The paper considers a non-reductionist theory of consciousness, which is not reducible to theories of reality and to physiological or psychological theories. Following D.I.Dubrovsky's "informational approach" to the "Mind-Brain Problem", we consider the reality through the prism of information about observed phenomena, which, in turn, is perceived by subjective reality through sensations, perceptions, feelings, etc., which, in turn, are information about the corresponding brain processes. Within this framework the following principle of the Information Theory of Consciousness (ITS) development is put forward: the brain discovers all possible causal relations in the external world and makes all possible inferences by them. The paper shows that ITS built on this principle: (1) also base on the information laws of the structure of external world; (2) explains the structure and functioning of the brain functional systems and cellular ensembles; (3) ensures maximum accuracy of predictions and the anticipation of reality; (4) resolves emerging contradictions and (5) is an information theory of the brain's reflection of reality. The non-reductionist theory of consciousness is not reducible to any theory of reality and to any physiological or psychological theory. At the Seventh International Conference on Cognitive Science, K.V.Anokhin said "The problem is not that the existing neurophysiological theories are imperfect... The correlative approaches used in them simply cannot answer questions about the nature of mind and subjective experience... This requires a non-reductionist fundamental theory" [3].

The human capability to reason about one domain by using knowledge of other domains has been researched for more than 50 years, but models that are formally sound and predict cognitive process are sparse. We propose a formally sound method that models associative reasoning by adapting logical reasoning mechanisms. In particular it is shown that the combination with large commensense knowledge within a single reasoning system demands for an efficient and powerful association technique. This approach is also used for modelling mind-wandering and the Remote Associates Test (RAT) for testing creativity. In a general discussion we show implications of the model for a broad variety of cognitive phenomena including consciousness.

In science, macro level descriptions of the causal interactions within complex, dynamical systems are typically deemed convenient, but ultimately reducible to a complete causal account of the underlying micro constituents. Yet, such a reductionist perspective is hard to square with several issues related to autonomy and agency: (1) agents require (causal) borders that separate them from the environment, (2) at least in a biological context, agents are associated with macroscopic systems, and (3) agents are supposed to act upon their environment. Integrated information theory (IIT) (Oizumi et al., 2014) offers a quantitative account of causation based on a set of causal principles, including notions such as causal specificity, composition, and irreducibility, that challenges the reductionist perspective in multiple ways. First, the IIT formalism provides a complete account of a system's causal structure, including irreducible higher-order mechanisms constituted of multiple system elements. Second, a system's amount of integrated information ($\Phi$) measures the causal constraints a system exerts onto itself and can peak at a macro level of description (Hoel et al., 2016; Marshall et al., 2018). Finally, the causal principles of IIT can also be employed to identify and quantify the actual causes of events ("what caused what"), such as an agent's actions (Albantakis et al., 2019). Here, we demonstrate this framework by example of a simulated agent, equipped with a small neural network, that forms a maximum of $\Phi$ at a macro scale.

This paper aims at demonstrating how a first-order logic reasoning system in combination with a large knowledge base can be understood as an artificial consciousness system. For this we review some aspects from the area of philosophy of mind and in particular Baars' Global Workspace Theory. This will be applied to the reasoning system Hyper with ConceptNet as a knowledge base. Finally we demonstrate that such a system is very well able to do conscious mind wandering.

We know that they have the same sensors – called nociceptors – that cause us to flinch or cry when we are hurt. And they certainly behave like they are sensing something unpleasant. When a chef places them in boiling water, for instance, they twitch their tails as if they are in agony. But are they actually "aware" of the sensation? Or is that response merely a reflex?

Some problems in science are so hard, we don't really know what meaningful questions to ask about them -- or whether they are even truly solvable by science. Consciousness is one of those: Some researchers think it is an illusion; others say it pervades everything. Some hope to see it reduced to the underlying biology of neurons firing; others say that it is an irreducibly holistic phenomenon. The question of what kinds of physical systems are conscious "is one of the deepest, most fascinating problems in all of science," wrote the computer scientist Scott Aaronson of the University of Texas at Austin. "I don't know of any philosophical reason why [it] should be inherently unsolvable" -- but "humans seem nowhere close to solving it." Now a new project currently under review hopes to close in on some answers. It proposes to draw up a suite of experiments that will expose theories of consciousness to a merciless spotlight, in the hope of ruling out at least some of them.

Actual causation is concerned with the question "what caused what?" Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system's causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the "what caused what?" question. Counterfactual accounts of actual causation based on graphical models, paired with system interventions, have demonstrated initial success in addressing specific problem cases in line with intuitive causal judgments. Here, we start from a set of basic requirements for causation (realization, composition, information, integration, and exclusion) and develop a rigorous, quantitative account of actual causation that is generally applicable to discrete dynamical systems. We present a formal framework to evaluate these causal requirements that is based on system interventions and partitions, and considers all counterfactuals of a state transition. This framework is used to provide a complete causal account of the transition by identifying and quantifying the strength of all actual causes and effects linking the two consecutive system states. Finally, we examine several exemplary cases and paradoxes of causation and show that they can be illuminated by the proposed framework for quantifying actual causation.

The only thing you know for sure is that you are conscious. All else is inference, however reasonable. There is something in your head that generates experiences: the words you are reading on this page, the snore of a bulldog on a red carpet, the perfume of roses on a desk. Your experience of such a scene is exclusive to you, and your impressions are integrated into one unified field of perception. It is like something to be you reading, hearing a dog, smelling flowers.

Using the mathematical language of information theory, Hoel and his collaborators claim to show that new causes--things that produce effects--can emerge at macroscopic scales. They say coarse-grained macroscopic states of a physical system (such as the psychological state of a brain) can have more causal power over the system's future than a more detailed, fine-grained description of the system possibly could. Just as codes reduce noise (and thus uncertainty) in transmitted data--Claude Shannon's 1948 insight that formed the bedrock of information theory--Hoel claims that macro states also reduce noise and uncertainty in a system's causal structure, strengthening causal relationships and making the system's behavior more deterministic. With Albantakis and Tononi, Hoel formalized a measure of causal power called "effective information," which indicates how effectively a particular state influences the future state of a system.

But University of Wisconsin-Madison research psychiatrist Giulio Tononi, who was recently selected to take part in the creation of a "cognitive computer," says the goal of building a computer as quick and flexible as a small mammalian brain is more daunting than it sounds. Tononi, professor of psychiatry at the UW-Madison School of Medicine and Public Health and an internationally known expert on consciousness, is part of a team of collaborators from top institutions who have been awarded a $4.9 million grant from the Defense Advanced Research Projects Agency (DARPA) for the first phase of DARPA's Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project. Tononi and scientists from Columbia University and IBM will work on the "software" for the thinking computer, while nanotechnology and supercomputing experts from Cornell, Stanford and the University of California-Merced will create the "hardware." Dharmendra Modha of IBM is the principal investigator. "Every neuron in the brain knows that something has changed," Tononi explains. "It tells the brain, 'I got burned, and if you want to change, this is the time to do it.''