Rough Notes on Virtual Reading, On literary study in the Fourth Arena

Title above, links, Abstract, Introduction, and Summary Below.

Academia.edu: https://www.academia.edu/150286029/Rough_Notes_on_Virtual_Reading SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6145009 
ResearchGate: https://www.researchgate.net/publication/400147214_Notes_on_Virtual_Reading_On_literary_study_in_the_Fourth_Arena

Abstract

This discussion develops a state-space framework for linking brains, texts, and literary history in a way that extends both traditional interpretation and current digital humanities methods. We begin with a neuroanatomical asymmetry: large language models (LLMs) operate primarily on linguistic traces, whereas human poetic production and reception emerge from whole-brain dynamics that include affect, memory, perception, attention, and bodily regulation. If meaning is understood in the language of complex dynamics, it is not a static property “contained” in words but a temporally unfolding trajectory through a high-dimensional cognitive state space. Texts are therefore treated as traces of such trajectories.

From this premise we propose virtual reading: since a text necessarily projects into lexical–semantic activity, and since word embeddings provide a tractable high-dimensional geometry for lexical relations, a text can be modeled as a path through embedding space. While this path reflects purely lexical structure, its global form—drift, recurrence, looping, discontinuity, return—also bears the imprint of extra-lexical constraints that shape lexical choice. In principle, neuroimaging of readers during reading supplies a second coupled trajectory (whole-brain activity over time), enabling empirical alignment between semantic paths and brain dynamics. Drawing on Walter Freeman and Hermann Haken, poetic form is framed as a cultural technology of dimensionality reduction: it extracts low-dimensional, shareable coordinates from otherwise intractable semantic dynamics.

Finally, we connect micro-trajectory analysis to macro-history via cultural evolution. Quantitative DH findings on directional change in large corpora (e.g., similarity structures that spontaneously align with time) become intelligible as movement through a cultural “design space.” The approach does not dissolve disciplinary differences, but provides a richer conceptual arena where close reading calibrates computational exploration, and state-space models open new pathways for scholarly and public understanding of literature as dynamics in time.

Introduction: This is a strange way to assemble a working paper

Over the last 15 years or so I’ve written a bunch of working papers and posted them to the web. Most of them consist of expository prose from beginning to end and a number of them have a few of many diagrams of one kind or another. A few of them are argued as carefully as a formal academic paper, though perhaps not so dense with supporting apparatus. Most of them are not so formal; some are more like popular scientific writing, though not on standard scientific topics; others are even more relaxed. But coherent prose, all of them, sentences and paragraphs, some headings and subheadings. That’s it.

This working paper is different. It’s a transcript of a long conversation I had with ChatGPT that began with the functional organization of the human brain and ended up somewhere beyond those pesky Two Cultures than so many earnest academics like to rattle on about. In between I talk about something I call virtual reading, which involves literary texts, high-dimensional lexical spaces and computing. Then I toss in brain study. After that it gets complex. Here and there we have some longish passages of prose, but mostly it’s one or three sentences at a time strung between a passel of bulleted lists and a blither of headings and subheadings. Not prose.

Why would I inflect that on you. Two reasons: 1) if you think carefully about it, it turns out to be challenging and interesting and 2) I just don’t have time to turn it all into properly argued prose.

This working paper is based on a dialog I had with ChatGPT 5.2 on January 16, 17, and 18, 2026. Most of it is, in fact, an almost direct transcription of that dialog. Why would I Issue such a crude and unpolished text?

I note, first of all, that you do not have to read that transcript if you are curious about what’s in this document. I have provided both an abstract (288 words) and a summary (846 words), both created by ChatGPT. You don’t have to slog through that transcript if you are interested. If you want details, though, you’ll find them in the transcript.

Note, furthermore, that here and there throughout the dialog you’ll find islands of coherent prose. ChatGPT produced some of them without prompting from me; these tend to be single paragraphs. It generated others in response to prompts from me; these tend to be multi-paragraphed, and somewhat long. Look for them. Finally, look for the hyperlinks ChatGPT embedded in the text.

What’s the Fourth Arena?

You may be wondering about that “Fourth Arena” in the title. It also shows up in the text. Here it is: “Fourth Arena” is a term I am using to refer to an emerging domain beyond matter, life, and culture, made possible by the deep integration of humans with computational systems. As AI increasingly participates in cognition, memory, and coordination, new hybrid forms of agency arise that are neither purely human nor merely mechanical. In this sense, the Fourth Arena echoes Pierre Teilhard de Chardin’s idea of a noosphere—a new layer of collective mind—but grounds it technologically and institutionally. Its defining shift is not greater efficiency, but a reorientation of human value away from work and toward play, meaning, and shared exploration.

Who Did What Work?

One might ask: “If ChatGPT provided all the words, why is yours the only name on the document?” Because I created the prompts, I gave ChatGPT direction. Analogy: I told it which cities I want to visit. I figured out how to get from one to the other.

The Cover Image 

Now that ChatGPT can create images I general ask it to do so for the front page of all my working papers. Once I’ve finished the text I ask ChatGPT to create an image for the cover of the working paper. Thus the prompt it is working from is rich and specific.

Sometimes I accept the first image it creates. Sometimes I don’t. In this case I didn’t. The first image was like the one I’ve used, but it was in full color, had a label across the top (“Virtual Reading”) and was in a portrait aspect ratio. I asked it to get rid of the lettering, which it did. Then I asked it to make the aspect ratio square, which I liked.

I was now bothered by the “plastic” or “fruity” look, so I asked it to produce a version in the style of a Renaissance etching. That’s the one you see on the cover.

Summary by ChatGPT: From brains to texts, from texts to trajectories, from DH to cultural evolution

We began with a basic neuroanatomical asymmetry: the human brain contains roughly 86 billion neurons, but only a fraction of them reside in the neocortex. If we loosely treat the neocortex as the main substrate of linguistic competence and symbolic manipulation, then any model that primarily operates on linguistic representations (as LLMs do) is at best a partial proxy for what a whole brain is doing. Even if such a model produces compelling outputs, the underlying generative process in humans has access to many other neural resources—affect, perception, bodily regulation, episodic memory, social attunement—distributed well beyond the lexical–semantic machinery. The point is not that language “lives” only in one place, but that language production and comprehension are embedded in a much larger dynamical system.

From there we introduced a dynamical picture of meaning: instead of treating meaning as a static object contained in words, we treat meaning as a trajectory through a high-dimensional state space. This is a standard maneuver in complex systems thinking: when the system is too complex to describe exhaustively, one looks for low-dimensional phenomena—order parameters, attractors, and structured trajectories—that make the system intelligible. In this view, a text can be understood as a trace left by a cognitive trajectory unfolding in time. The words on the page are not the process itself; they are a record of the process in a compressed medium.

That led to the idea of virtual reading. A text, as a whole-brain trace, must necessarily contain (at least) a trace of lexical processing, since words are the overt medium. Modern word-embedding spaces provide a plausible external analogue of lexical organization: a high-dimensional space where words occupy locations and exhibit neighborhood structure. This makes it possible, in principle, to map the unfolding of a text as a path through embedding space. Such a path would reflect lexical relations—synonymy, topical association, semantic clustering—but it would also carry the fingerprints of non-lexical forces that constrain lexical choice and sequencing: affective pressure, attentional gating, memory recall, rhetorical stance, narrative architecture. Virtual reading is thus a way of using a lexical projection to infer the presence of whole-brain shaping forces that are not themselves explicitly represented in the embedding.

At that point we strengthened the “whole brain” grounding by observing that, in principle, one can have a person read a text while brain activity is imaged. This yields two coupled trajectories: the virtual reading (a lexical-space path derived from the text) and a brain-dynamics trace (whole-brain activity over time). The text becomes a structured stimulus that entrains a temporally organized cognitive process in the reader. One cannot expect a transparent one-to-one mapping, but one can treat the relationship as an empirical alignment problem: where does the semantic path tighten, jump, loop, or recur, and what changes in whole-brain recruitment accompany those shifts?

A key historical anchor for this line of thinking came from earlier correspondence with Walter Freeman. The proposal there was essentially that poetic form functions as a cultural technology of dimensionality reduction: it “carves” a few sharable coordinates out of otherwise high-dimensional semantic dynamics, making aesthetic contemplation possible as a shared meeting space between minds. Even the speculative idea of extracting such induced dimensions from EEG signals points toward the same program: locate low-dimensional order in complex cognitive dynamics by coupling textual structure to neural data.

We then connected this micro-level trajectory view to macro-level literary history through the concept of cultural evolution. Digital humanists have already produced quantitative studies of large-scale change over time—semantic drift, shifting literary standards, long-term directionality—but have often avoided explicitly evolutionary language. Your own earlier work on Jockers (Macroanalysis 2013) is central here, because it shows that when thousands of nineteenth-century novels are located in a ~600-dimensional feature space, similarity relations among them generate a diagonal aligned with chronological time even though time is not encoded in the feature set. This suggests that the process generating literary production and reception is directional in design space: a historical population is moving through a space of possibilities under constraints that are social, aesthetic, and cognitive. As you put it, “The literary system is evolving in a 600 dimensional feature matrix. As time unfolds, the links between highly similar books trace a diagonal through the matrix.”

Finally, we returned to what this offers the humanities now. The state-space framework does not magically “unify” the Two Cultures. It does something more useful: it supplies a richer conceptual arena in which different methods can operate together. Traditional close attention to texts remains indispensable, not as the whole enterprise but as a calibration discipline—ensuring that computational trajectories and projections are aligned with what texts are actually doing. At the same time, state-space methods let scholars explore patterns—directionality, recurrence, innovation, propagation—that are simply inaccessible to unaided reading. This not only opens new research questions; it also gives humanists new ways to interact with the educated public: new objects of thought, new visualizations, and new accounts of how texts organize minds in time, rather than an academic posture limited to boundary policing and gatekeeping.