“Differences That Make a Difference”: Why Constraint-Based Explanations Succeed Where “Basal Cognition” Fails

A difference only “makes a difference” if it can persist long enough to constrain what comes next. Everything else is noise that thermodynamics erases.

Prologue: The Question That Would Not Stay Answered

In 1979, the year I was born, Gregory Bateson published Mind and Nature: A Necessary Unity and posed a question that would haunt systems theory, cybernetics, and philosophy of mind for nearly half a century. What is the pattern which connects all patterns? Bateson circled the answer beautifully, calling it “a metapattern,” the pattern which connects is itself a pattern of patterns. His earlier formulation in Steps to an Ecology of Mind (1972), “a difference which makes a difference,” gave the elementary unit of information, the minimal act of distinction that allows anything to be known at all. He never specified the constraint structure that would make such an answer operational. He sadly passed away the following summer, on July 4, 1980, the question still open.

The candidates accumulated across decades. Mind, said Bateson himself, tentatively. Information, said the cyberneticians following Shannon and Wiener. Recursion, proposed Douglas Hofstadter in Gödel, Escher, Bach. Process, argued the Whiteheadians. Mathematics, claimed Max Tegmark. Consciousness, insisted the panpsychists. Self-organization, offered Stuart Kauffman. Free energy minimization, suggested Karl Friston. The list is effectively unbounded. Any concept claiming universality becomes a candidate. That generates the deeper problem: without criteria for distinguishing correct answers from merely plausible ones, the question produces discourse without resolution.

This essay takes a different approach to Bateson’s question, grounded in thermodynamics, constraint theory, and falsification-first methodology. The answer that emerges adds nothing to the ontological inventory. It identifies a selection principle that explains why any pattern persists at all: constraint satisfaction under thermodynamic bounds. Differences that persist constrain what comes next. Differences that do not constrain what comes next are erased by noise, dissipation, or indifference. The pattern which connects is the invariant condition under which information, mind, structure, or process could exist long enough to be observed.

The principle does not compete with other frameworks. It subsumes them. It describes neither what exists, nor how we know, nor why there is something rather than nothing. It marks the boundary of what can remain available to explanation at all. And it is falsifiable. Find a pattern that persists without thermodynamic cost. Find a connection mechanism that does not reduce to constraint satisfaction. The principle fails.

What follows makes that claim rigorous, shows why recent metaphysical interpretations of biological efficiency fail where constraint-based explanations succeed, and demonstrates that the real explanatory work is already being done by frameworks that are experimentally grounded, mechanistically specified, and willing to lose. The question of what connects is also, it turns out, the question of what survives.

What This Essay Does Not Claim

Before the argument proceeds, a discipline of explicit modesty. This essay does not claim that consciousness has been explained away. It does not claim that subjective experience is illusory. It does not claim that panpsychism has been refuted by fiat. It does not claim that “basal cognition” researchers are wrong about their phenomena, only that the phenomena are already explained by constraint propagation under thermodynamic limits and that the cognitive vocabulary adds no predictive content that the constraint-based account does not already supply.

What is claimed is narrower and more defensible: that adding “cognition,” “intelligence,” “purpose,” or “experience” as primitives to explain efficient biological function is unfalsifiable, that the operative explanatory work is being done by constraint closure and dissipative dynamics, and that frameworks that cannot specify their own failure conditions are not contributing to scientific knowledge regardless of their fecundity in producing papers, conferences, and vocabulary.

Nor does this essay endorse a naive demarcation criterion between science and metaphysics. Following Lakatos rather than the early Popper, the issue is not whether individual claims are falsifiable in isolation but whether entire research programmes remain progressive: generating novel predictions, recovering from anomalies through principled modification, and staying continuous with our best empirical knowledge. Metaphysics that implies how empirical reality must be structured is doing legitimate work even when not directly testable. The worry is reserved for frameworks that construct themselves to survive every possible observation, which is a different thing entirely. Following Ladyman and Ross’s principle of naturalistic closure, metaphysics earns its keep when it remains continuous with our best empirical science, when it does the work of unifying or interpreting empirical findings rather than insulating itself from them.

The strongest version of the claim is what the rest of the essay argues for. Weaker versions, that some cognitive framing is heuristically useful, that the phenomena are real and important, that further empirical work is warranted, are conceded without contest.

Part I: The Structural Problem (Efficiency Is Not a Mechanism)

The Recurring Argument Pattern

A recurring temptation runs through the sciences of mind, life, and complexity. When confronted with systems that are efficient, robust, adaptive, or astonishingly competent, the move is to reach for a special noun. Intelligence. Cognition. Purpose. Agency. Consciousness. The word changes, but the move stays the same. Observe an impressive outcome, compare it to a weak or inappropriate null model, declare the gap too large to be bridged by ordinary mechanisms, install a new primitive to do the explanatory work. The noun becomes the answer. The question quietly disappears.

Consider the structure carefully. A biological system solves a problem efficiently. Regeneration. Error correction. Morphological repair. Goal-directed behavior. That performance gets compared to a null model, typically random search or maximal entropy sampling. The gap is large, sometimes astronomically large. The inference follows: something special must be doing the work. Intelligence. Cognition. A problem-space being searched. A goal being represented. The move should set off alarms. It conflates two fundamentally different claims: that the system is impressive, and that a cognitive primitive explains why.

Efficiency is an output, not a mechanism. It is the result of a process, not an explanation of one. Defining intelligence as efficient problem-solving, measuring efficiency, and concluding intelligence discovers nothing. The conclusion was already in the premise. The same logical structure plagued “irreducible complexity” arguments in intelligent design discourse: define a property such that it cannot arise gradually, find systems matching the definition, announce that design is required. Vocabulary varies; structure persists.

The null model matters enormously. Comparing an evolved, developmentally constrained, historically filtered system to random assembly is not illuminating; it is misleading. Evolution is the search process. Development is constraint propagation. The present phenotype embodies compressed history. Billions of years of selection, developmental canalization, and constraint satisfaction have shaped every molecular interaction. Measuring the endpoint against an incoherent starting distribution and calling the gap “cognition” mistakes the record of selection for a new causal ingredient.

William Wimsatt’s work on robustness analysis (DOI: 10.1086/288763) provides the methodological corrective. Wimsatt showed that what we take to be “real” in scientific practice is typically what remains invariant across multiple independent methods of detection, derivation, or measurement. Robustness is a property of how things survive different forms of probing. Applied to the efficiency-cognition inference: does the “intelligence” we detect remain invariant when we change our detection method, or does it dissolve when we stop comparing to inappropriate null models? The answer, repeatedly, is the latter. Constraint satisfaction remains robust. The cognitive gloss dissolves.

Why This Matters Beyond Academic Dispute

The concern is narrower than “unfalsifiable frameworks are bad.” Some metaphysical claims are empirically inert by their honest design, certain modal claims, mathematical structuralism about possibilia, particular categorial frameworks, and pose no threat to anyone. The concern is frameworks that make implicit causal or structural claims about the empirical world while being constructed such that no observation could revise them. Those frameworks do harm because they spend the credibility of empirical inquiry without paying its costs. They make claims that the world should be able to talk back to and then arrange themselves so it cannot.

Worse, the move normalizes a specific inferential pattern, observe efficiency gap, invoke special cause, immunize against revision, that has been used to defend genuinely anti-scientific positions throughout the history of biology, from vitalism through intelligent design to current claims about Platonic minds influencing development. The pattern is what’s at issue, not the existence of metaphysics.

The stakes are epistemic and ethical. Epistemic, because frameworks that insulate themselves from contact with empirical reality accumulate interpretations rather than knowledge. Ethical, because generations of scientists, thinkers, and experimenters spent their lifetimes building constraint-based frameworks precisely because intuition and metaphor kept failing. Every advance in thermodynamics, information theory, developmental biology, and neuroscience came from refusing to stop at vibes. People froze, starved, died of infection, got exiled, or were ignored for insisting that explanations cash out in mechanisms, constraints, and testable consequences. Installing a primitive that has been designed to be untouchable by any conceivable evidence, after that labor, is a quiet betrayal of the discipline that made knowledge possible.

Part II: Information Is Physical (The Thermodynamic Ground Floor)

Landauer’s Principle and the Cost of Distinction

Constraint-based explanation rests on physical law rather than philosophical preference. In 1961, Rolf Landauer published a paper that reshaped the relationship between information and physics: “Irreversibility and Heat Generation in the Computing Process” (IBM Journal of Research and Development, DOI: 10.1147/rd.53.0183). Landauer demonstrated that logically irreversible operations, operations that destroy information, such as erasing a bit, necessarily produce heat. The minimum energy dissipated per bit erased is kT ln 2, where k is Boltzmann’s constant and T is temperature. At room temperature (300 K), with k = 1.380649 × 10⁻²³ J/K and ln 2 ≈ 0.69315, this comes to approximately 2.87 × 10⁻²¹ joules per bit.

This is no engineering limitation to be overcome with better technology. It is a fundamental constraint arising from the second law of thermodynamics. Information is physical. Maintaining a distinction between 0 and 1 requires physical substrate. Erasing that distinction releases energy. There is no free information, no costless computation, no disembodied memory. An explanation that treats information or cognition as causally efficacious while exempting it from energetic accounting is incomplete by definition, missing the physics rather than committing some subtle philosophical error.

Charles Bennett extended Landauer’s work in his 1973 paper “Logical Reversibility of Computation” (IBM Journal of Research and Development, DOI: 10.1147/rd.176.0525). Bennett demonstrated that while computation itself can be thermodynamically reversible (dissipating arbitrarily close to zero energy per step), maintaining a result against thermal noise cannot be. The essential irreversible step is erasure, the forgetting of intermediate states, rather than measurement or computation per se. This resolved the century-old puzzle of Maxwell’s Demon. The demon appears to violate the second law by selectively allowing fast molecules through a gate, but the demon must remember which molecules it allowed. Eventually the demon’s memory fills up and must be erased, and that erasure produces exactly the entropy the demon seemed to avoid.

In 2012, Bérut and colleagues experimentally verified Landauer’s principle directly (Nature, DOI: 10.1038/nature10872). Using a colloidal particle trapped in a double-well potential created by optical tweezers, they measured the heat dissipated during bit erasure and found it saturated at the Landauer bound in the limit of slow erasure cycles. The measured heat dissipation matched the theoretical minimum to within a few percent. This is laboratory physics, not thought experiment. Information erasure costs energy, and the cost has been measured.

Every distinction maintained, every boundary, every memory, every pattern that persists, pays thermodynamic rent. Bennett’s 1982 review (International Journal of Theoretical Physics, DOI: 10.1007/BF02084158) surveyed biological molecular machines and found, in his exact wording, that “the molecular apparatus of DNA replication, transcription, and protein synthesis, whose components are truly microscopic, has a relatively high energy efficiency, dissipating 20-100 kT per nucleotide or amino acid inserted under physiological conditions” (p. 907). Far above the Landauer minimum, but still operating within thermodynamic constraints. The gap between biological dissipation and the theoretical minimum represents room for optimization, not exemption from physics. Life navigates within thermodynamic constraints with extraordinary skill accumulated over billions of years of selection.

The Death of Disembodied Information

This eliminates an entire class of explanatory moves in the cognition debate. Once information processing has irreducible physical cost, “information” cannot be invoked as a causal agent without specifying where the energy comes from and where the entropy goes. “Patterns” cannot be appealed to as causes without explaining how those patterns are maintained against thermal degradation. Cognition cannot be treated as a primitive without asking what physical substrate implements it and what thermodynamic transactions sustain it.

The Jarzynski equality (Physical Review Letters, 1997, DOI: 10.1103/PhysRevLett.78.2690) and subsequent work on fluctuation theorems have sharpened this. Jarzynski showed that the relationship between work and free energy holds through a precise equality involving exponentials of work: ⟨e^(-βW)⟩ = e^(-βΔF). This allows extraction of equilibrium free energy differences from non-equilibrium measurements and gives a rigorous framework for understanding how far systems can deviate from minimum dissipation. Crooks’ fluctuation theorem (Physical Review E, 1999, DOI: 10.1103/PhysRevE.60.2721) extended this, relating the probability of observing a given entropy production to the probability of observing its time-reverse. Seifert’s comprehensive review (Reports on Progress in Physics, 2012, DOI: 10.1088/0034-4885/75/12/126001) synthesized decades of work into stochastic thermodynamics, a complete framework for understanding thermodynamic processes at the scale where fluctuations matter.

Physics provides not just constraints but accounting. Any proposed mechanism can be audited for thermodynamic consistency. The minimum cost of maintaining distinctions can be calculated. Dissipation can be identified and measured against predictions. This converts metaphysical speculation into empirical science. The question shifts from “does cognition exist?” to “does the proposed cognitive process pay its thermodynamic rent?” When the answer is no, the proposal violates physics rather than merely raising philosophical concerns.

Part III: Constraint Closure (How Organization Persists Without Goals)

The Montévil-Mossio Framework

Thermodynamics provides the accounting. Constraint theory provides the mechanism. The question is how entire organizations persist, how living systems maintain themselves far from equilibrium, how they reproduce, repair, and adapt, all without invoking goals, purposes, or cognitive primitives. The answer emerging from theoretical biology is constraint closure.

Maël Montévil and Matteo Mossio’s 2015 paper “Biological organisation as closure of constraints” (Journal of Theoretical Biology, DOI: 10.1016/j.jtbi.2015.02.029) provides the most rigorous formalization. They distinguish two fundamentally different causal roles: processes and constraints. Processes are thermodynamic changes, chemical reactions, flows, transformations. Constraints are entities that act upon processes while remaining conserved (at the relevant time scale) by those processes. A catalyst enables a reaction without being consumed by it. A membrane channels flow without being destroyed by it. A developmental boundary shapes morphogenesis without being erased by it.

The crucial insight is that biological organization arises when constraints exhibit closure. In their precise formulation: “in biological systems, constraints realise closure, i.e. mutual dependence such that they both depend on and contribute to maintaining each other.” This is organizational closure rather than circular causation in any vicious sense. The constraints that enable the system’s behavior are themselves produced and maintained by that behavior. Break the closure, and the system collapses. Maintain it, and robust function emerges without any need for external supervision, goals, or cognitive direction.

Moreno and Mossio’s book Biological Autonomy (Springer, 2015, DOI: 10.1007/978-94-017-9837-2) develops this framework into a full theory of biological organization. Autonomy, the capacity of living systems to self-maintain, arises precisely from constraint closure. An autonomous system goes beyond mere self-organization (many physical systems self-organize). It is organizationally closed: its constraints depend on one another in a way that constitutes a self-maintaining network. This is what distinguishes a cell from a crystal, an organism from a whirlpool. Both involve self-organization; only the former involves constraint closure.

The connection to thermodynamics is immediate. Maintaining constraint closure requires continuous energy throughput. Living systems are dissipative structures in Prigogine’s sense (DOI: 10.1007/978-3-663-01511-6_10). They maintain organization by exporting entropy to their environment. But they are organizationally closed dissipative structures, which is why they persist, reproduce, and evolve in ways that mere dissipative patterns like Bénard cells or hurricanes do not.

RAF Theory and the Origin of Closure

Stuart Kauffman anticipated much of this in his work on autocatalytic sets, beginning with his 1986 paper “Autocatalytic sets of proteins” (Journal of Theoretical Biology, DOI: 10.1016/S0022-5193(86)80047-9). Kauffman asked how self-sustaining organization could arise in a pre-biotic world without enzymes, without genetic information, without design. His answer was that reaction networks can close on themselves. If molecule A catalyzes the formation of molecule B, and B catalyzes C, and C catalyzes A, the network is autocatalytic. It produces the very catalysts it needs to function.

Wim Hordijk, Mike Steel, and Kauffman formalized this intuition in RAF (Reflexively Autocatalytic Food-generated) theory. Their 2004 paper (Journal of Theoretical Biology, DOI: 10.1016/j.jtbi.2003.11.020) established the mathematical foundations, and subsequent work (DOI: 10.1007/s10441-012-9165-1; DOI: 10.3390/ijms12053085) determined the conditions under which RAFs emerge. The key finding: autocatalytic closure is not rare. With realistic assumptions about catalytic promiscuity, where each molecule catalyzes only 1-2 reactions on average, well within what biochemistry allows, RAFs emerge with high probability once reaction networks reach modest size.

Xavier and colleagues (Proceedings of the Royal Society B, 2020, DOI: 10.1098/rspb.2019.2377) brought this into contact with actual biochemistry. They identified RAF structures within the metabolic networks of ancient anaerobic autotrophs, showing that autocatalytic closure is not merely theoretically possible but present in the deep branches of the tree of life. Hordijk’s historical review (Biological Theory, 2019, DOI: 10.1007/s13752-019-00330-w) traces the development of these ideas and summarizes Kauffman’s central insight: catalytic closure is what transforms a collection of inert molecular species into a collectively self-sustaining whole. Life, on this account, is an emergent collective property of catalytic polymers rather than a property of any single component.

The implications for the cognition debate are direct. If organizational closure explains how living systems persist, repair, and adapt, cognition need not be invoked as a primitive. Constraint closure is the mechanism. What looks like goal-directed behavior is the statistical signature of systems whose internal constraints maintain themselves more effectively than external perturbations destroy them. Purpose is an externally legible outcome of constraint closure under selection, rather than an inner property.

Part IV: Memory Without Representation (The Embedded Memory Framework)

Attractor Landscapes and Persistent States

If constraint closure explains how organizations persist, what explains memory? The traditional answer involves storage, patterns encoded in stable substrates and retrieved on demand. This model faces deep problems when applied to biological systems. Neural memory bears little resemblance to computer memory. Synaptic weights change constantly, neurons die and are replaced, and memories persist through upheavals that would destroy any conventional storage system. Morphological memory is stranger still. Planarian worms retain body-plan information through complete cellular turnover, and organisms regenerate complex structures without consulting any identifiable blueprint.

The emerging alternative treats memory as constraint closure applied to dynamical systems. John Hopfield’s landmark 1982 paper, “Neural networks and physical systems with emergent collective computational abilities” (PNAS, DOI: 10.1073/pnas.79.8.2554), showed that neural networks could store memories as stable fixed points of an energy landscape. Memory occupies a basin of attraction, a region of state space that the system tends to enter and remain in, rather than a particular location. Recall is relaxation to an attractor, not retrieval from storage.

This insight extends far beyond neural networks. Miller’s review (F1000Research, 2016, DOI: 10.12688/f1000research.7698.1) surveys how attractor dynamics explain working memory, decision-making, and cognitive flexibility. The framework applies equally to bioelectric memory. Pezzulo, LaPalme, Durant, and Levin (Philosophical Transactions of the Royal Society B, 2021, DOI: 10.1098/rstb.2019.0765) demonstrated that somatic pattern memories in regenerating organisms are realized through bistability and multistability, multiple stable states in a constraint landscape rather than explicit representation.

Law and Levin (Theoretical Biology and Medical Modelling, 2015, DOI: 10.1186/s12976-015-0019-9) modeled bioelectric memory mathematically, showing how resting potential bistability in cells creates memory without any representational machinery. A cell “remembers” its state by persisting in one of two stable voltage configurations. The memory is the constraint closure of ion channel dynamics rather than something stored anywhere. Perturb the cell, and it may shift to the other attractor, a different “memory” that then persists without further input.

The Durant Experiments: Memory as Constraint, Not Blueprint

The most striking experimental demonstration comes from Durant and colleagues in the Levin lab. Their 2017 paper, “Long-Term, Stochastic Editing of Regenerative Anatomy via Targeting Endogenous Bioelectric Gradients” (Biophysical Journal, DOI: 10.1016/j.bpj.2017.04.011), showed that brief bioelectric manipulation using the gap junction blocker octanol can permanently alter planarian body plans. Approximately 25% of amputated trunk fragments treated with octanol regenerated as two-headed worms, while 72% regenerated as normal one-headed worms. The crucial finding: these two-headed planarians remain two-headed through subsequent rounds of regeneration, even though the original perturbation is long gone. The “memory” of being two-headed persists indefinitely.

This result contradicts Platonic interpretations of morphogenesis directly. If organisms were accessing an abstract morphospace or consulting an ideal template, transient perturbations should be corrected and the system should return to the canonical form. Instead, the result is path dependence. The constraint landscape was reshaped by the intervention, and the system now relaxes to a different attractor. No reference back to an ideal; only forward propagation of whatever constraints currently obtain.

Durant and colleagues extended this in 2019 (Biophysical Journal, DOI: 10.1016/j.bpj.2019.01.029), identifying the critical bioelectric window. In their precise words: “briefly manipulating the endogenous bioelectric state by depolarizing the injured tissue during the first 3 h of regeneration alters gene expression by 6 h postamputation.” Early bioelectric signals establish anterior-posterior polarity, and altering those signals creates cryptic phenotypes, organisms that appear morphologically normal but harbor altered bioelectric states that manifest in subsequent regeneration. The memory lives in the voltage pattern, a distributed constraint closure that persists because it is dynamically stable, rather than in the genes or the morphology.

Memory does not require representation. It requires constraint closure that makes certain future states more likely than others. A system “remembers” insofar as its past has narrowed its future possibilities. This is what I have called the Embedded Memory Framework (EMF): memory is the reduction of admissible future states due to past dynamics. No storage required. No retrieval mechanism. Constraints that close on themselves and persist because they can.

Part V: Structure Without Substance (Why Relations Are Primary)

Ontic Structural Realism

If constraints are the mechanism and closure is the principle, what is the ontology? The answer emerging from philosophy of physics is radical. Substances drop out; structures remain. Relations are ontologically primary; objects are derivative.

James Ladyman and Don Ross’s Every Thing Must Go: Metaphysics Naturalized (Oxford, 2007, DOI: 10.1093/acprof:oso/9780199276196.001.0001) is the landmark statement of ontic structural realism (OSR). They argue that our best physical theories, quantum mechanics, quantum field theory, general relativity, systematically undermine the notion of objects with intrinsic properties. Quantum particles fail the principle of identity of indiscernibles; spacetime points lack individuality in general relativity; fields are more fundamental than particles in quantum field theory. What remains invariant across theory change is the structure of relations rather than the inventory of objects.

The slogan “relations without relata” captures the radical thesis. This goes beyond epistemic humility (we can only know structure) to ontological commitment (only structure exists). Ladyman’s original 1998 paper (Studies in History and Philosophy of Science, DOI: 10.1016/S0039-3681(98)80129-5) distinguished epistemic from ontic versions; subsequent work by French and Ladyman (Synthese, 2003, DOI: 10.1023/A:1024156116636) and French’s comprehensive The Structure of the World (Oxford, 2014, DOI: 10.1093/acprof:oso/9780199684847.001.0001) developed the framework in detail.

French and Krause’s Identity in Physics (Oxford, 2006, DOI: 10.1093/0199278245.001.0001) provides the technical foundations. They argue that quantum particles cannot be treated as classical individuals subject to standard identity relations, a claim that would be incoherent for ordinary objects but is straightforwardly defensible for entities that can be permuted without physical consequence. Their earlier paper with Redhead (British Journal for the Philosophy of Science, 1988, DOI: 10.1093/bjps/39.2.233) demonstrated that quantum statistics violate the principle of identity of indiscernibles, suggesting that quantum particles lack individual identity altogether.

Ladyman and Ross are also the source of the principle this essay leans on most heavily for keeping metaphysics honest: naturalistic closure. Their argument is not that metaphysics is bad. It is that metaphysics must remain continuous with our best empirical science to earn its keep. Metaphysics that does the work of unifying scientific findings is doing legitimate work. Metaphysics that floats free of any scientific accountability, which they call “neo-scholastic,” has stopped doing that work. The distinction matters for the rest of the argument.

Rovelli’s Relational Quantum Mechanics

Carlo Rovelli’s relational interpretation of quantum mechanics (International Journal of Theoretical Physics, 1996, DOI: 10.1007/BF02302261) pushes this further. Rovelli argues that quantum mechanics describes relations between systems rather than absolute states. In his own formulation: “quantum mechanics is a theory about the physical description of physical systems relative to other systems, and this is a complete description of the world” (1996, pp. 1638–9). There are no observer-independent values; there are only relative facts, facts that hold for one system relative to another.

This dissolves the measurement problem by denying its presupposition. The puzzle arose from assuming that quantum systems have definite states that measurements reveal. If there are no absolute states, only correlations between systems, then “measurement” is simply the establishment of correlation. No collapse, no mystery, relational facts all the way down.

The connection to constraint-based explanation is direct. If only relations exist, what we call “objects” are crystallizations of relations, patterns of constraint that maintain themselves under interaction. Identity is relational rather than intrinsic. Persistence is constraint closure rather than substance. What survives under perturbation is what remains relationally stable. Everything else was always already nothing.

Eastern Philosophy Already Knew This

The convergence with certain Eastern philosophical traditions is striking and non-accidental. Nāgārjuna’s Mūlamadhyamakakārikā (second century CE) developed the doctrine of emptiness (śūnyatā): phenomena are empty of intrinsic existence (svabhāva). This is not nihilism. It is the claim that nothing exists independently of the relational web that permits it. The two truths doctrine distinguishes conventional truth (how things appear) from ultimate truth (their dependent origination). Westerhoff’s Nāgārjuna’s Madhyamaka (Oxford, 2009) and Garfield’s translation provide accessible introductions.

The structural parallel with OSR is precise. Both deny intrinsic natures. Both affirm relations as primary. Both dissolve the question “what is X really?” by showing that “really” presupposes a substance metaphysics that closer examination undermines. The methods differ, Nāgārjuna used dialectical analysis; modern physicists use mathematical formalism, but the destination is recognizably the same.

This convergence is evidence of something real. When independent traditions, using different methods, across thousands of years, arrive at the same structural insight, coincidence becomes implausible. What persists under recursive examination across scales, substrates, and cultures is more likely to be tracking something invariant than something parochial.

Part VI: The Free Energy Principle (What Survives the Critique)

Friston’s Framework

Karl Friston’s free energy principle (FEP) has become enormously influential in neuroscience and theoretical biology. His 2010 paper “The free-energy principle: a unified brain theory?” (Nature Reviews Neuroscience, DOI: 10.1038/nrn2787) has over 6,500 citations on Semantic Scholar as of mid-2026 and has spawned an industry of active inference research. The core claim is that biological systems minimize variational free energy, a measure of the difference between their internal model and the environmental states they encounter. This provides a principled explanation for perception (minimizing prediction error by updating models), action (minimizing prediction error by changing the world), and learning (optimizing model structure).

The mathematical framework is sophisticated. Parr, Pezzulo, and Friston’s textbook Active Inference (MIT Press, 2022, DOI: 10.7551/mitpress/12441.001.0001) provides a comprehensive treatment. The free energy principle connects to predictive processing (Rao and Ballard, Nature Neuroscience, 1999, DOI: 10.1038/4580; Clark, Behavioral and Brain Sciences, 2013, DOI: 10.1017/S0140525X12000477; Hohwy’s The Predictive Mind, 2013, DOI: 10.1093/acprof:oso/9780199682737.001.0001) and can be derived from first principles as a consequence of maintaining existence under changing conditions.

The Unfalsifiability Problem

The FEP faces a serious critique. Friston himself has acknowledged that the principle is not falsifiable in the usual sense. He has described it as more like Hamilton’s Principle of Stationary Action: a variational principle from which dynamics can be derived, rather than a contingent empirical claim. Colombo and Wright’s philosophical analysis (Synthese, 2021, DOI: 10.1007/s11229-018-01932-w) puts the problem directly: “FEP’s epistemic status is muddled. It has been called an unfalsifiable platitude, an imperative, a tautology, a stipulative definition, paradigm, law of the life sciences, law of nature, an a priori first principle.” The framework functions sometimes as an empirical hypothesis, sometimes as a mathematical framework, sometimes as a conceptual schema. These roles are not clearly distinguished.

Friston’s response to the “dark room problem” (Frontiers in Psychology, 2012, DOI: 10.3389/fpsyg.2012.00130) is instructive. Critics asked why organisms don’t simply minimize uncertainty by shutting themselves in dark rooms and dying quietly. Friston’s answer involves priors: organisms come equipped with expectations about environmental fluctuation, including expectations that they will act and explore. This resolves the puzzle but at the cost of packing more and more content into the prior. When the prior can be adjusted to fit any behavior, the framework slides from constraint into accommodation.

What We Can Keep

The charitable interpretation, and the one most consistent with Friston’s own variational-principle framing, is to take the FEP as a specific instantiation of constraint satisfaction under energetic bounds rather than as an inferential or cognitive process. Systems maintain themselves by minimizing the divergence between their internal states and environmental conditions, subject to the thermodynamic costs of doing so. This is dynamics under constraints, not inference in the folk-psychological sense. The brain is a physical system whose states are coupled to environmental states through constraint propagation, not literally a scientist testing hypotheses.

So read, the FEP becomes a specific instantiation of the general principle this essay is articulating. Systems that persist maintain constraint closure. One way to maintain closure is to minimize surprise, keeping internal states within viable bounds relative to external fluctuations. This is thermodynamics with feedback rather than cognition. The mathematical formalism is valuable because it makes predictions about neural dynamics, perceptual phenomena, and adaptive behavior. The cognitive vocabulary is optional and potentially misleading.

Part VII: Causation as Constraint Propagation

From Hume to Pearl

Causation has been contested since Hume declared it nothing but constant conjunction plus psychological habit. The interventionist revolution in causal reasoning, led by Judea Pearl and James Woodward, provides a rigorous alternative that aligns with constraint-based explanation.

Pearl’s work on causal diagrams (Biometrika, 1995, DOI: 10.1093/biomet/82.4.669) and his subsequent book Causality introduced the do-calculus, a formal framework for reasoning about interventions as distinct from observations. His 2010 introduction (International Journal of Biostatistics, DOI: 10.2202/1557-4679.1203) provides an accessible summary. The key insight is that causation is about what happens under intervention, not merely what correlates with what. A causes B if intervening on A changes B; correlation is evidence for but not proof of causation.

Woodward’s Making Things Happen (Oxford, 2003, DOI: 10.1093/0195155270.001.0001) develops an interventionist theory of causal explanation. Explanations are causal when they describe relationships that would be stable under at least some interventions, when changing the explanans would change the explanandum in systematic ways. This captures what distinguishes genuine explanations from merely predictive correlations.

Constitutive vs. Efficient Causation

Kaiser and Krickel (British Journal for the Philosophy of Science, 2017, DOI: 10.1093/bjps/axv058) distinguish constitutive from efficient causation. Efficient causes are events that bring about subsequent events, what we typically mean by “causes.” Constitutive causes explain what something is, not how it came to be. The arrangement of atoms constitutively explains the solidity of a table, though no atom “caused” the table to become solid.

This distinction matters for cognition debates. When someone claims that bioelectric patterns “cause” regeneration, the right question is: efficient or constitutive? If efficient, what is the mechanism? If constitutive, the description of a process is not being invoked as an additional cause; it is being identified with what the process is. Confusing constitutive with efficient causation creates phantom agencies, treating the description of a process as though it were an additional cause of that process.

Constraint-based explanation dissolves this confusion. Constraints neither push nor pull; they eliminate. What remains after constraint application is what the constraints permit, rather than what they efficiently cause. This is why constraint-based explanations are so powerful. They explain without invoking additional causes. They show why certain outcomes were the only ones consistent with the operative constraints.

Part VIII: Fractals, Scale Invariance, and the Geometry of What Survives

Mandelbrot’s Insight

Benoit Mandelbrot famously observed: “Clouds are not spheres, mountains are not cones, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line.” Euclidean geometry describes idealized forms that rarely appear in nature. Fractal geometry describes what actually persists.

This is diagnostic of constraint satisfaction rather than mere mathematical curiosity. Hutchinson’s contraction mapping theorem (Indiana University Mathematics Journal, 1981, DOI: 10.1512/iumj.1981.30.30055) proves that iterated function systems with contractivity s < 1 have unique fixed points, attractors. The fractal structures observed in nature are these fixed points. They are what remains after repeated application of constraints.

Why don’t spheres and cones appear in nature? Because they cannot persist under iteration. Cones erode. Spheres collapse under gravity. Straight lines diffuse. What survives repeated interaction with physical processes is what we actually observe: branching structures, rough surfaces, self-similar patterns at multiple scales. Fractal geometry describes the statistical signature of constraint satisfaction rather than some hidden Platonic realm.

Biological Scaling Laws and Causal Emergence

West, Brown, and Enquist’s model for allometric scaling (Science, 1997, DOI: 10.1126/science.276.5309.122) demonstrates this principle in biology. They derive the ubiquitous 3/4-power scaling of metabolic rate with body mass from constraints on resource distribution networks. The fractal branching of circulatory and respiratory systems is what minimizes transport costs under space-filling constraints, rather than arbitrary morphological choice. The geometry is selected because it satisfies thermodynamic constraints better than alternatives.

Bak, Tang, and Wiesenfeld’s self-organized criticality (Physical Review Letters, 1987, DOI: 10.1103/PhysRevLett.59.381) shows how systems naturally evolve toward critical states characterized by power-law distributions. Stanley’s review (Reviews of Modern Physics, 1999, DOI: 10.1103/RevModPhys.71.S358) connects this to universality classes in statistical mechanics. The same scale-invariant structures appear in earthquakes, forest fires, neural avalanches, and financial markets, not because these systems share mechanisms, but because they share constraint geometry.

Cross and Hohenberg’s comprehensive review of pattern formation (Reviews of Modern Physics, 1993, DOI: 10.1103/RevModPhys.65.851) catalogs how ordered patterns emerge from constraints in systems far from equilibrium. Gierer and Meinhardt’s reaction-diffusion theory (Kybernetik, 1972, DOI: 10.1007/BF00289234) explains how activator-inhibitor dynamics generate the spots and stripes we see in animal coats. Diego and colleagues (Physical Review X, 2018, DOI: 10.1103/PhysRevX.8.021071) show that pattern-enabling features are determined purely by network topology, by constraint structure.

A 2025 empirical demonstration directly bears on the framework’s scale-dependence claim. Franck and colleagues (Ageing Research Reviews, 2025, DOI: 10.1016/j.arr.2025.102880; Nature Aging 5: 1471–1480, DOI: 10.1038/s43587-025-00888-0) demonstrated the non-universality of inflammaging across populations. The molecular signatures of immune-system aging that are well-established in Western industrialized cohorts do not generalize cleanly to Tsimane forager-horticulturalists or Orang Asli hunter-gatherers. This is precisely what a multi-scale constraint framework predicts: the relevant causal grain is not “inflammation as universal driver of aging” but “inflammation under specific ecological constraint regimes.” Causal claims that are stable at one scale dissolve at another when the constraint structure changes. The finding is not a defeat of the constraint framework; it is the framework working correctly, identifying which scale-dependent regularities hold under which perturbation regimes (cf. Hoel, Albantakis, and Tononi 2013 on causal emergence, DOI: 10.1073/pnas.1314922110).

Part IX: Indigenous Epistemologies (Long-Horizon Empirical Programs)

Songlines as Constraint Paths

Knowledge systems designed to transmit constraint patterns across generations without writing have encoded the pattern which connects with remarkable precision. The framing here matters. Aboriginal Australian knowledge systems are not data points to be subordinated to Western argument; they are independent intellectual traditions whose convergence with constraint theory is meaningful precisely because the convergence is independent. The argument runs in both directions.

Aboriginal Australian songlines are not merely mnemonic devices or cultural artifacts. They are constraint-propagation paths through physical landscape. Lynne Kelly’s The Memory Code (Allen & Unwin, 2016) documents how Aboriginal Australians encoded navigation, ecology, law, kinship, and cosmology in sung narratives that must be walked to be fully activated. The singing and the walking together regenerate the country. Knowledge that does not remain coupled to land, season, and practice does not survive transmission. Songlines are literally falsification-resistant: when following the song leads someone off a cliff, the song gets corrected.

Reser and colleagues (PLOS ONE, 2021, DOI: 10.1371/journal.pone.0251710) experimentally tested Aboriginal memory techniques against Western methods. In a randomized study at Monash Rural Health–Churchill, first-year graduate-entry medical students (N = 76, 2018 Year A cohort) were trained in either an Australian Aboriginal songline-style narrative method, a classical memory palace technique, or received no memory training. Students who learned the Aboriginal method showed an odds ratio of 2.82 (95% CI 1.15–6.90) for improving to perfect recall of a 20-item butterfly species list, compared with 2.03 (95% CI 0.81–5.06) for the memory palace and 1.5 (95% CI 0.54–4.59) for the untrained control. Roughly a threefold versus twofold improvement. The Aboriginal method was also rated as significantly more enjoyable and engaging than the alternatives. Empirical measurement, not cultural romanticism. A knowledge system developed under survival pressure outperforms systems developed in libraries.

Tempone-Wiltshire and Yunkaporta’s recent analysis, “Contributions From Aboriginal Australian Psychology: Songlines, Memory, and Relational Knowledge Systems” (Psychotherapy and Counselling Journal of Australia, 2025, DOI: 10.59158/001c.143975), articulates the epistemology underlying these practices. Knowledge exists within relationships, not in isolated propositions. Yunkaporta’s Sand Talk: How Indigenous Thinking Can Save the World (2019) makes this vivid: knowledge drawn in sand cannot be reified. It must be regenerated through practice. This is constraint-based epistemology operating on its own terms. Only what can be reproduced survives.

The Dreaming as Constraint Structure

The Dreaming (Dreamtime) is often misunderstood as a mythological past. It is more accurately understood as atemporal constraint structure, the generative network from which actual configurations emerge. What Ladyman and Ross call “real patterns,” Aboriginal cosmology calls the Dreaming. The Dreaming is the ongoing constraint-satisfaction process through which Country maintains coherence, not a separate “realm.”

This is convergent discovery rather than mere analogy. Cultures that survived for long horizons under survival pressure did not center creation myths alone. They encoded elimination myths. What gets cut. What fails. What cannot be spoken without collapsing. The pattern which connects was already known to peoples whose archaeological record of continuous occupation in Australia extends at least 65,000 years (Clarkson et al., 2017, Nature, DOI: 10.1038/nature22968), with a minority of archaeologists arguing for closer to 47,000 years (O’Connell & Allen 2015, Journal of Archaeological Science 56: 73–84).

Kampanelis, Elizalde, and Ioannides (SSRN, 2023, DOI: 10.2139/ssrn.4594688) found that Aboriginal trade routes are strongly associated with current economic activity patterns measured via nighttime satellite light density and population density, with the mechanism attributed to path dependence from transport infrastructure laid along these routes during European colonization. Empirical validation comes in many forms, and tens of millennia of continuous knowledge practice constitutes a long-running experiment of a kind that Western institutional science has not yet replicated at scale.

The convergence of organizational closure theory, ontic structural realism, process ontology, and Aboriginal knowledge systems is significant because the lines of evidence are genuinely independent. Different methods, different starting assumptions, different cultural matrices, different time horizons. Convergence under those conditions is evidence for invariant structure rather than shared bias. That is Whewell’s consilience of inductions operating across millennia and continents.

Part X: Why “Nothing” Cannot Persist (Dissolving the Primordial Question)

Grünbaum’s Critique

“Why is there something rather than nothing?” has been called the fundamental question of metaphysics. Adolf Grünbaum’s analysis, “The Poverty of Theistic Cosmology” (British Journal for the Philosophy of Science, 2004, DOI: 10.1093/bjps/55.4.561), reveals that it contains a hidden presupposition: the “Spontaneity of Nothingness” (SoN), the assumption that nothingness is the ontologically natural default state requiring no explanation while existence demands justification.

Grünbaum traces this assumption to a distinctly Christian precept going back to the second century, the claim that the very existence of any and every contingent entity is utterly dependent on God at any and all times, imported into philosophy without rational justification. Once the presupposition is identified, the question dissolves. We can equally ask: “Why should there be nothing rather than something?” Without independent justification for privileging nothingness, the original question becomes a non-starter.

Grünbaum’s earlier paper, “A new critique of theological interpretations of physical cosmology” (British Journal for the Philosophy of Science, 2000, DOI: 10.1093/bjps/51.1.1), develops the critique in detail. The physical vacuum has positive zero-point energy, undergoes measurable fluctuations, and exhibits definite structural properties. True metaphysical “nothing,” no spacetime, no laws, no potentiality, cannot be addressed by physics because it has no properties to analyze.

Constraint-Based Dissolution

The constraint-based answer is more direct: “nothing” cannot persist because it cannot support constraint. The moment any difference persists, it does so by excluding alternatives, and exclusion under finite resources is constraint satisfaction. No extra metaphysical floor is needed.

This reframes the question from a metaphysical “why” to a dynamical “how come.” The universe is determinate because determinacy is what survives recursive elimination, rather than because it must be. That is a Darwinian answer applied to ontology itself. What we observe is explained by persistence, not by necessity or design.

“Nothing” is mathematically and conceptually unstable. Pure unconstrained possibility is paradoxically self-constraining; the absence of all constraints is itself a maximal constraint on what can be distinguished. Quantum vacuum fluctuations, Gödel-type self-reference, and topological considerations all suggest that coherent “nothing” cannot be maintained.

Part XI: Derivation of Intelligibility Conditions

From Thermodynamics to the Floor

The boldest claim of this essay is that the conditions for intelligibility itself, distinction, identity, relation, constraint, are not additional axioms that must be assumed. They are derivable consequences of constraint satisfaction under thermodynamic bounds.

Distinction emerges from boundary maintenance. Landauer’s principle shows that maintaining a bit, distinguishing 0 from 1, requires energy expenditure. Without energetic work, distinctions dissolve into thermal noise. Distinction is the signature of boundaries maintained against entropy, rather than a metaphysical primitive.

Identity emerges from constraint closure. What makes something “the same” over time is invariance under perturbation, not a magical soul or haecceity. An entity persists as itself insofar as its constraint network regenerates through change. The Montévil-Mossio framework makes this precise: identity is closure that maintains itself. The Durant experiments demonstrate it empirically: morphological identity is bistable attractor, not Platonic form.

Relation emerges from constraint propagation. If Rovelli and the structural realists are right, relations are not between pre-existing things; things are crystallizations of relations. Constraint propagation is inherently relational. Constraints couple systems, create dependencies, enable and forbid. Relation is what remains when substances are dissolved, rather than an addition to an object-based ontology.

Constraint is the fundamental primitive. Unlike other proposed primitives, constraint comes with a derivation: thermodynamic state-space structure. The Second Law is a statistical consequence of vastly more high-entropy than low-entropy microstates, not a stipulation. Constraint emerges from combinatorics plus dynamics. We do not need to posit it; it falls out of the mathematics of possibility.

The Selection Principle That Precedes Categories

What philosophers have called “categories of understanding,” the preconditions for any thought or experience, are not a priori impositions by mind onto world. They are the structure that any world capable of supporting thought must already have. Intelligibility is the shadow cast by constraint satisfaction, not a human projection.

The framework operates beneath the level at which traditional philosophical questions are posed. It is a selection principle on what can remain available to ontology, epistemology, or cosmology rather than an instance of any of them. It explains why there is anything to know rather than what we know.

The framework feels austere, even deflationary, because it removes furniture rather than adding it. Ontologies describe what passed the filter. Epistemologies describe how filtered things are tracked. Cosmologies describe how filters evolve. The work here is at the level of the filter itself.

Part XII: Falsification and the Discipline of Loss

Beyond Naive Falsificationism

A research programme that cannot specify what would force its own revision is not engaging empirical reality, whatever else it may be doing. Karl Popper’s Logic of Scientific Discovery (Routledge, 1959, DOI: 10.4324/9780203994627) introduced falsifiability as a demarcation criterion between science and pseudoscience. The criterion in its original strong form did not survive Laudan’s “The Demise of the Demarcation Problem” (1983) or the subsequent post-Popperian consensus that Pigliucci, Boudry, Hansson and others have developed. There is no necessary and sufficient criterion that cleanly separates science from non-science. Popper himself, in later work, refined his position considerably; the original 1934 Logik der Forschung posture was not where he stayed.

What did survive Laudan’s critique, and what this essay relies on, is the methodological core: research programmes that move from anomaly to ad hoc modification without generating novel testable predictions are doing something different from research programmes that recover from anomaly by sharpening their claims. Mayo’s severe testing programme, Lakatos’s progressive-versus-degenerating distinction, Tetlock’s accountability-based forecasting, and Whewell’s consilience of inductions all converge on this point from different directions. Methodological evaluation through prediction, severe testing, and progressive problem-shift remains indispensable, even after the strong demarcation criterion is abandoned.

Imre Lakatos refined Popper in his methodology of scientific research programmes (DOI: 10.1017/CBO9780511621123.003). Lakatos distinguished the “hard core” of a research programme, the central commitments that define it, from the “protective belt” of auxiliary hypotheses that can be modified. A programme is progressive if its modifications lead to novel predictions; it is degenerating if modifications serve only to shield the core from refutation. The distinction is comparative and historical rather than binary. A programme can be in degenerating phase for a generation and then make a genuine novel prediction; another can look progressive and then stop generating differential content. The judgment is made over time, against rivals, and against the programme’s own past performance, rather than at any single moment by any single test.

Antony Flew’s “Theology and Falsification,” originally presented at the Oxford Socratic Club in 1950 and published in 1955 in New Essays in Philosophical Theology (eds. Flew & MacIntyre), applied this logic to religious claims with devastating effect. Flew observed that theistic claims often die “the death of a thousand qualifications,” each apparent counterexample absorbed by reinterpretation until nothing empirical remains. The diagnostic Flew supplied was not “metaphysics is bad” but “claims that absorb every counterexample have stopped functioning as claims about the world.” This is consistent with Ladyman and Ross’s principle of naturalistic closure: metaphysics earns its keep when it remains continuous with our best empirical science, when it does the work of unifying or interpreting empirical findings rather than insulating itself from them. The same diagnostic applies to “basal cognition.” If every observation is compatible with the framework, the framework has stopped doing the work it claims to do.

The 2025 adversarial collaboration of the Cogitate Consortium (Nature, DOI: 10.1038/s41586-025-08888-1) demonstrates that structural theories of consciousness ARE genuinely falsifiable, contrary to the common charge that “structural” criteria are inherently unfalsifiable. The consortium ran a preregistered direct test of integrated information theory (IIT) and global neuronal workspace theory (GNWT) using fMRI, MEG, and intracranial EEG across 256 human participants. Their finding: results substantially challenged key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT faces challenges from the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. Structural theories took specific empirical hits, exactly the behavior that distinguishes progressive scientific programmes from degenerating ones.

How Constraint-Based Explanation Can Fail

Constraint-based explanation is falsifiable in the sense that matters: failure conditions can be specified in advance, and the framework can degenerate if its modifications stop generating novel content. Here are the failure conditions, stated openly.

Violation of the Landauer bound. If information processing occurred without thermodynamic cost in isolated systems, the foundation would collapse.

Constraint closure without robustness. If organizationally closed systems showed no greater persistence than open ones under perturbation, the Montévil-Mossio framework would be wrong.

Path independence in bioelectric memory. If the Durant results failed to replicate, if transient perturbations reliably corrected toward canonical form regardless of intervention timing, the attractor interpretation would fail.

RAF failure at realistic catalysis levels. If autocatalytic closure required unrealistic levels of catalytic promiscuity to emerge, Kauffman-Hordijk-Steel would be wrong.

Scale non-invariance breaking the pattern. If the fractal signatures and scaling laws broke down systematically rather than transitioning predictably between regimes, the constraint-survival interpretation would need revision.

Superior predictions from cognitive frameworks. If “basal cognition” generated predictions that constraint-based models could not match, predictions about specific outcomes, intervention effects, or system behavior, the cognitive framework would earn its place.

Computational reducibility of co-constituting systems. If a part of a coupled organism-environment system could shortcut the computation of the system it co-constitutes, the constraint-propagation account of inside-view phenomena would need revision. Israeli and Goldenfeld 2004 (Physical Review Letters, DOI: 10.1103/PhysRevLett.92.074105) demonstrated computational irreducibility for a broad class of cellular automata: outputs cannot be obtained faster than running the system. The framework predicts that this irreducibility is a feature, not a bug; what is called the inside view may be what it is like to be the part doing irreducible computation from within the system it co-constitutes.

None of these failures have occurred. The framework survives because it can fail and has not.

Part XIII: The Question That Remains

What Does “Basal Cognition” Add?

The challenge stands, and it is simple. What concrete, falsifiable prediction does “basal cognition” make that is not already made, tested, and explained by constraint propagation under thermodynamic limits? How would we tell if it is wrong?

This is the minimal bar for any scientific claim, not a rhetorical question. If “basal cognition” predicts something specific, some pattern of behavior, some response to intervention, some measurable quantity, that constraint-based models do not predict, then it is doing real work. State the prediction. Design the experiment. Run the test.

If “basal cognition” makes no predictions that differ from constraint satisfaction, it functions as a vocabulary preference rather than an alternative explanation. Vocabulary preferences are not nothing. They shape research directions, attract funding, organize communities. What they do not do is add mechanism. And in the specific case where the vocabulary implicitly imports causal claims (cognition as efficient cause of regeneration, intelligence as efficient cause of repair) while remaining unable to specify how those efficient causes could be tested independently of the phenomena they were invoked to explain, the vocabulary has crossed from organizing inquiry into degrading it.

The phenomena are real. Biological systems exhibit stunning efficiency, robustness, and adaptability. The empirical work documenting these phenomena is invaluable. What is contested is whether labeling them “cognitive” does explanatory work, or whether the labeling merely relabels what constraint-based frameworks already explain while immunizing itself from falsification.

Load-Bearing vs. Scaffolding

A discipline of honest citation: the support marshaled in this essay is not all of one kind. The load-bearing empirical pieces are Landauer’s principle and its experimental verification (Bérut et al. 2012), the constraint closure formalization (Montévil & Mossio 2015), RAF theory (Hordijk-Steel-Kauffman), the Durant et al. bioelectric memory experiments, the Reser et al. memory study, the Clarkson et al. archaeological dating, and the recent Cogitate Consortium adversarial collaboration. These supply differential evidential weight from laboratory measurement and field data.

Other elements function as conceptual lineage and analogy: Whitehead, Nāgārjuna, Yunkaporta, Hofstadter, Maturana and Varela, Bateson. Their convergence with the empirical results is meaningful, but the convergence is conceptual rather than experimental. Pretending otherwise inflates the case. The stronger version of the argument keeps these layers distinct.

What Persists Is What Constrains

A simpler principle remains once metaphysical excess is stripped away. Differences that persist are differences that constrain what comes next. Persistence is evidence of constraint satisfaction. The rule applies at every scale. It explains why there is determinate existence rather than indeterminate nothing without appealing to necessity or intention. Nothing cannot persist. Anything that does persist has already passed a filter.

This is the pattern which connects. The invariant condition under which information, mind, structure, or process could exist long enough to be observed, measured, or thought, rather than any of those things themselves.

Gregory Bateson asked the question in 1979, the year I was born. The answer was always implicit in his formulation: a difference which makes a difference. But what makes a difference make a difference? Persistence. And what makes things persist? Constraint satisfaction under thermodynamic bounds.

Bateson circled the answer without landing. The Indigenous knowledge systems encoded it without formalizing. The Eastern philosophers described it without mechanizing. The physicists derived it without seeing its full scope. What emerges from convergence across traditions, methods, and millennia is consilience rather than coincidence, independent lines of evidence pointing to the same invariant.

What persists is what constrains. Everything else is commentary.

Appendix: Key Scholarly Citations by Domain

Thermodynamics and Information Theory

• Landauer, R. (1961). DOI: 10.1147/rd.53.0183
• Bennett, C.H. (1973). DOI: 10.1147/rd.176.0525
• Bennett, C.H. (1982). DOI: 10.1007/BF02084158
• Bennett, C.H. (2003). DOI: 10.1016/S1355-2198(03)00039-X
• Bérut, A. et al. (2012). DOI: 10.1038/nature10872
• Jarzynski, C. (1997). DOI: 10.1103/PhysRevLett.78.2690
• Crooks, G.E. (1999). DOI: 10.1103/PhysRevE.60.2721
• Seifert, U. (2012). DOI: 10.1088/0034-4885/75/12/126001
• England, J.L. (2013). DOI: 10.1063/1.4818538
• England, J.L. (2015). DOI: 10.1038/nnano.2015.250

Biological Organization and Constraint Closure

• Montévil, M. & Mossio, M. (2015). DOI: 10.1016/j.jtbi.2015.02.029
• Moreno, A. & Mossio, M. (2015). DOI: 10.1007/978-94-017-9837-2
• Mossio, M. & Moreno, A. (2010). PMID: 21162371
• Mossio, M. et al. (2009). DOI: 10.1093/bjps/axp036
• Kauffman, S.A. (1986). DOI: 10.1016/S0022-5193(86)80047-9
• Hordijk, W. & Steel, M. (2004). DOI: 10.1016/j.jtbi.2003.11.020
• Hordijk, W. et al. (2011). DOI: 10.3390/ijms12053085
• Hordijk, W. (2019). DOI: 10.1007/s13752-019-00330-w
• Xavier, J.C. et al. (2020). DOI: 10.1098/rspb.2019.2377

Bioelectric Research and Memory

• Levin, M. (2014). DOI: 10.1113/jphysiol.2014.271940
• Levin, M. & Martyniuk, C.J. (2018). DOI: 10.1016/j.biosystems.2017.08.009
• Durant, F. et al. (2017). DOI: 10.1016/j.bpj.2017.04.011
• Durant, F. et al. (2019). DOI: 10.1016/j.bpj.2019.01.029
• Pezzulo, G. et al. (2021). DOI: 10.1098/rstb.2019.0765
• Law, R. & Levin, M. (2015). DOI: 10.1186/s12976-015-0019-9
• Hopfield, J.J. (1982). DOI: 10.1073/pnas.79.8.2554

Structural Realism and Physics

• Ladyman, J. & Ross, D. (2007). DOI: 10.1093/acprof:oso/9780199276196.001.0001
• Ladyman, J. (1998). DOI: 10.1016/S0039-3681(98)80129-5
• French, S. & Ladyman, J. (2003). DOI: 10.1023/A:1024156116636
• French, S. (2014). DOI: 10.1093/acprof:oso/9780199684847.001.0001
• French, S. & Krause, D. (2006). DOI: 10.1093/0199278245.001.0001
• Rovelli, C. (1996). DOI: 10.1007/BF02302261
• Cogitate Consortium (2025). DOI: 10.1038/s41586-025-08888-1
• Israeli, N. & Goldenfeld, N. (2004). DOI: 10.1103/PhysRevLett.92.074105

Causation, Methodology, and Demarcation

• Pearl, J. (1995). DOI: 10.1093/biomet/82.4.669
• Pearl, J. (2010). DOI: 10.2202/1557-4679.1203
• Woodward, J. (2003). DOI: 10.1093/0195155270.001.0001
• Kaiser, M.I. & Krickel, B. (2017). DOI: 10.1093/bjps/axv058
• Hoel, E.P., Albantakis, L. & Tononi, G. (2013). DOI: 10.1073/pnas.1314922110
• Wimsatt, W.C. (1981). DOI: 10.1086/288763
• Popper, K. (1959). DOI: 10.4324/9780203994627
• Lakatos, I. (1970). DOI: 10.1017/CBO9780511621123.003
• Laudan, L. (1983). “The Demise of the Demarcation Problem.” In Physics, Philosophy and Psychoanalysis, ed. Cohen & Laudan.
• Mayo, D. (2018). Statistical Inference as Severe Testing. Cambridge.
• Pigliucci, M. & Boudry, M. (eds., 2013). Philosophy of Pseudoscience. Chicago.
• Grünbaum, A. (2000). DOI: 10.1093/bjps/51.1.1
• Grünbaum, A. (2004). DOI: 10.1093/bjps/55.4.561

Free Energy Principle

• Friston, K. (2010). DOI: 10.1038/nrn2787
• Parr, T. et al. (2022). DOI: 10.7551/mitpress/12441.001.0001
• Rao, R.P.N. & Ballard, D.H. (1999). DOI: 10.1038/4580
• Clark, A. (2013). DOI: 10.1017/S0140525X12000477
• Hohwy, J. (2013). DOI: 10.1093/acprof:oso/9780199682737.001.0001
• Colombo, M. & Wright, C. (2021). DOI: 10.1007/s11229-018-01932-w
• Friston, K. et al. (2012). DOI: 10.3389/fpsyg.2012.00130

Indigenous Knowledge Systems

• Kelly, L. (2016). The Memory Code. Allen & Unwin.
• Yunkaporta, T. (2019). Sand Talk.
• Reser, D.H. et al. (2021). DOI: 10.1371/journal.pone.0251710
• Tempone-Wiltshire, J. & Yunkaporta, T. (2025). DOI: 10.59158/001c.143975
• Kampanelis, S., Elizalde, A. & Ioannides, Y.M. (2023). DOI: 10.2139/ssrn.4594688
• Clarkson, C. et al. (2017). DOI: 10.1038/nature22968
• O’Connell, J.F. & Allen, J. (2015). Journal of Archaeological Science 56: 73–84.

Fractals, Scale Invariance, and Aging

• Hutchinson, J.E. (1981). DOI: 10.1512/iumj.1981.30.30055
• West, G.B. et al. (1997). DOI: 10.1126/science.276.5309.122
• Bak, P. et al. (1987). DOI: 10.1103/PhysRevLett.59.381
• Cross, M.C. & Hohenberg, P.C. (1993). DOI: 10.1103/RevModPhys.65.851
• Gierer, A. & Meinhardt, H. (1972). DOI: 10.1007/BF00289234
• Stanley, H.E. (1999). DOI: 10.1103/RevModPhys.71.S358
• Diego, X. et al. (2018). DOI: 10.1103/PhysRevX.8.021071
• Franck, M. et al. (2025). DOI: 10.1016/j.arr.2025.102880
• Franck, M. et al. (2025). DOI: 10.1038/s43587-025-00888-0

What survives is what can. 🧬🌀