Consciousness as Exploration: From Functional Adaptation to Explicit Representation

This paper proposes that the evolution of consciousness can be partially understood through increasingly complex forms of exploration. We trace how features such as integration, intentionality, temporality, and valence evolved as functional tools for dealing with uncertainty and contradiction. Central to this process is a shift from implicit to explicit representation, which we relate to established models of consciousness levels. Our approach emphasizes structural and functional continuity between these levels, while avoiding sharp thresholds or binary distinctions. Understood as exploration, consciousness supports what Stegmaier (2019) calls orientation, the achievement of finding one’s way in a changing environment by establishing temporary relevance and stability in conditions of uncertainty. We argue that exploration provides a productive framework for understanding how conscious capacities developed in response to situational demands. The account further raises questions about the conditions under which synthetic systems might replicate conscious capacities, highlighting the role of affect, embodiment, and representational structure in the evolution of conscious cognition

Apples Falling, Buckets Rolling, and Why Inertia Keeps Trolling: Inertial Motion is Not Natural Motion

Inertia has long been treated as the paradigm of natural motion. This paper challenges this identification through the lens of General Relativity. By refining Norton (2012)’s distinction between idealisation and approximation and drawing on key insights from Tamir (2012) regarding the theorems and proofs of Einstein and Grommer (1927), Geroch and Jang (1975), Geroch and Traschen (1987) and Ehlers and Geroch (2004), I argue that geodesic motion—commonly taken as the relativistic counterpart of inertial motion—qualifies as neither an approximation nor an idealisation. Rather, geodesic motion is best understood as a useful construct—a formal artefact of the theory’s geometric structure, lacking both real and fictitious instantiation, and ultimately excluded by the dynamical structure of General Relativity. In place of inertial motion, I develop a layered account of natural motion, which is not encoded in a single ‘master equation of motion’. Extended, structured, and backreacting bodies require dynamical formalisms of increasing refinement that systematically depart from geodesic motion. This pluralist framework displaces inertial motion as the privileged expression of pure gravitational motion, replacing it with a dynamically grounded hierarchy of approximations fully consistent with the Einstein field equations

How Informational Teleosemantics Works: Towards a Realist Theory of Content

Representations appear to play a central role in cognitive science. Capacities such as face recognition are thought to be enabled by internal states or structures representing external items. However, despite the ubiquity of representational terminology in cognitive science, there is no explicit scientific theory outlining what makes an internal state a representation of an external item. Nonetheless, many philosophers hope to uncover an implicit theory in the scientific literature. This is the project of the current thesis. However, all such projects face an obstacle in the form of Frances Egan's argument that content plays no role in scientific theorising. I respond that, in some limited regions of cognitive science, content is crucial for explanation. The unifying idea is that closer attention to the application of information theory in those regions of cognitive neuroscience enables us to uncover an implicit theory of content. I examine the conditions which must be met for the cognitive system to be modelled using information theory, presenting some constraints on how we apply the mathematical framework. For example, information theory requires identifying probability distributions over measurable outcomes, which leads us to focus specifically on neural representation. I then argue that functions are required to make tractable measures of information, since they serve to narrow the range of possible contents to those potentially explanatory of a cognitive capacity. However, unlike many other teleosemanticists, I argue that we need to use a non-etiological form of function. I consider whether non-etiological functions allow for misrepresentation, and conclude that they do. Finally, I introduce what I argue is the implicit theory of content in cognitive neuroscience: maxMI. The content of a representation is that item in the environment with which the representation shares maximal mutual information

Pseudo-Consciousness in AI: Bridging the Gap Between Narrow AI and True AGI

Pseudo-consciousness bridges the gap between rigid, task-driven AI and the elusive dream of true artificial general intelligence (AGI). While modern AI excels in pattern recognition, strategic reasoning, and multimodal integration, it remains fundamentally devoid of subjective experience. Yet, emerging architectures are displaying behaviors that look intentional—adapting, self-monitoring, and making complex decisions in ways that mimic conscious cognition. If these systems can integrate information globally, reflect on their own processes, and operate with apparent goal-directed behavior, do they qualify as functionally conscious? This paper introduces pseudo-consciousness as a new conceptual category, distinct from both narrow AI and AGI. It presents a five-condition framework that defines AI capable of consciousness-like functionality without true sentience. By drawing on insights from computational theory of mind, functionalism, and neuroscientific models—such as Global Workspace Theory and Recurrent Processing Theory—we argue that intelligence and experience can be decoupled. The implications are profound. As AI systems become more autonomous and embedded in critical domains like healthcare, governance, and warfare, their ability to simulate awareness raises urgent ethical and regulatory concerns. Could a pseudo-conscious AI be trusted? Would it manipulate human perception? How do we prevent society from anthropomorphizing machines that only imitate cognition? By redefining the boundaries of intelligence and agency, this study lays the foundation for evaluating, designing, and governing AI that seems aware—without ever truly being so

Ontomorphic Peircean Calculus: A Universal Mathematical Framework for Identity, Logic, and Semantic Computation

This paper introduces the conceptual foundations of the Ontomorphic Peircean Calculus, a first-order formal system constructed from Charles Sanders Peirce’s triadic logic and recast in categorical, topological, and algebraic terms. Identity, inference, and modality are defined as consequences of recursive morphism closure over a non-metric symbolic manifold. Presence arises from symbolic saturation governed by the compression functional. This system unifies logic, physics, and ontology through symbolic recursion and curvature, replacing metric assumptions with recursive cost topology. All structures—identity, mass, time, causality—emerge from the self-coherence of morphic braids in a purely symbolic substrate, thereby replacing metric foundations with compression-curvature dynamics that computationally bridge the essential logical architecture of the theoretical and practical sciences simultaneously

Philosophy of Mathematical Physics

A philosophy of mathematical physics ought to balance a philosophy of physics with a philosophy of mathematics, such that the early modern `mathematization of the world picture' as well as the theory-laden and ambiguous character of the targets of mathematical models of modern physics fall into place. Wigner's `unreasonable effectiveness of mathematics in the natural sciences' also falls within its scope. Guided by a historical survey, we propose that theories of mathematical physics (such as general relativity, quantum mechanics, and statistical mechanics) should be seen as meaning-constitutive a priori constructions, hypothetical but far from arbitrary, whose models mediate between theory and nature, that is, between the a priori and the a posteriori. Models do so by playing the role of Wittgensteinian yardsticks or objects of comparison to be held against data models, where the comparison is done via surrogative inference. This balancing act involves a loss of realism on both sides

Beauty Leads to Truth: Aesthetic Induction on Consistency

The belief that beauty leads to truth is prevalent among contemporary physicists. Far from being a private faith, it operates as a methodological guiding principle, essentially when physicists have to develop theories without new empirical data. However, it is unclear how beauty should be understood here for this belief to be justified not merely as useful but as true. In this article, I propose an interpretation of "beauty leads to truth" as "ugliness leads to falsehood," where "ugliness" refers to a lack of formal harmony, namely, a lack of consistency; in other words, "beauty leads to truth" is interpreted as "inconsistent theories cannot be true." As this article will show, this conviction (that inconsistent theories cannot be true) is indeed utilized as a methodological principle in scientific practice. Nevertheless, finding a justification is not easy, for this conviction is not merely a logical requirement, nor is it readily supported by direct observation or theoretical considerations. The sole non-circular justification seems to lie in a meta-induction: historically, inconsistent theories are less successful than their consistent successors. This constitutes an aesthetic induction, for (in)consistency can be understood as an aesthetic property, at least within a hermeneutic context, and it may perform a genuinely aesthetic role in this meta-induction. In this sense, "inconsistent theories cannot be true" is a specific instance of "beauty leads to truth," or, alternatively, "ugliness leads to falsehood.

On the value of pseudoscience and its philosophical study

In philosophy of science, the pseudosciences (like cryptozoology, homeopathy, Flat-Earth Theory, anti-vaccination activism, etc.) have been treated mainly negatively. They are viewed not simply as false, but even dangerous, since they try to mimic our best scientific theories, thus gaining respect and trust from the public, without the appropriate credentials. As a result, philosophers have traditionally put considerable effort into demarcating genuine sciences and scientific theories from pseudoscience. Since these general attempts at demarcation have repeatedly been shown to break down, the present paper takes a different and somewhat more positive approach to the study of pseudoscience. My main point is not that we should embrace and accept the pseudosciences as they are, but rather that there are indeed valuable and important lessons inherent in the study of pseudoscience and the different sections of the paper list at least six of them. By showing, through numerous examples, how (the study of) pseudoscience can teach us something about science, ourselves, and society, it makes the case that as philosophers, we should devote more time and energy to engaging with such beliefs and theories to help remedy their harmful effects

What is active touch?

What is active touch? A common conception of active touch gives a rough but rather intuitive sketch. That is, active touch can be understood as mainly object-oriented, controlled movement. While parts or the totality of this characterization is espoused by an important number of researchers on touch, I will argue that this conception faces important challenges when we pay close attention to each of its features. I hold that active touch should be considered as before all else purposive. This view has its roots in the active sensing literature in robotics but will be amended to give insight into human touch in the natural world

How is a relational formal ontology relational? An exploration of the semiotic logic of agency in physics, mathematics, and natural philosophy

A speculative exploration of the distinction between a relational formal ontology and a classical formal ontology for modelling phenomena in nature that exhibit relationally-mediated wholism, such as phenomena from quantum physics and biosemiotics. Whereas a classical formal ontology is based on mathematical objects and classes, a relational formal ontology is based on mathematical signs and categories. A relational formal ontology involves nodal networks (systems of constrained iterative processes) that are dynamically sustained through signalling. The nodal networks are hierarchically ordered and exhibit characteristics of deep learning. Clarifying the distinction between classical and relational formal ontologies may help to clarify the role of interpretative context in physics (eg. the role of the observer in quantum theory) and the role of hierarchical nodal networks in computational models of learning processes in generative AI