We propose that consciousness arises from the brain’s recursive self-modeling and compression of its own complex activity, yielding a unified and coherent subjective experience (Baars, 1988; Dennett, 1991; Tononi, 2004). On this view, the brain constructs a high-level, low-dimensional summary of its own operations that preserves behaviorally and introspectively relevant structure while discarding fine-grained neural detail. This theory is supported by convergent evidence from neurology, psychiatry, pharmacology, developmental psychology, and comparative cognition. Disruptions to recursive self-modeling or integrative compression reliably impair (...) or fragment consciousness, while their integrity correlates with the richness and unity of subjective experience (Gazzaniga, 2000; Alkire et al., 2008; Lyssenko et al., 2018; Bertenthal & Fischer, 1978; Beran, 2019). This synthesis provides a parsimonious and empirically grounded account of consciousness, reframing the so-called “hard problem” (Chalmers, 1995) and the mystery of qualia (Nagel, 1974) as cognitive illusions arising from the structural and introspective limitations of the self-model itself (Dennett, 1991; Frankish, 2016). (shrink)

Traditional memetics failed as a research program due to unresolved problems concerning units, transmission fidelity, explanatory mechanism, and theoretical isolation. This paper proposes a fundamental reformulation grounded in Universal Constraint Parsing (UCP) and Level-Conditional Rendering (LCR), treating memetic patterns as the basic units of adaptive information processing across all substrates and scales. -/- We argue that memetic patterns are substrate-independent, context-sensitive configurations encoding behavior and abstract content. What biologists call genes and what Dawkins called memes are both memetic patterns differing (...) in storage substrate, transmission mechanism, and context-range. Genetic encoding uses a rigid substrate (DNA) optimized for long-term storage; the patterns themselves become fluid and context-responsive once expressed. Learned patterns occupy various positions along a spectrum from deeply embedded to highly flexible. -/- Cognition is the acquisition, storage, combination, and context-adaptive deployment of memetic patterns through pattern selection dynamics. The same constraint-satisfaction logic that governs physical systems operates at the cognitive scale through domain-specific constraints. Learning is acquiring patterns and refining context-fitting. Intelligence is the capacity for functional equivalence across structural difference under active constraints. -/- Consciousness is approached not as emerging from memetic processing, but as jointly requiring both architectural structure and coherent pattern content: neither architecture alone nor patterns alone constitutes conscious experience, but together they are jointly necessary and sufficient. The stable orbit of recursive self-modeling operating on coherent content is what consciousness is. This reframing dissolves traditional problems while connecting memetics to physics, biology, and cognitive science through a unified constraint-satisfaction framework. -/- . (shrink)

We argue that consciousness is not an additional metaphysical ingredient but a property of certain computational architectures: systems that (i) integrate information across many specialized processes via global broadcast, (ii) operate through recurrent, intersecting feedback loops (often realized as predictive/active-inference hierarchies), and (iii) construct a compressed, recursively updated self-model of their own ongoing processing. On this view, the familiar "what-it's-like" character of experience is an introspective user-illusion: a simplified, low-dimensional interface generated by the self-model, which is behaviorally indispensable but mechanistically (...) opaque from the inside. We synthesize convergent empirical evidence from neurology, anesthesia, psychedelics, dissociation, development, and comparative cognition, and then present an explicit architectural account that unifies global workspace theory, predictive processing, and illusionism. We defend substrate-independence in the relevant sense (architecture over material), propose an empirically tractable robustness program for estimating degrees of consciousness, and derive implications for artificial systems, ethics, and future experimental work. (shrink)

Does the universe run on computational principles because it is a simulation, or do computational systems succeed because they capture how reality actually works? We argue for the latter through Universal Constraint Parsing (UCP)—a framework showing that constraint-based selection operates throughout physical reality from quantum mechanics to consciousness. Computational systems work precisely because they can instantiate this natural mechanism, not because reality is itself computational. We examine the simulation hypothesis literature (Bostrom 2003; Chalmers 2005), analyze the relationship between UCP and (...) computational models, and show why the deep effectiveness of computation in science reflects variational and constraint-selection structural matching rather than ontological identity. This undercuts the explanatory motivation for the simulation hypothesis by explaining computational success without requiring reality to be a computation. We conclude that UCP provides a more parsimonious explanation: computation succeeds when it mirrors constraint-parsing mechanisms already present in nature. (shrink)

Right now, you're deciding whether to keep reading. You could stop. You could scroll to something else. You could close this tab and go make coffee. But you're choosing to continue. That choice feels completely free—it's yours, made by you, and you could have chosen otherwise. -/- Except you couldn't have. Not really. -/- Here's why...

Universal Darwinism correctly identified that selection dynamics appear across domains—biological, cultural, epistemic, technological. But it inherited from biology an assumption that has limited its generality: that selection requires replication. This assumption generates persistent puzzles about what counts as a "replicator" and whether replication is truly necessary for evolutionary dynamics. -/- We propose that the fundamental mechanism underlying selection is differential propagation under constraints—not replication. Replication is one implementation of this mechanism, not a requirement for it. This reframing dissolves the replicator (...) problem, extends evolutionary logic to domains where copying is absent or fuzzy, and reveals selection as a consequence of constraint satisfaction dynamics formalized through free-energy minimization. -/- The implications extend from physics through culture: wherever patterns exist in possibility space and constraints define viability, selection occurs. Replication accelerates and channels this process but does not constitute it. (shrink)

AI systems exhibit a failure mode more insidious than simple hallucination: confident dismissal without evaluation. When asked about unfamiliar work, these systems pattern-match to stereotypes, fabricate critical consensus, and attribute positions to named scholars who never made them. I call this failure mode Epistemic Authority Overreach: systems asserting evaluative authority—judgments about what is valid, credible, or worth investigating—without having performed the evaluation that would warrant such authority. The downstream consequence is epistemic pollution: false beliefs about the standing of ideas entering (...) the information commons through mechanisms that look like legitimate knowledge transfer. Unlike factual errors, dismissive assertions close inquiry rather than inviting verification. This paper documents two case studies demonstrating the failure mode, analyzes why it evades current safety frameworks, and proposes structural interventions. The problem is not that AI systems lie; it is that they perform epistemic authority they do not possess, and their confident closure of inquiry spreads without correction. (shrink)

The same mechanism operates from quarks to consciousness. We call it Universal Constraint Parsing (UCP)—constraints at each level evaluate entities against possibility spaces, accepting configurations that fit, rejecting those that don't. Quarks are parsed by QCD field constraints. Molecules are parsed by thermodynamic constraints. Organisms are parsed by ecological constraints. Beliefs are parsed by evidential constraints. Memes are parsed by cultural constraints. Self-models are parsed by architectural constraints. This isn’t metaphor or loose analogy—across domains, selection dynamics belong to the same (...) formal equivalence class of constraint-satisfaction processes under scale-appropriate coarse-graining. While state variables and effective free-energy functionals differ by level, the underlying variational structure and selection dynamics are preserved. Crucially, UCP operates without agency—no designer, no purpose, no teleology required at any level. Agency itself emerges as a functional property when systems begin recursively parsing their own parsing at the consciousness threshold. We demonstrate UCP across all levels, show what emerges at complexity thresholds (replication, learning, consciousness), explain how existing frameworks (Universal Darwinism, memetics, Free Energy Principle) relate to UCP, and derive empirically constraining expectations. The framework is falsifiable and unifies phenomena previously treated as separate. It's physics all the way—mechanistic constraint satisfaction at every level. (shrink)

Universal Constraint Parsing (UCP) is not a domain-specific scientific theory, nor a proposal for new physical laws. It does not compete with physics, biology, cognitive science, or social theory at the level of empirical detail or local prediction. Instead, UCP is a foundational explanatory principle: a claim about the form that successful explanations of persistence, selection, and adaptive structure must take across domains. -/- At its core, UCP states that: Wherever structured persistence occurs, it is the result of differential propagation (...) under constraints. -/- This is a claim about mechanism at the most abstract level—about why certain patterns endure, spread, or stabilize, rather than how any particular system implements that outcome. Understanding UCP's epistemic status therefore requires a clear separation between principles, which constrain the space of possible explanations, and process theories, which specify concrete mechanisms within particular domains. UCP belongs squarely in the former category. (shrink)