Within the philosophy of mind, consciousness is currently understood as the expression of one or other cognitive modality, either intentionality , transparency , subjectivity or reflexivity . However, neither intentionality, subjectivity nor transparency adequately distinguishes conscious from nonconscious cognition. Consequently, the only genuine index or defining characteristic of consciousness is reflexivity, the capacity for autonoetic or self-referring, self-monitoring awareness. But the identification of reflexivity as the principal index of consciousness raises a major challenge in relation to the cognitive mechanism responsible (...) for operationalizing such a reflexive state. Current reliance by higher-order and intrinsic self-representational theories on self-representing data structures to achieve reflexive self-awareness is highly problematic, suggesting a solution in terms of a self-referential processing regime. (shrink)

This is the first in a series of instalments aiming at a minimal model explanation for conscious experience, taking the phenomenal character of “pure consciousness” or “pure awareness” in meditation as its entry point. It develops the concept of “minimal phenomenal experience” as a candidate for the simplest form of consciousness, substantiating it by extracting six semantic constraints from the existing literature and using sixteen phenomenological case-studies to incrementally flesh out the new working concept. One empirical hypothesis is that the (...) phenomenological prototype of “pure awareness”, to which all such reports refer, really is the content of a predictive model, namely, a Bayesian representation of tonic alertness. On a more abstract conceptual level, it can be described as a model of an unpartitioned epistemic space. (shrink)

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key property characteristic (...) of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector.. It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one. (shrink)