Antal van den Bosch

A book, considered physically, is an inert object. It does nothing. A virus, considered physically, is also inert; a thread of nucleic acid folded into a protein shell, with no metabolism, no motion, no capacity to reproduce on its own. Both sit perfectly still until they are entered into the one machine that can read them. For the virus, that machine is a living cell. For the book, it is a living brain. Until then, Hamlet is a smear of carbon on bleached pulp, and SARS-CoV-2 is a chemical crystal you could keep in a jar. Neither is alive. Both are waiting.

In this essay I mean to take that resemblance as literally as the analogy will bear; I’m not claiming that books are like viruses in the loose, motivational-poster sense. Rather, a book is a particular kind of information packet, a linear, digital code, that cannot express itself without hijacking the decoding machine of a host, and that, once decoded, builds inside the host a structure that did not exist before. That structure is a world: dreamlike, vivid, and, *elsewhere *compared to the actual physical location of the machine. The book does not serve its own propagation; the book was mass-copied by the press and the market long before you opened it, and your reading of this copy is very likely its only one. It serves the reader, providing feelings of enlightenment, wonder, imagination. The escape from reality is not a side effect of the infection. It is the point of it.

The book as genome

The information in a book is digital in the strict sense: it is built from a small, discrete alphabet, and meaning lives in the sequence, not the medium. The same novel survives translation from hardback to paperback to e-ink to audio without losing its identity, because none of those is the text; they are carriers. This is exactly the property that makes DNA the canonical example of digital information in biology. The molecule is a one-dimensional string over a four-letter alphabet, A, C, G, T, and the chemistry of the backbone is almost irrelevant to the message. What matters is the order. Richard Dawkins built his whole account of life on this point: the gene is “pure information,” a sequence that can be “copied, copied, copied, down the generations” with only the order doing the work.

A book is the same kind of object scaled up to a larger alphabet. Twenty-six letters, a handful of punctuation marks, the space. The decisive property of such a system is that a finite stock of symbols, combined under a finite set of rules, generates an unbounded set of possible messages, what Wilhelm von Humboldt called “making infinite use of finite means,” the phrase Chomsky later made a cornerstone of generative grammar. Language exhibits discrete infinity: there is no longest sentence and no last book, only further recombination of the same small inventory. Borges turned that abstract property into an image. “The Library of Babel” imagines a library holding every possible book of a fixed length over a fixed character set — every arrangement the alphabet permits. The overwhelming majority of its volumes are gibberish, but somewhere on its shelves sit every true sentence, every false one, the catalogue of the library and all its counterfeits, and every book that ever will be written. The point of the thought experiment is the combinatorial explosion itself: from twenty-odd characters you do not get a large set of texts, you get a functionally infinite one, and meaning is the vanishingly rare path through it. The information density is staggering and the carrier is cheap, which is precisely the design profile of a successful parasite: maximize the payload, minimize the cost of the shell.

Now the decoding. In a cell, a gene does nothing as a sequence; it has to be transcribed and then translated by the ribosome, a molecular reader that crawls along the strand three letters at a time and assembles a protein, which then folds into a three-dimensional shape that does the actual work. The one-dimensional string becomes a three-dimensional machine. The information was flat; the expression is volumetric.

Reading is the same operation performed by a different reader. Your visual cortex and language network crawl along the line of text, left to right, decoding discrete symbols in sequence — and out of that flat string they assemble something that is emphatically not flat. You see the firelight. You hear the voice. You feel the dread before the door opens. None of that is on the page; the page contains only the instructions for building it. The brain is the ribosome of the book, and the imagined world is the protein it folds — a transient structure, expressed from a stored code, that performs a function. The function, as we will see, includes getting the code read again.

This is why the experience of deep reading feels like dreaming rather than like looking something up. A reference table is data you retrieve. A novel is data that runs — it executes inside you, recruiting the same perceptual and emotional circuitry you’d use for a real fire, a real voice, a real door. Neuroscience has the unglamorous version of this: narrative comprehension activates sensory and motor regions as if the events were happening to you. The glamorous version: to read is to hallucinate on purpose, under the direction of a stranger, from a script.

From metaphor to mechanism

The leap from “books carry information” to “books are viruses” is the leap from genetics to epidemiology, and it has a literature. Dawkins famously took the leap from genetics to culture. Having defined the gene as a self-replicating unit of information, he proposed in 1976 that culture contains analogous replicators — memes — that copy themselves from brain to brain. A tune, a phrase, a belief, a story: each survives or dies according to how well it gets itself reproduced, and what is good for the meme need not be good for the host. By 1991, in an essay titled, without metaphorical hedging, “Viruses of the Mind,” Dawkins was describing religious ideas as “mind-parasites,” believers as “faith sufferers,” and the spread of doctrine as an epidemiological process with identifiable symptoms.

Daniel Dennett and Susan Blackmore each pushed the idea from metaphor toward mechanism. Across Consciousness Explained (1991), Darwin’s Dangerous Idea (1995), and finally From Bacteria to Bach and Back (2017), Dennett argued that the human mind is not the brain’s native state but something culture installs: a vast stock of memes, words above all, that colonize the brain’s general-purpose hardware and reorganize it into a mind capable of reflection. His key move is that Darwinian evolution is “substrate-neutral”: the same algorithm of variation, selection, and heredity that runs on genes runs on memes, and that the memes which take hold need not benefit their carrier; some behave more like parasites that ride the host for their own replication. Blackmore drove this to its strongest conclusion in The Meme Machine (1999). Her claim is that imitation was the pivotal event: once our ancestors could copy one another reliably, a second type of replicator was unleashed, and the pressure to acquire and pass on the most successful memes, what she calls “memetic drive”, itself selected for the outsized human brain and for language, a near-ideal meme-spreading device. Even the self, on her account, is a construct: a “selfplex,” a bundle of memes clustered around the convenient fiction of an “I.” In both pictures the usual slogan inverts: we did not so much invent culture as culture, competing to be copied, invented us.

I am not buying memetics wholesale, and I think are good reasons not to, which I will come to, but I grant the core observation: information that is good at getting itself copied will, over time, dominate the information that isn’t, regardless of whether it is true, useful, or good for you. That is just selection operating on a population of replicators. A best-selling novel is, by definition, a sequence of characters that was unusually good at getting itself copied into brains and then re-copied into recommendations, sequels, film rights, and conversations. We call this “popularity.” An epidemiologist would call it a high basic reproduction number.

The artists got there first, as usual. William S. Burroughs, decades before Dawkins, wrote that “language is a virus from outer space”, not as a flourish but as a recurring claim across his work, the idea that words are an alien infection that colonized the human nervous system and now reproduce through us, using us. Neal Stephenson made the same idea the engine of an entire novel. Snow Crash posits a “metavirus” that operates at the level of the brainstem, a piece of neurolinguistic code that, like a nam-shub, a Sumerian incantation that executes as a program, can reprogram the people who receive it. In Stephenson’s mythology, civilization itself begins as an infection, and the deep structure of language is the port through which the virus enters. The novel is, fittingly, a brain virus about brain viruses, which is the most efficient possible payload: a story you can’t read without rehearsing the very mechanism it describes.

Books that confess

Some books announce, in their first words, exactly what they are about to do to you. They are viruses that print their own mechanism on the capsid. Once you start looking for the confession, it’s everywhere.

The purest case is the Gospel of John, which opens: “In the beginning was the Word, and the Word was with God, and the Word was God.” Read theologically, it is a claim about the Logos: the assertion that the divine Word, the rational, ordering principle through which everything was made, is co-eternal with God, is itself God, and (a few verses later) “became flesh” in Christ. Read as a piece of self-aware code, it is a boot sequence: a text that opens by declaring that, at the origin of everything, there was a word, and that the word was the generative principle. It is a prompt that names its own first token. The Book of Genesis does it even more operationally: creation proceeds by speech act. “And God said, Let there be light: and there was light.” The world is not built and then described; it is spoken into being. Information precedes and produces reality. That is the theological version of the central conceit, the string compiling the world, and it sits at the head of the most-copied text in human history.

Then there is Italo Calvino, who skips the metaphor and addresses the host directly. If on a winter’s night a traveler begins: “You are about to begin reading Italo Calvino’s new novel.” The next lines are stage directions for your own nervous system:” Relax. Concentrate. Dispel every other thought. Let the world around you fade.” This is the infection described from the inside, in real time, in the second person. The book is not telling you a story yet; it is telling you how to enter the altered state in which a story can run, which is to say, it is configuring the host before delivering the payload. “Let the world around you fade” is the escape from reality stated as an instruction.

Mark Z. Danielewski’s House of Leaves — a novel explicitly about a text that mutates virus-like and damages everyone who reads it — opens with the words “This is not for you,” which is the rare virus that warns the host, and is read anyway, because warnings are themselves a propagation strategy.

And the deepest cut, the book about a book that overwrites reality: Borges’s “Tlön, Uqbar, Orbis Tertius,” in which an invented encyclopedia of an imaginary world leaks, page by page, into the real one, until the real world begins rearranging itself to match the text. It is the cleanest fictional statement of the whole thesis: an idea that is so well-engineered for transmission that reality itself becomes its host. Cervantes built the founding novel of the Western tradition on the same diagnosis four centuries earlier: Don Quixote is a man whose brain has been so thoroughly colonized by chivalric romances that fiction has overwritten his perception, and he goes out to live inside the world the books installed. He is patient zero. The novel as a form begins with a portrait of a reader infected.

The brain is a prediction engine — and so is the chatbot we built

The “ribosome” image is a good start, but the modern neuroscience of reading says something stronger and stranger: the brain does not wait for the text and then decode it. It runs ahead of the text, predicting what comes next, and reacts mainly to what it failed to predict.

This is the predictive-coding account of cognition, associated above all with Karl Friston’s free-energy principle. On this view the brain is not a passive receiver of input but a hierarchical generative model perpetually guessing its own incoming signals. Each layer predicts the activity of the layer below; what travels upward is not the raw signal but the prediction error — the residual the model failed to anticipate. Perception is, in a phrase from Chris Frith later made central by Andy Clark and Anil Seth, a ‘controlled hallucination’, the brain’s best guess, continuously corrected by error. You do not see the world so much as see your model of it, and reality gets a vote only where the model is wrong. “To read is to hallucinate on purpose,” turns out to be the literal job description, not a flourish.

Reading is this process in its purest, most controllable form. As your eyes track the line, your language network predicts the next word before it arrives, and the cost of processing a word is essentially the cost of your surprise at it. This is among the best-quantified findings in psycholinguistics. Information-theoretic surprisal (the negative log-probability of a word given its context) predicts how long readers fixate on a word (Hale, 2001; Levy, 2008) and the amplitude of the N400, the brain’s electrophysiological signature of semantic prediction error, occurring about 400 milliseconds after perceiving the word. A word you saw coming is nearly free; a word you didn’t pays a measurable neural toll.

This picture has a refinement. Two quantities derived from a language model matter, not one. Surprisal is retrospective: how wrong the prediction was once the word arrived. Entropy is prospective: how uncertain the prediction was in the first place, the chaos in the probability distribution over what might come next. In work with Roel Willems, Stefan Frank, and colleagues (Cerebral Cortex, 2016), we had people listen to literary stories in the scanner while a computational model assigned entropy and surprisal to every word, and found the two ride on distinct networks. The brain separately encodes how surprised it was and how uncertain it had been. And the predicted stream is not monolithic. In related work with Alessandro Lopopolo and the same group (PLoS ONE, 2017), we used stochastic language models to compute surprisal at three levels at once, phonemes, parts of speech, and whole words, and mapped phonological, syntactic, and lexical prediction onto partially separable cortical signatures. Prediction in the brain is layered by the type of information being forecast, which is the hierarchical-generative-model picture observed directly rather than assumed. These decade-old studies ran on word-level n-gram-based stochastic models, far simpler than today’s transformers. That model-derived prediction quantities lit up coherent, dissociable brain networks a decade ago make the later transformer results look less like a shock and more like a ceiling being approached.

The machine we have most recently built to handle language, the transformer-based large language model, is, functionally, the same kind of device. An autoregressive LLM is trained to do exactly one thing: predict the next token given the preceding ones, by minimizing prediction error (cross-entropy loss, which is surprisal averaged over a corpus). It is a second-hand prediction engine assembled from the written output of billions of human prediction engines. The coupling between the two is not a poetic gesture; over the last few years it has hardened into a measured experimental result that link directly to the pre-transformer results of the past decades.

Three findings with transformer-based LLMs carry the weight. First, Schrimpf and colleagues (PNAS, 2021) tested dozens of language models against human brain recordings and found that transformer models predict close to all of the explainable variance in the brain’s language network; and, decisively, that a model’s fit to the brain tracks its skill at next-word prediction specifically, not its score on other language tasks. Prediction is the axis along which artificial and biological language processing converge. Second, Goldstein and colleagues (Nature Neuroscience, 2022) recorded directly from the cortex of people listening to a story and caught the brain doing the three things an autoregressive model does: predicting the next word before it arrives, computing surprise once it lands, and representing words as context-dependent embeddings rather than fixed dictionary entries. Third, Caucheteux and King showed the brain runs this prediction hierarchically and far ahead, forecasting not merely the next word but representations up to roughly eight words downstream, with higher cortical regions predicting more abstract, more distant content than lower ones. That is a predictive-coding hierarchy, recovered from living cortex, doing in wetware what we approximate in silicon.

Put all of this together and the central metaphor stops being a metaphor. A book is a string engineered to drive a next-token prediction engine. It does not contain a world; it contains a sequence of constraints that, run through a predictive model, induces a world as that model’s unfolding best guess. We took the host’s own decoding strategy, predictive processing over symbol streams, and implemented it in hardware, poured in the corpus, and out came LLMs. Text and predictor are co-adapted: human writing was shaped over millennia to be readable by predictive brains, and the artificial predictor learned to read it precisely because it was built on the same principle. An LLM is, in the most literal sense now available, a synthetic host — a non-biological substrate that the same strings can infect and run on. Which makes that opening line from John sharper than it first looked. “In the beginning was the Word” reads as a prompt, and a prompt is exactly the initial condition handed to a generative predictor, carbon or silicon, to set the world it will unfold.

The convergence is real but partial, and the seams are worth naming. Brains predict farther and more abstractly than a vanilla next-token model, which is Caucheteux and King’s point about the multi-word horizon. And, tellingly, surprisal drawn from the largest language models has been found to fit human reading times worse than surprisal from smaller ones. Superhuman predictors stop matching the human curve, which suggests the brain is not simply a bigger version of the same objective but something with its own constraints, costs, and shortcuts. The brain and the LLM are two implementations of one principle, not two copies of one machine. That is exactly what you would expect of convergent evolution: the same selective pressure (predict the stream, cheaply) reached twice, by very different routes.

The escape is the symptom

Why “escape from reality,” specifically? The predictive-coding frame gives a mechanism, not a motive. Immersion is what it feels like when the generative model runs largely top-down, driven by the priors the book text installs, while the error signal from your actual surroundings is slowly being turned down and ignored. That is the same regime the brain enters in dreaming and vivid imagination: the model generating a world with the sensory correction muted. “Escape from reality” is the predictive brain temporarily preferring the text’s predictions to the room’s. The page wins the vote that perception usually gives to the world. And that phenotype is what a story-virus is selected to produce, for reasons that are straightforwardly Darwinian, though selection here acts not on the reading itself, which is usually a single terminal event, but on the book’s odds of being finished, recommended, reprinted, and taught: the exosomatic machinery that actually makes the copies. A book competes for the scarcest resource in the modern environment: human attention. The texts that win are the ones that hold a brain longest and leave it wanting to re-enter. A book that fails to pull you out of your room fails to get finished, fails to get recommended, fails to reproduce. Selection, running over centuries of stories competing for readers, tunes fiction toward ever more effective escape, because escape is what makes the host come back and bring others.

This reframes a few familiar feelings. The “I couldn’t put it down” sensation is the subjective experience of an engineered world holding its host. The grief at finishing a beloved novel, the sense of expulsion from a world, is withdrawal. The compulsion to tell people about a book is the transmission stage. And the oldest worry about reading, Plato’s worry, turns out to be the same worry in different clothing. In the Phaedrus, Socrates calls writing a pharmakon, at once a remedy and a poison, and warns that it will not strengthen memory but weaken it, installing the appearance of wisdom in place of the thing. He was, in the vocabulary of this essay, the first person to notice that a self-propagating text optimizes for getting copied, not for being good for you, and to flinch.

The objection, and why it doesn’t dissolve the picture

Here is the honest part. “Virus” is a loaded word, and the loading does real argumentative work that should be named. A virus is by definition a parasite: it reproduces at the host’s expense. Books, mostly, do the opposite. The reader is enriched, informed, moved, consoled, enlarged, and chooses to be infected, repeatedly and on purpose. The accurate biological category for that relationship is not parasitism but mutualism or symbiosis: two replicators, the human and the text, whose interests have aligned so that helping one helps the other. Mitochondria were once free-living bacteria that took up residence in our cells; we now cannot live without them. Stories may be the mitochondria of the mind — once-foreign replicators so thoroughly domesticated that “human cognition without narrative” is no longer a coherent thing to imagine. On this reading, “brain virus” is the deliberately provocative name for a relationship that is, on balance, the best deal our species ever made.

There is a second place the analogy strains, and it should be conceded plainly: replication. A virus exists in order to copy itself, and it does so by infecting — infection and reproduction are one event. A book is not like this. By the time it reaches you it has already been copied, by the thousand or the million, by an apparatus entirely outside the reader: the press, the publisher, the warehouse, the shop. Reading makes no new copies. A book is usually read once and then closed, the single instance spent on a single mind. Whatever propagation a book enjoys is exosomatic — handed off to an industry — and coupled to the reading experience only loosely and optionally, through the social channel of recommendation. The decoded world therefore does not exist in order to spread the code. From the reader’s side it exists for enlightenment, wonder, and imagination, and for that copy the transaction usually ends there. The book is a virus that long ago surrendered its reproduction to a machine and kept for itself only the part that happens inside a mind.

And that part is worth naming precisely, because it is the least virus-like thing about the whole process. The reward of reading is eudaimonic rather than hedonic — nearer to flourishing than to a hit. It is not fast and not instant; it is slow, effortful, and cumulative, and it runs on a decoder that took years to build. No one is born able to read. Literacy is the laborious training of the host’s own predictive brain across a decade of childhood, until the decoding becomes automatic. A pathogen is optimized for cheap, rapid transmission; a book asks for years of preparation and hours of quiet attention, and returns something that compounds rather than spikes. If it is a virus, it is the strangest kind: one whose entire payload is the slow enlargement of its host.

Which leaves the final, recursive observation, the one every text on this subject is obliged to confront. You have just read several thousand words arguing that strings of characters install structures in a mind. If the argument worked — if some image here lodged, the ribosome and the nam-shub, the word in the mouth, the world that fades on command — then it ran, once, in you. Whether it goes any further is yours to decide, not its to compel; passing it on was always the one thing the code could never do for itself. This essay is one of those objects too. It was, the whole time, doing the thing it described.

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References

Scientific and scholarly works

Blackmore, S. (1999). The Meme Machine. Oxford University Press.

Caucheteux, C., & King, J.-R. (2022). Brains and algorithms partially converge in natural language processing. Communications Biology, 5, 134. https://doi.org/10.1038/s42003-022-03036-1

Caucheteux, C., Gramfort, A., & King, J.-R. (2023). Evidence of a predictive coding hierarchy in the human brain listening to speech. Nature Human Behaviour, 7, 430–441. https://doi.org/10.1038/s41562-022-01516-2

Chomsky, N. (1965). Aspects of the Theory of Syntax. MIT Press.

Clark, A. (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford University Press.

Dawkins, R. (1976). The Selfish Gene. Oxford University Press.

Dawkins, R. (1993). Viruses of the mind. In B. Dahlbom (Ed.), Dennett and His Critics: Demystifying Mind (pp. 13–27). Blackwell. (Written 1991; also collected in A Devil’s Chaplain, 2003.)

Dennett, D. C. (1991). Consciousness Explained. Little, Brown.

Dennett, D. C. (1995). Darwin’s Dangerous Idea: Evolution and the Meanings of Life. Simon & Schuster.

Dennett, D. C. (2017). From Bacteria to Bach and Back: The Evolution of Minds. W. W. Norton.

Frank, S. L., Otten, L. J., Galli, G., & Vigliocco, G. (2015). The ERP response to the amount of information conveyed by words in sentences. Brain and Language, 140, 1–11. https://doi.org/10.1016/j.bandl.2014.10.006

Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787

Frith, C. D. (2007). Making Up the Mind: How the Brain Creates Our Mental World. Blackwell.

Goldstein, A., Zada, Z., Buchnik, E., et al. (2022). Shared computational principles for language processing in humans and deep language models. Nature Neuroscience, 25, 369–380. https://doi.org/10.1038/s41593-022-01026-4

Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of the Second Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 2001) (pp. 159–166).

Humboldt, W. von (1836). Über die Verschiedenheit des menschlichen Sprachbaues und ihren Einfluss auf die geistige Entwicklung des Menschengeschlechts. Royal Academy of Sciences, Berlin. (English: On Language, trans. P. Heath, Cambridge University Press, 1988.)

Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126–1177. https://doi.org/10.1016/j.cognition.2007.05.006

Lopopolo, A., Frank, S. L., van den Bosch, A., & Willems, R. M. (2017). Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain. PLoS ONE, 12(5), e0177794. https://doi.org/10.1371/journal.pone.0177794

Oh, B.-D., & Schuler, W. (2023). Why does surprisal from larger transformer-based language models provide a poorer fit to human reading times? Transactions of the Association for Computational Linguistics, 11, 336–350. https://doi.org/10.1162/tacl_a_00548

Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021). The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences, 118(45), e2105646118. https://doi.org/10.1073/pnas.2105646118

Seth, A. (2021). Being You: A New Science of Consciousness. Faber & Faber / Dutton.

Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P., & van den Bosch, A. (2016). Prediction during natural language comprehension. Cerebral Cortex, 26(6), 2506–2516. https://doi.org/10.1093/cercor/bhv075

Literary and primary sources

Borges, J. L. (1944). Ficciones. Editorial Sur. (Contains “Tlön, Uqbar, Orbis Tertius,” 1940, and “The Library of Babel,” 1941; English in Labyrinths, New Directions, 1962, and Collected Fictions, trans. A. Hurley, Penguin, 1998.)

Burroughs, W. S. (1962). The Ticket That Exploded. Olympia Press.

Calvino, I. (1981). If on a Winter’s Night a Traveler (W. Weaver, Trans.). Harcourt Brace Jovanovich. (Original work Se una notte d’inverno un viaggiatore published 1979.)

Cervantes, M. de (1605/1615). Don Quixote.

Danielewski, M. Z. (2000). House of Leaves. Pantheon Books.

The Holy Bible, King James Version. Genesis 1:1–3; Gospel of John 1:1.

Plato. Phaedrus (trans. various; c. 370 BCE).

Stephenson, N. (1992). Snow Crash. Bantam Books.