Form, computation, and plot summary: A short note on method

I’ve just made a short post about To Catch a Thief. It’s been on my mind for weeks but I didn’t post because: 1) what I had in mind was simple, really, but 2) as I thought about it I realized that I would get tangled up in recounting the plot, more so than I wanted for such a post. Well, I managed to skim over the plot rather quickly, but I fear, alas, the post is likely to seem a bit cryptic.

But then, going back to graduate school days this has been an issue. When dealing with narrative I’m always summarizing the plot. Why?

Well, because I feel it’s necessary in order to understand what I have to say, which often seems obvious once I’ve said it. Why is it necessary?

Because – now that I think of it – what I have to say is ultimately about computational form. I need to summarize in order to set out the computational framework; the syntagmatic structure governs the computation, so you need to see it.

Jakobson’s Poetic Function and Textual Closure

I'm bumping this to the top of the queue, 1) on general principle, and 2) because, with almost 2900 reads, it seems to be unusually popular for a quasi-technical post on literary form. It was originally published on 12.25.2017.

Roman Jakobson’s poetic function [1] is one of the best-known and most obscure ideas in modern poetics. I believe that it extends beyond the kinds of examples Jakobson himself gave, to include, for example, ring-form narratives. It may as well be considered a computational principle applying to texts considered as strings of word forms.

Literary Form

Two years ago Sandra MacPherson wrote [2] that she's looking for “for a genuinely formalist critical practice, a little formalism that would turn one away from history without shame or apology” (p. 385). What does she mean by form? She means “nothing more—and nothing less—than the shape matter (whether a poem or a tree) takes” (p. 390).

The basic shape that literary matter takes is simple, a string. When spoken the string is an acoustic wave. When written the string is a collection of written symbols that generally take rectangular form on the page but that are read as though they were one long string – which they are. The rectangular arrangement is but a convenient way of fitting the string onto sheets of paper.

Music takes the form of a string. That’s one example for us, and poetry is often likened to music. Beads on a wire is another example – a metaphor sometimes used to characterize DNA. I suggest that Roman Jakobson’s poetic function is an abstract statement of a formal principle for things strung together in linear order, such as words.

Jakobson’s formulation of this principle is one of the most enigmatic statements in the critical literature (p. 358):

The poetic function projects the principle of equivalence from the axis of selection into the axis of combination. Equivalence is promoted to the constitutive device of the sequence.

What does that mean? The sequence, of course, is our string. A bit later he says (p. 358):

Measure of sequences is a device that, outside of the poetic function, finds no application in language. Only in poetry with its regular reiteration of equivalent units is the time of the speech flow experienced, as it is — to cite another semiotic pattern — with musical time.

Almost all of his examples are from poetry. Take rhyme. Line endings occur at regular measured intervals. When similar syllables occur at specific fixed intervals, that is projection from the axis of selection (one syllable or another, one word or another) to the axis of combination. That is rhyme. Rhyme is a simple and obvious example of the poetic function. Jakobson goes on to give other, more sophisticated, examples.

But I want to move out of the domain entirely. Let me suggest that ring-composition also exemplifies the poetic function. You may recall that ring-composition involves linear arrangements of this form:

A, B, C...X...C’, B, A’.

The letters indicate ‘slots’ in the sequence while the identity of the letters indicates the pattern of symmetrical matching that is characteristic of ring composition. Matching pairs are equivalent in some semantic sense and the form requires that they be deployed in a certain sequence.

That, I realize is a highly abstract paragraph. As I intend this as only a short note, I have no intention of filling that out [3]. My object is simply to point out that ring-composition can be seen as exemplifying Jakobson’s poetic function, thereby extending its applicable range beyond the kinds of examples Jakobson himself gave and that others typically give. The poetic function isn’t specifically about poetry. It operates in non-poetic narrative forms as well.

I have no reason to believe that the poetic function will account for all aspects of literary form. Just how many aspects it accounts for, I wouldn’t hazard a guess. That will require more work.

The poetic function as a computational principle

Not only can Jakobson’s poetic function be extended beyond the examples he gave, which came from poetry, to other formal features, such as ring composition. I now want to suggest that it is a computational principle as well. What do I mean by computation [4]? That’s always a question in these discussions, isn’t it?

When Alan Turing formalized the idea of computation he did so with the notion of a so-called Turing Machine: “The machine operates on an infinite memory tape divided into discrete cells. The machine positions its head over a cell and ‘reads’ (scans) the symbol there.”[5] There’s more to it than that, but that’s all we need here. It’s that tape that interests me, the one with discrete cells, each containing a symbol. Turing defined computation as an operation on the contents of those cells. Just what kind of symbols we’re dealing with is irrelevant as long as the basic rules governing their use are well-specified. The symbols might be numerals and mathematical operators, but they might also be the words and punctuation marks of a written language.

Linguists frequently refer to strings; an utterance is a string of phonemes, or morphemes, or words, depending on what you’re interested in. Of course it doesn’t have to be an utterance; the string can consist of a written text. What’s important is that it’s a string.

Well, Jakobson’s poetic function places restrictions on the arrangement of words on the string, restrictions independent of those made by ordinary syntax. Let us recall his definition:

The poetic function projects the principle of equivalence from the axis of selection into the axis of combination. Equivalence is promoted to the constitutive device of the sequence.

The sequence, of course, is our string. As for the rest of it, that’s a bit obscure. But it’s easy to see how things like meter and rhyme impose restrictions on the composition of strings. And we have just seen, if only briefly, that ring composition may be seen as restrictions of the composition of a string. In a working paper on ring composition, I have already pointed out how the seven rules Mary Douglas gave for characterizing ring composition [6] can be given a computational interpretation (pp. 39-42).

Textual closure and literary form

I propose these as central to a computational account of literary form:

1. The process whereby word forms, whether spoken, written, or gestured (signed), are linked to meaning/semantics is irreducibly computational.
2. A complete text is well-formed if and only if its meaning is resolved once the last word form has been taken up.
3. It is in this context that Roman Jakobson’s poetic function may be considered a principle of literary form.

1 and 2 are about language in general.

It is not clear to me whether 1 is a matter of definition or a statement empirical fact subject to investigation. If it is to be construed as fact, what would the investigation be like? What counts as evidence? If it is a matter of definition, what is the more general definition of computation of which this would be a particular instance? Would Alan Turing’s definition, via the Turing Machine, be sufficient?

On 2, I rather imagine there is relevant literature, though I don’t know. Obviously there is a huge literature about computational completion, and 2 would fall within the scope of that literature. Whether or not a computation will complete is one thing. This is much more specific. It says that we are

1. computing the value of a string by
2. moving through the string from left to right (though I suppose we can allow some back-tracking and some looking ahead) and that
3. the value of the string will have been completed shortly after the rightmost character has been read.

“Shortly after” means, say, less than 1/1000 to 1/100 of the time it takes to read the string from beginning to end–something like that; it needs to be adjusted to allow for both haiku and triple-decker Victorian novels.

On 3, I note that Obama’s eulogy for Clementa Pinckney takes the form of a sermon, but it exhibits ring-composition and thus falls within the scope of Jakobson’s poetic function [7]. I note as well that Alan Liu’s essay, “The Meaning of the Digital Humanities”, exhibits ring-composition, but is expository and argumentative prose [8]. It is possible, but by no means obvious, that all literary texts are governed by the poetic function (whatever “govern” means), but that non-literary texts exhibit it as well. It is also possible that some literary texts exhibit it and some do not. Finally, we might ask whether or not, and if so, in what way, the use of the poetic function in a text contributes to its closure, as defined in 2.

References

[1] Roman Jakobson, “Linguistics and Poetics,” in Thomas Sebeok, ed., Style in Language (Cambridge, Ma.: MIT Press,1960), 350-77.

[2] Sandra Macpherson, A Little Formalism, ELH, Volume 82, Number 2, Summer 2015, pp. 385-405.

[3] For that, see, e.g., Mary Douglas, Thinking in Circles: An Essay on Ring Composition, Yale University Press, 2007. I have numerous posts on ring form at New Savanna, https://new-savanna.blogspot.com/search/label/ring-form
and a number of working papers at Academic.edu: https://independent.academia.edu/BillBenzon

[4] I have argued at some length that literary form is computational: Literary Morphology: Nine Propositions in a Naturalist Theory of Form. PsyArt: An Online Journal for the Psychological Study of the Arts, August 2006, Article 060608. https://www.academia.edu/235110/Literary_Morphology_Nine_Propositions_in_a_Naturalist_Theory_of_Form

[5] Turing machine, Wikipedia, accessed Sept. 19, 2017: https://en.wikipedia.org/wiki/Turing_machine

[6] Ring Composition: Some Notes on a Particular Literary Morphology, Working Paper, September 28, 2014, 70 pp.
https://www.academia.edu/8529105/Ring_Composition_Some_Notes_on_a_Particular_Literary_Morphology

[7] Obama’s Eulogy for Clementa Pinckney: Technics of Power and Grace, Working Paper, July 2015, 37 pp., https://www.academia.edu/14487024/Obama_s_Eulogy_for_Clementa_Pinckney_Technics_of_Power_and_Grace

On finding Donkey Kong transistors in a MOS 6502 microprocessor chip – Whoops! the methods of the neurosciences have problems, no?

This is from June 2019. I'm bumping it to the top of the queue because I'm thinking about these things.

A couple of days ago I posted a conversation with Rodney Brooks on the limitations of the computing metaphor as a vehicle for understanding the brain. Brooks mentioned an article, "Could a Neuroscientist Understand a Microprocessor?". The point of the article is that if you attempt to understand a microprocessor using the same methods neuroscientists use to understand the brain you're going to come up with gibberish.

I've located that article along with an informal account of the work in The Atlantic. I conclude some some observations of my own.

* * * * *

Jonas E, Kording KP (2017) Could a Neuroscientist Understand a Microprocessor? PLoS Comput Biol 13(1): e1005268. https://doi.org/10.1371/journal.pcbi.1005268

Abstract

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.

Author Summary

Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used naïvely, fall short of producing a meaningful understanding.

* * * * *

Ed Yong, Can Neuroscience Understand Donkey Kong, Let Alone a Brain? The Atlantic, June 2, 2016. From the article:

The human brain contains 86 billion neurons, underlies all of humanity’s scientific and artistic endeavours, and has been repeatedly described as the most complex object in the known universe. By contrast, the MOS 6502 microchip contains 3510 transistors, runs Space Invaders, and wouldn’t even be the most complex object in my pocket. We know very little about how the brain works, but we understand the chip completely. [...]

Even though the duo knew everything about the chip—the state of each transistor and the voltage along every wire—their inferences were trivial at best and seriously misleading at worst. “Most of my friends assumed that we’d pull out some insights about how the processor works,” says Jonas. “But what we extracted was so incredibly superficial. We saw that the processor has a clock and it sometimes reads and writes to memory. Awesome, but in the real world, this would be a millions-of-dollars data set.”

Last week, the duo uploaded their paper, titled “Could a neuroscientist understand a microprocessor?” after a classic from 2002. It reads like both a playful thought experiment (albeit one backed up with data) and a serious shot across the bow. And although it has yet to undergo formal peer review, other neuroscientists have already called it a “landmark paper”, a “watershed moment”, and “the paper we all had in our minds but didn't dare to write”. “While their findings will not necessarily be surprising for a chip designer, they are humbling for a neuroscientist,” wrote Steve Fleming from University College London on his blog. “This kind of soul-searching is exactly what we need to ensure neuroscience evolves in the right direction.”

Five Observations

First, I've noted at various times that I find philosophical arguments about the human mind/brain and computing to be rather empty, mainly because they don't usefully engage the ideas actually used in investigating the mind or the brain. They don't provide pointers for doing better, whether in computational or other terms. I suspect that some of my humanist colleagues are attracted to these arguments because they don't want to entertain, even at a distance, any explicit account of mental operations. Some of them might balk at mind-body dualism as an explicit intellectual program, but they are, effectively, mind-body dualists and therefor mysterians as well. That is they want the mind to be shrouded in mystery. Is humanistic thought (in that view) such that it cannot in principle embrace or approach explicit accounts of the mind? If so, why? Is this a methodological or a metaphysical commitment (does it matter?)?

Second, if the computer metaphor isn't adequate, does it nonetheless have some role to play in understanding the mind? For instance, I've been arguing that language is the simplest thing humans do that involves computation. In this view computation is a very high-level brain process. That implies, of course, that we're going to need other concepts for understanding the brain – such as control theory and complex dynamics. [I note in passing basic that  the arithmetic computation we learn in primary school is a highly constrained and specialized form of language.]

Third, we do know quite a bit about biological mechanisms at the molecular and cellular levels. But we don't yet know how those processes "add up to" a brain, through we're working on it. See, for example, the OpenWorm project, which is an attempt to simulate the roundworm Caenorhabditis elegans at the cellular level. C. elegans has 959 cells, including 302 neurons and 95 muscle cells. Then we have the Blue Brain Project, which is an attempt to simulate a rodent brain at some neuronal level. What's going on in these simulations? I note that, while Searle said nothing about biology in his original formulation of his well-known Chinese Room argument (against the computational view of mind), he has some more recent observations in which he explicitly references biology, wondering how much of biological mechanism is "essential for duplicating the causal powers of the original."

Fourth, the upshot of Searle's Chinese Room argument is that computation is a purely syntactic process. It cannot encompass meaning. It lacks intention and so cannot be about anything. All of which is to say, it cannot connect with a world outside itself. A Universal Turing Machine certainly seems to be that kind of thing, doesn't it?

Fifth, it is nonetheless interesting and telling that computers give us a way simulating anything we can describe in sufficient detail of just the right kind. Including neurons and brains.

What is computation? That is to say, what do I mean by computation? [putting things in order]

As far as I know, the nature of computation is still under investigation. I’m not really qualified to or in fact interested in addressing the question in its full scope and generality. I’m interested in a more limited question – though not, as these things go, all that limited – of the computational aspects of the human mind. And within that, I’m particularly interested in language, and in literature, which of course includes language, but more as well. How much more...who knows?

So, I start out with the abstract idea of computation, then introduce the idea of implementing computation in a physical system and conclude by observing that the computational simulation of a system is not to be confused with the thing itself.

Abstract computation

Abstractly considered, Turing defined computation in terms of a machine that had, 1) a set of symbols, 2) a paper tape on which symbols could be written and from which they could be erased, 3) a device that read from and wrote to the tape, and 4) an instruction set defining relations between the symbols and specifying writing to and erasing from the tape. We need not go beyond that. My point is that we do have a well-known and thoroughly explored account of computation, and that that account is stated in terms of an abstract machine.

Real computation requires physical implementation

I’m not interested in abstract computation on an abstract machine. I’m interested in real computation on a real device of some kind. Given some device, how can we implement computation on that device. It is the idea of implementation that is key.

The device that interests me, of course, is the human brain. And the conclusion I’ve reached over the past few years is that natural language is the simplest activity that requires computation. Language cannot be explained and understood without reference to computation. By implication then we should be able to understand, for example, visual perception without reference to computation. This implies that the full powers of an advanced primate brain are necessary for implementing computation. I suppose that’s the take-out from a paper David Hays and I published in the 1988:

William Benzon and David Hays, Principles and Development of Natural Intelligence, Journal of Social and Biological Structures, Vol. 11, No. 8, July 1988, 293-322, https://www.academia.edu/235116/Principles_and_Development_of_Natural_Intelligence

We didn’t quite put things in those terms, but that paper justifies them. We need not going into the details here.

And so, going back to March of 2016, I’ve written a series of posts on that theme. I’ve collected them under the label, “computational envelope”. This, of course, is another post in that series.

Simulation is not the thing itself

Now we need one more idea, that of simulation. Digital computers can be, have been, and are being used to simulate all sorts of things. But the simulation of a thing is not to be confused with the thing itself. A simulation of an atomic explosion is quite a different phenomenon from a real atomic explosion. And so it is for many other things as well.

And then we have the brain, of any animal, and the human mind. In this case there seems to be some difficulty in distinguishing between a simulation of the thing and the thing itself. It’s not that anyone is confused about the difference between a digital computer, but rather that there is a suspicion that, if we simulate mental processes on a digital computer with sufficient precision and power, then perhaps that computer is not merely running a simulation of a mind, but is in fact a mind. I say let’s set that one aside until we actually confront the situation. So far, we are no where near that.

Now we’ve arrived at the point of this post, a passage from a most interesting book by Peter Gärdenfors, Conceptual Spaces (MIT 2000) p. 253:

On the symbolic level, searching, matching, of symbol strings, and rule following are central. On the subconceptual level, pattern recognition, pattern transformation, and dynamic adaptation of values are some examples of typical computational processes. And on the intermediate conceptual level, vector calculations, coordinate transformations, as well as other geometrical operations are in focus. Of course, one type of calculation can be simulated by one of the others (for example, by symbolic methods on a Turing machine). A point that is often forgotten, however, is that the simulations will, in general be computationally more complex than the process that is simulated.

I rather suspect that all of these kinds of processes take place in the human brain. Only the symbolic level processes however, are irreducibly computational as implemented in the human brain. The other processes are implemented in some non-computational way.

Pattern recognition and transformation might be implemented in neurodynamics while coordinate transformations might, in part, be carried out by the physical structure of region to region mapping in the brain. Whatever. But the scientific investigation of human perception and cognition may require us to simulate any and all of these processes in a computer – as indeed, Walter Freeman has implemented dynamical processes in understanding how odors are recognized and remembered. The fact that we can simulate these processes computationally does not, of course, imply that they are computational in the brain.

Coda

It is my impression that a have of confusion has arisen through a failure to distinguish between the need for implementation on the one hand and the difference between simulation and reality on the other. That’s more than I want to go into here and now.

The computational envelope of language – Once more into the breach

Time to saddle-up and once more ride my current hobby horse, or one of them at least. In this case, the idea that natural language is the simplest aspect of human activity that is fundamentally and irreducibly computational in nature.

Let’s back into it.

* * * * *

Is arithmetic calculation computational in kind?

Well yes, of course. If anything is computation, that sure is.

Well then, in my current view, arithmetic calculation is language from which meaning has been completely removed, squeezed out as it were, leaving us with syntax, morphology, and so forth.

Elaborate.

First, let’s remind ourselves that arithmetic calculation, as performed by writing symbols on some surface, is a very specialized form of language. Sure, we think of it as something different from language...

All those years of drill and practice in primary school?

Yes. We have it drilled into our heads that arithmetic is one thing, over here, while language is something different, over there. But it’s obvious, isn’t it, that arithmetic is built from language?

OK, I’ll accept that.

So, arithmetic calculation has two kinds of symbols, numerals and operators. Both are finite in number. Numerals can be concatenated into strings of any length and in any order and combination.

OK. In the standard Arabic notation there are ten numerals, zero (0) through (9).

That’s correct.

And we’ve got five operators, +, -, * [times], ÷, and =. And, come to think of it, we probably should have left and right parenthesis as well.

OK. What’s the relationship between these two kinds of symbols?

Hmmmm....The operators allow as to specify various relationships between strings of numerals.

Starting with, yes, starting with a basic set of equivalences of the form, NumStr Op NumStr = NumStr, where Op is one from +, -, *, and ÷ and NumStr is a string of one or, in the case of these primitive equivalences, two numerals. [1]

Thus giving us those tables we memorized in grade school. Right!

What do you mean by semantics being removed?

Well, what are the potentially meaning-bearing elements in this collection?

That would be the numerals, no?

Yes. What do they mean?

Why, they don’t meaning anything...

Well... But they aren’t completely empty, are they?

No.

Elaborate. What’s not empty about, say, 5?

5 could designate...

By “designate” you mean “mean”?

Yes. 5 could designate any collection with five members. 5 apples, 5 oranges, 5 mountains, 5 stars...

What about an apple, an orange, a mountain, a star, and a dragon?

Yes, as long as there’s five of them.

Ah, I see. The numerals, or strings of numerals, are connected to the world though the operation of counting. When we use them to count, they, in effect, become numbers. But, yes, that’s a very general kind of relationship. Not much semantics or meaning there. [2]

Right. And that’s what I mean by empty of semantics. All we’ve got left is syntax, more or less.

Sounds a bit like Searle in his Chinese Room.

Yes, it does, doesn’t it?

The idea is that the mental machinery we use to do arithmetic calculation, that’s natural computation, computation performed by a brain, from which semantics has been removed. That machinery is there in ordinary language, or even extraordinary language. Language couldn’t function without it. That’s where language gets its combinatorial facility.

And THAT sounds like Chomsky, no?

Yes.

* * * * *

And so it goes, on and on.

When the intellectual history of the second half of the twentieth century gets written, the discovery of the irreducibly computational nature of natural language will surely be listed as one of the highlights. Just who will get the honor, that’s not clear, though Chomsky is an obvious candidate. He certainly played a major role. But he didn’t figure out how an actual physical system could do it (the question was of little or no interest to him), and surely that’s part of the problem. If so, however, then we still haven’t gotten it figured out, have we?

* * * * *

[1] Isn’t that a bit sophisticated for the Glaucon figure in this dialog? Yes, but this is a 21st century Glaucon. He’s got a few tricks up his sleeve.

[2] Sounds a bit like the Frege/Russell set theory definition of number: a natural number n is the collection of all sets with n elements.

Border Patrol: Arguments against the idea that the mind is (somehow) computational in nature

I’ve been through this before, the idea that arguments such as those by Dreyfus and Searle seem curious and empty to me.

Eye-hand coordination

But before I get to that I want to acknowledge what seems to me some remarkable work by researchers at OpenAI, a robotic hand that solves Rubik’s Cube. The cube algorithm is (old school) symbolic (as Gary Marcus points out) but visual perceptual and manipulation are achieved by (new school) neural networks. I think the visuo-manipulative work is wonderful.

But does it display intelligence? I don’t much care. I think it’s wonderful. And perhaps the most wonderful aspect is the interaction between visual perception on the one hand and hapsis (touch) and movement on the other. My instinct/intuition is to say that THERE’s where you’re going to get “intelligence”, whatever that is, from that intermodal coordination. Why? Because that interaction is just a bit more abstract than either perception or movement alone; it must take place in an abstract space that encompasses both but isn’t OF either.

Of course, back in the old school world of symbolic intelligence, the sensorimotor world of manipulation was peripheral to intelligence, which was about things like theorem proving, chess, and expert systems of scientific and technical knowledge. That began to change, I believe, in the 1970s. For one thing work on natural language forced researchers to integrate perception (of the auditory signal) with cognition (semantic meaning). And that’s when things began to collapse.

But we needn’t go into that here.

So what?

I remember reading Searle’s (in)famous Chinese Room argument when it came out in Behavior and Brain Sciences in, I believe 1980, and being both puzzled and unimpressed. By that time I’d been reading quite widely in computational linguistics and written a dissertation in which I made use of computational semantics in analyzing a Shakespeare sonnet and in discussing narrative order. Searle’s argument simply did not connect with any of the many concepts and techniques used in modeling thought. Understanding Searle’s argument wasn’t going to help us develop better computer models nor, for that matter, would it be of much use to psychologists and neuroscientists.

Just what was it good for?

I suspect the same is true for the older arguments of Hubert Dreyfus, which I’ve never read in full. Of course, where Searle was arguing within the Anglo-American analytic philosophical tradition, Dreyfus was a Heideggerian arguing within the Continental tradition. The intellectual style is different, but the result is the same.

At the moment I’ve been looking at an article Dreyfus published in 2007, “Why Heideggerian AI failed and how fixing it would require making it more Heideggerian” (Artificial Intelligence 171, 2007, 1137-1160). He reviews a half century of work in AI, including the line of research pioneered by Rodney Brooks in the 1980s, which he finds akin to an idea advanced by Merleau-Ponty, “that intelligence is founded on and presupposes the more basic way of coping we share with animals” (p. 1141) and that requires giving up on the notion of internal symbolic representations of the world. That, as far as I can tell, is what he means by Heideggerian AI. He goes on to critique other varieties of “pseudo Heidegerian AI” until he arrives at Walter Freeman’s “Merleau-Pontian” neurodynamics (pp. 1150 ff).

Of which he approves. Nor am I surprised at this. I’d read quite a bit of Freeman when I was working on my book about music, Beethoven’s Anvil, and adopted his neurodynamics as the basic framework in which to understand music as a medium of group interaction. I had quite a bit of correspondence with him and know he was talking to Dreyfus and, for that matter, knew he was interested in Continental philosophy. But, I wonder, how much did he actually owe to Continental philosophy?

I don’t know. Freeman’s neurodynamic approach was pretty mature by the time he had these conversations with Dreyfus. It’s possible that he’d been influenced by Continental thought early in his career, as I had been, but I don’t actually know that. As far as I know, Freeman did all the work: laboratory observation, mathematical analysis, and computational modeling. Continental philosophy came late to his game and perhaps more as a vehicle for presenting his ideas to a wider intellectual community, and in opposition to AI, than as a fundamental source of technical insight.

For that is what is required, technical insight. Did he get technical insight from early-career reading of Continental thought? I don’t know.

A bit about how I got here

While I have never really thought the brain/mind was computational top-to-bottom, at least I don’t think I did, I have long be fascinated by the insight computation affords us into the mind and currently believe that some aspects of the mind are irreducibly computational. But not the whole shebang, top-to-bottom.

And I was certainly influenced by Continental thought in my undergraduate years, Merleau-Ponty in particular. I studied his Phenomenology of Perception, not for any course, but on my own, underlining passages in two or three colors, making marginal annotations, and indexing key passages on the end pages. Perhaps that “inoculated” me against going whole-hog for a computational view of the mind.

I was also strongly influence by Lévi-Strauss, also, of course, a Continental thinker, but of a somewhat different style. As I was interested in literature, it was his thinking on symbolic systems and, above all, mythology, that captured my attention. I like his tables, diagrams, and pseudo-mathematical expressions. It seemed to me that if THAT’s what was going on in myth, then we need more of it. And that, in turn, led me to the cognitive sciences (by way of “Kubla Khan” [1]).

When I read The Savage Mind I was particular struck by what he called the totemic operator:

There’s no need to explain it. I just liked it.

More on speech as computation [what disfluencies tell us]

I continue to think about language as the basic computational operation of the mind/brain. The idea is that this business of stringing words together into coherent, intelligible, utterances is irreducibly computational; the linking of signifier to signified, that’s the basic “atom” of computation. Think of that as roughly analogous to a basic proposition in arithmetic as given in the tables for addition, subtraction, multiplication, and division. Stringing signifiers together into utterances is then roughly like performing arithmetic operations involving two or more of those basic propositions. Consider, for example, adding a two-digit number and a one digit number: e.g. 25 + 8. In that particular case we invoke “5 + 8 = 13” and “1 + 2 = 3” in just the right way to yield “33”. That’s computation. And so is a multi-word utterance, but the computation is ‘hidden’.

The following passage from one of my papers on “Kubla Khan” [1] is about linguistic computation in that sense:

Nonetheless, the linguist Wallace Chafe has quite a bit to say about what he calls an intonation unit, and that seems germane to any consideration of the poetic line. In Discourse, Consciousness, and Time Chafe asserts that the intonation unit is “a unit of mental and linguistic processing” (Chafe 1994, pp. 55 ff. 290 ff.). He begins developing the notion by discussing breathing and speech (p. 57): “Anyone who listens objectively to speech will quickly notice that is not produced in a continuous, uninterrupted flow but in spurts. This quality of language is, among other things, a biological necessity.” He goes on to observe that “this physiological requirement operates in happy synchrony with some basic functional segmentations of discourse,” namely “that each intonation unit verbalizes the information active in the speaker’s mind at its onset” (p. 63).

While it is not obvious to me just what Chafe means here, I offer a crude analogy to indicate what I understand to be the case. Speaking is a bit like fishing; you toss the line in expectation of catching a fish. But you do not really know what you will hook. Sometimes you get a fish, but you may also get nothing, or an old rubber boot. In this analogy, syntax is like tossing the line while semantics is reeling in the fish, or the boot. The syntactic toss is made with respect to your current position in the discourse (i.e. the current state of the system). You are seeking a certain kind of meaning in relation to where you are now.

Chafe identifies three different kinds of intonation units. Substantive units tend to be roughly five words long on average and, as the term suggests, present the substance of one’s thought. Regulatory units are generally a word or so long (e.g. and then, maybe, mhm, oh, and so forth), and serve to regulate the flow of ideas, rather than to present their substance. Given these durations, a single line of poetry can readily encompass a substantive unit or both a substantive and a regulatory unit.

The third kind of unit, fragmentary, results when one of the other types is aborted in mid-execution. That is to say, one is always listening to one’s own speech and is never quite sure, at the outset of a phrase, whether or not one’s toss of the syntactic line will reel-in the right fish. If things do not go as intended, the phrase may be aborted. Fragments do not concern us, as we are dealing with a text that has been thought-out and, presumably, edited, rather than with free speech, which is what Chafe studied.

The starting and stopping, the disfluencies, all betray the operations of the underlying computation [2]. The goal of the computation is to produce a coherent, an intelligible, utterance. How is that judged? Both by how the speaker interprets the unfolding utterance and how they judge the interlocutor’s response.

[1] William Benzon, “Kubla Khan” and the Embodied Mind, PsyArt: A Hyperlink Journal for the Psychological Study of the Arts, Article 030915, Published to the web on 14 November 2003.
http://www.psyartjournal.com/article/show/l_benzon-kubla_khan_and_the_embodied_mind.

[2] See my earlier post, Speech as computation [Trump's speaking], New Savanna, September 23, 2019, https://new-savanna.blogspot.com/2019/09/speech-as-computation-trumps-speaking.html.

Speech as computation [Trump's speaking]

Posted to Language Log as a comment to a post on Trump’s 'incoherence' while speaking:

If I might indulge a current hobby horse, I've been playing with the idea that language is the simplest thing humans do that requires a computational account. From this premise it follows, for example, that however the minds/brains of chimpanzees, dogs, bees, ants, or c. elegans work, it's not through communication. Something else is going on, complex dynamics, for example. OK.

I'm thinking that all these bumps, hesitations, fillers, whatever, of conversation betray the inner workings of these mechanisms. We've got, say, a dynamical system implementing a computational process, speech. And it doesn't always go smoothly. The right word or phrase isn't always available; it's not like they're all queued up just waiting to be entered into the speech stream. So the system has to hunt around looking for them. That is, we're listening to and making sense of our own speech via the auditory system even as the motor system is placing words into the speech stream.

Now, when we write, he can clean things up so it appears perfect. The language computer can parse those sentences readily (that is, map words and phrases onto semantic structures) and it all makes sense. But we all know that writing can often be quite difficult. We have to do quite a bit of reworking to produce computationally fluid prose.

Segmenting the language stream [words are tricky]

It is sometimes useful to reflect of the fact that, aurally, the speech stream is continuous, not segmented. The segmentation is something we impose on the stream through cognitive mechanisms – that, I argue, is the computational foundation of language. Thus early forms of writing often consisted of a continuous stream of characters, with no segmentation into separate words. Victor Mair has a post at Language Log that speaks to this, The challenging importance of spacing in Korean:

Who'da thunk it? – spacing is the most difficult aspect of Korean writing. One might have thought it would be a simple task, that word spacing / separation is innate for all speakers of a given language. Apparently that is not so.

In Hanyu Pinyin, it is called fēncí liánxiě 分詞連寫 ("word division; parsing"). Of course, it has its problems, but we do have rules to guide us, viz., zhèngcífǎ 正詞法 ("orthography").

This morning in my "Language, Script, and Society in China" course, I embarked on a discussion of the difference between zì 字 ("character") and cí 詞 ("word"). Although this seems like a simple, straightforward question, it is always one of the most difficult topics encountered in the course — especially for students of Chinese background. It took me a whole semester to get the idea across to the 72 very smart students in my language studies class at the University of Hong Kong in 2002-2003. Even at the conclusion of the semester, there were still some of the students who just couldn't comprehend the distinction.

Be sure to read the comments.

Addendum: In fact, I'll reprint one of them in full. Victor Mair, who started the thread, posts this on behalf of an unnamed colleague

Spacing–word division–assumes shared knowledge among users of what constitutes a language's words. This is not a trivial matter, and Korean linguists, lexicographers and publishers have been working the issue for decades.

The basic problem, as one of the commentators intimates, is that words, like (morpho)phonemic spelling, are an artifact of writing. They are not a given to be plucked from someone's brain. Orthography takes it upon itself to regularize (adjudicate) the intuitions users have about what constitutes the lexical units of their language, which are far from uniform and constantly shifting. Korean lacked that tradition and is catching up, although in a sense all written languages that use word division are continuously "catching up." I don't see it as a major problem, or a problem at all.

What I do find problematic in Asian languages is fluid "standards" for sentence representation, namely, where the period goes. This is not an issue (for me) in Korean, probably because the language does use word division, which enforces a discipline on writers that carries beyond the identification of (agreement on) word boundaries to one's whole approach to sentence structure. Chinese sentences–the text between periods–are often by western standards two sentences, five sentences, or partial sentences. Japanese writers also seem to have more liberty in this regard than a westerner would expect. Vietnamese sentences, in earlier novels at least, end or don't end seemingly at whim. And I question if Tibetans even have the concept of "sentence."

I've been out of this field for too long so my thinking may be dated. But there may be psycholinguistic issues at play here that merit serious study.

This too is relevant to the issue of computation in the mind. And so: I've just been thinking about this. And I'm wondering if the problem isn't similar to the problem that adolescent and post-adolescent second language learners have with pronunciation. I don't know what the current literature says about that, but in the past I've seen it attributed to a lack of neuro-plasticity. I don't find that terribly convincing. My intuition – and it's no more than that – is that the problem is more like conscious access. For some reason conscious access to (something in) the aural-motor channel has been, if not lost, somewhat degraded.

Could the same thing be going on in the transfer of segmentation from the aural-motor channel to the visuo-orthographic?

Computation in Language Process

New working paper. Title above, abstract, table of contents, and introduction below. Download here:

• Academic.edu: https://www.academia.edu/37973310/Computation_in_Language_Process
• SSRN: https://ssrn.com/abstract=3300999

Abstract: Language is the locus of computational processing in the mind/brain. In this view computation is not fundamental to the nervous system. Rather it is derived. Computation is the means though which speech binds word forms to elements of meaning (syntactic processing). We can think of the mechanisms of speech as a Turning-like device of limited power. The speech signal itself is analogous to the paper tape while the auditory system reads from that tape and the vocal system writes to it. The neocortex contains the table of instructions and the state register.

Contents

The Computational Mind, Losses and Gains 2

Words, Binding, and Conversation as Computation 4

What’s Computation? What’s Literary Computation? 7

Writing, Computation and, Well, Computation 8

The Computational Envelope of Language 11

Computation in Language: From speech input/output to writing, calculation, and electronic computers 13

Appendix 1: The importance of real-time processing of language 16

Appendix 2: A quick note on computing in the mind 17

Limits to the Computational Mind

For most of my career I have held that the human mind is, in some respect, computational in nature. In this I follow many others in the cognitive sciences and related disciplines. But, in what respect and to what extent computational? Chomsky took the view that much of language is syntax and that syntax is inherently computational. Though I am no longer sympathetic to Chomsky’s specific views on syntax, I was and remain sympathetic to the view that syntax is computational.

A decade before Chomsky began publishing his views on language Warren MuCulloch and Walter Pitts published “A logical calculus of the ideas immanent in nervous activity” in which they argued:

Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed.

The article contain illustrations that looked like highly stylized neurons:

That article helped set the stage for thinking that perhaps it was computation all they way down, or at least down to the level of individual neurons.

That view certainly has had and continues to have proponents. I can’t say that I’ve ever believed it; I’ve certainly never said so in print. And, as I learned about the nervous system, it seemed rather a bit too messy to operate on logical principles at its most basic level. In this working paper I explicitly reject the idea that it is computation all the way down and assert that language is the simplest human activity that involves computation. In my view linguistic computation serves as an input/output device for symbolic computation and thought.

What about those neurons then? If they’re not doing computation, what are they doing? That’s not my concern here. Whatever it is, it is something else. That something else may well be simulated by computational means, just as we simulate atomic explosions, the weather, and traffic patterns. The fact that we can simulate those phenomena computationally doesn’t imply that they are computational phenomena. And so it is with the nervous system. What is interesting about the nervous system, then, is that it evolved to the point where it could implement a computational process, speech, and that then became the partial basis for a shared cultural life different in kind and extent from animal cultures. But that is beyond the scope of this paper, which is limited to my reasoning about language.

* * * * *

Words, Binding, and Conversation as Computation – Here I argue that conversation is a computational process, that the binding of word forms to meanings (syntax) requires computation.

What’s Computation? What’s Literary Computation? – Continues the previous section and leads to the next.

Writing, computation and, well, computation – Here I talk about ordinary arithmetic calculation which is, of course, computation, and then say a word about algorithms.

The Computational Envelope of Language – In which I return to Saussure.

Computation in Language: From speech input/output to writing, calculation, and electronic computers – We can think of the mechanisms of speech as a Turning-like device of limited power. The speech signal itself is analogous to the paper tape while the auditory system reads from that tape and the vocal system writes to it. The neortex contains the table of instructions and the state register.

Appendix 1: The importance of real-time processing of language – A abstract of a recent article about the information processing bottleneck that speech processing must deal with.

Appendix 2: A quick note on computing in the mind – An abstract of an article that David Hays and I published in 1988, “Principles and development of natural intelligence.” That article speaks to the issue of non-computational or pre-computational processes in the brain and places language in that context.

Computation in language 3: Myths, stories, and writing

I’ve been arguing that speech communication, in effect, is a computational input/output device for symbols. Its computational power is necessarily quite limited, for the “tape” is short and can move only in one direction. The power is in the symbols themselves. I offer two further remarks, one about myths and stories, and the other about writing.

Myths and stories

We need to think a bit about myths and tales, strings of “tape” the culture seeks to preserve fairly intact. The preservation is not exact, of course, not word-for-word, but the general ‘drift’ of events is preserved. Just what is being preserved in these stories? It’s not the stories as such, but whatever it is that is embedded in those stories – values, customs, what? Lévi-Strauss has his binary oppositions and, yes, they’re important. But like he says, they’re only a code. What are they encoding?

All of a sudden I don’t know what’s going on, though I’ve certainly thought about it a lot and written a bit about it. And what I wrote talks about the nervous system, and in particular about the so-called lizard brain [1]. Surely that’s what’s important about these stories, the way they engage those deep neural structures and thus make their activities public and sharable in a protected setting.

Writing

Writing (Rank 2) in effect extends the length of the tape in our Turing machine input/output device and allows us to move over in, not only in both directions, but even in random ways (e.g. open a document to any page). Written words are of course stable over time and from reader to reader and can accumulate indefinitely. Does this change the character of our language Turing machine?

Moreover it is writing that facilitates the development of computation in the narrow sense of arithmetic calculation. Arithmetic operations, from mere counting through complex calculations, bring a new range of phenomena within range of human cognition.

And so it goes with calculation (Rank 3) and computation (Rank 4) [2].

References

[1] William Benzon, The Evolution of Narrative and the Self, Journal of Social and Evolutionary Systems, 16(2): 129-155, 1993, https://www.academia.edu/235114/The_Evolution_of_Narrative_and_the_Self.

[2] William Benzon and David G. Hays, The Evolution of Cognition, Journal of Social and Biological Structures. 13(4): 297-320, 1990, https://www.academia.edu/243486/The_Evolution_of_Cognition.

Computation in language, Turing machine edition

Continuing on....

So, I’m exploring the idea that language is the simplest thing humans do that involves computation. Thus, in my current view, whatever it is that goes on in the brain of a chimpanzee, a chameleon, or a roundworm, for example, it isn’t computation. Just what it is, that’s not my concern at this point. It follows as well that linguistic computation is grounded in something else, likely several things, none of which are my direct concern here. To be clear on this point, I reject the view the individual neurons are the basic elements of mental computation as was suggested by McCulloch and Pitts 1943, “A logical calculus of the ideas immanent in nervous activity.” Of course neurons and neuronal circuits can be simulated by a computer, but that’s something else. Computers can simulated atomic explosions too, but we don’t take that as evidence that atomic explosions are computational phenomena.

But what do I mean by computation? I mean a Turing machine, albeit one of somewhat limited capacity. As language is first of all speaking, that’s where we start. The vocal system writes to the tape while the auditory system reads from it; taken together they are the head of the device. The brain contains that table of instructions – I’m indifferent at this point as to whether those instructions are symbolic, pre-symbolic, or both – and the state register. The speech stream itself is the tape.

Therein lies the limitation of this Turing machine. In standard Turing machines the tape can move over the head in either direction. This “tape” moves in only one direction. The tape in a universal Turing machine in indefinitely long. This tape is quite limited in length. Experiments on the length of short-term memory put it at about 3 to 4 seconds. That’s the length of this one-directional tape. It can carry a single line of poetry.

Those limitations gives this Turing device the character of a very specialized input-output system. It’s a way, of course, for people to exchange symbolic “information” with one another, sending outputs to others and receiving inputs from them. Most interstingly, and very curiously, it allows us to exchange inputs and outputs with ourselves in ways otherwise impossible. Just why and how that is so is something I’ve thought about a great deal over the years, but do not understand. And don’t think I’ll get there now. But I note it.

Rather than go on and on I’ll conclude with a passage about the poetic line from my 2003 paper, “Kubla Khan” and the Embodied Mind. Note, in particular, the few lines about syntax and its relation to semantics:

Considered as a unit of analysis, the line is a conjunction of units of thought, or sense, and units of physical realization – speaking and hearing.

The significance of the poetic line is easily demonstrated by the common experiment of taking some fragment of ordinary prose and breaking it into separate lines. The result is rarely good poetry, but the poetry-like presentation invites one to consider each line as a unit by itself in addition to its connections with the lines before and after. The quasi-autonomy of the poetic line belongs to the cultural conventions governing how we read poetry. The psychological, not to mention the neural, underpinnings of this effect are, as far as I know, obscure.

Nonetheless, the linguist Wallace Chafe has quite a bit to say about what he calls an intonation unit, and that seems germane to any consideration of the poetic line. In Discourse, Consciousness, and Time Chafe asserts that the intonation unit is “a unit of mental and linguistic processing” (Chafe 1994, pp. 55 ff. 290 ff.). He begins developing the notion by discussing breathing and speech (p. 57): “Anyone who listens objectively to speech will quickly notice that is not produced in a continuous, uninterrupted flow but in spurts. This quality of language is, among other things, a biological necessity.” He goes on to observe that “this physiological requirement operates in happy synchrony with some basic functional segmentations of discourse,” namely “that each intonation unit verbalizes the information active in the speaker’s mind at its onset” (p. 63).

While it is not obvious to me just what Chafe means here, I offer a crude analogy to indicate what I understand to be the case. Speaking is a bit like fishing; you toss the line in expectation of catching a fish. But you do not really know what you will hook. Sometimes you get a fish, but you may also get nothing, or an old rubber boot. In this analogy, syntax is like tossing the line while semantics is reeling in the fish, or the boot. The syntactic toss is made with respect to your current position in the discourse (i.e. the current state of the system). You are seeking a certain kind of meaning in relation to where you are now.

Chafe identifies three different kinds of intonation units. Substantive units tend to be roughly five words long on average and, as the term suggests, present the substance of one’s thought. Regulatory units are generally a word or so long (e.g. and then, maybe, mhm, oh, and so forth), and serve to regulate the flow of ideas, rather than to present their substance. Given these durations, a single line of poetry can readily encompass a substantive unit or both a substantive and a regulatory unit.

The third kind of unit, fragmentary, results when one of the other types is aborted in mid-execution. That is to say, one is always listening to one’s own speech and is never quite sure, at the outset of a phrase, whether or not one’s toss of the syntactic line will reel-in the right fish. If things do not go as intended, the phrase may be aborted. Fragments do not concern us, as we are dealing with a text that has been thought-out and, presumably, edited, rather than with free speech, which is what Chafe studied.

Chafe’s notion is consistent with an observation made initially by Ernst Pöppel. After reviewing studies by others and offering some of his own, Pöppel concluded that our awareness of the present extends roughly three to four seconds. That suggested that lines of poetry last no longer than that and that, where written lines appeared to take longer to read, they have a strong break in the middle. Working with a poet and critic, Frederick Turner, Pöppel found evidence for these notions in the poetry of several cultures, thus showing how versification technique deals with this constraint (cf. Turner and Pöppel 1983, Pöppel 1985, pp. 75-82).

Computation in Language

For some time now I’ve been pursuing the idea that language is the basic locus of computation in the human mind and, correlatively, that it is grounded in processes that are non-computational in kind. This might not seem strange to a computational linguist, but other of course have other ideas. Many humanists are at best skeptical, if not horrified at the idea that the mind is computational in some way. And many cognitive scientists would like to think it’s computation all the way down to individual neurons. Humanistic skepticism is simply out of date while the cognitive scientists are over-eager, especially now that we have other ways of thinking about basic neural processes (e.g. complex dynamics).

More specifically, I identify computation with establishing the mapping between a language string and elements of meaning, whether in comprehension or production. The problem, of course, is that the elements of the string are all there together in one place, one after the other. But the elements of meaning are scattered all over the place in neuro-mental space. Here and there might be a pair of contiguous elements, but for the most part, not. We need a computational process to establish the coupling.

But why? Or, conversely, why doesn’t this problem exist for any other activity?

Questions questions questions.

The importance of real-time processing of language

Erin S.Isbilen, Morten H. Christiansen, Nick Chater, It's about time: Adding processing to neuroemergentism, Journal of Neurolinguistics, Volume 49, February 2019, Pages 224-227, https://doi.org/10.1016/j.jneuroling.2018.04.005.

Linguistic exchanges occur in real time, on a moment-to-moment basis. The rapid rate of linguistic input (10-15 phonemes per second; Studdert-Kennedy, 1987), and its transience (50-100 ms; Elliott, 1962; Remez et al., 2010) pose a fundamental challenge to processing, with information being delivered at a rate that strains the limit of the human auditory threshold (∼10 non-speech sounds; Miller & Taylor, 1948). The additive effects of the linguistic signal's fast rate and fleeting nature are further exacerbated by the limitations of human working memory, which on average can retain no more than 41 (Cowan, 2001) to 7 [plus or minus] 2 items at a time (Miller, 1956). Together, these challenges form a Now-or-Never Bottleneck (Christiansen & Chater, 2016a,b): if input is not processed as soon as it is encountered, the signal is either overwritten or interfered with by new incoming material. In order to sustain linguistic functions, the cognitive system must overcome this bottleneck. Importantly, the Now-or-Never Bottleneck is not limited to linguistic processing. Rather, it extends to the perception of haptic (Gallace, Tan, & Spence, 2006), visual (Haber, 1983), and non-linguistic auditory input (Pavani & Turatto, 2008). Understanding how the cognitive system deals with this bottleneck can therefore provide fundamental insights into the emergence not only of language, but also of the other complex cognitive abilities discussed by HCRCSWY.

The dynamics of how the linguistic signal unfolds in real-time underscores the importance of memory processes in considering how the cognitive system deals with the Now-or-Never bottleneck. Building on the basic memory process of chunking, Christiansen and Chater (2016b) suggest that the cognitive system engages in Chunk-and-Pass Processing to overcome the bottleneck. Using Chunk-and-Pass Processing, the cognitive system builds a multi-level representation of incoming input, by rapidly compressing and recoding the input into chunks of increasing levels of abstraction as soon as it is encountered. This process of compression and abstraction enables information to be held in memory for longer periods of time. To provide an example from language, the raw acoustic input may be chunked into syllables, syllables into words or multi-word phrases, and so on up to complex representations of the discourse. Throughout the multi-level process of chunking, top-down information driven by predictions from semantic, pragmatic and discourse expectations augmented by real-world knowledge will enrich the resulting representations. The reverse is hypothesized to happen during language production, with the intended message being broken down into chunks of increasing specificity. [...]

From the viewpoint of the Chunk-and-Pass framework, language acquisition involves learning how to process input – that is, learning how to effectively chunk linguistic input using top-down information in the face of the Now-or-Never bottleneck. Importantly, the real-time pressures from language processing not only shapes language acquisition, but also the cultural evolution of language itself. [...]

Similarly, the incorporation of multiple cues in natural language can also facilitate both the usefulness and learnability of linguistic structures. Because the Now-or-Never Bottleneck makes back-tracking very hard, the language system needs to rely on all available information to be right-the-first-time when chunking the input.

And I'm guessing that it's the pressure of real-time processing that gives language its computational 'nature'.

Though we must  be careful here. I AM NOT asserting that computation is a basic or even the basic neural process. Rather, I am working within the scope of my conjecture that language processing is the most primitive form of computational process in the mind/brain. In particular, the mapping between linguistic form and meaningful content is where computation is necessary. What's computed, then, is the relation between chunks of form and chunks of meaning.

See my post, The Computational Envelope of Language, and the posts it cites as leading up to it.

Language, Computation, and Literary Form

I’ve been making a lot of posts over the past year or so about language, computation, and literary form, with a particular flurry in the last month or two linking Jakobson’s poetic function into the mix. This is going to be another one of those posts. I figure I’ve got to keep going over it until I feel that I’ve got it right, whatever that means.

What I’m NOT saying

I’m not saying that the human mind, or brain, is essentially computational, or digital. Those may or may not be true, but my assertion is more limited.

It limited to language and, within language, to the process whereby word forms are linked to semantic objects and structure (informally, to meaning). Let me emphasize process. It is something the mind does rather than something the mind is – to put it rather sketchily.

Other processes may or may not be fundamentally computational – sensation, perception, movement, pattern recognition, feeling, whatever. Off hand I’d think there’s computation in the sense that word-form-binding is computation, but I also think there are non-computational processes.

Moreover, I’m NOT saying that word-form-binding can be modeled by or usefully thought of as computation. I’m saying that it IS computation. And linguistic form is computational form. Linguistic form guides the binding process.

In what sense computation?

In the sense that we say the earth is a planet that revolves around the sun, and the moon revolves around the earth. Neither of those assertions can be verified by direct perception. Direct perception tells us that the earth is stationary and that both the sun and the moon move over the earth’s surface. Where does the sun go at night? Direct observation doesn’t tell us. Where does the moon go during the day? Direct observation doesn’t tell us.

The heliocentric model of the solar system is an abstract idea. It’s based on a wide range of observations by many observers at many times and places. But not simply observations. Reasoning, physical reasoning and mathematical reasoning.

It’s model subject to revision as needed. Thus Pluto has recently been demoted from planet status, a relatively minor matter of definition. More consequentially, the advent of complexity theory and digital simulation has allowed us to realize that, over the long term, the system is chaotic. The bodies in the system are constantly influencing one another and, consequently, orbits are gradually changing.

Well, that language involves computation is like that, irreducibly so. We don’t understand the system nearly so well. And, to some extent the argument has to be a negative one: What else could it be? As far as I know there simply are no other proposals on the table.

Alan Turing has defined computation in a way that’s independent of any particular physical realization, and THAT’s the kind of thing that can perform the binding task. We know that primarily because we have built artificial system that perform the binding task in limited domains. We have no reason to think that the limitations of those systems can be attributed to computation itself.

Conversation

In looking over my posts I realized that back in August of 2016 I’d posted, Words, Binding, and Conversation as Computation, and What’s Computation? What’s Literary Computation? YES. The back and forth of computation strikes me as being inherently computational (read those two posts).

That, of course, involves the interaction of autonomous agents, which is not something we ordinarily think of in conjunction with computation, which has been characterized as something done by a single agent. I don’t see that as a problem, however. In fact, that may be how computation (in this sense) got started. And through a process perhaps first described by Vygotsky (in his account of language learning) the process that had been distributed across two agents becomes internalized in one.

Also

Anaphoric reference and duality of patterning – but I’m not going to remark on these here and now. I note, however, that duality of patterning pretty much implies the binding problem. And it is, of course, related to indexing as Hays and I discussed it in Principles and development of natural intelligence [1].

Form on a string

Language is manifest as a string of word forms, one after the other. In the case of some but not all written language the word-forms are sharply separated from one another. They are not sharply separated in spoken form nor, I believe, in the gestures of signing. Where the string does not naturally exist in discrete forms the perceptual system must do the job of segmenting; sometimes there are failures.

In a way and somehow I want to say that computation is somehow necessitated by the fact that semantic structures (meaning) are inherently multi-dimensional and cotemporaneous while language takes the material form of one-dimensional strings. It takes computation to go back and forth between these two – not, alas, a very felicitious formulation.

Literary form and description

It is because literature is made of language that literary form is, like linguistic form, computational in nature [2]. Note that the literary string may be subject to quasi-independent sources of ordering (as in verse, where sound may be ordered independently of sense).

I’m thinking – pace yesterday’s discussion of ring composition – the routine description of literary form is only possible in the context of the explicit recognition of the computational nature of linguistic and therefore literary form. The ring composition literature seems to me a bit ‘spotty’ and in a way ‘opportunistic’. It’s a bunch of local accommodations and fixes without any overall system. Also, in some forms it is overly reliant on spatial metaphors and references to oral practice, both of which are beside the point (and the spatial metaphors are misleading).

Routinization requires systematic thought and description. In the case of literary form we must explicitly recognize that literary texts ARE strings. It’s not that anyone doesn’t know that, but it’s not something that’s thought about and theorized. And we must recognize that literary form words outside the bounds of conscious thought and deliberation (as, indeed, does linguistic form as well).

Literary form is necessarily about restrictions on the structure of literary strings. That’s what Jakobson’s poetic function is about. It remains to be seen whether the poetic function is absolutely general, providing a ‘complete’ account.

More later.

References

[1] William L. Benzon and David G. Hays. Principles and development of natural intelligence. Journal of Social and Biological Structures 11, 1988, pp. 293-322. https://www.academia.edu/235116/Principles_and_Development_of_Natural_Intelligence

[2] My 2006 article on literary morphology is my major systematic statement about literary form, Literary Morphology: Nine Propositions in a Naturalist Theory of Form, PsyArt: An Online Journal for the Psychological Study of the Arts, August 2006, Article 060608. https://www.academia.edu/235110/Literary_Morphology_Nine_Propositions_in_a_Naturalist_Theory_of_Form