The notion of loop seems to be ubiquitous in the study of organisms, the human mind and symbolic systems. With the possible exception of quantum-mechanical approaches, the treatments of consciousness we are acquainted with crucially appeal to the concept of loop. The uses of loops in this context fall within two broad classes. In the first one, loops are used to express the control of the organism’s interaction with the environment; in the second one, they are used to express self-reference. (...) Both classes are tied with investigations which aim at accounting for symbolic capabilities, and ultimately for subjectivity. Neurophysiological research detects loops in the animal nervous system since its very inception (e.g., all the work on the reflex arc). Recently, Gray proposed a model purporting to explain the mechanism supporting the contents of consciousness in the human CNS, in ways that are practically indistinguishable from models formulated within the cybernetical point of view. Both types of models apply loops in strikingly similar ways. While there is no conclusive evidence that loops are necessary to support consciousness, they are nonetheless as good a candidate as today can be found for inclusion in the list of essential ingredients for subjectivity to arise. In the first two sections we discuss the above mentioned uses by means of significant examples. In the third section, we compare Gray’s recent model to MacKay’s early cybernetical model of a selfobserving system, in the setting of a broader discussion on loops for consciousness. (shrink)

We isolate some critical aspects of the reuse notion in Anderson's massive redeployment hypothesis (MRH). We notice that the actual rearranging of local neural circuits at a timescale comparable with the reactivity timescale of the organism is left open. We propose the concept of programmable neural network as a solution.

Against the opinion that DNA as program is not sufficiently explanatory, we maintain that the cellular machinery is entirely computational, and we identify the crucial notion of the interpreter that expresses the gene with the minimal gene set. Epigenetics research does not so much need paradigm shifts as the unraveling of an exceedingly complex computational machine.

Nijhawan argues convincingly that predictive mechanisms are pervasive in the central nervous system (CNS). However, scientific understanding of visual prediction requires one to formulate empirically testable neurophysiological models. The author's suggestions in this direction are to be evaluated on the basis of more realistic experimental methodologies and more plausible assumptions on the hierarchical character of the human visual cortex.