22.1 The Move the Last Chapter Left Open

Chapter 21 closed on a diagnosis: neither operator-level rule-following nor fluent output establishes understanding by the implemented whole. A computationalist can grant all of that and still think the argument has missed him, because (he will say) the mind was never supposed to be the symbols. The symbols are just the visible spelling. The mind is the realized computation: recurrence, memory, integration, planning, and control, implemented as one causal organization whether built from neurons or silicon. Attack the operator all you like; the candidate system remains untouched.

That reply forces a distinction the slogan the program usually blurs. An abstract program is a formal type, available for indefinitely many implementations and carrying no token history merely by existing. A running implementation is a physical process with determinate causal powers, a provenance, and whatever relations it bears to an environment. This chapter denies mentality to neither by definition. It argues that the abstract type leaves the relevant worldly and system-level facts open, while the running token must be inspected to learn which of them it realizes. A wide computationalist who includes the complete content-bearing and integrative organization can therefore agree with the positive account and disagree only about its name.

Take the question in its rudest form, the one philosophers play as a parlor game. There is always someone (there is always someone) who announces, with the calm of a person about to enjoy themselves, that the radiator against the far wall is, at this very moment, running Microsoft Word. Not metaphorically. Not “could be wired to.” Running it, now. The radiator’s molecules occupy a physical state too complicated ever to write down, and that state grinds, moment to moment, into other states. Write down the sequence of computational states Word passes through as it runs, then pair each one, in order, with one of the radiator’s physical states. Given enough physical states to draw on, and enough freedom in how you pair them, the pairing exists. So the radiator runs Word. It also, by the identical trick, runs a chess engine, and the flight software of a 747, and the chess engine’s exact opposite, and it dreams of Provence on alternating Tuesdays.

The pairing works on these permissive terms. The question is whether pairing states in a sequence amounts to implementing a program. A proper account must distinguish the radiator from the computer without assuming that only one can think. Causal structure supplies that distinction.

22.2 The Wall That Allegedly Runs Any Program

Functionalism defines a mental state by its causal role and permits the same mind to run on different materials. The triviality argument takes that promise and runs it off a cliff. If a mind is the right functional organization, then having the mind is instantiating the organization, and organization comes cheap. To say a physical system “implements” a finite-state automaton is only to say that some mapping from physical states to automaton states makes the physical transitions line up with the transition table. Take any system with enough internal states changing fast enough (a wall warming unevenly, a bucket of water, the radiator) and, with no rule against gerrymandering, such a mapping always turns up. Putnam proved a sharp form of it: every ordinary open physical system implements every inputless finite-state automaton.¹

And Putnam inflicted the wound himself: he wanted minds to be organization precisely so they could be multiply realized. The triviality result says the realization relation is so unconstrained that implementing-this-automaton achieves nothing at all — a permission slip you write yourself. So the radiator already instantiates the right automaton, and already has my mind, and yours, and the mind of a reader this argument persuades, and the mind of one it leaves cold, all at once, settled only by which mapping a bored interpreter lays over it. A theory on which the radiator already understands everything has not explained understanding. It has mislaid the word.

The point arrives from the other direction too. Searle reached it through physics, not automata. Whether a lump of matter counts as a “symbol,” a “computational state,” a case of “running a program” is not, he argued, an intrinsic feature of it the way mass and charge are intrinsic. An observer assigns that feature to it by reading the computational description on. Syntax, in his flat phrase, is not intrinsic to physics.² You can describe the same wall as implementing any program, or its negation, or nothing, depending entirely on the interpretation projected onto its molecular states — and a physical system has the causal properties it has, full stop, whether or not anybody reads them as a computation.

Putnam and Searle, who agreed about almost nothing in the philosophy of mind, reached the same wall from opposite sides — Putnam from inside functionalism, Searle as its standing critic.³ The natural reply is that the mappings are gerrymandered: genuine implementation must respect causal structure and counterfactuals, not merely trace an arbitrary sequence through matter.⁴ That is the strongest repair, and it deserves a clean hearing.

The defect is real and fixable. Chalmers’s counterfactual repair defeats the wall; a different question survives: whether intrinsic computation fixes meaning.

22.3 The Best Repair, Granted and Set Aside

A stronger objection says a careful account of implementation defeats the triviality argument, so the wall does not run every program after all.

Chalmers supplied functionalism’s strongest defense two chapters ago; he supplies the implementation repair here too. He grants that unconstrained mappings trivialize, then argues that genuine implementation was never unconstrained. For a physical system to implement a computation, its causal structure must mirror the computation’s formal structure: its states must divide into components that transition under counterfactual-supporting causal laws in an isomorphic pattern. He formalizes this with what he calls combinatorial-state automata. Ma and Kanai press the repair further: the organization must be specifiable without an observer’s labels and displayed in the system’s intervention profile. A rock does not implement every finite-state automaton, because its physics carries none of the rich, counterfactual-supporting, component-wise structure genuine implementation demands.⁵ Putnam’s mapping laid a story over the system instead of finding one in it. Implementation, on this account, is a substantive constraint. Most systems implement rather few computations. The wall is innocent.

The repair wins. Intrinsic, counterfactual-supporting causal structure fences the gerrymander out; the radiator, disciplined, does not run Word. Grant all of it.

Chalmers has shown that a system’s causal structure constrains which computations it implements — a real fact, not a free interpretive gift. The constraint has not shown that fixing the computation fixes a meaning. It delivers a pattern in the shape of causal flow. The same abstract transition structure can serve chess, a tax calculation, or nothing anyone cares about. Causal organization fixes the implemented pattern; history and world-relations fix what, if anything, the pattern is about.

Re-instantiation repeats a pattern without deciding whether the later token inherits the first token’s content, continues the same subject, understands, or feels. Provenance and world-involving history bear on content; the realized cognitive organization bears on attribution.

22.4 What the Wall Lacks

With implementation established, a different question begins: what makes a computational state mean something? A system can really compute while its abstract transition pattern leaves the content open.

Not the formal structure alone. The structure has to come tethered: selection, learning, and causal traffic must constrain which worldly conditions a state answers to.⁶ A trained producer-consumer mechanism can thereby acquire content through that history. A nervous system may earn predator rather than sandwich crumbs while leaving finer distinctions unsettled; no interpreter assigned the pairing, but neither did selection carry a taxonomist’s pencil. The radiator dreaming of Provence on Tuesdays cannot dream of Provence because it has neither the relevant history nor an integrated cognitive organization in which such content could figure. A brain has both. Content depends on mechanisms developing and operating in worlds, not on formal organization alone.

The brain’s chemistry supplies determinate causal joints, but causal specificity alone is not content; a radiator has determinate chemistry too. Formal computation groups systems by transition structure and omits the selection, learning, and world history that do the tethering.

An artificial system might develop the relevant history too. The implementation repair gives us a genuine computation to investigate; it does not bar its states from acquiring content.

22.5 What the Model Does and What the Storm Does

So the computationalist stops defending the symbols and stops defending the bare notion of implementation, and asks instead for the whole apparatus at once, properly tightened. Fine, he says. Grant me Chalmers’s disciplined implementation — counterfactual-supporting causal structure, no gerrymander. Grant me Deutsch’s physical principle: the brain is a finitely realizable physical system, so a universal model computing machine can perfectly simulate it. Now build me a perfect computational duplicate of a living brain (every neuron, every synapse, every ionic flux, tracked and updated in lockstep) and run it. I am no longer claiming the wall thinks. I am claiming that this, the faithful running model of a brain, thinks. What could it possibly lack?

Grant disciplined implementation, physical simulability, and the perfect duplicate. Which features does the running system merely describe, and which does it actually realize? We have to inspect the deed.

Imagine a hurricane forecast. The model takes in pressure gradients, sea-surface temperatures, and the rotation of the earth. Its projected storm intensifies, drifts west-northwest, and makes landfall near Galveston. Suppose the forecast proves good enough that Texas evacuates cities on its say-so. Nobody evacuates the data center.⁷

The computationalist can grant all of that: a simulated calculator really adds, and a simulated chess engine really plays chess. Perhaps mentality, too, consists in organization that the running computer actually realizes. If so, the dry data center tells us nothing against its thinking. I agree that the hurricane cannot decide which sort of capacity mind involves.

Watch what the weather model lacks. It updates numerical values for temperature, pressure, and wind velocity through atmospheric equations. The hurricane moves air, lifts heat off a warm ocean, and drives water ashore. The computer’s voltages do not produce those atmospheric powers. Refining the grid improves the forecast without turning the calculation into a storm. This comparison blocks an automatic transfer of powers from model to modeled thing; it supplies no general barrier to artificial realization.

A simulation of metabolism burns no calories — or rather burns only the watts the hardware draws, which is precisely the wrong expenditure, the cost of the substrate being itself rather than the cost of a body doing its living. To realize a phenomenon, a system has to possess the causal powers of that phenomenon, not merely a description rich enough to predict its behavior.⁸

Coupling can change the answer. A simulated controller wired through transducers to a boiler really controls the boiler. Its variables now participate in the activity previously only modeled. Calculation, control, digestion, and weather have different realization conditions; the word simulation tells us none of them.

That shifts the burden to the theory of mind. If consciousness depends only on intrinsic causal-computational organization, a system realizing it is no counterfeit merely because we call it a simulation. If consciousness requires world-involving content, those powers must be realized too. Wire a model into the right traffic and it may acquire a power it previously depicted. The noun settles nothing; what the running system does settles the case.

For more than forty years Searle has pressed the comparison, usually with rainstorms and five-alarm fires: “The brain simulator program by itself no more causes consciousness than the fire simulator program burns the house down or the rain simulator program leaves us all drenched.”⁹ The hurricane is mine; the analogy is his. Its application to consciousness requires the further argument about what a mind realizes.

22.6 Simulability Is Not Realization

The original Church–Turing thesis concerns functions calculable by an effective procedure. Deutsch’s physical principle says that every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means.¹⁰¹¹ Neither claim says that computing a model of a process instantiates every causal power of the process modeled. A brain may be perfectly simulable without its simulation thereby possessing the powers that make a mind.

22.7 The Perfect Brain, and What It Still Lacks

Give the computationalist the limit case: a simulation tracking every neuron, synapse, neurotransmitter release, and ionic flux in a human brain. Its modeled neurons reproduce the patterns of a living brain having lunch, reading Proust, or falling in love. Hold fixed the fine-grained causal organization inside the brain, including its responses to alternative inputs at the sensory boundary. Leave the new implementation’s provenance and environmental relations unspecified. Surely, the intuition says, somewhere in there the lights come on.

That stipulation fixes internal organization, not the original subject’s entire world-involving history. Two questions remain.

The first gap opens from the side of meaning, the gap Part Three prepared: tracking, selection, and learning relations constrain what a state is about—the causal-historical relations Dretske naturalized and Tye used to secure wide content, which Chapter 6 then identifies with phenomenal character in the conscious case.¹² A match in internal organization does not by itself settle whether a new token inherits those relations. An accidental duplicate has no claim on the donor’s coffee-history; a simulation produced by causally copying a particular brain may stand at the end of that history, just as a speaker can inherit a name’s reference through a chain she did not begin. Any inheritance would come from the copy’s provenance, not from the firing pattern alone. A separately trained copy may instead earn content through its own history. Internal duplication settles neither route.

Ned Block reached the second opening from the functionalist’s own side, by way of the homunculus-nation the reader already met in Chapter 20.¹³ Here it takes the shape Block gave it in 1995, tailored to precisely this case: the separation of phenomenal consciousness (what it is like to undergo an experience) from access consciousness, the availability of information for reasoning and control.¹⁴ An exact reproduction of the relevant access functions delivers access consciousness by the functional characterization. Whether it delivers the phenomenal remains the point under dispute, not a conclusion contained in the vocabulary. The perfect duplicate therefore gives the functionalist her strongest case. It does not get to borrow the donor brain’s history for free, and the word simulation cannot deny it consciousness for free either.

A determined defender sees the shape of the reply and reaches for the obvious patch. Granted, he says, a brain model on indifferent hardware has no contact with a world. So I will not run a disembodied brain. I will run it inside a simulated body, inside a simulated environment — simulated sensors taking in a simulated world, simulated effectors acting on it, a simulated developmental history. Everything Part Three said a mind needed, my system has. Virtually.

The patch cannot be dismissed by repeating the word simulated. A virtual environment contains real digital objects and real causal relations among the physical states that implement them. A collision can genuinely redirect a process; a virtual predator can genuinely alter what its agent does next. Chalmers is right to insist on that much. The harder question is whether the agent’s relations to that environment fix content for the agent, or whether the whole ecology remains a drama whose semantic roles come from its makers. More model is not automatically more grounding. Neither is it automatically none.

The argument does not say no machine or virtual agent could realize a mind. It says that reproducing internal organization does not by itself reproduce the history content may require. A system becomes a candidate understander when content-bearing states participate in a unified, persistent organization of perception, memory, inference, correction, planning, and action. Its environment may contain trees or digital objects; actual means causally actual for the system, not pleasingly non-virtual to us.

The concession’s best advocate is Chalmers, who has argued at book length that virtual worlds deserve better than their scare quotes: virtual objects are genuine digital objects, virtual events genuinely occur, and a life lived in a well-built virtual world would be a real life in a real environment. He has aimed the point at the present juncture. “Some people will say this doesn’t count for what’s needed for grounding because the environments are virtual,” he writes, and answers: “I don’t agree. … I’ve argued that virtual reality is just as legitimate and real as physical reality for all kinds of purposes.”¹⁵ He is right. The relations inside a simulated ecology can be causally real, and no general principle makes their identity forever borrowed from a designer. Designers also built the laboratory, named the stimulus, and chose the frog; none of that prevents the frog from earning content in the resulting traffic. The semantic question turns instead on whether selection, learning, and causal history distinguish among equally structure-preserving readings. A bare web cannot (Section 22.8); a mechanism with the right consequential history may. Cups and predators can become genuine contents because the history fixes what the relevant states are for, not because the maker labels the sprites.

Dangerous-for-it marks a separate achievement. A pattern that threatens an organization the system must maintain can ground a practical interest, but it does not tell us what any state is about. Chapter 24 takes up that question separately from meaning and consciousness.

The stronger patch makes the system model its own continued existence and act to preserve it — a self-evidencing agent holding its states within viable bounds.¹⁶ Modeling viability is not enough. The regulation has to maintain the organization whose loss would end it; otherwise the self-concern is depicted along with everything else.

22.8 The Objection That Takes Mind to Be the Exception

The organizational reply beside the hurricane has a specific semantic version. Vladimír Havlík, writing in Synthese in 2025, argues that linguistic understanding has realization conditions a language model can meet.¹⁷ His position, semantic fragmentism, treats training through the contextual and inferential relations of human language as enough to ground minimal semantic contents and bounded but genuine understanding. On that account, acquiring and using the right dense, context-sensitive relations among symbols realizes understanding rather than merely describing it. A data center need not move air to achieve that.

Training can give internal mechanisms rich causal structure and genuine content about linguistic contexts. When several content-guided capacities correct and constrain one another, the book also grants a basis for partial understanding. Havlík and I therefore need not disagree about every bounded achievement. The remaining questions concern what fixes the content and how broadly the larger system integrates its use. Neither a demand for adult human breadth nor the hurricane can answer those questions for us.

But I do not want to rest the reply on “you assumed your conclusion,” because the fragmentist can fairly say the same of me. An independent reason limits the portable web—a theorem’s worth of reason, not a clash of starting points. A system of relations fixes its relata only up to structure. Give me any web of symbol-to-symbol relations, however dense, and I can relabel every node, and so long as the swap preserves the pattern of relations, the new assignment satisfies the web exactly as well as the old. The relations cannot tell the two assignments apart, because the relations are all there is. M. H. A. Newman raised this objection in 1928 against a purely structural account of knowledge, and Putnam made it the decisive step of his model-theoretic argument: no amount of internal or structural constraint, by itself, pins a term to its referent.¹⁸ A distributional web inherits the predicament. Its geometry alone cannot select which worldly thing each symbol is about. Training may supply genuine content about linguistic contexts; a further causal-historical chain may also fix distal reference. The theorem blocks structure alone, not those historical routes.

Nor does richness buy an exemption. Probe a trained network and you can find internal structure that genuinely mirrors a domain—researchers have recovered a working map of a game board from a model trained only to predict moves, and “world model” is not an unfair name for what they found. Grant that training made the geometry a network achievement and that the mechanism uses it causally. Whether the resulting content is original still turns on what fixes the task’s semantic standard. The permutation shows that geometry alone does not settle that question or distal worldly reference. Nor does mechanism content by itself establish understanding by the larger system.

The result is multiplicity: every relation-preserving relabeling satisfies the web equally well, so the relations select no distal reading. Reference remains possible if something beyond the geometry breaks the tie — selection history, corpus-mediated causal chains, or live world traffic. Co-occurrence alone gives us more symbols: the merry-go-round, spun faster.¹⁹

A reader who knows the literature will have a reply ready, and it deserves stating at full strength. David Lewis answered the permutation argument a generation ago: reference gets fixed by structure plus eligibility—some properties carve the world at its joints, so interpretations assigning those properties stand ahead of gerrymandered rivals.²⁰ No literal magnet tugs at a symbol. Eligibility constrains the best overall interpretation. Grant that the world’s natural joints break some structural ties. That may help fix reference, especially when selection history and causal traffic engage those properties. It still does not, by itself, turn a self-enclosed web into an understanding subject; whole-system attribution remains a further question. The hurricane survives intact.

Chapter Summary

The claim. A wall does not run every program because an observer can map its states. A strict causal test can show that a system truly computes, but causal form alone fixes neither meaning nor mind. Calling the system a simulation settles nothing else.

Where the argument stands. Putnam exposes how loose mappings trivialize implementation; Chalmers’s repair ties computation to the system’s own causal structure. Church–Turing results concern computability rather than every modeled power, while the hurricane blocks automatic transfer without deciding the mind case. History and use fix original content only where correctness standards are not exhausted by interpretation; derived content remains real, and neither kind establishes whole-system understanding.

What’s left open. A real system may combine its own computation, world-directed content, and a unified cognitive life; neither a virtual setting nor an engineered origin bars it. Self-maintenance may add autonomy and interests, but it creates neither meaning nor consciousness.

The hand-off. The wall has left the witness stand. Actual systems now require judgment by their histories and organization, not by a loose mapping or the word simulation.

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Notes

1. Hilary Putnam, Representation and Reality (Cambridge, MA: MIT Press, 1988), ch. 5 and the Appendix, esp. pp. 120–125 (the page range at which Chalmers locates the theorem): every ordinary open physical system realizes every abstract inputless finite-state automaton. The result requires the system be open (interacting with an environment so its states do not cycle) over the relevant interval; the proof constructs the needed state-to-state mapping by exploiting the density of distinct physical states. For automata with specified inputs and outputs, Putnam offers a weaker result: any physical system with the right input–output profile realizes indefinitely many compatible automata, not literally every one. The strength of the inputless claim — every automaton, not merely many — is what makes it a triviality result rather than a mere restatement of multiple realizability. ↩
2. John R. Searle, “Is the Brain a Digital Computer?,” Proceedings and Addresses of the American Philosophical Association 64, no. 3 (1990): 21–37, at 26–28, where Searle argues that “syntax is not intrinsic to physics” — that computational description is observer-relative, no fact intrinsic to a physical system settling whether it is computing at all. Searle’s 1990 paper does work distinct from the Chinese Room of Chapter 21: the Chinese Room argues that syntax does not suffice for semantics; “Is the Brain a Digital Computer?” argues that nothing in a physical system is intrinsically syntactic in the first place. The restatement in less technical register appears in The Mystery of Consciousness (New York: New York Review of Books, 1997), ch. 1. The observer-relativity of syntax is the governing premise; for its extension to semantics see Teodor Negru, “Intentionality and Background: Searle and Dreyfus against Classical AI Theory,” Filosofia Unisinos 14, no. 1 (2013): 18–34. ↩
3. Putnam, Representation and Reality, esp. chs. 5–6. The reversal is genuine and unhedged: the multiple realizability that recommended functionalism in the 1960s is, by Putnam’s 1988 lights, exactly what dissolves it once the realization relation is seen to be unconstrained. On the philosophical and rhetorical weight of functionalism’s founder arriving at this verdict, see Oron Shagrir, “The Rise and Fall of Computational Functionalism,” in Hilary Putnam, ed. Yemima Ben-Menahem (Contemporary Philosophy in Focus; Cambridge: Cambridge University Press, 2005). This chapter treats the Putnam–Searle convergence as evidential, not merely rhetorical: independent derivations of one conclusion from incompatible starting points. ↩
4. Peter Godfrey-Smith, “Triviality Arguments against Functionalism,” Philosophical Studies 145, no. 2 (2009): 273–295, esp. secs. 4–5. Godfrey-Smith’s inventory separates the versions of the triviality argument that succeed from those that overreach. His response requires realizers to possess the right lower-level causal organization: appropriately similar physical states, independently variable components, and suitably localized role occupants. That retreat from wholly autonomous functional description converges with the position Section 22.4 defends. Godfrey-Smith explicitly brackets teleological versions of functionalism in this paper. ↩
5. David J. Chalmers, “Does a Rock Implement Every Finite-State Automaton?,” Synthese 108, no. 3 (1996): 309–333; the compressed version is in The Conscious Mind: In Search of a Fundamental Theory (New York: Oxford University Press, 1996), ch. 10. Chalmers requires a combinatorial-state automaton structure: internal states factor into components whose transitions are reliable, counterfactual-supporting, and isomorphic to the formal computation’s state-transition structure. Ron Chrisley, “Why Everything Doesn’t Realize Every Computation,” Minds and Machines 4, no. 4 (1994): 403–420, presses a kindred constraint. For the subsequent debate see Matthias Scheutz, “When Physical Systems Realize Functions…,” Minds and Machines 9, no. 2 (1999): 161–196; Mark Sprevak, “Three Challenges to Chalmers on Computational Implementation,” Journal of Cognitive Science 13 (2012): 107–143; and Michael Rescorla, “The Computational Theory of Mind,” Stanford Encyclopedia of Philosophy. Shuqin Ma and Ryota Kanai push the repair further in “Intrinsic Computational Functionalism: From Observer-Relative Maps to Observer-Independent Structures,” arXiv:2606.06424 (2026): their criteria require observer-independent structure grounded in causal-dynamical organization displayed under intervention. The chapter grants that intrinsic realization without remainder. Their companion paper, Ryota Kanai and Shuqin Ma, “Intrinsic Computational Functionalism and Simulated Consciousness,” arXiv:2606.15348 (2026), rightly shifts the burden: a critic must identify a consciousness-relevant intrinsic structure a purported simulation fails to realize. Chapter 24 accepts that burden without treating thermodynamic self-maintenance as a consciousness requirement; it separates world-involving content, whole-system integration, consciousness-relevant poising and identity, and the autonomy and welfare a realized stake may ground. ↩
6. The teleosemantic grounding gestured at here is developed in Part Three; see Chapter 15 (teleosemantics) and Chapter 16 (semantic externalism), resting on Ruth Garrett Millikan, Language, Thought, and Other Biological Categories (Cambridge, MA: MIT Press, 1984), chs. 1–2 on proper functions, and Fred Dretske, Explaining Behavior: Reasons in a World of Causes (Cambridge, MA: MIT Press, 1988), for the parallel project through learning history. Artificial training can in principle supply producer-consumer functions and task-relative correctness conditions. Jumbly Grindrod, “Large Language Models and Linguistic Intentionality,” Synthese 204 (2024): article 71, https://doi.org/10.1007/s11229-024-04723-8, presses the further possibility that corpus-mediated causal-historical chains can support linguistic reference. The text grants both routes while keeping distal reference and whole-system understanding distinct. ↩
7. The hypothetical hurricane forecast is mine; the simulation/realization distinction it dramatizes is Searle’s (see n. 9). Improving the numerical resolution of a conventional atmospheric model does not give its implementation the atmospheric powers represented. This blocks an inference from predictive fidelity alone to possession of those powers. It does not show that fidelity supplies no evidence about any modeled capacity, or that computation cannot realize mentality. The realization conditions of the particular capacity must be established independently. ↩
8. John Searle, “Minds, Brains, and Programs,” Behavioral and Brain Sciences 3 (1980): 417–424, at 420–423: the distinction between a system that has the relevant causal powers and a system that merely models them is central to his reply to the brain-simulator version of strong AI. “Causal powers” is Searle’s term of art. The book retains the demand to realize the powers relevant to the attributed capacity without accepting a biological restriction or inferring that a model cannot also be a realization. Sections 22.5–22.7 assess that further question separately. ↩
9. John Searle, The Mystery of Consciousness (New York: New York Review of Books, 1997), 60. The point recurs across Searle’s work from 1980 onward, in a formula he states variously as simulation is not duplication and a simulation should not be confused with the thing simulated. Searle’s rainstorm, fire, and digestion cases are interchangeable illustrations of one structural point: a model of a causal process does not inherit the process’s downstream physical effects. ↩
10. Gualtiero Piccinini, “Computationalism, the Church–Turing Thesis, and the Church–Turing Fallacy,” Synthese 154, no. 1 (2007): 97–120, esp. 99–101 and 114–15. Piccinini distinguishes computationalism proper — the empirical thesis that cognition consists in the brain’s computation of certain Turing-computable functions — from the far weaker claim that brain functions are Turing-computable. Even the weaker claim requires a physical computability premise; physicalism alone does not supply it. The inference from computability to computationalism is the Church–Turing fallacy, and Piccinini’s paper shows several historically influential versions of it (Newell, Pylyshyn, and others) to be unsound. The coinage is B. Jack Copeland’s — Piccinini writes of “what Jack Copeland has called the Church–Turing fallacy” — see Copeland, “Narrow Versus Wide Mechanism: Including a Re-Examination of Turing’s Views on the Mind-Machine Issue,” Journal of Philosophy 97, no. 1 (2000): 5–32. The thesis and Deutsch’s physical principle are distinct claims doing distinct work — and the computationalist tends to draw on whichever one is not currently under examination. ↩
11. David Deutsch, “Quantum Theory, the Church–Turing Principle and the Universal Quantum Computer,” Proceedings of the Royal Society of London A 400 (1985): 97–117, at 99. Deutsch’s physical principle says that “every finitely realizable physical system can be perfectly simulated by a universal model computing machine operating by finite means.” It is an additional thesis about physics, not a consequence of physicalism. The crucial point is that the principle concerns simulation: it is silent on whether running the model instantiates every causal power of the system modeled. The principle remains contested in its strongest form; the chapter grants it precisely to show that even at full strength it licenses nothing the brain-simulation hypothesis needs. ↩
12. Michael Tye, Ten Problems of Consciousness (Cambridge, MA: MIT Press, 1995), develops the PANIC theory on which phenomenal character is identical to representational content that is Poised, Abstract, Non-conceptual, and Intentional; Fred Dretske, Naturalizing the Mind (Cambridge, MA: MIT Press, 1995), and earlier Explaining Behavior: Reasons in a World of Causes (Cambridge, MA: MIT Press, 1988), ground content in tracking relations secured by the system’s design or learning history. The identity claim this book defends at Chapter 6 holds in the main text that phenomenal character consists in representational content of the right kind; the “right kind” is cashed out by the world-involving, teleosemantic relations of Part Three (Chapters 14–16) — exactly the relations a merely formal simulation describes but does not thereby instantiate. ↩
13. The homunculus-nation and the absent-qualia objection are built and sourced in Chapter 20, Section 20.4.1 (notes 12–17); the primary text is Ned Block, “Troubles with Functionalism” (1978). The present chapter draws only on the later cut tailored to the brain-simulation case — Block’s access/phenomenal distinction (n. 14). ↩
14. Ned Block, “On a Confusion about a Function of Consciousness,” Behavioral and Brain Sciences 18, no. 2 (1995): 227–247, esp. 227–30. The phenomenal/access distinction has organized the consciousness literature since: access consciousness is functionally definable (information poised for reasoning, report, and the control of action); phenomenal consciousness is what it is like to undergo a state. Functional reproduction secures the first by construction and leaves the second untouched — which is exactly the leverage the brain-simulation hypothesis lacks. ↩
15. David J. Chalmers, “Could a Large Language Model Be Conscious?,” Boston Review, August 9, 2023 (from a NeurIPS 2022 address); the quoted sentences appear in his discussion of virtual embodiment. The book-length defense of virtual realism is Reality+: Virtual Worlds and the Problems of Philosophy (New York: W. W. Norton, 2022): virtual objects are genuine digital objects, virtual events genuinely occur, and virtual experience need involve no illusion. The text grants the metaphysics. Designer-assigned labels do not by themselves fix what an agent’s states represent, but selection, learning, and consequential traffic within a virtual ecology can in principle do so. Virtuality neither supplies grounding nor blocks it; self-maintenance bears separately on autonomy and welfare. ↩
16. The “self-evidencing” idiom belongs to Jakob Hohwy, “The Self-Evidencing Brain,” Noûs 50, no. 2 (2016): 259–285: a system that embodies a model of itself gathers evidence for its own existence by acting to keep its sensory states within the bounds the model expects. The underlying formal framework is Karl Friston’s free-energy principle — organisms resist disorder by minimizing variational free energy, an information-theoretic bound on the surprisal of their sensory states: Karl Friston, “The Free-Energy Principle: A Unified Brain Theory?,” Nature Reviews Neuroscience 11 (2010): 127–138. Neither author draws the present distinction. The question is not whether self-evidencing can be implemented in software in the abstract, but whether a particular physical implementation’s regulation helps constitute the maintenance of the very organization doing the regulating. Chapter 24 calls that condition thermodynamic self-maintenance and treats it as substrate-general. ↩
17. Vladimír Havlík, “Meaning and Understanding in Large Language Models,” Synthese 205, art. 9 (2025), esp. secs. 3.5–3.6, https://doi.org/10.1007/s11229-024-04878-4. Havlík’s semantic fragmentism holds that LLMs achieve genuine, bounded linguistic understanding because training embeds their internal states in dense contextual and inferential relations, supports minimal semantic contents, and can ground meanings without requiring conscious awareness or direct sensory contact. The view is the most serious recent attempt to argue that understanding, unlike weather, is the kind of phenomenon a formal system can realize — which is why it, rather than the boosters’ enthusiasm, is the objection the chapter answers at strength. ↩
18. That a system of relations fixes its relata only up to structure—leaving distal reference undetermined by structure alone—is M. H. A. Newman’s 1928 objection to Russell’s structuralism (“Mr Russell’s ‘Causal Theory of Perception,’” Mind 37, no. 146 [1928]: 137–148) and the decisive step in Hilary Putnam’s model-theoretic argument (Reason, Truth and History [Cambridge: Cambridge University Press, 1981], ch. 2 and the Appendix). A distributional or fragmentist web inherits that limited result: its geometry alone cannot fix which worldly thing a symbol is about. Selection history may still privilege an encoding, and causal-historical chains may help fix distal reference. Chapter 23, note 8, brings the same distinction to large language models. Interventionist probing has recovered rich, behavior-controlling internal structure from sequence models—the model case is Kenneth Li et al., “Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task,” ICLR 2023 (arXiv:2210.13382). Training can make that geometry causally operative and content-bearing. Where an interpreting practice exhausts the correctness standard, the content remains derived; inheritance of the task alone does not establish that dependence. The permutation result limits internal geometry alone, not an account that also appeals to actual content-fixing history and use. Neither geometry nor mechanism content by itself establishes whole-system understanding. ↩
19. The “merry-go-round” is Stevan Harnad’s image for the symbol grounding problem: symbols defined only by other symbols, circling endlessly without ever touching what they are about. Chapter 23 (Section 23.2) gives the problem its source and its bearing on language models. The point here is limited: a web of intra-symbolic relations does not by itself fix distal worldly reference, though training history and corpus-mediated causal chains may add what bare geometry lacks. ↩
20. The reply is David Lewis’s, “Putnam’s Paradox,” Australasian Journal of Philosophy 62, no. 3 (1984): 221–236, esp. 227–29, which answers the permutation argument by adding a constraint of eligibility—reference biased toward the more natural properties—to the structural ones Putnam allows. J. R. G. Williams reconstructs and presses the eligibility view in “Eligibility and Inscrutability,” Philosophical Review 116, no. 3 (2007): 361–399, esp. 372–80. Eligibility constrains interpretations globally; it need not operate through a causal “pull” on a particular symbol. The text grants that naturalness can break some structural ties and may help fix reference when joined to selection history and causal engagement. That semantic concession leaves whole-system understanding as a separate attribution question. For sustained skepticism about treating reference magnetism as a Lewisian doctrine at all, cf. Wolfgang Schwarz, “Against Magnetism,” Australasian Journal of Philosophy 92, no. 1 (2014): 17–36. ↩