According to the American Society for Cybernetics (2012), there is no unified comprehensive account of a far-reaching narrative that takes into account all of the Macy Conferences and what was discussed and accomplished at these meetings. This chapter will thus propose how group dialogues on concepts such as information and feedback allowed the Macy Conferences to act as a catalyst for second-order systems theory, when fi rstorder, steady-state models of homeostasis became supplanted by those of self-reference in observing systems. I will trace how such a development transpired through a conferences-wide interdisciplinary mindset that promoted the idea of refl exivity. According to N. Katherine Hayles, the conferences’ singular achievement was to create a “new paradigm” for “looking at human beings … as information-processing entities who are essentially similar to intelligent machines,” by routing Claude Shannon’s information theory through Warren McCulloch’s “model of neural functioning” and John von Neumann’s work in “biological systems” and then capitalizing on Norbert Wiener’s “visionary” talent for disseminating the “larger implications” of such a paradigm shift. From this perspective, the most crucial work would achieve its fruition after the end of the Macy conferences. Yet the foundations for such work were, perforce, cast during the discussions at the conferences that epitomize science in the making and, as such, warrant our careful attention.

A theoretical model is proposed that explicates the generation of conceptual structures from unitary sensory objects to abstract constructs that satisfy the criteria generally stipulated for concepts of “number”: independence from sensory properties, unity of composites consisting of units, and potential numerosity. The model is based on the assumption that attention operates not as a steady state but as a pulselike phenomenon that can, but need not, be focused on sensory signals in the central nervous system. Such a view of attention is compatible with recent findings in the neurophysiology of perception and provides, in conjunction with Piaget’s postulate of empirical and reflective abstraction, a novel approach to the analysis of concepts that seem indispensable for the development of numerical operations.

Key words: philosophy of mathematics, cognition, development

The theory of Autopoiesis describes what the living systems are and not what they do. Instead of investigating the behavior of systems exhibiting autonomy and the concrete implementation of this autonomy (i.e. the system structure), the study addresses the reason why such behavior is exhibited (i.e. the abstract system organization). This article explores the use of autopoietic concepts in the field of Image Processing. Two different approaches are presented. The first approach assumes that the organization of an image is represented only by its grayvalue distribution. In order to identify autopoietic organization inside an image’s pixel distribution, the steady state Xor-operation is identified as the only valid approach for an autopoietic processing of images. The effect of its application on images is explored and discussed. The second approach makes use of a second space, the A-space, as the autopoietic-processing domain. This allows for the formulation of adaptable recognition tasks. Based on this second approach, the concept of autopoiesis as a tool for the analysis of textures is explored.

Key words: autopoiesis, steady state, image processing, auto-projective operators, texture analysis, texture retrieval, systems, autopoietic-agents.

In this paper, I argue that enactivism and computationalism – two seemingly incompatible research traditions in modern cognitive science – can be fruitfully reconciled under the framework of the free energy principle (FEP). FEP holds that cognitive systems encode generative models of their niches and cognition can be understood in terms of minimizing the free energy of these models. There are two philosophical interpretations of this picture. A computationalist will argue that as FEP claims that Bayesian inference underpins both perception and action, it entails a concept of cognition as a computational process. An enactivist, on the other hand, will point out that FEP explains cognitive systems as constantly self-organizing to non-equilibrium steady-state. My claim is that these two interpretations are both true at the same time and that they enlighten each other.

Key words: Active inference, autopoiesis, computational theory of mind, enactivism, predictive processing, self-organization.

Single unit activity was recorded from principal cells in the A-laminae of the cat dorsal lateral geniculate nucleus (dLGN). A steady state pattern of afferent activation was induced by presenting a continuously drifting square wave grating of constant spatial frequency to the eye (the dominant eye) that provided the excitatory input to the recorded cell. Intermittently, a second grating stimulus was presented to the other, nondominant, eye. In most neurones nondominant eye stimulation led to inhibition of relay cell responses. The latency of this suppressive effect was unusually long (up to 1 s) and its intensity and duration depended critically on the similarity between the gratings that were presented to the two eyes. Typically suppression was strongest when the gratings differed in orientation, direction of movement and contrast and when the nondominant eye stimulus was moving rather than stationary. Ablation of visual cortex abolished these long latency and feature-dependent interferences. We conclude that the visual cortex and the corticothalamic projections are involved in the mediation of these interocular interactions. We interpret our results as support for the hypothesis that corticothalamic feedback modifies thalamic transmission as a function of the congruency between ongoing cortical activation patterns and afferent retinal signals.

Purpose: The purpose of this paper is to demonstrate that cybernetic theory explains learning curves and sets the curves as legitimate and efficient tools for a pro-active energy technology policy. Design/methodology/approach – The learning system is a non-trivial machine that is kept in non-equilibrium steady state at minimum entropy production by competitive, equilibrium markets. The system has operational closure and the learning curve expresses its eigenbehaviour. This eigenbehaviour is analysed not in calendar time but in the characteristic time of the system, i.e., its eigentime. Measured in eigentime, the minimum entropy production in the steady-state learning system is constant. The double closure mechanism described by Heinz von Förster makes it possible for the learning system to change (adapt) its eigenbehaviour without compromising its operational closure. Findings: By obeying basic laws of second order cybernetics and of non-equilibrium thermodynamics the learning system self-organises its learning to follow an optimal path described by the learning curve. The learning rates are obtained through an operator formalism and the results explain observed distributions. Application to solar cell (photo-voltaic) modules indicates that the silicon scarcity bubble 2005–2008 produced excess entropy corresponding to costs of the order of 100 billion US dollars. Research limitations/implications: Grounding technology learning and learning curves in cybernetics and non-equilibrium thermodynamics open up new possibilities to understand technology shifts through radical innovations or paradigm changes. Practical implications: Learning curves are legitimate and efficient tools for energy policy and industrial strategy. Originality/value – Grounding of technology learning and learning curves in cybernetic and thermodynamic theory provides a stable theoretical basis for applications in industry and policy.

Key words: eigenbehavior, learning curves, …