The article analyzes the epistemological mechanism of discussion concerning descriptive theoretical knowledge and formulates rules for conducting such discussion that enable the proponent and opponent to understand each other and acknowledge each other’s arguments. It is demonstrated that before the discussion begins, during the research stage, the proponent formulates a theoretical problem, finds its solution, and substantiates it. The purpose of the discussion is merely to test the results of this three-stage work, to determine whether the descriptive theoretical knowledge constituting the content of the proponent’s thesis actually corresponds to its subject matter. Since both the proponent and the opponent are interested in correctly resolving this task, theoretical discussion can be represented as a joint search for a solution to a common problem. The article distinguishes three stages of theoretical discussion. At the first stage, the proponent’s thesis is tested for internal consistency; at the second stage, in case of success, it is tested for correspondence with those theoretical knowledge claims whose truth has already been recognized by the scientific community; at the third stage, the main examination begins, namely the testing of the theoretical thesis for correspondence with indisputable empirical facts. At each stage, the discussion participants face fundamental philosophical questions, the answers to which determine their understanding of the structure of the discussion, its principles, and its outcomes. Does a world exist beyond the boundaries of their consciousness? Is it knowable? Are its statistical laws secondary in relation to dynamical ones? Does theoretical knowledge arise independently of empirical knowledge or in the process of its historical self-development? The article offers participants in theoretical discussions answers to these questions based on the principles of realism, consistent determinism, aposteriority, and the classical theory of truth. The possibility of constructing theoretical discussion on alternative methodological principles is acknowledged but not analyzed in the article. The research concludes with the formulation of universal rules for conducting scientific dialogue – from the presumption of good faith of the parties to the principle of observability – adherence to which enables the transformation of a clash of opinions into a procedure for testing and developing scientific knowledge.

The article presents an approach to defining the category of scientific knowledge that integrates the principles of scientific realism and constructivism. The authors propose a coherent model designed to overcome the limitations of the traditional definition of knowledge as “justified true belief” (JTB) and to account for the complexity of actual scientific inquiry, a process in which it is not always possible to evaluate current concepts and theories strictly in terms of truth. A central tenet of the paper is that knowledge should be understood as a reflection of reality in an ideal (mental) form. Within this framework, knowledge is categorized into two types: truth (which corresponds entirely to an object's established properties) and error (which corresponds only partially). The paper demonstrates that even refuted scientific concepts, despite being formally false, frequently contain objective elements that drive scientific progress. The authors analyze the correspondence between knowledge and the object of cognition, offering a focused critique of radical constructivism. They argue that the constructive nature of cognition does not contradict its reflective essence, but rather complements it; constructivist principles prove most productive when grounded in scientific realism. At both the sensory-empirical and rational-theoretical levels, this constructivity involves creating mental models that closely approximate reality. Furthermore, the study outlines a framework of criteria for evaluating scientific cognition. This includes standards for verifying truth, establishing scientific validity, ensuring disciplinary autonomy, and fostering a rigorous research culture. The article also explores the ontological status of knowledge. By critically examining Karl Popper’s “World 3” framework, the authors argue that knowledge exists exclusively as a mental phenomenon. In this view, texts and other semiotic systems act merely as transmission codes rather than direct containers of knowledge. Ultimately, the proposed coherent model establishes two boundaries: an external boundary demarcating science from non-science, and an internal boundary distinguishing verified true knowledge from knowledge yet to be validated. This approach maintains a firm commitment to the correspondence theory of truth (reflection theory) while successfully accommodating the constructivist dimensions of human cognition.

The article presents a philosophical and methodological analysis of technical cybernetic languages, focusing on the emergence of a “third artificial nature” – an environment comprising cyber-technical systems capable of self-reflection and self-development. The study traces three evolutionary orders of cybernetics: Norbert Wiener’s first-order cybernetics, Heinz von Foerster’s second-order cybernetics of observing systems, and V.E. Lepskiy’s post-nonclassical third-order cybernetics, which deals with self-developing, poly-subject environments. For each level, the author establishes a mapping between the type of cyber-technical system and the structural characteristics of its descriptive language. The analysis shows that while second-nature computing systems implementing Turing-complete formalisms reach the theoretical limits of machine information processing, they remain fundamentally inadequate for describing systems that can autonomously alter their own semantic rules. The shift toward self-developing technical systems requires adopting a processual approach to semantics (drawing on the work of V.A. Lektorsky and A.V. Smirnov). In this framework, meaning is not a statically defined correspondence, but rather a dynamic, time-bound interpretive process that continuously generates new semantic linkages. To capture this shift, the article introduces and substantiates the concept of “open semantics” as the defining feature of languages belonging to the third artificial nature. The author defines semantic openness as the capacity for recursive-inductive extensions of a language’s semantic domain during technical semiosis, wherein recursion acts as the mechanism by which a system alters the semantic scope of its own syntactico-semantic rules. The article also proposes the concept of “Gödel completeness” to characterize the extensional completeness of such open-semantic languages. This reframes the traditionally negative implications of Kurt Gödel’s incompleteness theorems: the semantic incompleteness of formal languages that allow for self-referential extensions is instead reinterpreted as a positive feature – one that guarantees the evolutionary potential of the linguistic model. The languages of the third artificial nature (Gödel-complete languages of recursive processes) provide a crucial foundation for modeling artificial consciousness, including the representation of both the world and the subject within it (following V.A. Lefebvre). Furthermore, these languages offer a theoretical pathway to overcoming the fundamental limitations of modern large language models, which remain constrained by the static and probabilistic nature of their semantics.

Given the growing significance of neuroscience for political theory and practice, the article explores the problem of integrating contemporary neurobiological data into the conceptual framework of the philosophy of politics. The study analyzes the results of empirical research revealing the neurobiological mechanisms underlying conscious political experience. The primary aim is to articulate the philosophical implications of recent neuroscientific findings, thereby mapping the theoretical landscape of the emerging interdisciplinary field of the neurophilosophy of political consciousness. Drawing on a review of highly cited Russian and international research published between 2019 and 2024, the paper frames political consciousness as a network of neural patterns whose activity corresponds to dynamic conscious experiences shaped by politically oriented mental imagery. This political dimension of consciousness – comprising the mental representations and psychophysiological mechanisms used to process politically significant stimuli – encompasses core concepts of political philosophy, such as power, the state, citizenship, and freedom. The author argues that neuroscientific data can significantly enrich our philosophical understanding of political consciousness, particularly through the lens of methodological naturalism. Conversely, the philosophy of politics can provide an ontology suitable for the empirical, natural-scientific study of these phenomena. Philosophical reflection offers vital theoretical and methodological grounding for neurobiological research, safeguarding it against radical reductionism while helping to integrate disparate empirical findings. The study concludes by emphasizing the need to construct a general theory of political consciousness based on the convergence of methods from neurobiology, philosophy, psychology, and the social sciences.

The article explores the challenges of applying philosophical and neuroscientific theories of consciousness to artificial intelligence (AI) architectures, particularly large language models (LLMs). It argues that most of these theories were developed to explain biological consciousness and therefore cannot be seamlessly mapped onto artificial systems without risking oversimplified or unfounded analogies. This presents a conceptual challenge in the study of modern AI: while these systems demonstrate a capacity for complex “thinking,” there remains no compelling basis to attribute consciousness to them. To address this gap, the study adopts David Dubrovsky’s information theory of consciousness as its methodological foundation. Unlike classical philosophical approaches that postulate special entities to account for conscious perceptual experience, Dubrovsky’s framework offers a functional-informational account of subjective reality. Drawing on the principles of code dependence and substrate independence (the invariance of information relative to its physical carrier), the theory frames consciousness as the product of self-determination within a multilevel informational structure – an “ego-system.” This perspective provides a viable pathway for modeling consciousness on non-biological substrates. To operationalize these theoretical premises, the paper proposes two experimental architectures. The first aims to equip LLMs with an intrinsic or “self-context” – an autonomous, dynamic informational field that serves as a functional analogue to inner experience. Through interaction with the external environment and other agents, the model employs reinforcement learning to optimize this field, effectively emulating the ability to process “information about information.” The second architecture introduces a two-tiered cognitive system that separates continuous (sub-symbolic) computation from a discrete linguistic interface. By enabling controlled modifications, these designs allow researchers to formulate testable hypotheses and empirically investigate the mechanisms behind phenomena typically associated with consciousness, disentangling them from the machine’s baseline cognitive functions. The proposed approach does not purport to resolve the hard problem of consciousness. Rather, it highlights the substantial heuristic potential of Dubrovsky’s theory, whose principles of structural and functional organization of informational systems show a notable convergence with current trajectories in the development of advanced AI architectures.

The article addresses the problem of mental causation – the question of how phenomena of subjective reality, which lack physical characteristics, can determine bodily processes. The author examines the distinctive features of the informational approach to the mind–brain problem and traces the development of his informational theory of consciousness from its origins in the 1960s, while also engaging with methodological objections raised against it from within analytic philosophy. A fundamental distinction is drawn between informational and physical causation: unlike the latter, the effect in informational causation is determined not by the physical properties of the substrate, but by an established code dependence that remains invariant with respect to those properties. Mental causation is understood as a specific form of informational causation, realized through neurodynamic code transformations in the brain. The author also explores the relationship between the mental and the physical, the problem of subjective reality, the category of the ideal, the principle of isofunctionalism of systems, and its epistemological implications. The article demonstrates the significance of the informational approach for contemporary neuroscience, particularly for decoding the neurodynamic brain codes of subjective reality phenomena. The author critically examines the interpretation of his theory offered by A.A. Zhudina and refutes her claims regarding the reduction of the mental to the physical, the identity of the mental and the physical, the causal closure of the physical domain, and the principle of supervenience. The study concludes that without a solid theoretical account of mental causation, neither a coherent theory of consciousness nor a scientifically rigorous explanation of free will is achievable.

The article analyzes David Dubrovsky’s approach to informational causation in comparison with research on mental causation in the analytic philosophy of mind. Central to this analysis is the question of whether consciousness and mental properties can serve as causes of physical events. Building on Dubrovsky’s premise that his conceptual framework is commensurable with the terminology of analytic philosophy, the paper discusses three core principles underlying contemporary debates on mental causation: the causal completeness of the physical, the no-overdetermination principle, and supervenience. Dubrovsky positions his theory as a form of non-reductive materialism and explicitly critiques reductionist accounts. However, the present author asserts that Dubrovsky’s core thesis – that information must necessarily be instantiated in neurodynamic processes – is structurally equivalent to the principle of the causal closure of the physical. Furthermore, within this context, the terms “materialism” and “physicalism” should be treated as interchangeable, as should “the principle of the causal completeness of the physical” and Dubrovsky’s “principle of the material unity of the world.” Embracing the causal completeness of the physical naturally preserves the ban on overdetermination, leading to the conclusion that mental states lack independent causal efficacy over physical processes. Consciousness can influence behavior only by virtue of its embodiment in the physical, and this embodiment of consciousness in brain processes constitutes a core tenet of Dubrovsky’s theory. The article argues that the mind–brain relationship explored by Dubrovsky effectively corresponds to the logical supervenience of the mental upon the physical, thereby allowing for a reductive explanation of both informational causation and consciousness as a whole. The author concludes that Dubrovsky’s framework most closely aligns with a reductive variant of physicalism – specifically, mind–brain identity theory.