signals, and effect changes on constraints in the controlled mechanism. They thereby allow living organisms to adapt to internal and external variations and to coordinate their parts in such a manner as to maintain viability. Because living organisms contain a vast number of control mechanisms, a central challenge is to understand how they are themselves organized. With the support of examples from both unicellular and multicellular systems we argue that control mechanisms are organized heterarchically, and we discuss how this type of control architecture can, without invoking top-down and centralized forms of organizations, succeed in coordinating internal activities of organisms.

Allostasis, Action, and Affect in Depression: Insights from the Theory of Constructed Emotion

The theory of constructed emotion is a systems neuroscience approach to understanding the nature of emotion. It is also a general theoretical framework to guide hypothesis generation for how actions and experiences are constructed as the brain continually anticipates metabolic needs and attempts to meet those needs before they arise (termed allostasis). In this review, we introduce this framework and hypothesize that allostatic dysregulation is a trans-disorder vulnerability for mental and physical illness. We then review published findings consistent with the hypothesis that several symptoms in major depressive disorder (MDD), such as fatigue, distress, context insensitivity, reward insensitivity, and motor retardation, are associated with persistent problems in energy regulation. Our approach transforms the current understanding of MDD as resulting from enhanced emotional reactivity combined with reduced cognitive control and, in doing so, offers novel hypotheses regarding the development, progression, treatment, and prevention of MDD.

Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence.

Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality

The truly surprising thing about evolution is not how it makes individuals better adapted to their environment, but how it makes individuals. All individuals are made of parts that used to be individuals themselves, e.g., multicellular organisms from unicellular organisms. In such evolutionary transitions in individuality, the organised structure of relationships between component parts causes them to work together, creating a new organismic entity and a new evolutionary unit on which selection can act. However, the principles of these transitions remain poorly understood. In particular, the process of transition must be explained by “bottom-up” selection, i.e., on the existing lower-level evolutionary units, without presupposing the higher-level evolutionary unit we are trying to explain. In this hypothesis and theory manuscript we address the conditions for evolutionary transitions in individuality by exploiting adaptive principles already known in learning systems. Connectionist learning models, well-studied in neural networks, demonstrate how networks of organised functional relationships between components, sufficient to exhibit information integration and collective action, can be produced via fully-distributed and unsupervised learning principles, i.e., without centralised control or an external teacher. Evolutionary connectionism translates these distributed learning principles into the domain of natural selection, and suggests how relationships among evolutionary units could become adaptively organised by selection from below without presupposing genetic relatedness or selection on collectives. In this manuscript, we address how connectionist models with a particular interaction structure might explain transitions in individuality. We explore the relationship between the interaction structures necessary for (a) evolutionary individuality (where the evolution of the whole is a non-decomposable function of the evolution of the parts), (b) organismic individuality (where the development and behaviour of the whole is a non-decomposable function of the behaviour of component parts) and (c) non-linearly separable functions, familiar in connectionist models (where the output of the network is a non-decomposable function of the inputs). Specifically, we hypothesise that the conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions of this type (or deep model induction) familiar in connectionist models of cognition and learning.

Scaffolding layered control architectures through constraint closure: insights into brain evolution and development

The functional organization of the mammalian brain can be considered to form a layered control architecture, but how this complex system has emerged through evolution and is constructed during development remains a puzzle. Here we consider brain organization through the framework of constraint closure, viewed as a general characteristic of living systems, that they are composed of multiple sub-systems that constrain each other at different timescales. We do so by developing a new formalism for constraint closure, inspired by a previous model showing how within-lifetime dynamics can constrain between-lifetime dynamics, and we demonstrate how this interaction can be generalized to multi-layered systems. Through this model, we consider brain organization in the context of two major examples of constraint closure-physiological regulation and visual orienting. Our analysis draws attention to the capacity of layered brain architectures to scaffold themselves across multiple timescales, including the ability of cortical processes to constrain the evolution of sub-cortical processes, and of the latter to constrain the space in which cortical systems self-organize and refine themselves. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Lessons from behaviorism: The problem of construct-led science

Yarkoni makes a number of valid points in his critical analysis of psychology, but he misses an opportunity to expose the root of its problems. That root is the poor practice around the derivation of explanatory constructs. We make comment on this with an example from behaviorist history and relate this to the recent discussion of scientific understanding in the philosophy of science.

Evolutionary origin and the development of consciousness

This review seeks to combine advances in anthropology and neuroscience to investigate the adaptive value of human consciousness. It uses an interdisciplinary perspective on the origin of consciousness to refute the most common fallacies in considering consciousness, particularly, disregarding the evolutionary origin of the subjective reality in looking for the neural correlates of consciousness and divorcing studies in neuroscience and behavioural sciences. Various explanations linked to consciousness in the field of neuroscience, supplemented with the theoretical explanation of an experience as an ongoing process of overlap between intrinsic neural dynamics and stimulation can be summarised as the stochastic dynamics of one's control system experienced by the individual in the form of subjective reality. This framework elaborates on the world-brain research program and lays foundation for the quantitative description of one's qualitative feelings and naturalistic science of consciousness. Furthermore, this study highlights the philosophical perspective of the inseparability between the physical correlates and the subjective reality contributing to the realistic ontology of conscious processes.

A Minimal Framework for Describing Living Systems: A Multi-Dimensional View of Life Across Scales

The almost limitless complexity of biology has led to two general approaches to understanding biological phenomena. One approach is dominated by reductionism in which high-level phenomena of whole systems are viewed as emerging from relatively simple and generally understood interactions at a substantially lower level. Although this approach is theoretically general, it can become intractable in practice when attempting to simultaneously explain a wide range of systems. A second approach is for specialists to investigate biological phenomena within one of many different hierarchical levels of description that are separated to decouple from concerns at other levels. Although this approach reduces the explanatory burden on specialists that operate within each level, it also reduces integration from insights gained at other levels. Thus, as beneficial as these approaches have been, they limit the scope and integration of knowledge across scales of biological organization to the detriment of a truly synoptic view of life. The challenge is to find a theoretical and experimental framework that facilitates a broader understanding of the hierarchy of life-providing permeability for the exchange of ideas among disciplinary specialists without discounting the peculiarities that have come to define those disciplines. For this purpose, coarse-grained, scale-invariant properties and resources need to be identified that describe the characteristic features of a living system at all spatiotemporal scales. The approach will be aided by a common vernacular that underscores the realities of biological connections across a wide range of scales. Therefore, in this vision paper, we propose a conceptual approach based on four identified resources-energy, conductance, storage, and information (ECSI)-to reintegrate biological studies with the aim of unifying life sciences under resource limitations. We argue that no functional description of a living system is complete without accounting for at least all four of these resources. Thus, making these resources explicit will help to identify commonalities to aid in transdisciplinary discourse as well as opportunities for integrating among the differently scoped areas of specialized inquiry. The proposed conceptual framework for living systems should be valid across all scales and may uncover potential limitations of existing hypotheses and help researchers develop new hypotheses addressing fundamental processes of life without having to resort to reductionism.

Reframing cognition: getting down to biological basics

The premise of this two-part theme issue is simple: the cognitive sciences should join the rest of the life sciences in how they approach the quarry within their research domain. Specifically, understanding how organisms on the lower branches of the phylogenetic tree become familiar with, value and exploit elements of an ecological niche while avoiding harm can be expected to aid understanding of how organisms that evolved later (including Homo sapiens) do the same or similar things. We call this approach basal cognition. In this introductory essay, we explain what the approach involves. Because no definition of cognition exists that reflects its biological basis, we advance a working definition that can be operationalized; introduce a behaviour-generating toolkit of capacities that comprise the function (e.g. sensing/perception, memory, valence, learning, decision making, communication), each element of which can be studied relatively independently; and identify a (necessarily incomplete) suite of common biophysical mechanisms found throughout the domains of life involved in implementing the toolkit. The articles in this collection illuminate different aspects of basal cognition across different forms of biological organization, from prokaryotes and single-celled eukaryotes-the focus of Part 1-to plants and finally to animals, without and with nervous systems, the focus of Part 2. By showcasing work in diverse, currently disconnected fields, we hope to sketch the outline of a new multidisciplinary approach for comprehending cognition, arguably the most fascinating and hard-to-fathom evolved function on this planet. Doing so has the potential to shed light on problems in a wide variety of research domains, including microbiology, immunology, zoology, biophysics, botany, developmental biology, neurobiology/science, regenerative medicine, computational biology, artificial life and synthetic bioengineering. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.