Maturana’s cognitive perspective on the living state, Dretske’s insight on how information theory constrains cognition, the Atlan/Cohen cognitive paradigm, and models of intelligence without representation, permit construction of a spectrum of dynamic necessary conditions statistical models of signal transduction, regulation, and metabolism at and across the many scales and levels of organisation of an organism and its context. Nonequilibrium critical phenomena analogous to physical phase transitions, driven by crosstalk, will be ubiquitous, representing not only signal switching, but the recruitment of underlying cognitive modules into tunable dynamic coalitions that address changing patterns of need and opportunity at all scales and levels of organisation. The models proposed here, while certainly providing much conceptual insight, should be most useful in the analysis of empirical data, much as are fitted regression equations.

Key words: cognition, living state, mutual information, phase transition, rate distortion

How might one account for the organization in behavior without attributing it to an internal control structure? The present article develops a theoretical framework called behavioral dynamics that inte- grates an information-based approach to perception with a dynamical systems approach to action. For a given task, the agent and its environment are treated as a pair of dynamical systems that are coupled mechanically and informationally. Their interactions give rise to the behavioral dynamics, a vector field with attractors that correspond to stable task solutions, repellers that correspond to avoided states, and bifurcations that correspond to behavioral transitions. The framework is used to develop theories of several tasks in which a human agent interacts with the physical environment, including bouncing a ball on a racquet, balancing an object, braking a vehicle, and guiding locomotion. Stable, adaptive behavior emerges from the dynamics of the interaction between a structured environment and an agent with simple control laws, under physical and informational constraints.

Key words: perception and action, perceptual–motor control, dynamical systems, self-organization, locomotion

It is my opinion that Humberto Maturana’s contributions to the state of human knowledge about both humans and their knowledge are nothing short of monumental. At least that’s the magnitude of impact his ideas had on me, both as a doctoral student and in my various subsequent roles researching how information technologies interact with human cognitive capacities, interpersonal communication, and directed collaboration, for example. Though already familiar with the work of scholars like Ashby, Beer, von Foerster, Angyal, Bateson, and Pask, I had remained adrift in deciding how best to apply the wisdom of cybernetics to matters like group decision support.

We present two original computational models – globular universe and autopoietic automata – capturing the basic aspects of an evolution: a construction of self–reproducing automata by self–assembly and a transfer of algorithmically modified genetic information over generations. Within this framework we show implementation of autopoietic automata in a globular universe. Further, we characterize the computational power of lineages of autopoietic automata via interactive Turing machines and show an unbounded complexity growth of a computational power of automata during the evolution. Finally, we define the problem of sustainable evolution and show its undecidability.

We introduce a new formal computational model designed for studying the information transfer among the generations of offspring-producing evolving machines – so-called autopoietic automata. These can be seen as nondeterministic finite state transducers whose “program” can become a subject of their own processing. An autopoietic automaton can algorithmically generate an offspring controlled by a program which is a modification of its parent’s program. Autopoietic automata offer a neat framework for investigating computational and complexity issues in the evolutionary self-reproducing processes. We show that the computational power of lineages of autopoietic automata is equal to that of an interactive nondeterministic Turing machine. We also prove that there exists an autopoietic automaton giving rise to an unlimited evolution, provided that suitable inputs are delivered to individual automata. However, the problem of sustainable evolution, asking whether for an arbitrary autopoietic automaton and arbitrary inputs there is an infinite lineage of its offspring, is undecidable.

Key words: Autopoietic automata, Self-reproducing automata, Interactive Turing machine, Simulation, Computational complexity

Recent work in cognitive and computational neuroscience depicts the human cortex as a multi-level prediction engine. This ‘predictive processing’ framework shows great promise as a means of both understanding and integrating the core information processing strategies underlying perception, reasoning, and action. But how, if at all, do emotions and sub-cortical contributions fit into this emerging picture? The fit, we shall argue, is both profound and potentially transformative. In the picture we develop, online cognitive function cannot be assigned to either the cortical or the sub-cortical component, but instead emerges from their tight co-ordination. This tight co-ordination involves processes of continuous reciprocal causation that weave together bodily information and ‘top-down’ predictions, generating a unified sense of what’s out there and why it matters. The upshot is a more truly ‘embodied’ vision of the predictive brain in action.

Key words: predictive processing, affective neuroscience, embodied mind, affordance competition, pulvinar

This chapter discusses a formal model of human and machine learning called participatory learning. This model allows the representation of machine learning in a constructivist framework. In this model, the learner’s previous beliefs play an important role in the assimilation of further information. A central aspect of the theory is the degree of compatibility between observations and belief. In a constructivist theory, learning is a bootstrap process. The name participatory learning highlights the fact that the learner’s current knowledge of the subject participates intimately in the learning process. Central to participatory learning is the idea that an exogenous observation has the greatest impact on learning when the observation is largely compatible with the present belief system. In particular, observations in conflict with current core constructs or strongly held beliefs are discounted. The role of arousal or anxiety can be thought of as salient and/or massed negative feedback.

The paper describes results of a teaching experiment with five high school (Year 10 and 11) students. Four qualitative characteristics were established: the first step of solution, main information extracted from the problem, generalisation from a problem and completion of solution. From these characteristics the corresponding quantitative indices were introduced and analysed. The structure of two of them, specific SFS and common SHP,are given in detail. Investigation of quantitative indices and their qualitative characteristics gives an opportunity to find out more about interrelations between different stages of the problem-solving process.

As the results of man-engineered experiments with social design, social “revolution”, socialist “architectures”, and other feats of “social engineering”, are crumbling down, they are causing large-scale human suffering through their failures. There is a renewed awareness that self-organizing and spontaneous properties of complex social systems are much too powerful (and much too vulnerable at the same lime) to respond or be exposed to the endless, reductionistic “tinkering” of policy “makers”, “scientists” of the artificial, and “engineers of human souls”. The mankind is again ready to learn how to “trigger”, “catalyze”, “sustain” and “lead-manage” a spontaneous process of social self-organization; it is becoming less inclined to design another “central super-controller”, “information-processing command system”, or “World Brain”. The purpose of this paper is to show: (1) that theories dealing with “spontaneous social orders” have deep historical roots and (2) that systems sciences are in a good position (better than economics, engineering or sociology) to build upon these roots and expand the theories into useful, practical methodologies. For example, modern theories of autopoiesis and order through fluctuations, especially their rich, computer-based simulation experiments, provide a good and solid point of departure.

Key words: Self-organization, self-production, autopoiesis, spontaneous social orders, social autopoiesis.