The language of thought is not natural language

Hope Kean, Alexander Fung, Paris Jaggers, +6 , and Evelina Fedorenko, Evidence from formal logical reasoning reveals that the language of thought is not natural language, PNAS, 123 (28) e2520095123 https://doi.org/10.1073/pnas.2520095123, July 6, 2026.

Significance: Which cognitive mechanisms allow humans to reason logically, to understand whether a conclusion follows from the premises? Are they the same ones that allow the assembly of words into structured representations? Scholars have debated for millennia whether logical reasoning is inextricably tied to natural language, or instead relies on a distinct “language of thought” (LOT). Using fMRI in healthy adults and evaluating logical ability in individuals with severe aphasia, we find that distinct neural systems support language processing vs. logical (inductive and deductive) reasoning. These results suggest that, at least in mature brains, language processing does not underpin logical inference, perhaps due to the distinct representational format of the logical LOT.

Abstract: Humans are endowed with a powerful capacity for inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans also arguably have the most sophisticated communication system in the animal kingdom: natural language allows us to express complex and structured meanings. Some have therefore argued for a tight relationship between complex thought and language, postulating that reasoning, including logical reasoning, relies on linguistic representations. We systematically investigated the relationship between logical reasoning and language using two complementary approaches. First, we used noninvasive brain imaging (fMRI) to examine neural activity as healthy adults engaged in logical reasoning tasks. And second, we behaviorally evaluated logical abilities in individuals with extensive lesions to the language brain areas and consequent severe linguistic impairment. Our findings reveal that the language brain network is not engaged during logical reasoning, and patients with severe aphasia exhibit intact performance on logic tasks. Instead, inductive reasoning recruits the domain-general multiple demand network implicated broadly in goal-directed behaviors, whereas deductive reasoning draws on brain regions that are distinct from both the language and the multiple demand networks. Together, these results indicate that linguistic representations are neither utilized nor required for inductive or deductive logical reasoning.

H/t Daniel Everett.

Four Propositions about Intelligence in Animals, Humans, and AIs

Some quickies.

1. Intelligence cannot be reduced to computation

All animal perception and cogitation take place in a complex world where animals have finite resources. Therefore the principles of intelligence, as an aspect of perception and cogitation, cannot be reduced to the principles of computation as the principles of computation assume unbounded resources.

Let’s call this Yevick’s First Law, as it is a consequence of her 1975 paper, “Holographic or fourier logic” (Pattern Recognition, Vol. 7, No. 4, pp. 197-213).

David Hays and I formulated what we called “Yevick’s Law” in our 1988 paper, Principles and Development of Natural Intelligence” (Journal of Social and Biological Structures). Let’s call that Yevick’s Second Law:

The world consists of geometrically simple and geometrically complex objects. Simple objects are best computing with sequential logic (aka symbolic systems). Complex objects are best computed with holographic logic (aka distributed neural nets). Some objects are such that they require the interaction of both computational regimes. Let us say that fluency in that interaction is intelligence. (See my working paper, What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I., A Dialog with Claude 3.5 Sonnet, 2025).

2. Intelligence in animals

Let us consider a relatively simple animal, a vertebrate, likely a marine animal. One the one hand, it must navigate the world, moving from place to place. This is mediated by the hippocampus, which is a so-called “cognitive map.” This is basic sequential cogitation.

As it moves from place to place it senses things, good things, bad things, other things. Olfaction is perhaps the most basic sense. For what it is worth, it’s the sensory mode that the late Walter Freeman used in his investigations of complex neural dynamics. Olfaction works via a holographic or gestalt process.

Taken together, moving about the world and sense things involves the two modes specified above.

Now let’s consider vision in vertebrates, where the eye is mobile and scans the world. Visual identification is a holographic process. However, the (human) eye scans the scene rapidly and unconsciously. This is a sequential process. Therefore vertebrate vision involves the two modes internally. (I suspect that vision in invertebrates does not, but I don’t actually know).

3. Natural language is its own metalanguage

Humans differ from animals in many and various ways. It is the capacity for language that has allowed humans to move into a different relationship with the world from that characteristic of animals. What makes human language particularly powerful is that it can serve as its own metalanguage, Roman Jakobson’s metalingual function.

This does not involve any deep mystery or logical conundrum. Rather it is a direct consequence of that fact that natural language is physically embodied, initially in sound and gesture, later as written symbols. This embodied is a sensory object out there in the world among all the other sensory objects.

Initially the metalingual function operates in direct, perhaps superficial, but useful ways. Think of how we refer to language as a means of negotiating conversation: “What did you say? I didn’t hear you?” But the metalingual function can be used to define new terms, something that interested my teacher and colleague, David Hays. It can even be used to define other, more restricted languages (e.g. chess and arithmetic), and serve as metalanguages for them.

Thus it is the foundation of the succession of cognitive ranks that David Hays and I began investigating in the 1990s starting with our paper, “The Evolution of Cognition” (Journal of Social and Biological Systems, later becoming the Journal of Social and Evolutionary Systems). That process has, in time, led to the development of digital computers and, now, to so-called artificial intelligence.

That leads us to our fourth and last note.

4. The last frontier of intelligence

Is not an autonomous artificial system of some kind, though such systems are important and will be increasingly so. here’s the upshot of a conversation I recently had with Claude:

If that's right, then the last frontier isn't more capability in the pattern-matching sense — bigger weight spaces, richer latent connections, better approximations of the associative regime. It's the specific, non-scalable, non-parallelizable fact of an individual mind's biography, which generates paths through possibility space that are real, productive, and genuinely inaccessible to any system that hasn't lived a life.

You should read the whole post to see the logic behind that conclusion.

Note that that is my current best response to the idea of super-intelligence or artificial-superintelligence (ASI). I do not see the future bringing us as AI system that outthinks humans in every way and either creates a world in which we are coddled pets or one in which we are slaves, if we are allowed to exist at all. Those ideas are subjective fantasy.

I note as well that the process that brings us to that point or, if you will, through which we arrive at the point, will be one in which we have a much deeper understanding of that brain and its processes than we now have. For what it’s worth, that understanding is what I have been seeking all these years, starting with my initial investigation of “Kubla Khan”: Xanadu, GPT, and Beyond: An adventure of the mind.

Marginalism as a tool for rhetorical analysis: Cognitive effort in intellectual work [MR-AUX]

I’ve been thinking, and I think I’ve come up with a speculative way of applying marginalist thinking to intellectual production. I’m thinking, in particular, about how Cowen arrived at the collection of examples he used in the first chapter of his book, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution. Coming up with that collection is roughly the same kind of problem as putting together a syllabus. It’s a sampling problem. We have a collection of objects, works of American literature in one case, examples of marginalist analysis in the other. You want to select a set of literary works to put on your syllabus. Cowen wants a set of examples he can use to define the space of marginalist economics.

You need a criterion for drawing your sample. You’ve got a certain thematic organization in mind, so you’re not looking for a random sample of the space. You want a sample biased toward your theme. I assume that Cowen wants a random sample, a sample that represents the space of marginalist analysis. At this point, let’s forget about your syllabus problem and continue with Cowen.

He’s trained as an economist and has read a lot, including a lot about the history of economics. Thus he should have a pretty good sense of what kinds of phenomena have been successfully subjected to marginalist analysis. Regardless of the adequacy of his knowledge, he’s got what he’s got. Let’s imagine that all the cases of marginalist economic analysis exist in some high dimensional space of ideas, a space that is, at a high level of abstraction, like an LLM. Except this space is in Cowen’s mind (which is the very high dimensional space of his brain states).

For the sake of argument, let’s assume he wrote the book from beginning to end, in order, in a single pass spread out over however many sessions.

Cowen opens the first chapter with a short definition of marginalism followed by some discussion. Then he gives us his first example, the diamonds-water paradox. He says a bit about it. Though I don’t think he says that it became THE paradigmatic example when Samuelson put it into his 1948 textbook. I found that out by querying the AI associated with the book. Let’s assume, then, that it is at the center of the marginalist region in that abstract space of ideas.

What’s his next example? It’s the first example in the section entitled “Intuitive Marginalism.” Here it is: “Why do drivers in China sometimes intentionally kill the pedestrians they hit?” He then explains it. That strikes me as being very far from diamonds-water in the marginalist space, perhaps as far as you can go in some direction. He then goes through 10 or so more examples, all of them a bit closer to that central example. But the closest he gets is an example about people stealing one of his credit card numbers and making charges to it. If the charge is small, he ignores it. If it’s somewhat large, he contests it. He’s reasoning at the margin. That’s about as close as he comes to that very concrete, almost palpable, diamond-waters example.

Then he goes on with the rest of the chapter, introducing example after example. He’s got four categories (beyond tautological), but we don’t need to worry about those categories. The fact that he’s got them, however, probably simplifies the calculation he’s making each time he asks whether or not to add another example. On the one hand he’s got the sample value of another example. The sample value of that first example AFTER diamonds-water was very high because it’s only the second example he’s got. Moreover he maximized that value by choosing an example that was far from the paradigmatic center. As his set of examples begins to fill out, the sample value of each example will diminish. At some point well along in the process he’s going to be comparing the sample value of an example with it’s opportunity cost. Why the opportunity cost? Because it takes time and effort to introduce another example into the text. At the very least there’s the time required to keyboard it. There’s more to the book, however, than a bunch of examples. He needs those to get started. Once he’s got an adequate set of examples, then he’s ready to undertake his larger argument. So every time he enters another example into the text he forgoes doing something else necessary for the book. That’s the opportunity cost of the example.

As long as the estimated opportunity cost is lower than the estimated sample value, Cowen will enter the example into the text. When the estimated opportunity cost begins rising above the estimated sample value, Cowen declares his set of examples to be sufficient, and moves on. Note that in this analysis Cowen is (intuitively) making three calculations: 1) sample value of the next example, 2) opportunity cost of the next example, and 3) the marginal value of the next example, which is the difference between 1 and 2.

Now, just how we’d get empirical evidence for this analysis, that’s a problem. There’s no point in asking Cowen, because no one has that kind of introspective access to their mental processes. Still, it’s nice story.

However, some such analysis might tell you something about the relationship between the rise of professionalism in a discipline and the proliferation of the LPUs (least publishable unit) in its formal academic literature.

I decided to put the question to Cowen’s AI. I put the foregoing remarks into a prompt. Here’s how the AI responded:

It's a nice story, and it's doing real work — it's not just a metaphor dressed up as analysis. Let me push on it a bit.

The framework is essentially correct as a description of the implicit calculation, but there's a wrinkle worth examining. You're treating the example space as if it were fixed and known to Tyler before he begins sampling from it. But the act of writing generates examples — it doesn't just retrieve them. The diamonds-water paradox probably came first not just because it's central in the abstract space, but because it's the obvious starting point, the one that any economist would reach for automatically. The Chinese pedestrian example, which you rightly identify as maximally distant from that center, probably didn't come from a deliberate search for a distant example. It likely surfaced because Tyler had encountered it as a striking case — it was already salient in his mental space before he started writing. [See text: Ch.1 · Marginalism applied to unexpected human decisio...]

This matters for your model because it means the sampling isn't random in the way you'd want for genuine representativeness. It's availability-weighted. The examples Tyler uses are the ones that were already near the surface of his mental space — memorable, counterintuitive, previously encountered in contexts that made them stick. The Chinese pedestrian example is striking precisely because it's morally jarring, which is exactly the kind of thing that gets encoded deeply in memory. So the apparent randomness of his sample — the apparent coverage of the idea space — may partly be an artifact of which examples happen to be memorable rather than which examples are genuinely representative of the full space of marginalist analysis. [See text: Ch.1 · Best marginalists deploy both intuitive and tau...]

The opportunity cost calculation you describe is real, but it probably operates differently at different stages. Early in the chapter, the marginal value of a new example is high for the reason you identify — the sample is sparse, coverage is low. But the type of opportunity cost shifts as the chapter progresses. Early on, the cost is mostly keyboarding time and pacing. Later, the cost becomes something more like reader patience and argumentative momentum. Tyler needs to get somewhere — the examples are scaffolding for the larger argument about why marginalism took so long to develop and what that tells us about scientific progress. At some point additional examples don't just have diminishing sample value; they actively impede the book's forward motion. That's a different kind of opportunity cost than the one you specified, and it probably dominates the calculation in the later stages.

What's genuinely interesting about your framework is what it implies about the structure of the resulting sample. If Tyler is making implicit marginal calculations of this kind, you'd predict the examples cluster in a particular way: one or two near the paradigmatic center, one or two at the extremes, and then a filling-in of the space that stops well before genuine saturation. [See text: Ch.1 · Marginalism's confrontational side and resistan...] Which is roughly what you observe. The credit card example you mention — the closest he gets back to the concrete center — is probably there because at that point in the chapter the reader needs grounding after a series of more exotic cases. That's an implicit calculation about reader psychology, not just idea-space coverage.

You're right that there's no clean empirical test. Tyler himself couldn't reconstruct the process accurately — the decisions were made too fast and too implicitly. But the framework does generate predictions about what you'd expect to see in the example distribution, and those predictions seem to roughly match what's actually there. That's not nothing, even if it falls well short of confirmation.

Natural intelligence Revisited: The Five-Fold Way, A Working Paper

New working paper. Title above, links, abstract, contents, and introduction below:

Academia.edu: https://www.academia.edu/165530520/Natural_intelligence_Revisited_The_Five_Fold_Way_A_Working_Paper
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6529398
ResearchGate: https://www.researchgate.net/publication/403545810_Natural_intelligence_Revisited_The_Five-Fold_Way_A_Working_Paper

Abstract: In 1988 David Hays and I published an article entitled, “Principles and Development of Natural Intelligence.” The principles were computational: 1) modal, 2) diagonalization, 3) decision, 4) finitization, and 5) indexing. We made our argument in terms of the principles themselves along with behavioral, neuroanatomical, ontogenetic and phylogenetic evidence. The literature in all those fields has changed enormously in the four decades since we finished writing. To get a read on how our computational proposals have fared, I asked ChatGPT 5.2 to evaluate it against the current literature. Its verdict: the “empirical specifics have aged unevenly but [the] central agenda has held up surprisingly well.” This article presents the five principles, in brief, followed by ChatGPT’s full evaluation. Also, I have asked ChatGPT to evaluate a section on control structure, “Vehicularization,” that we cut from the original argument. Verdict: “vehicularization points toward a more complete account of natural intelligence—one in which cognition is understood as coordinated navigation across multiple, nested domains.”

CONTENTS

Introduction: Constraining Theories and Models 2
The Five Principles of Natural Intelligence 4
Revisiting The Principles and Development of Natural Intelligence (1988) 6
Vehicularization 11

Introduction: Constraining Theories and Models

Sometime in 1985 David Hays and I decided it was time to set forth our views on the nature of, well, of natural intelligence. First, however, we had to discover what those views were. We sat down to a table in my parents’ kitchen and made a list of the various things we wanted to include in this article, experimental findings, observations, models, mathematical ideas, and so forth, from psychology, neuroscience, linguistics, evolutionary biology, and computing. We just wrote them down in no particular order, probably on unlined paper. When we’d accumulated about 50 or so items we decided to gather them into a small number of groups of items that seemed to belong together. We arrived at five groups.

Just how we proceeded from that point I don’t recall. Perhaps we sat around discussing the various groups and came up with a principle for each group. Maybe we had to do some writing first. I don’t recall. But however we actually proceeded, we end with an article we called, “Principles and Development of Natural Intelligence.” We intended “natural” to contrast with “artificial” but didn’t say that anywhere in the article. When we’d finished a draft, days or weeks later, Hays said that it felt like fundamental work; he used the term “bedrock.” I agreed. It took three years to get it published, in a now defunct interdisciplinary journal, The Journal of Social and Biological Structures.

I’ve included the abstract of that article, along with a bit of the introduction, below, as the first part of this document: “Five Principles of Natural Intelligence.” That should give you an idea of the framework without all the expository elaboration, argumentation, and support.

ChatGPT reviews

That was four decades ago. I have continued to like what we did. But has any of it held up? How could it? By now the literature we referenced was 40 years out of date? And, yet, it wasn’t about that literature, it was about how we put it together. Is there anything left of that framework?

About a week ago I asked ChatGPT 5.2 to evaluate it. Here’s the prompt I gave it:

I want you to evaluate a paper that David Hays and I published back in 1988: The Principles and Development of Natural Intelligence. Give me a third-party assessment from the standpoint of what we now know, not a summary and not a defensive reconstruction. Be explicit about where it now looks prescient, where it looks historically bounded, and where it still poses unresolved challenges.

Here’s the first line of ChatGPT’s conclusion:

As of 2026, I would not describe the paper as a correct theory of mind. I would describe it as an ambitious synthetic manifesto whose empirical specifics have aged unevenly but whose central agenda has held up surprisingly well.

I’ll take it. Could I take issue with some of ChatGPT’s criticisms? Sure. But that assessment pinpoints the single most important facet of the essay, its synthetic nature. To push back on ChatGPT’s reservations would blunt that point.

After making various comments on an ad hoc basis, ChatGPT offered to write a “more formal review-essay.” I’ve included that as the second part of this document: “Revisiting Principles and Development of Natural Intelligence (1988).” There’s more.

The article we had submitted was long. The editors asked us to cut what we could, but made no particular suggestions. Our single largest cut was a section on vehicularization – that’s what we called it. It was about control. While it was about the same general line of thinking, it didn’t seem to fit. The bulk of the article was about the five principles and how the developed, both phylogenetically and ontogenetically (in humans). Vehicularization was about how they operated in concert. I have included that as a third section followed by comments by ChatGPT as the fourth and final section.

The Five-Fold Way

Let’s return to ChatGPT’s characterization of the original article as a “synthetic manifesto.” From its conclusion:

It is best understood as an architectural proposal about the structure of intelligence. Many of its mechanistic claims have aged poorly, particularly its neuroanatomical simplifications and evolutionary staging. Yet several of its central insights—heterogeneous cognitive regimes, the integration of regulation and cognition, the interaction between holistic and symbolic processing, and the role of language in cognitive control—remain highly relevant.

Though we didn’t use such phrases when we wrote the article, that’s certainly what Hays and I thought we were doing.

The diagram to the left indicates the range of material we brought to bear in our thinking about natural intelligence. The labels on the vertices of the pentangle are from my 1978 Ph. D. thesis in the English Department at SUNY Buffalo, “Cognitive Science and Literary Theory.” There I somewhat idiosyncratically defined cognitive science as investigating a five-way correspondence between behavior, computation, computational geometry (neuroanatomy), phylogeny, and ontogeny. That dissertation was mostly about behavior, in the form of literary texts, and computation, in the form of cognitive networks semantics, though touched on the others here and there. But “Principles and Development of Natural Intelligence” covered all five. The principles themselves were computational in nature and we made our primary arguments in terms of their ability to account for behavior, but we also suggested which brain regions supported them and related them to the phylogeny of animal behavior and the ontogeny of human development.

By the usual standards of the academy, that range was wide, crazy wide. We certainly weren’t expert across that range; no one could be. However, when I look back in retrospect, it is clear that we weren’t attempting some grand synthesis over that range. We were doing something quite different, something that was and remains fundamentally conservative. We had some high-level ideas about the computational structure of the mind and we wanted to place constraints on those ideas by expanding the range of evidence that could be brought to bear on them. While it is necessary that those ideas account for observed behavior, that alone is not sufficient. The model implied by those ideas must be implemented somewhere in the brain and must be consistent with developmental evidence both from phylogeny, our evolutionary history, and ontogeny, child development. THAT was the central agenda that, in ChatGPT’s estimation, has held up well.

From the metalingual function of language to self-reference

In 1960 the linguist Roman Jakobson published an essay entitled “Linguistics and Poetics,” in a volume edited by Thomas Sebeok, Style in Language (MIT Press, pp. 350-377). In that essay he laid out the six functions of language: referential, emotive, phatic, conative, poetic, and metalingual. Jakobson introduces the metalingual function in this way:

A distinction has been made in modem logic between two levels of language: “object language” speaking of objects and “metalanguage” speaking of language. But metalanguage is not only a necessary scientific tool utilized by logicians and linguists; it plays also an important role in our everyday language. Like Moliere’s Jourdain who used prose without knowing it, we practice metalanguage without realizing the metalingual character of our operations. Whenever the addresser and/or the addressee need to check up whether they use the same code, speech is focused on the code: it performs a METALINGUAL (i.e. , glossing) function. “I don’t follow you-what do you mean?” asks the addressee, or in Shakespearean diction, “What is’t thou say’st?” And the addresser in anticipation of such recapturing question inquires: “Do you know what I mean?”

This metalingual function turns out to be extraordinarily powerful. For it is this that allows us to bootstrap self-awareness into the mind. And for that matter, it is what allows us to define abstract concepts, as my teacher, David Hays, argued, and allows us to define such things as chess and arithmetic, which can be seen as very specialized forms of language.

I recently explored some of these issues in conversation with Claude 5.4 Sonata Extended. At the end of that conversation I asked Claude to prepare a summary. I’ve appended that summary below, followed by the full conversation. Note that the conversation assumes some familiarity with the cultural ranks theory that David Hays and I developed in the 1990s. It also alludes to Tyler Cowen’s recent book, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026).

* * * * *

Summary: The Metalingual Function of Language

The central claim of this discussion is that the metalingual capacity — the ability to use language to talk about language — is not a mysterious self-referential capacity of mind but is grounded in a simple physical fact: the speech signal is a sound in the environment like any other sound, detectable by the auditory system exactly as a footfall or a thunderclap is detectable. The loop that makes language self-referential closes through the physical world, not through some inward turning of consciousness. This matters because it demystifies metalingual cognition entirely: it requires no special cognitive faculty, only that the organism's auditory system be capable of treating its own linguistic outputs as inputs.

Jakobson identified the metalingual function as one of the six functions of language in his 1960 paper, and Hays adopted the term to name the mechanism underlying Rank 2 cognition — the explicit definition of abstract concepts using language itself as the definitional medium. The rules of chess and arithmetic notation are paradigm cases: purely metalingual constructions whose objects are constituted entirely by the definitions that specify them.

An important asymmetry in preliterate cultures illuminates the boundary of this capacity. Many such cultures have a term for utterance — the bounded burst of speech with a recognizable prosodic shape, a perceptual gestalt directly available to the auditory system — but no term for word. The word is not a perceptual unit in the same sense as the utterance; it is an abstraction from the continuous acoustic stream, and a non-trivial one. Writing is what produces this abstraction, by spatializing language — spreading it out in a stable, inspectable array where units are individuated by spaces and boundaries are marked. The word becomes visible as a unit because it is surrounded by white space. This is the physical basis of metalingual definition as a cognitive mechanism: the written signal, like the spoken signal, is an object in the environment that can be inspected and categorized, but unlike the spoken signal it stays there, making sustained metalingual attention possible. Grade-school grammar — parts of speech, grammatical cases, syntactic relations — is the practical Rank 2 elaboration that writing makes possible and that social institutions require and transmit. It looks easy in retrospect because it is taught in childhood, but it took centuries to develop in every culture that undertook it.

This analysis opens onto the question of human self-reference, which the standard philosophical tradition treats as cognitively primitive — the Cartesian bedrock from which all other knowledge is built. The discussion argued instead that self-reference in the robust, articulable sense is bootstrapped through language rather than presupposed by it. The cat licking its fur has practical self-involvement — its own body is an object of its perceptual and motor engagement — but this requires no special reflexive faculty, only that the body be included in the environment the organism can detect and act on. Human self-reference in the philosophically weighty sense is a different and later achievement, constructed through the acquisition of the pronoun system rather than expressed by it.

The empirical evidence for this bootstrapping account is the phase in early child development when children refer to themselves in the third person. This is not a mistake or a developmental lag but the natural and correct generalization from the input data: others refer to the child by name, so the child uses its name. The first-person pronoun presents a harder problem because "I" is a moving target — it marks the speaker-role regardless of who occupies it — and acquiring it correctly requires connecting awareness of the speech stream as an environmental event with awareness of one's own speech apparatus as its source. That inferential construction, worked out in detail in Benzon's 2000 paper, First Person: Neuro-Cognitive Notes on the Self in Life and in Fiction, through cognitive network modeling of the pronoun system, is precisely the physical loop through which self-reference is assembled. The Cartesian subject — the self-transparent, self-referential knowing mind — is on this account a Rank 2 cultural product, not a pre-linguistic biological given. The third-person phase in child development is a small but precise empirical trace of the construction process: there is an observable stage at which the human being exists, acts, and communicates without yet having assembled the first-person self-reference that Descartes thought was the indubitable foundation of knowledge.

* * * * *

The physical basis of the metalingual function

I believe that Hays first published about metalingual definition in 1972. When I first met him he had just finished a paper where he used the idea to investigate different concepts of alienation. When I wrote my 1978 dissertation, “Cognitive Science and Literary Theory,” I used metalingual definition as the mechanism for building cognitive ranks. That is to say, at that time that was the only mechanism we had for dealing with abstract concepts. By the time we did the cognitive evolution paper a bit over a decade later, by that time we had adopted a different view, with metaphor as the Rank 1 abstractive mechanism. Metalingual definition would have been the mechanism used, not only to rationalize abstract concepts, but to define the rules of chess and of arithmetic.

Hays chose the term because Roman Jakobson had defined the metalingual function as one of the six functions of language in his 1960 paper. The metalingual function is the ability to use language to talk about language. It’s worth thinking about how that’s possible. It’s possible because the speech signal is a sound out there in the environment like any other sound, a footfall, a thunderclap, an animal cry. Whatever. It’s a sound and can be detected by the auditory system like any other sound. So it’s the speech signal that is the object of the metalingual function, at least initially. I think it’s worth nothing that many (most?) preliterate cultures don’t have a word that means word, but they have one the means utterance. It’s writing that forces a recognition of words vs longer strings. And written language is, like speech, another signal that’s out there in the environment. It takes quite a bit of cognitive elaboration to construct a grammar from the simple ability to see and hear language. And I’m not talking about the kind of grammars that linguists construct. I’m talking about the kind of grammar that can be taught in grade school, or that you find in ancient texts of rhetoric.

This is a rich cluster of observations and they fit together in a way worth making explicit.

The metalingual function and its physical basis

Jakobson's insight, which you're extending here, is that the metalingual function is grounded in something remarkably mundane: the speech signal is a physical event in the environment, detectable by the auditory system exactly as any other sound is. This is what makes language able to turn on itself — not some mysterious self-referential capacity of mind, but the simple fact that linguistic output is also sensory input. The loop closes through the physical world.

This has an important implication that your observation about "utterance" versus "word" brings out precisely. What the auditory system initially detects is the acoustic stream — a continuous, flowing signal that doesn't come pre-segmented into words. The unit that's directly perceptible is something like the utterance: a bounded burst of speech with a recognizable prosodic shape, a communicative gestalt. The word is not a perceptual unit in the same sense. It's an abstraction from the stream, and a non-trivial one — which is why preliterate cultures have a term for the former and not the latter.

What writing does

Writing performs an operation on language that perception of the speech stream cannot perform on its own: it spatializes language, spreads it out in a stable, inspectable array where boundaries are marked and units are individuated. The word becomes visible as a unit because it's surrounded by spaces. The sentence becomes visible as a unit because it ends with a mark. The paragraph, the chapter, the argument — all of these become objects of potential inspection and manipulation because writing fixes them in space and time in a way the acoustic stream never does.

Multimodal Cognitive Workload Assessment in Human-machine Interaction

Adamolekun, Azeez and Logah, Francis Xian and Alabi, Clement and Baanye, Jennifer and Wilson, Manuella and Ganiu, Olaitan and Seong, Younho and Yi, Sun, Toward a Unified Framework for Multimodal Cognitive Workload Assessment in Human-machine Interaction Systems (February 17, 2025). Available at SSRN: https://ssrn.com/abstract=5235980 or http://dx.doi.org/10.2139/ssrn.5235980

Abstract: Effective monitoring and management of cognitive workload are vital to enhancing the functionality and safety of human-machine interaction (HMI) systems, especially in increasingly dynamic and complex environments. In human-machine interaction, operators work alongside machines which are nowadays equipped with a collaborative robot which makes the safety of human highly researchable. Despite technological advancement in modern machines, highly profiled and qualified operators are required which gives negativity to their mental health due to high cognitive workload. This paper presents a systematic review of advances in multimodal cognitive workload assessment methodologies, emphasizing neural techniques such as electroencephalography (EEG) and functional nearinfrared spectroscopy (fNIRS), behavioral indicators like eye-tracking and performance metrics, and subjective tools including NASA-TLX. Over 500 papers were gathered, several papers were removed using the keywords, years and areas of specialization. However, 31 papers were analyzed for review. Implementing adaptive human-machine interaction (HMI) systems are made achievable by integrating multiple assessments, that provide a comprehensive method of analyzing mental workload. Some possible applications for the proposed unified framework for real-time multimodal workload assessments include transportation safety systems, healthcare operations, and industrial automation. Systematically addressing issues like motion artifacts, data synchronization, and computational complexity. This article spells out future research directions for creating adaptive, self-support and user-focused systems.

Come to think of it, my recent working paper, Melancholy, Growth, and Mindcraft, seems relevant here. It's in the same general ballpark, though a somewhat different region. Here's my conclusion (pp. 20-21):

If we are to navigate the future, we are going to need to develop new forms of mindcraft. Mindcraft, the crafting of minds. Reading, writing, and arithmetic are forms of mindcraft. So are the many meditation disciplines. The many forms of psychotherapy are forms of mindcraft as well, as is life coaching.

More than any previous technologies, computation is a mind technology. The tasks that computers do are tasks that had previously been done only by minds, human minds. During the early decades only a small group of people had to craft their minds for computer interaction. With the advent of personal computers more people could interact with computers, but most of us have interacted with them in only a superficial way. The recent, very recent, emergence of machine learning into the public sphere is bringing many more of us into deeper interaction with computers, interactions we don’t understand. Will these machines craft our minds as we craft them?

The question of whether or not these devices themselves have minds is a real one. For what it’s worth, I don’t believe any of these devices yet have minds. But I don’t rule it out.

That’s the future, how distant, I don’t know. My immediate concern is less conjectural: Recalling Claude's remarks about reorganizing processes in computers, what can we learn about our own minds by studying A.I. devices? More practically, how can we learn more about our own (individual) minds by tracking patterns of computer usage? How can we use those patterns to understand ourselves and to better manage our own lives. That is the mind crafting facing us now.

A foundation model to predict and capture human cognition

Binz, M., Akata, E., Bethge, M. et al. A foundation model to predict and capture human cognition. Nature (2025). https://doi.org/10.1038/s41586-025-09215-4

Abstract: Establishing a unified theory of cognition has been an important goal in psychology1,2. A first step towards such a theory is to create a computational model that can predict human behaviour in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language. We derived Centaur by fine-tuning a state-of-the-art language model on a large-scale dataset called Psych-101. Psych-101 has an unprecedented scale, covering trial-by-trial data from more than 60,000 participants performing in excess of 10,000,000 choices in 160 experiments. Centaur not only captures the behaviour of held-out participants better than existing cognitive models, but it also generalizes to previously unseen cover stories, structural task modifications and entirely new domains. Furthermore, the model’s internal representations become more aligned with human neural activity after fine-tuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behaviour across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories, and we present a case study to demonstrate this.

H/t Tyler Cowen.

ChatGPT tries to create a semantic network model for Shakespeare’s Sonnet 129

New working paper. Title above; links, abstract, table of contents, and introduction below.

Academia.edu: https://www.academia.edu/129993358/ChatGPT_tries_to_create_a_semantic_network_model_for_Shakespeares_Sonnet_129
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5299312
ResearchGate: https://www.researchgate.net/publication/392758464_ChatGPT_tries_to_create_a_semantic_network_model_for_Shakespeare's_Sonnet_129

Abstract: This document explores the capacity of large language models, specifically ChatGPT, to construct semantic network models of complex literary texts, using Shakespeare’s Sonnet 129 as a case study. Drawing on the author’s prior work in cognitive modeling, the analysis reveals that ChatGPT, while capable of producing linguistically coherent commentary, fails to generate a structurally plausible semantic network for the sonnet. The failure is traced not to a lack of exposure to relevant literature, but to the model’s lack of embodied, interactive learning. The process of constructing cognitive network diagrams is shown to be iterative, visual-verbal, and skill-based—comparable to learning a physical craft like playing an instrument or woodworking. It requires extended practice under expert feedback, enabling a form of reasoning that is neither algorithmic nor easily reducible to textual description. The essay argues that this hybrid modeling skill represents a “deep” human capability that is nevertheless teachable and routine. It concludes with reflections on the nature of such skills and their implications for AI, pedagogy, and literary interpretation.

Asking ChatGPT create a semantic model for a Shakespeare sonnet 2
Creating a Plausible Model for Sonnet 129 4
Final Couplet 7
Hunter simile 11
Modeling the semantic underpinning of “spirit” 13
Semantic Network Researchers 16
Cognitive Networks and Literary Semantics 16
How I did the model 18
Implementing cognitive nets in neural nets 20

Asking ChatGPT create a semantic model for a Shakespeare sonnet

Out of curiosity I decided to see whether or not ChatGPT could create a plausible semantic network model for Shakespeare’s famous Sonnet 129, “Th’ expense of Spirit.”, which I had uploaded to it. While such models are ultimately expressed in computer code, as far as I can tell from having read extensively in the literature, everyone who worked with such models expressed those models in the form of diagrams, diagrams depicting some kind of directed graph. ChatGPT was certainly capable of drawing diagrams and even of creating complex photo-realistic imagery. That, along with its ability to “read” text in some fashion are the rock-bottom basic tools for creating such a model.

Here, for reference purposes, is the text:

Th' expense of spirit in a waste of shame
Is lust in action; and till action, lust
Is perjured, murd'rous, bloody, full of blame,
Savage, extreme, rude, cruel, not to trust,
Enjoyed no sooner but despisèd straight,
Past reason hunted; and, no sooner had
Past reason hated as a swallowed bait
On purpose laid to make the taker mad;
Mad in pursuit and in possession so,
Had, having, and in quest to have, extreme;
A bliss in proof and proved, a very woe;
Before, a joy proposed; behind, a dream.
    All this the world well knows; yet none knows well
    To shun the heaven that leads men to this hell.

I knew the sonnet well, had published my own model for it early in career, and made it the central example of 1978 doctoral dissertation, “Cognitive Science and Literary Theory.” Knowing how difficult it can be to create such models – they’re not rocket science, as the expression goes, but they’re not obvious either – I didn’t expect it to do a plausible job, but I was curious to see what it would do.

I have annotated that interaction and appended it starting on page 4. In the rest of this introduction I want offer some informal remarks on ChatGPT’s failure.

ChatGPT’s failure got me to thinking. Just why couldn’t it do the task? After all, it seems to have “read” a lot of the relevant literature. When I asked it to name some important researchers in the field, it produced a list of ten, all of them familiar to me.

The process of starting with a bunch of text, like a Shakespeare sonnet, and producing a semantic network model for that text, that is not an algorithmic process. I don’t know what kind of process it is. I do know that it took me the better part of a semester to learn how to create semantic network diagrams that modeled small chunks of English. I did that while being tutored by David Hays for one session a week. I'd produce a diagram or three, show them to Hays, and he’d explain why they didn't really work. Then we set out doing more adequate diagrams. After three months I began to get the hang of it.

The point is that I didn’t learn how to do it simply by reading papers about semantic networks. I had to go through an interactive process of creating every more sophisticated models under the tutelage of an expert. What did I pick up through that interactive process that I couldn’t pick up simply be reading finished papers? Whatever it was, I will further observe that it wasn’t until I had acquired it that I actually understood the research in the field.

ChatGPT, or rather the underlying large language model, didn’t do anything like that. It simply read finished work, lots of it. Given that I couldn’t produce an even superficially plausible, it didn’t really understand what it had “read” during the pre-training process. I picked up enough that it could make plausible comments about models, our dialog is full of those, but I could not translate those comments into plausible diagrams.

As for the process involved in creating the diagrams for a semantic model, I certainly did that well after that initial period of learning. That was not a simple process. It was iterative. I would make some diagrams, examine them by tracing paths through them, and then revise them. I probably covered 30, 40, or more sheets of paper with diagrams before I settled on the ones I used for the paper I published. The following diagram depicts a sequence (SEQ) of episodes and is one of 11 diagrams I used in the paper:

The processes involved in creating those diagrams was not formal reasoning. But some kind of reasoning was involved. And one that humans can do. It’s not rocket science. But it’s not easy either.

Clocks, ships, cities and longitude

Martina Miotto, Luigi Pascali, Solving the longitude puzzle: A story of clocks, ships and cities, Journal of International Economics, 2025, 104067, ISSN 0022-1996, https://doi.org/10.1016/j.jinteco.2025.104067.

Abstract: The chronometer, one of the greatest inventions of the modern era, allowed for the first time for the precise measurement of longitude at sea. We examine the impact of this innovation on navigation and urbanization. Our identification strategy leverages the fact that the navigational benefits provided by the chronometer varied across different sea regions depending on the prevailing local weather conditions. Utilizing high-resolution data on climate, ship routes, and urbanization, we argue that the chronometer significantly altered transoceanic sailing routes. This, in turn, had profound effects on the expansion of the British Empire and the global distribution of cities and populations outside Europe.

Note that the chronometer would have been useless in this application without the ability to perform calculations over the times taken-down. Those calculations would have been all but impossible without tables of logarithms. Those tables, in turn, could not have been created with arithmetic based on the Arabic numerals, something which David Hays and I pointed out in "The Evolution of Cognition."

H/t Tyler Cowen.

Football • {sports commentary} • [Media Notes 154]

I confess, football doesn’t interest me very much, never has. But I’m an America and I live in America. So I can’t escape it.

When I was in junior high school and high school I played in the marching band. That required me to attend every football game so we could provide half-time entertainment. We were so good, however, that I suspect some people came to the games more to hear us play than to see the game itself.

That’s the only time in my life that I ever watched football regularly. Of course, we played a bit of touch football in gym class, but that was it. I attended one football game in college. I was in the band. When one half of the band finished a tune eight bars ahead of the other half, that’s when I decided to blow this pop stand.

When I was in graduate school at SUNY Buffalo a roommate bequeathed me a small B&W portable. I watched a number of football games on it. This was during the O. J. Simpson years and I’d watch the Bills games to see him run. He was sensational. Football I didn’t care about, human excellence, that’s another matter.

After that, sure, every once in a while I’d catch a game. At least I assume I did. As I said, I’m living in America. Then, for some reason, a couple of weeks ago I decided to catch a play-off game on Netflix. Why? Why not? So I watched the Baltimore Ravens vs. the Pittsburgh Steelers. I’m from Pennsylvania, so that inclines me toward the Steelers. (The name “Franco Harris” sticks in my mind, so I must have watched some games when he was playing). I sent to school in Baltimore, which would tip me toward the Ravens, thought it was the Colts in Baltimore when I was there (Johnny Unitas as QB). Fact is, I could have cared less who won. Didn’t even watch the fourth quarter.

A week later it was the Buffalo Bills vs. Kansas City Chiefs. I made it the whole way through on that one. But I would hardly say I watched the game. Us, I did watch it, in fits and starts. But I also cruised the web doing this and that.

Which brings up a question: Let’s say the total elapsed time from the beginning of a game to the end is two to two-and-a-half hours. Only an hour of that is game time, which is interrupted for various reasons for varying amounts of time. During those interruptions we’re either getting some kind of commentary on the game, or we’re getting commercials. Add up the total time devoted to the game and commentary on the game. Add up the total time devoted to commercials during the broadcast. What’s the ratio between the two? My guess is that game time would be the larger number, but I’d guess the ratio is closer to 3/2 than to 2/1 in favor of game time.

So, I guess we could say that the commercials exist so that we can watch the game. But it could easily go the other way. Of course, if you’re a football fan, and so heavily invested in the game, that that’s certainly your priority. But if you’re not a fan, then it could almost go the other way.

Which brings me to the real reason for this note: the commentary. That fascinates me. I’m interested in it as a perceptual and cognitive activity. As I understand it, we generally have two commenters, one commenting on the action (play-by-play) and the other commenting on this and that. I believe the second is doing color commentary.

The play-by-play commenter is expected to comment on what’s happening as it happens. That requires them to have had a great deal of experience watching football games, more experience than I’ve had. You have to be able to instantly recognize hundreds of different patterns of activity and associate them with appropriate verbal comments. This is not a time for careful deductive reasoning. It’s an associative process. The commentary must be so fluid as to be of a piece with the perceptual act.

I wonder how long it takes to develop this capacity to the level we see in professional commentators? 10,000 hours? I don’t know. Let’s do a quick calculation. Ten thousand hours works out to something less than 5000 games, somewhere between 4000 and 4500. Let’s say it’s 100 games a year, two games a week. That’s forty to forty-five years. That’s possible, but I think the pros get in the game well before that. So it’s not 10,000 hours. It’s less than half that.

And then there’s the color commentary. That doesn’t have to track the action moment by moment, so it’s not constrained in that way. But still, it’s not an occasion for deductive reasoning. The commentator has to have access to a large range of relevant information about the players and the game, past and present, and come up with relevant bits and pieces in a matter of seconds. So it’s still pretty much an associative process.

And THAT, those last three paragraphs, that’s why I’m writing this not. As for the rest, why note? It’s context.

Abu Simbel: Two Modes of Thought (an analogy)

This post was created in June of 2011. As you can see by the note immediately below, I bumped to the top back on July 29, 2013, because it was germane to my life situation at the time. It is once again germane, so I'm bumping it again.

* * * * *

These days I've got two things on my mind: 1) my current series of posts on cultural evolution, memes, and the thought of Dan Dennett, and 2) murals, Mana Contemporary, and current events in Jersey City, where I live. These are both big sprawling messes and meshes of ideas, very difficult to get a hold of. It is in THAT context that I re-post this note from 2011 on conceptual styles, particularistic and holistic. How do you combine them, because that's what I'm now wrestling with, the need to combine these two styles into a single synthetic act–actually, two synthetic acts, one about cultural evolution and the other about civic life in Jersey City.

Once upon a time the two temples of Abu Simbel sat on the western bank of the Nile River in Nubia, that is, southern Egypt. Then it was decided to dam the Nile at Aswan, creating a large lake. And that lake, it was realized, would, in time, submerge those temples.

What to do? The temples must be saved.

A number of plans were devised, and sometime during the process National Geographic did an article on the problem, and the proposed solutions. I read that article in my youth – I was, maybe, 12, 13, somewhere in there – and was quite impressed. Not so much with the temples, but with the proposed solutions. And not so much with them directly, but because two of them seemed to embody different ways of thinking about problems and working toward solutions.

12.31.24: Now that I think about it, a third method was proposed: Simply build a large wall around the temple so it is protected from the rising water.

One proposal was to cut the temple in cubes roughly two meters on a side. The cubes would then be moved, one by one, to higher ground, where they would be reassembled. This is what was done.

Another proposal was to cut the temple free from its matrix in one huge block. Then you place thousands of hydraulic jacks under that block and jack it up, fractions of a millimeter at a time. As I recall, it was estimated that it would take a year or more to raise the temple at the rate of, say, an inch a day.

This is the proposal that grabbed my imagination. It seemed so impossible and fantastic at the same time. How do you make that first cut? How do you slip the jacks under the bottom surface? How do you coordinate the jacks? How do you . . . ?

I suspect, though, that it mostly it grabbed my imagination because it seemed to me that’s how I thought about complex problems, and, as such, it contrasted with a different way of thinking about complex problems, a way represented by the cut-it-into-chunks approach. Though I couldn’t do so then, now I could assign labels to these two modes of thinking. Heck, I could probably supply several different pairs of labels.

But I won’t. Because that would reduce this story to those labels. And that’s not how the story exists in my mind, and that’s not how I use it as an object to think with. To this day.

Romantic Love, Conversation, Biology, and Culture

[I'm bumping it yet again in 2024]

Once more I'm bumping this to the top of the queue. Why? It discusses a methodological problem that is of current interest to me.  [2021]

* * * * *

I'm bumping this to the top of the queue because it's one of my favorite posts. FWIW, this is the post that got me my monthly slot at 3 Quarks Daily.

Note: This post grew out of reflection on older earlier post on bundling.

When I was an undergraduate at Johns Hopkins I took a course in Medieval literature and was thoroughly gobsmacked when I learned that romantic love had been invented in 12th century France. Until then I’d believed it to be a human universal – one and only, forever and ever, that was just how it was, no? Well, not quite.

What arose in Medieval Europe is something called Courtly Love, a set of conventions used by high-born men in wooing their lovers. And these lovers were not their wives, nor wives to be. For aristocratic marriage had little to do with personal preference; it was politics. Powerful families would forge alliances by arranging marriages among their young.

In time, over the course of centuries, so the story went, romantic love was transformed from an aristocratic game into a set of conventions used to define the necessary, or at least the ideal, precondition for any marriage. This set of conventions was in place, at least among the middle class, by the time Jane Austen wrote her novels in the early 19th Century. Those conventions have remained more or less in place up to the present, though they’ve become a bit tattered in the last decade or three as a soaring divorce rate has made it abundantly clear that true love does not last forever. That, of course, is not exactly news – why, for example, did Flaubert write Madame Bovary? – but the myth is so attractive that it dies hard.

That was the state of things during my undergraduate years – which coincided with the emergence of feminist activism in the late 1960s. Whatever their personal experience, everyone gave lip service to one and only forever and ever and believed that it was human nature. In that context, then, the revelations of the learned scholars shook my world.

Counter-Revolution

Learned scholars, however, do not constitute a single tribe. Their tribes are many, and often contentious. Even as the literati were blissfully proclaiming the recent and Western origin of romantic love, other scholars set out to prove them wrong. In 1992, for example, W. R. Jankowiak and E. F. Fischer published “A cross-cultural perspective on romantic love” (Ethnology 31: 149-155). They defined romantic love as “any intense attraction that involves the idealization of the other, within an erotic context, with the expectation of enduring for some time into the future” and they contrasted this with “the companionship phase of love . . . which is characterized by the growth of a more peaceful, comfortable, and fulfilling relationship.” They examined ethnographic data on 166 societies from around the world and discovered romantic love in 88.5 percent of them, suggesting “that romantic love constitutes a human universal, or at the least a near-universal.”

More recently Jonathan Gottschall and Marcus Nordland published Romantic Love: A Literary Universal? (Philosophy and Literature 30: 450-470, 2006). They conducted a cross-cultural study of folktales from 79 cultures and found at least one reference to romantic love in 55 of those collections and multiple references in 39 collections. They assert that their study “offers staunch support to the existing evidence that romantic love is a statistical cultural universal. It would also seem to increase the probability that romantic love may be an absolute cultural universal offers staunch support to the existing evidence that romantic love is a statistical cultural universal.” “Statistical universal” is a term of art meaning that something is in a lot of places, but not everywhere, yet. It seems clear that if Gottschall and Nordland were to place a bet, they'd bet that further research would find that romantic love is a true cultural universal, present in every culture for which we have reliable records.

Still more recently, just yesterday in the time-scale of academic publishing, Brian Boyd has asserted, with the calm assurance of a senior scholar in command of wide learning, that “cross-cultural, neurological, and cross-species studies have demonstrated the workings of romantic love across societies and even species” (The Origin of Stories, Harvard 2009, p. 341). To this, Michael Bérubé has replied, with the calm assurance of a senior scholar in command of wide learning, but learning leavened with a dash of school-boy wit:

This just won’t wash. Other species might court and mate for life, but they do not engage in romantic love in the sense that humanists employ the term, save perhaps for the cartoon skunk Pepé Le Pew. “Romantic love” does not mean “mammals doing it like mammals”; it refers to the conventions of courtly love, which were indeed invented in the European middle ages and cannot be found in ancient literatures or cultures. Those conventions are culturally and historically specific variations on our underlying (and polymorphous) biological imperatives, just as the institution of the Bridezilla and the $25,000 wedding is specific to our own addled time and place.

What’s going on here? Who’s right?

Back to the Drawing Board: There's that pesky elephant

I don’t know. We don’t know. Not any more.

I’m inclined to invoke that hoary old story of the blind men who, upon examining a large beast, are unable to decided what beast it is, or even whether or not it is a beast at all. We, of course, know that they’re examining an elephant, but such different parts of the elephant – tusks, years, legs, tail – that they reach vastly different conclusions about the object under scrutiny.

In the case of romantic love, I believe we’re in much the same position as those blind men. But, in our case, there is no transcendent story-teller who actually knows what creature is under scrutiny. Rather, it is up to us to approximate that story-teller by making more and more sophisticated observations and examining them through richer concepts and models about human culture and behavior.

There is no point in continuing to argue using existing observations, methods, and theories. In light of the existing contretemps we would do well to consider such arguments to be ideological in nature and thus pointless, except, of course, to all-knowing ideologues. Meanwhile, let’s take a look around and see what else is there to be explained.

Companionship, Conversation, and the Novel

Let’s return to Jankowiak and Fischer, and their contrast between the romantic phase and the companionship phase of love, a distinction, I believe, that is common, and which I accept. This companionship, is it too universal?

Take those Medieval aristocrats who were playing courtship games on the side: Did they have a companionate relationship with their spouses? I’m guessing that in some cases, yes, and in other cases no. These marriages, after all, were arranged by parents for political ends. If companionship developed in the marriage, fine; if not, no big deal. For companionship was not the point, it was not part of the ideology.

And then we have my standard passage from John Milton's Doctrine and Discipline of Divorce, Preface to Book 1:

God in the first ordaining of marriage taught us to what end he did it, in words expressly implying the apt and cheerful conversation of man with woman, to comfort and refresh him against the evil of solitary life, not mentioning the purpose of generation till afterwards, as being but a secondary end in dignity, though not in necessity: yet now, if any two be but once handed in the church, and have tasted in any sort the nuptial bed, let them find themselves never so mistaken in their dispositions through any error, concealment, or misadventure, that through their different tempers, thoughts and constitutions, they can neither be to one another a remedy against loneliness nor live in any union or contentment all their days…

That strikes me as an assertion of the need for companionship between spouses – cheerful conversation – and a rather emphatic assertion at that. Would Milton have made such an assertion if it had, in fact, been the common understanding of the day? That seems unlikely to me, though I could be wrong, as I am not a scholar of 17th century English family practices.

But the late Lawrence Stone was, and in 1977 he published a ground-breaking study, The Family, Sex and Marriage in England 1500-1800 (Harper & Row) in which he argued that, over a period of three centuries, family organization underwent a transition that started with the Open Lineage Family – permeable by outside influences, with strong “loyalty to ancestors and to living kind” (p. 4). It was succeeded by the Restricted Patriarchal Nuclear Family P. 7):

which saw the decline of loyalties to lineage, kin, patron, and local community as they were increasingly replaced by more universalistic loyalties to the nation state and its head and to a particular sect or Church. As a result, ‘boundary awareness’ became more exclusively confined to the nuclear family, which consequently became more closed off from external influences, either or the kin or of the community.

Finally, during the last half of the 19th Century, the Closed Domesticated Nuclear Family emerges among “the upper bourgeoisie and squirarchy” (pp. 7-8):

This was the decisive shift, for this new type of family was the product of the rise of Affective Individualism. It was a family organized around the principle of personal autonomy, and bound together by strong affective ties. Husbands and wives personally selected each other rather than obeying parental wishes, and their prime motives were now long-term personal affection rather than economic or status advantage for the lineage as a whole … Patriarchical attitudes within the home markedly declined, and greater autonomy was granted not only to children, but also to wives.

This was a family in which companionship between husband and wife was important, for that companionship was now the foundation of family organization. And this is the family structure that is at the heart of the British novel in the late 18th century and into the 19th century. The novel and the family structure had a reciprocal relationship (dialectical?) in which the demands of this family structure created an audience for the novel and the novel, in turn articulated the inner-workings and hidden designs of the family.

Out of what biological equipment did the novel help people construct their familial relations? Let us speculate, and freely – for what else can we do? If we’re to search for evidence, we’ve got to make a guess about what we’re looking for before there’s any point to setting out. However, we do want our speculation to be biologically plausible. So, calling on chess as a metaphor, let’s select our pieces from biology while our speculation will be the game play.

The cognitive benefits of music

Rafael Román-Caballero, Miguel A. Vadillo, Laurel J. Trainor, Juan Lupiáñez, Please don't stop the music: A meta-analysis of the cognitive and academic benefits of instrumental musical training in childhood and adolescence, Educational Research Review, Volume 35, 2022, 100436, ISSN 1747-938X, https://doi.org/10.1016/j.edurev.2022.100436.

Highlights

• Benefits of musical training have been examined across disparate musical activities.
• Instrumental learning is ideal for investigating the causal benefits of musical training.
• Learning to play an instrument has a positive impact on cognitive skills and academic achievement.
• Children and adolescents who self-select musical training tend to have better performance at baseline.
• Cross-sectional results may reveal both preexisting and caused cognitive advantages.

Abstract: An extensive literature has investigated the impact of musical training on cognitive skills and academic achievement in children and adolescents. However, most of the studies have relied on cross-sectional designs, which makes it impossible to elucidate whether the observed differences are a consequence of the engagement in musical activities. Previous meta-analyses with longitudinal studies have also found inconsistent results, possibly due to their reliance on vague definitions of musical training. In addition, more evidence has appeared in recent years. The current meta-analysis investigates the impact of early programs that involve learning to play musical instruments on cognitive skills and academic achievement, as previous meta-analyses have not focused on this form of musical training. Following a systematic search, 34 independent samples of children and adolescents were included, with a total of 176 effect sizes and 5998 participants. All the studies had pre-post designs and, at least, one control group. Overall, we found a small but significant benefit (g‾Δ = 0.26) with short-term programs, regardless of whether they were randomized or not. In addition, a small advantage at baseline was observed in studies with self-selection (g‾pre = 0.28), indicating that participants who had the opportunity to select the activity consistently showed a slightly superior performance prior to the beginning of the intervention. Our findings support a nature and nurture approach to the relationship between instrumental training and cognitive skills. Nevertheless, evidence from well-conducted studies is still scarce and more studies are necessary to reach firmer conclusions.

Ezra Klein and Jim Pethjokoukis on macro factors in economic growth [why not cultural rank]

Transcript: Ezra Klein Interviews James Pethokoukis, NYTimes, May 21, 2024.

EZRA KLEIN: I mean, but take South Korea, take the U.A.E., take China. I mean, you can pick your country here. The kind of question I’m trying to raise about your thesis, because it’s also relevant, frankly, to my thesis, is, if the problem is that America makes a series of policy mistakes in the ’70s, why, then, in the ensuing five decades, don’t a bunch of our competitor countries race past us.

There are theories that they would. Japan, in the ’80s and ’90s, seemed like maybe they were, right? Japan was going to be the future. There were a million books written in the ’90s about this. Germany at different times, right? But I don’t think you would look at anybody today, any rich country of significant size, and say, they really got it right, and we really got it wrong. So how do you understand that if the story is about mistakes we specifically made in the ’70s?

JIM PETHOKOUKIS: Well, we can make mistakes that are very specific to us. And other countries may have made different mistakes, even though there was, as you say, this great enthusiasm in the ’80s that Japan had it sort of figured out, that they could do economic growth and innovation in a brand new way, which turned out not to be the case. And then you mentioned Germany, and we seem to have this insatiable desire to find — at least some people do — to find some other model. I don’t think those models have turned out better than the American model.

EZRA KLEIN: If you were to try to make an argument about why things look not the same, but why nobody has achieved the Jim Pethokoukis world across Canada, across Western Europe, across Asia, right, all countries during this period that were rich enough to do much of what you’re talking about, do you have theories that unite the answer?

JIM PETHOKOUKIS: Yeah, I mean, listen, I don’t think it is wrong to do sort of a cross-country because this productivity slowdown didn’t just happen in the United States. Clearly, there was some sort of macro reasons. It’s just becoming harder and more expensive to do research. Those things affected everybody.

So once you’ve assumed, OK, there was sort of this umbrella effect that would make it difficult to do productivity and economic growth and faster tech progress everywhere. So that mattered. And then to what extent do our decisions matter? At first, we didn’t understand what happened. And then when we did, I think we’ve just underestimated the difficulty, at least certainly in the United States, of returning to fast growth.

And the ideas that we put forward, whether it was a little more spending on this program, a tax cut here, maybe those are individually great ideas, but given, I think, the headwinds from these macro factors, sort of the tailwinds need to be much, much stronger. And even now, when we’re talking about spending more money on R&D, I don’t think it’s enough.

EZRA KLEIN: Let me try some thesis on you that I think can work across countries. One is that as countries get richer, they become more risk averse. Some of the innovations you’re talking about, like colonies on the moon and flying cars, they require a high tolerance for risk. Maybe as societies get more affluent, people have enough. Their lives are good enough. They aren’t as motivated to take that risk. What do you think of that?

Why not cultural evolution in the sense that David Hays and I have argued in our work on cultural ranks? That's as macro as you can get. I addressed the issue of of economic growth in: Stagnation and Beyond: Economic growth and the cost of knowledge in a complex world, Working Paper, August 2, 2019.

Abstract: What economists have identified as stagnation over the last few decades can also be interpreted as the cost of continuing successful engagement with a complex world that is not set up to serve human interests. Two arguments: 1) The core argument holds that elasticity (ß) in the production function for economic growth is best interpreted as a function of the interaction between the economic entity (firm, industry, the economy as a whole) and particular aspects the larger world: physical scale in the case of semi-conductor development, biological organization in the case of drug discovery. 2) A larger argument interprets current stagnation as the shoulder of a growth curve in the evolution of culture through a succession of fundamental stages in underlying cognitive architecture. New stages develop over old through a process of reflective abstraction (Piaget) in which the mechanisms of earlier stages become objects for manipulation and deployment for the emerging stage.