Self-reference and recursion characterize a vast range of dynamic phenomena, particularly biological automata. In this paper we investigate the dynamics of self-referent phenomena using the Extended Calculus of Indications (ECI) of Kauffman and Varela, who have applied the ECI to mathematics, physics, linguistics, perception, and cognition. Previous studies have focused on the algebraic structure of the ECI, and on form dynamics using only the arithmetic of Spencer-Brown. We here examine the temporal behavior of self-referent or reentrant forms using the full power of the ECI to represent tangled hierarchies and multiple enfolded dimensions of space-time. Further, we explore the temporal convolution of static and recursive states in coherent fluctuation, providing a foundation for going beyond the Turing model of computation in finite automata. Novel results are presented on the structure of reentrant forms and the canonical elements of form eigenbehavior, the characteristic self-determined dynamic inherent in reentrant forms.

Open peer commentary on the article “Constructivism and Computation: Can Computer-Based Modeling Add to the Case for Constructivism?” by Manfred Füllsack. Upshot: Füllsack’s article offers many interesting ideas but falls short of elucidating the relationship between constructivism and computation. It could profit by taking into consideration stronger constructivist foundations such as the distinction between machine and organism, the relationship between reality and the observer, and Ceccato’s theory of attention.

Whilst the usefulness of the computational metaphor in many areas of psychology and neuroscience is clear, it has not gone unchallenged and in this article I will review a group of philosophical arguments that suggest either such unequivocal optimism in computationalism is misplaced – computation is neither necessary nor sufficient for cognition – or panpsychism (the belief that the physical universe is fundamentally composed of elements each of which is conscious) is true. I conclude by highlighting an alternative metaphor for cognitive processes based on communication and interaction. Relevance: This paper argues against computational accounts of mind and cognition, discussing Searle, Bishop and Penrose and suggesting a new metaphor for cognition based on interactions and communication. The new metaphor is sympathetic to modern post-symbolic, anti-representationalist, embodied, enactive accounts of cognition.

A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult. Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems. The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system’s circular organisation to define its boundary with its environment. In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation. The paper is conceptual in nature and the emphasis is on the review of other people’s work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.

Key words: autopoiesis, computing, computability, structural coupling

The main contribution of this paper is a naturalized account of the phenomenon of computation. The key idea for the development of this account is the identification of the notion of syntactical processing (or information processing) with the dynamical evolution of a constrained physical process, based on the observation that both evolve according to an arbitrary set of rules. This identification, in turn, revealed that, from the physical point of view, computation could be understood in terms of the operation of a component subdivided into two parts, (a) the constrained process and (b) the constraints that control its dynamics, where the interactions with the rest of the system are mediated by configurational changes of the constrained process. The immediate consequence of this analysis is the observation that this notion of computation can be readily integrated into the enactive paradigm of cognition.

Key words: enaction, computation, syntax, arbitrariness, computation, varela, organism, searle.

The work of physicist and theoretical biologist Howard Pattee has focused on the roles that symbols and dynamics play in biological systems. Symbols, as discrete functional switching-states, are seen at the heart of all biological systems in the form of genetic codes, and at the core of all neural systems in the form of informational mechanisms that switch behavior. They also appear in one form or another in all epistemic systems, from informational processes embedded in primitive organisms to individual human beings to public scientific models. Over its course, Pattee’s work has explored (1) the physical basis of informational functions (dynamical vs. rule-based descriptions, switching mechanisms, memory, symbols), (2) the functional organization of the observer (measurement, computation), (3) the means by which information can be embedded in biological organisms for purposes of self-construction and representation (as codes, modeling relations, memory, symbols), and (4) the processes by which new structures and functions can emerge over time. We discuss how these concepts can be applied to a high-level understanding of the brain. Biological organisms constantly reproduce themselves as well as their relations with their environs. The brain similarly can be seen as a self-producing, self-regenerating neural signaling system and as an adaptive informational system that interacts with its surrounds in order to steer behavior.

Key words: Adaptive systems, biological cybernetics, biological semiotics, dynamical systems, emergence, epistemology, evolutionary robotics, genetic code, neural code, neurocomputation, self-organization, symbols.

This chapter outlines a broad theory of sign use in natural and artificial systems that was developed over several decades within the context of theoretical biology, cybernetics, systems theory, biosemiotics, and neuroscience. Different conceptions of semiosis and information in nature are considered. General functional properties of and operations on signs, including measurement, computation, and sign-directed actions are described. A taxonomy of semiotic systems is built up from combinations of these operations. The respective functional organizations and informational capabilities of formal systems and computempiral-predictive scientific models, percept-action systems, purposive goal-seeking systems, and self-constructing systems are discussed. Semiotic relations are considered in terms of Morrisean semiotic triad of syntactics, semantics, and pragmatics. Analysis of statetransition structure is used to demarcate functional boundaries, such as epistemic and control cuts. Capabilities for open-ended behavior, combinatoric and emergent creativity, and umwelt expansion are taken up. Finally, basic problems of neurosemiotics, neural coding, and neurophenomenology are outlined.

Key words: Semiotics, biosemiotics, cybernetics, theoretical biology, adaptive systems, scientific models, purposive systems, theoretical neuroscience, functional neuroscience, neurosemiotics, neurophenomenology, neural coding, temporal coding, emergence, creativity

Human thinking uses other peoples’ experience. While often pictured as computation or based on the workings of a language-system in the mind or brain, the evidence suggests alternatives to representationalism. In terms proposed here, embodiment is interlaced with wordings as people tackle the integration problem. Using a case study, the paper shows how a young man uses external resources in an experimental task. He grasps a well-defined problem by using material resources, talking about his doings and switching roles and procedures. Attentional skills enable him to act as an air cadet who, among other things, connects action, leadership and logic. Airforce practices prompt him to draw timeously on non-local resources as, using impersonal experience, he interlaces language, action and perception. He connects the cultural and the metabolic in cognitive work as he finds a way to completion of the task.

Key words: cognitive science, distributed cognition, ecological psychology, enactivism, integration problem, interactivity, problem solving, river problem, systemic cognition, thinking

A case for a pluralistic approach to cognitive science is sketched. It is argued that cognitive scientists should take seriously the possibility that a single, unified framework for all of cognition is an unrealistic expectation for its diverse interdisciplinary goals and subject matter. A pluralistic approach instead seeks ways of integrating the multiple perspectives that have provided explanatory success in loosely interconnected sub-domains of cognitive phenomena. Research strategies recommended by this approach are discussed, with review of research currently carrying out such strategies and others that may hold promise for the future. The article ends with a discussion of seeking closer integration of the inquirer into consideration of which explanatory framework to choose. A systematic exploration of this transactional approach to cognitive science may grant coherence to pluralism even as it embraces diverse schemes of explanation.

Key words: pluralism, cognitive science, complexity, dynamics, computation, pragmatism

Little has been done yet to study the synchronization properties of the sources estimated from the MEG/EEG inverse problem, despite the growing interest in the role of phase relations in brain functions. In order to achieve this aim, we propose a novel approach to the MEG/EEG inverse problem based on some regularization using spectral priors: The MEG/EEG raw data are filtered in a frequency band of interest and blurred with some specific “regularization noise” prior to the inversion process. This formalism uses non quadratic regularization and a deterministic optimization algorithm. We proceed to Monte Carlo simulations using the chaotic Rössler oscillators to model the neural generators. Our results demonstate that it is possible to reveal some phase-locking between brain sources with great accuracy following the computation of the inverse problem based on scalp MEG/EEG measurements.