The deterministic component of the structure of the genetic code, derived from tRNA dimerization as proposed by the self-referential model, is described. Anticodon triplets form well defined modules of dimers that are sites for hosting the amino acid guests. The amino acids are the non-deterministic component, selected evolutionarily at the accomplishment of functions in the nucleoprotein ensembles and coevolving with metabolic pathways. The concomitance of the deterministic and the evolutionary components results in regionalization of the attributions in the matrix of encoded correspondences. The regionalized structure is what explains the error-minimizing property of the code structure. Relevance: Our utilization of the notion of self-reference may be relevant for the discussions on autopoiesis. Our model utilizes self-reference as the original and foundational attribute but compartmentalization is considered a derived function in the construction of the biomolecular machinery. The genetic code also reaches integration through the action of protein-protein interactions, between synthetases. Functional closure is reached when the punctuation subsystem is developed, but also in dependence on complex participation of other genes.

Traditionally, computational theory (CT) and dynamical systems theory (DST) have presented themselves as opposed and incompatible paradigms in cognitive science. There have been some efforts to reconcile these paradigms, mainly, by assimilating DST to CT at the expenses of its anti-representationalist commitments. In this paper, building on Piccinini’s mechanistic account of computation and the notion of functional closure, we explore an alternative conciliatory strategy. We try to assimilate CT to DST by dropping its representationalist commitments, and by inviting CT to recognize the functionally closed nature of some computational systems.

Key words: computational theory, dynamical systems theory, cognitive science, representation, functional closure.

In cognitive science, computationalism is the thesis that natural cognitive systems are computing systems. Traditionally, computationalism has understood computing and cognitive systems as functionally open systems, i.e., as systems that have functional entries through which they receive inputs, and exits through which they emit outputs. In opposition to this view, enactive theory claims that natural cognitive systems, unlike computing systems, are autonomous systems whose functional organization does not have inputs and outputs. Computationalism and enactivism seem to share an assumption that computing systems are input-output functional systems. In this paper, such an assumption will be critically reviewed by appealing to the cybernetic notion of functional closure. The notion of functional closure, as elaborated in Maturanas cybernetic neurophysiology, refers to a closed functional network in which, due to the circularity of the dynamics, we cannot distinguish inputs and outputs as intrinsic functional properties of the system. On the basis of this conceptualization, it will be argued that some paradigmatic cases of computing systems (notably a physically realized Turing machine) are actually functionally closed systems, and therefore computing systems without inputs and outputs. If this analysis is right, then the incompatibility that enactivists see between computing systems and organizationally closed functional systems would no longer hold, as it would not be true that computing systems must necessarily be understood as input-output systems.

Key words: computationalism enactivism functional closure turing machine input-output system