This paper reviews ideas developed by the late Gordon Pask as part of his Conversation Theory CT. CT uses theories of the dynamics of complex, self-organising systems, in conjunction with models of conceptual structures, in order to give an account of conceptual coherence for example, of a theory or a belief system as a form of organisational closure. In Pask’s own terms, CT is concerned both with the kinematics of knowledge structures and the kinetics of knowing and coming to know. The main features of Pask’s ways of modelling conceptual structures and processes are presented. The author goes on to present a summary two cycle model of learning, aimed to capture some of Pask’s key insights with respect to conceptual coherence and the organisational closure of conceptual systems. Parallels are drawn with other work in epistemology classic cybernetic studies of self-organisation and the concept of autopoiesis. The two cycle model is then applied recursively to generate learning cycles and conceptual structures at different levels of abstraction, as a contribution to Pask’s work on the topology of thought. Finally, the model is applied reflexively. That is, its own form is considered as a topic for conversation and conceptualisation. Carrying out such a reflection provides a coherent way of characterising epistemological limits, whilst retaining a clear sense of there being an in principle unlimited praxeology of awareness.

This paper reviews ideas developed by the late Gordon Pask as part of his conversation theory (CT). CT uses theories of the dynamics of complex, self-organizing systems, in conjunction with models of conceptual structures, in order to give an account of conceptual coherence (for example, of a theory or a belief system) as a form of organizational closure. In Pask’s own terms, CT is concerned both with the kinematics of knowledge structures and the kinetics of knowing and coming to know. The main features of modelling conceptual structures and processes used by Pask are presented. We continue by presenting a summary two-cycle model of learning, aimed to capture some of Pask’s key insights with respect to conceptual coherence and the organizational closure of conceptual systems. Parallels are drawn with other work in epistemology, classic cybernetic studies of self-organization, and the concept of autopoiesis. The two-cycle model is then applied recursively to generate learning cycles and conceptual structures at different levels of abstraction, as a contribution to the work of Pask on the topology of thought. Finally, the model is applied reflexively. That is, its own form is considered as a topic for conversation and conceptualization. Carrying out such a reflection provides a coherent way of characterizing epistemological limits, while retaining a clear sense of there being an (in principle) unlimited praxeology of awareness.

Purpose: To present sociocybernetic models of observers in interaction with the aim of encouraging reflection on what is good practice in human communication. Design/methodology/approach – Foundational cybernetic concepts of process and product are drawn upon to develop models of belief, meaning, truth and power. Findings: Belief, following Pask and Rescher, is modelled as a coherent, self-reproducing system of concepts. Meaning, following Peirce, is modelled in terms of the pragmatic consequences of holding certain beliefs to be true. The concept of truth is modelled as justified true belief, the classic ideal of the objective sciences. Power is modelled as the pragmatic consequences of socialinteraction. Originality/value – The paper invites the members of the sociocybernetics community to reflect on the reflexive nature of these models and to critically monitor and evaluate the quality of the communication within that community.

Key words: Modelling, Sociocybernetics, Systems analysis

Embodiment has become the raison d’etre for much of the new ‘cognitive robotics’. It fills a gap in the non-interactivist approach of traditional artificial intelligence (AI) in which ‘intelligence’ is viewed as the manipulation of symbols in a vacuum. However, a foundational question for the new AI is, can embodiment lead to a strong AI, i.e. a robot mind? To address this question, two extreme poles of embodiment are distinguished here, mechanistic and phenomenal. A detailed exploration of each type of embodiment is provided together with an appraisal of whether strong embodiment is possible for robotics, or whether robotics merely provides a tool for scientific exploration and modelling, i.e. weak embodiment? It is argued that strong embodiment, either mechanistic or phenomenal, is not possible for present day robots. However, weak embodiment may provide an enlightened approach to using robots for modelling cognition.

Key words: artificial intelligence, autopoiesis, cognitive robotics, mechanistic embodiment, phenomenal embodiment

Cognitive systems research has predominantly been guided by the historical distinction between emotion and cognition, and has focused its efforts on modelling the “cognitive” aspects of behaviour. While this initially meant modelling only the control system of cognitive creatures, with the advent of “embodied” cognitive science this expanded to also modelling the interactions between the control system and the external environment. What did not seem to change with this embodiment revolution, however, was the attitude towards affect and emotion in cognitive science. This paper argues that cognitive systems research is now beginning to integrate these aspects of natural cognitive systems into cognitive science proper, not in virtue of traditional “embodied cognitive science,” which focuses predominantly on the body’s gross morphology, but rather in virtue of research into the interoceptive, organismic basis of natural cognitive systems.

Key words: proper embodiment, embodied cognition, affective neuroscience, interoception, internal robotics, enactive cognitive science.

A great deal of research in science education reports on the contents of students’ minds: what they know, think, believe, and understand. This new book offers an analysis of the processes by which we can come to claims about the minds of others and highlights the logical impossibility of ever knowing for sure what someone else knows or thinks. I argue here that researchers in science education need to be much more explicit about the extent to which research into learners’ ideas in science is necessarily a process of developing models. Many research reports fail to acknowledge this, and make claims that are much less tentative than is justified, leading to misleading and sometimes contrary findings in the literature. In everyday life we commonly take it for granted that finding out what another knows or thinks is a relatively trivial or straightforward process. We come to take the “mental register” (the way we talk and think about the “contents” of minds) for granted and so teachers and researchers may readily underestimate the challenges involved in their work. The book sets out in stages the necessary processes and challenges involved in modelling student thinking, understanding and learning. Relevance: Concerns how researchers develop understandings of how learners construct knowledge in science classes

Context: In previous papers, I suggested six rules proposed by Varela, Maturana and Uribe as a validation test to assess the autopoietic nature of a complex dynamic system. Identifying possible non-biological autopoietic systems is harder than merely assessing self-organization, existence of embodied boundaries and some observable autonomous behavioural capabilities: any rigorous assessment should include a close observation of the “intra-boundaries” phenomenology in terms of components’ self-production, their spatial distribution and the temporal occurrence of interaction events. Problem: Under which physical and components’ relational conditions can some social systems be properly considered as autopoietic unities compliant with the six rules? Results: Dynamic systems can be classified according to “degrees of autonomous behaviour” that they may acquire as a result of the emergence of organizational closure (i.e., autonomy. Also, the different “degrees of attainable systemic autonomy” depend on the “degrees of autonomy” shown by a system’s dynamic components. For human social systems, a necessary balance between individuals’ autonomy and the heteronomous behaviour brought about on people by social norms (laws, culture, tradition or coercion) sets limits to the “degree of systemic autonomy” that human organizations may acquire. Therefore social systems, defined as dynamic systems composed of physical agents, could not attain the high “levels of systemic autonomy” ascribable to autopoietic systems without constraining the autonomy of agents to “levels” that are incompatible with spontaneous human behaviour. Also, social organizations seen as composed of physical agents interacting in physical space cannot be construed as autopoietic systems. Alternatively, if seen as composed of “process-like” entities, where agents participate as actors within processes, some social systems could be described as autopoietic wholes existing in the abstract space in which we distinguish interactions between processes, provided that we can assess compliance with the rules for some specific cases. Implications: These conclusions contribute to the debate on the possible autopoietic nature of some human social systems and to grasping the opportunity to shift focus to the more interesting issue of the “degrees of systemic autonomy” that human organizations could acquire (if needed) without imposing unbearable limitations on the autonomy of human actors. Also, the conceptual framework of this explanatory approach could be used in practical terms to assist the development of new dynamic modelling languages capable of simulating social systems.

Key words: Autopoiesis, autonomy, heteronomy, self-organization, social system, organizational closure, causation, Maturana, Varela, Luhmann.