For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into 14 themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields. Relevance: Artificial life has contributed to philosophy of biology and of cognitive science, thus making it an important field related to constructivism.

Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications – with a firm commitment to modular, internalistic mental representation – to more moderate views emphasizing the importance of ‘body-representations’, and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory (e.g., of attention or consciousness) must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal ‘representations’ arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.

Key words: Predictive processing, embodied cognition, interoception, active inference, connectionism, computationalism, enactivism.

The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus.

Purpose: Attention is drawn to a principle of “significance feedback” in neural nets that was devised in the encouraging ambience of the Biological Computer Laboratory and is arguably fundamental to much of the subsequent practical application of artificial neural nets. Design/methodology/approach – The background against which the innovation was made is reviewed, as well as subsequent developments. It is emphasised that Heinz von Foerster and BCL made important contributions prior to their focus on second-order cybernetics. Findings: The version of “significance feedback” denoted by “backpropagation of error” has found numerous applications, but in a restricted field, and the relevance to biology is uncertain. Practical implications: Ways in which the principle might be extended are discussed, including attention to structural changes in networks, and extension of the field of application to include conceptual processing. Originality/value – The original work was 40 years ago, but indications are given of questions that are still unanswered and avenues yet to be explored, some of them indicated by reference to intelligence as “fractal.”

Key words: cybernetics, neural nets, learning.

The article considers the complexities of thinking about the computer as a model of the mind. It examines the computer as being a model of the brain in several very different senses of “model‘. On the one hand the basic architecture of the first modern stored-program computers was „modeled on“ the brain by John von Neumann. Von Neumann also sought to build a mathematical model of the biological brain as a complex system. A similar but different approach to modeling the brain was taken by Alan Turing, who on the one hand believed that the mind simply was a universal computer, and who sought to show how brain-like networks could self-organize into Universal Turing Machines. And on the other hand, Turing saw the computer as the universal machine that could simulate any other machine, and thus any particular human skill and thereby could simulate human intelligence. This leads to a discussion of the nature of “simulation” and its relation to models and modeling. The article applies this analysis to a written correspondence between Ashby and Turing in which Turing urges Ashby to simulate his cybernetic Homeostat device on the ACE computer, rather than build a special machine.

This chapter sketches an intellectual portrait of W. Ross Ashby’s thought from his earliest work on the mechanisms of intelligence in 1940 through the birth of what is now called artificial intelligence (AI), around 1956, and to the end of his career in 1972. It begins by examining his earliest published works on adaptation and equilibrium, and the conceptual structure of his notions of the mechanisms of control in biological systems. In particular, it assesses his conceptions of mechanism, equilibrium, stability, and the role of breakdown in achieving equilibrium. It then proceeds to his work on refining the concept of “intelligence,” on the possibility of the mechanical augmentation and amplification of human intelligence, and on how machines might be built that surpass human understanding in their capabilities. Finally, the chapter considers the significance of his philosophy and its role in cybernetic thought.

Key words: artificial intelligence, machine intelligence, equilibrium, human intelligence, cybernetic thought

Excerpt: The purpose of this essay is to clarify some of the important senses in which the relationship between the brain and the computer might be considered as one of “modeling.” It also considers the meaning of “simulation” in the relationships between models, computers and brains. While there has been a fairly broad literature emerging on models and simulations in science, these have primarily focused on the physical sciences, rather than the mind and brain. And while the cognitive sciences have often invoked concepts of modeling and simulation, they have been frustratingly inconsistent in their use of these terms, and the implicit relations to their scientific roles. My approach is to consider the early convolution of brain models and computational models in cybernetics, with the aim of clarifying their significance for more current debates in the cognitive sciences. It is my belief that clarifying the historical senses in which the brain and computer serve as models of each other in the historical period prior to the birth of AI and cognitive science is a crucial task for an archeology of AI and the history of cognitive science.

This dissertation reconsiders the nature of scientific models through an historical study of the development of electronic models of the brain by Cybernetics researchers in the 1940s. By examining how these unique models were used in the brain sciences, it develops the concept of a “working model” for the brain sciences. Working models differ from theoretical models in that they are subject to manipulation and interactive experimentation, i.e., they are themselves objects of study and part of material culture. While these electronic brains are often disparaged by historians as toys and publicity stunts, I argue that they mediated between physiological theories of neurons and psychological theories of behavior so as to leverage their compelling material performances against the lack of observational data and sparse theoretical connections between neurology and psychology. I further argue that working models might be used by cognitive science to better understand how the brain develops performative representations of the world.

The concept of “autonomy,” once at the core of the original enactivist proposal in The Embodied Mind (Varela et al. in The embodied mind: cognitive science and human experience. MIT Press, Cambridge, 1991), is nowadays ignored or neglected by some of the most prominent contemporary enactivists approaches. Theories of autonomy, however, come to fill a theoretical gap that sensorimotor accounts of cognition cannot ignore: they provide a naturalized account of normativity and the resources to ground the identity of a cognitive subject in its specific mode of organization. There are, however, good reasons for the contemporary neglect of autonomy as a relevant concept for enactivism. On the one hand, the concept of autonomy has too often been assimilated into autopoiesis (or basic autonomy in the molecular or biological realm) and the implications are not always clear for a dynamical sensorimotor approach to cognitive science. On the other hand, the foundational enactivist proposal displays a metaphysical tension between the concept of operational closure (autonomy), deployed as constitutive, and that of structural coupling (sensorimotor dynamics); making it hard to reconcile with the claim that experience is sensorimotorly constituted. This tension is particularly apparent when Varela et al. propose Bittorio (a 1D cellular automata) as a model of the operational closure of the nervous system as it fails to satisfy the required conditions for a sensorimotor constitution of experience. It is, however, possible to solve these problems by re-considering autonomy at the level of sensorimotor neurodynamics. Two recent robotic simulation models are used for this task, illustrating the notion of strong sensorimotor dependency of neurodynamic patterns, and their networked intertwinement. The concept of habit is proposed as an enactivist building block for cognitive theorizing, re-conceptualizing mental life as a habit ecology, tied within an agent’s behaviour generating mechanism in coordination with its environment. Norms can be naturalized in terms of dynamic, interactively self-sustaining, coherentism. This conception of autonomous sensorimotor agency is put in contrast with those enactive approaches that reject autonomy or neglect the theoretical resources it has to offer for the project of naturalizing minds.

Key words: Autonomy, enactivism, operational closure of the nervous system, sensorimotor constitution of experience, mental life, habit, sensorimotor autonomous agency, autonomist sensorimotor enactivism.

Dynamicism has provided cognitive science with important tools to understand some aspects of “how cognitive agents work” but the issue of “what makes something cognitive” has not been sufficiently addressed yet and, we argue, the former will never be complete without the latter. Behavioristic characterizations of cognitive properties are criticized in favor of an organizational approach focused on the internal dynamic relationships that constitute cognitive systems. A definition of cognition as adaptive-autonomy in the embodied and situated neurodynamic domain is provided: the compensatory regulation of a web of stability dependencies between sensorimotor structures is created and pre served during a historical/developmental process. We highlight the functional role of emotional embodiment: internal bioregulatory processes coupled to the formation and adaptive regulation of neurodynamic autonomy. Finally, we discuss a “minimally cognitive behavior program” in evolutionary simulation modeling suggesting that much is to be learned from a complementary “minimally cognitive organization program”

Key words: minimal cognition, adaptive autonomy, neurodynamic organization, cognition-as-it-could-be, emotional embodiment