Naively, I certainly thought that all humans would have words for exact counting, and the
piraha don't, okay?
So they don't have any words for even one.
There's not a word for one in their language.
And so there's certainly not a word for two, three, or four.
And so that kind of blows people's minds off.
Yeah, that's blowing my mind.
That's pretty weird, isn't it?
How are you going to ask, I want two of those?
You just don't.
And so that's just not a thing you can possibly ask in the piraha.
It's not possible.
There's no words for that.
The following is a conversation with Edward Gibson, or Ted, as everybody calls him.
He is a psycholinguistics professor at MIT.
He heads the MIT Language Lab that investigates why human languages look the way they do,
the relationship between culture and language, and how people represent, process, and learn
language.
Also, he should have a book titled Syntax, A Cognitive Approach, published by MIT Press,
coming out this fall.
So look out for that.
This is the Lex Freeman Podcast.
To support it, please check out our sponsors in the description.
And now, dear friends, here's Edward Gibson.
When did you first become fascinated with human language?
As a kid in school, when we had to structure sentences and English grammar, I found that
process interesting.
I found it confusing as to what it was I was told to do.
I didn't understand what the theory was behind it, but I found it very interesting.
So when you look at grammar, you're almost thinking about it like a puzzle, almost like
a mathematical puzzle?
Yeah, I think that's right.
I didn't know I was going to work on this at all at that point.
I was really just, I was kind of a math geek person, computer scientist.
I really liked computer science.
And then I found language as a neat puzzle to work on from an engineering perspective,
actually, as I sort of accidentally, I decided after I finished my undergraduate degree, which
was computer science and math in Canada and Queen's University, I decided to go to grad
school.
It's like, that's what I always thought I would do.
And I went to Cambridge, where they had a master's in, a master's program in computational
linguistics.
And I hadn't taken a single language class before.
All I'd taken was CS, computer science, math classes, pretty much, mostly, as an undergrad.
And I just thought, oh, this was an interesting thing to do for a year, because it was a single
year program.
And then I ended up spending my whole life doing it.
So fundamentally, your journey through life was one of a mathematician and a computer scientist.
And then you kind of discovered the puzzle, the problem of language, and approached it from
that angle, to try to understand it from that angle, almost like a mathematician or maybe
even an engineer.
As an engineer, I'd say, I mean, to be frank, I had taken an AI class, I guess it was 83 or
84, 85, somewhere 84 in there, a long time ago.
And there was a natural language section in there.
And it didn't impress me.
I thought, there must be more interesting things we can do.
It didn't seem very, it seemed just a bunch of hacks to me.
It didn't seem like a real theory of things in any way.
And so I just thought this was, this seemed like an interesting area where there wasn't
enough good work.
Did you ever come across like the philosophy angle of logic?
So if you think about the 80s with AI, the expert systems where you try to kind of maybe
sidestep the poetry of language and some of the syntax and the grammar and all that kind
of stuff and go to the underlying meaning that language is trying to communicate and try
to somehow compress that in a computer-representable way, did you ever come across that in your
studies?
I mean, I probably did, but I wasn't as interested in it.
I was trying to do the easier problems first, the ones I could, thought maybe were handleable,
which seems like the syntax is easier, like, which is just the forms as opposed to the meaning.
Like, you're talking about, when you're starting talking about the meaning, that's a very
hard problem.
And it still is a really, really hard problem.
But the forms is easier.
And so I thought at least figuring out the forms of human language, which sounds really
hard, but is actually maybe more tractable.
So it's interesting.
You think there is a big divide.
There's a gap.
There's a distance between form and meaning.
Because that's a question you have discussed a lot with LLMs, because they're damn good at
form.
Yeah.
I think that's what they're good at, is form.
Yeah.
Exactly.
And that's why they're good, because they can do form.
Meaning's hard.
Do you think there's, oh, wow.
I mean, it's an open question, right?
Yeah.
How close form and meaning are.
We'll discuss it.
But to me, studying form, maybe it's a romantic notion, gives you, form is like the shadow of
the bigger meaning thing underlying language, as I, form is, language is how we communicate
ideas.
We communicate with each other using language.
So in understanding the structure of that communication, I think you start to understand
the structure of thought and the structure of meaning behind those thoughts and communication
to me.
But to you, big gap.
Yeah.
What do you find most beautiful about human language, maybe the form of human language,
the expression of human language?
What I find beautiful about human language is the, some of the generalizations that happen
across the human languages, within and across a language.
So let me give you an example of something which I find kind of remarkable, that is if
like a language, if it has a word order such that the verbs tend to come before their objects.
And so that's like English does that.
So we have the first, the subject comes first in a simple sentence.
So I say, you know, the dog chased the cat or Mary kicked the ball.
So the subject's first.
And then after the subject, there's the verb.
And then we have objects.
All these things come after in English.
So it's generally a verb.
And most of the stuff that we want to say comes after the subject.
It comes, it's the objects.
There's a lot of things we want to say they come after.
And there's a lot of languages like that.
About 40% of the languages of the world look like that.
They're subject, verb, object languages.
And then these languages tend to have prepositions, these little markers on the nouns that connect
nouns to other nouns or nouns to verbs.
So when I say a verb, I say a preposition like in or on or of or about, I say I talk about something.
The something is the object of that preposition that we have, these little markers come also,
just like verbs, they come before their nouns.
Okay, and then so now we look at other languages like Japanese or Hindi or some, these are so-called verb final languages.
Those is about maybe a little more than 40%, maybe 45% of the world's languages or more.
I mean, 50% of the world's languages are verb final.
Those tend to be postpositions.
Those markers, the states have the same kinds of markers as we do in English, but they put them after.
So, sorry, they put them first.
The markers come first.
So you say, instead of, you know, talk about a book, you say a book about, the opposite order there in Japanese or in Hindi.
You do the opposite.
And the talk comes at the end.
So the verb will come at the end as well.
So instead of Mary kicked the ball, it's Mary ball kicked.
And then if it's Mary kicked the ball to John, it's John to.
The to, the marker there, the preposition, it's a postposition in these languages.
And so the interesting thing, a fascinating thing to me is that within a language, this order aligns.
It's harmonic.
And so if it's one or the other, it's either verb initial or verb final, but then you, then you'll have prepositions, prepositions or postpositions.
And so that, and that's across the languages that we, we can look at.
We've got around a thousand languages for, for, there's around 7,000 languages around on the, on the earth right now.
But we have information about, say, word order on around a thousand of those, a pretty decent amount of information.
And for those thousand, which we know about, about 95% fit that pattern.
So they will have either verb, it's about, it's about half and half or half of verb initial, like English, and half of verb final, like, like Japanese.
So just to clarify, verb initial is subject, verb, object.
That's correct.
Verb final is still subject, object, verb.
That's correct.
Yeah.
The subject is generally first.
That's so fascinating.
I ate an apple or I apple ate.
Yes.
Okay.
And it's fascinating that there's a pretty even division in the world amongst those 40, 45%.
Yeah.
It's pretty, it's pretty even.
And, and those two are the most common by far.
Those two word orders, the subject tends to be first.
There's so many interesting things, but these things are, the thing I find so fascinating is there are these generalizations within and across a language.
And, and not only those are the, and there's actually a simple explanation, I think, for a lot of that.
And that is, you're trying to like minimize dependencies between words.
That's basically the story, I think, behind a lot of why word order looks the way it is, is you, we're always connecting.
What is it, what is the thing I'm telling you?
I'm, I'm talking to you in sentences.
You're talking to me in sentences.
These are sequences of words which are connected.
And the connections are dependencies between the words.
And it turns out that what we, what we're trying to do in a language is actually minimize those dependency links.
It's easier for me to say things if the words that are connecting for their meaning are close together.
It's easier for you in understanding if that's also true.
If they're far away, it's, it's hard to produce, produce that.
And it's hard for you to understand.
And the languages of the world within a language and across languages, you know, fit that generalization, which is, you know, so I, you know, it turns out that having verbs initial and then having prepositions ends up making dependencies shorter.
And, and having verbs final and having postpositions ends up making dependencies shorter than if you cross them.
If you cross them, it ends up, you just end up, it's possible, you can do it.
You mean within a language?
Within a language, you can do it.
It just ends up with longer dependencies than if you didn't.
And so languages tend to go that way.
They tend to minimize, they say, they call it harmonic.
So it was observed a long time ago by, without the explanation, by a guy called Joseph Greenberg, who's a famous typologist from Stanford.
He observes a lot of generalizations about how word order works.
And these are some of the harmonic generalizations that he observed.
Harmonic generalizations about word order.
There's so many things I want to ask you.
Let me just, sometimes basics, you mentioned dependencies a few times.
What do you mean by dependencies?
Well, what I mean is, in, in language, there's kind of three structures to, three components to the structure of language.
One is the sounds.
So cat is k, a, and t in English.
I'm not talking about that part.
I'm talking, and then there's two meaning parts.
And those are the words.
And, and you were talking about meaning earlier.
So words have a form and they have a meaning associated with them.
And so cat is a full form in English and it has a meaning associated with whatever a cat is.
And then the combinations of words, that's what I'll call grammar or syntax.
And that's like when I have a combination like the cat or two cats.
Okay.
So where I take two different words there and put them together and I get a compositional meaning from putting those two different words together.
And, and so that's the syntax.
And in any sentence or utterance, whatever I'm talking to you, you're talking to me, we have a bunch of words and we're putting together in a sequence.
It turns out they, it turns out they are connected so that every word is connected to just one other word in that, in that sentence.
And so you end up with what's, what's called technically a tree.
It's a tree structure.
So there, where there's a root of that, of that utterance of that sentence.
And then there's a bunch of dependence, like branches from that root that go down to the words.
The words are the leaves in this metaphor for a tree.
So a tree is also sort of a mathematical construct.
Yeah.
Yeah.
It's a graph theoretical thing.
Graph theory.
Yeah.
Yeah.
So in the, it's fascinating that you can break down a sentence into a tree and then one, every word is hanging on to another, it's depending on a word.
That's right.
And, and everyone agrees on that.
So all linguists will agree with that.
No one, no one.
This is not a controversial thing.
That is not controversial.
There's nobody sitting here listening mad at you.
I do not think so.
Okay.
There's no linguist sitting there mad at this.
No.
I think in every language, I think everyone agrees that all sentences are trees at some level.
Can I pause on that?
Sure.
Because it, it's to me, just as a layman, it, it's surprising that you can break down sentences in many, most, all languages, into a tree.
I think so.
That's weird.
I've never heard of anyone disagreeing with that.
That's weird.
The details of the trees are what people disagree about.
Well, okay.
So what's, what's at the root of a tree?
How do you construct?
How hard is it?
What is the process of constructing a tree from a sentence?
Well, this is where, you know, depending on what you're, there's different theoretical notions.
I'm going to say the simplest thing, dependency grammar.
It's like a bunch of people invented this.
Teniere was the first French guy back in, I mean, the paper was published in 1959, but he was working on the 30s and stuff.
So, and, and it goes back to, you know, philologist Panini was doing this in ancient India, okay?
And so, you know, doing something like this.
The simplest thing we can think of is that there's just connections between the words to make the utterance.
And so let's just say I have like two dogs entered a room, okay?
Here's a sentence.
And so we're connecting two and dogs together.
That's like, there's some dependency between those words to make some bigger meaning.
And then we're connecting dogs now to entered, right?
And we connect a room somehow to entered.
And so I'm going to connect to room and then room back to entered.
That's the tree is I, the root is entered.
That's the, the thing is like an entering event.
That's what we're saying here.
And the, the subject, which is whatever that dog is, is two dogs, it was, and, and the connection goes back to dogs, which goes back to, then that, that goes back to two.
I'm just, that, that's my tree.
It, it starts at entered, goes to dogs, down to two.
And then the other side, after the verb, the object, it goes to room.
And then that goes back to the, the determiner or article, whatever you want to call that word, ah.
So there's a bunch of categories of words here we're noticing.
So there are verbs, those are these things that typically mark, they refer to events and states in the world.
And there are nouns, which typically refer to people, places, and things is what people say.
But they can refer to other more, they can refer to events themselves as well.
They're, they're, they're marked by, you know, how they, how they, the category, the part of speech of a word is how it gets used in language.
It's, like, that's how you decide what the, what the category of a word is.
Not, not by the meaning, but how it's, how it gets used.
How it's used.
What's usually the root?
Is it going to be the verb that defines the event?
Usually, usually, yes, yes.
Okay.
Yeah.
I mean, if I don't say a verb, then there won't be a verb, and so it'll be something else.
What if you're messing, are we talking about language that's, like, correct language?
What if you're doing poetry and messing with stuff?
Is it, then, then rules go out the window, right?
Then it's, no.
You're still, no, no, no, no, no.
You're, you're constrained by whatever language you're dealing with.
Probably, you have other constraints in poetry, such that you're, like, usually in poetry, there's multiple constraints that you want to, like, you want to usually convey multiple meanings is the idea, and maybe you have, like, a rhythm or a rhyming structure as well.
And depending, so, but you usually are constrained by your, the rules of your language for the most part.
And so you don't violate those too much.
You can violate them somewhat, but not too much.
So it has to be recognizable as your language.
Like, in English, I can't say, dogs two entered room a.
I mean, I meant, you know, two dogs entered a room.
And I can't mess with the order of the articles, the articles and the nouns.
You just can't do that.
In some languages, you can, you can mess around with the order of words much more.
I mean, you speak Russian.
Russian has a much freer word order than English.
And so, in fact, you can move around words in, you know, I told you that English has the subject, verb, object, word order.
So does Russian.
But Russian is much freer than English.
And so you can actually mess around with the word order.
So probably Russian poetry is going to be quite different from English poetry because the word order is much less constrained.
Yeah, there's a much more extensive culture of poetry throughout the history of the last hundred years in Russia.
And I always wondered why that is.
But it seems that there's more flexibility in the way the language is used.
There's more, you're morphing the language easier by altering the words, altering the order of the words, messing with it.
Well, you can just mess with different things in each language.
And so in Russian, you have case markers, right, which are these endings on the nouns which tell you how it connects, each noun connects to the verb, right?
We don't have that in English.
And so when I say, Mary kissed John, I don't know who the agent or the patient is except by the order of the words, right?
In Russian, you actually have a marker on the end.
And if you're using a Russian name in each of those names, you'll also say, is it, you know, agent, it'll be the, you know, nominative, which is marking the subject, or an accusative will mark the object.
And you could put them in the reverse order.
You could put accusative first, you could put subject, you could put the patient first, and then the verb, and then the subject.
And that would be a perfectly good Russian sentence.
And it would still mean, Mary, I could say John kissed Mary, meaning Mary kissed John, as long as I use the case markers in the right way.
You can't do that in English.
And so...
I love the terminology of agent and patient and the other ones you used.
Those are sort of linguistic terms, correct?
Those are for like kind of meaning.
Those are meaning.
And subject and object are generally used for position.
So subject is just like the thing that comes before the verb, and the object is the one that comes after the verb.
The agent is kind of like the thing doing it.
That's kind of what that means, right?
The subject is often the person doing the action, right?
The thing.
So, yeah.
Okay, this is fascinating.
So how hard is it to form a tree in general?
Is there a procedure to it?
Like if you look at different languages, is it supposed to be a very natural, like is it automatable, or is there some human genius involved?
I think it's pretty automatable at this point.
People can figure out the words are.
They can figure out the morphemes, which are the technically morphemes are the minimal meaning units within a language, okay?
And so when you say eats or drinks, it actually has two morphemes in it in English.
There's the root, which is the verb, and then there's some ending on it, which tells you, you know, that's this third person singular.
Can you say what morphemes are?
Morphemes are just the minimal meaning units within a language.
And then a word is just kind of the things we put spaces between in English.
And they have a little bit more, they have the morphology as well.
They have the endings, this inflectual morphology on the endings on the roots.
It modifies something about the word that adds additional meaning.
They tell you, yeah, yeah, yeah.
And so we have a little bit of that in English, very little, you have much more in Russian, for instance.
But we have a little bit in English, and so we have a little on the nouns.
You can say it's either singular or plural.
And you can say, same thing for verbs.
Like simple past tense, for example, it's like, you know, notice in English, we say drinks, you know, he drinks, but everyone else says I drink, you drink, we drink.
It's unmarked in a way.
And then, but in the past tense, it's just drank.
For everyone, there's no morphology at all for past tense.
There is morphology, it's marking past tense, but it's kind of, it's an irregular now.
So we don't even, you know, drink to drank, you know, it's not even a regular word.
So in most verbs, many verbs, there's an ed we kind of add.
So walk to walked, we add that to say it's the past tense.
I just happened to choose an irregular because it's a high frequency word.
And the high frequency words tend to have irregulars in English for...
What's an irregular?
Irregular is just, there isn't a rule.
So drink to drank is an irregular.
Drink, drank, okay.
As opposed to walk, walked, talked, talked.
And there's a lot of irregulars in English.
There's a lot of irregulars in English.
The frequent ones, the common words tend to be irregular.
There's many, many more low frequency words.
And those tend to be, those are regular ones.
The evolution of the irregulars are fascinating.
It's essentially slang that's sticky because you're breaking the rules and then everybody
uses it and doesn't follow the rules.
And they say, screw it to the rules.
It's fascinating.
So you said morphemes, lots of questions.
So morphology is what?
The study of morphemes?
Morphology is the connections between the morphemes onto the roots, the roots.
So in English, we mostly have suffixes.
We have endings on the words.
Not very much, but a little bit.
And as opposed to prefixes, some words, depending on your language, can have mostly prefixes,
mostly suffixes, or mostly, or both.
And then even languages, several languages have things called infixes, where you have
some kind of a general form for the root, and you put stuff in the middle.
You change the vowels.
That's fascinating.
That is fascinating.
So in general, there's, what, two morphemes per word?
Usually one or two?
Or three?
Well, in English, it's one or two.
In English, it tends to be one or two.
There can be more.
You know, in other languages, you know, a language like Finnish, which has a very elaborate morphology,
there may be 10 morphemes on the end of a root, okay?
And so there may be millions of forms of a given word, okay?
Okay, I'll ask the same question over and over, but how does the, just sometimes to understand
things like morphemes, it's nice to just ask the question, how do these kinds of things
evolve?
So you have a great book studying sort of the, how the cognitive processing, how language
is for communication, so the mathematical notion of how effective a language is for communication,
what role that plays in the evolution of language, but just high level, like, how do
we, how does a language evolve with, where English has two morphemes or one or two morphemes
per word, and then Finnish has infinity per word?
So what, how does that, how does that happen?
Is it just people?
That's a really good question.
Yeah.
That's a very good question.
It's like, why do languages have more morphology versus less morphology?
And I don't think we know the answer to this.
I don't, I think there's just like a lot of good solutions to the problem of communication.
So I, like, I believe, as you hinted, that language is an invented system by humans for
communicating their ideas.
And I think we, it comes down to, we label the things we want to talk about.
Those are the, the, the morphemes and words.
Those are the things we want to talk about in the world, and we invent those things.
And then we put them together in ways that are easy for us to convey, to process.
But that, that, that's like a naive view.
And I don't, I mean, I, I think it's probably right, right?
It's naive and probably right.
Well, that's the, I don't know if it's naive.
I think it's simple.
Simple.
Yeah.
I think naive is, naive is an indication that it's an incorrect somehow.
It's a trivial, too, too simple.
I think it could very well be correct.
But it's interesting how sticky, it feels like two people got together.
It just feels like once you figure out certain aspects of a language, that just becomes sticky
and the tribe forms around that language.
Maybe the language, maybe the tribe forms first, then the language evolves.
And then you just kind of agree and you stick to whatever that is.
I mean, these are very interesting questions.
We don't know really about how words, even words, get invented very much about, you know,
we don't really, I mean, assuming they get invented, they, we don't really know how that
process works and how these things evolve.
What we have is kind of a current picture, a current picture of a few thousand languages,
a few thousand instances.
We don't have any pictures of really how these things are evolving, really.
And then the evolution is massively, you know, confused by contact, right?
So as soon as one language group, one group runs into another, we are smart.
Humans are smart.
And they take on whatever is useful in the other group.
And so any kind of contrast, which you're talking about, which I find useful, I'm going
to start using as well.
So I worked a little bit in specific areas of words, in number words and in color words.
And in color words, so we have in English, we have around 11 words that everyone knows
for colors and many more.
If you happen to be interested in color for some reason or other, if you're a fashion
designer or an artist or something, you may have many, many more words.
But we can see millions.
Like if you have normal color vision, normal trichrometric color vision, you can see millions
of distinctions in color.
So we don't have millions of words.
You know, the most efficient, no, the most, you know, detailed color vocabulary would have
over a million terms to distinguish all the different colors that we can see.
But of course, we don't have that.
So it's somehow, it's been, it's kind of useful for English to have evolved in some way to,
such as there's 11 terms that people find useful to talk about, you know, black, white,
red, blue, green, yellow, purple, gray, pink, and I probably missed something there.
Anyway, there's 11 that everyone knows.
And depending on your, but you go to different cultures, especially the non-industrialized
cultures, and there'll be many fewer.
So some cultures will have only two, believe it or not, that the Danai in Papua New Guinea
have only two labels that the group uses for color.
Those are roughly black and white.
They are very, very dark and very, very light, which are roughly black and white.
And you might think, oh, they're dividing the whole color space into, you know, light
and dark or something.
And that's not really true.
They mostly just only label the light, the black and the white things.
They just don't talk about the colors for the other ones.
And so, and then there's other groups.
I've worked with a group called the Chimani down in Bolivia, in South America.
And they have three words that everyone knows, but there's a few others that are, that several
people, that many people know.
And so they have, it's kind of depending on how you count, between three and seven words
that the group knows, okay?
And again, they're black and white.
Everyone knows those.
And red, red is, you know, like that tends to be the third word that everyone, that cultures
bring in.
Nice.
If there's a word, it's always red, the third one.
And then after that, it's kind of all bets are off about what they bring in.
And so after that, they bring in a sort of a big blue-green group.
Grew, they have one for that.
And then they have, and then, you know, different people have different words that they'll use
for other parts of the space.
And so anyway, it's probably related to what they want to talk, what they, not what they,
not what they see.
Because they see the same colors as we see.
So it's not like they have a weak, a low color palette in the things they're looking at.
They're looking at a lot of beautiful scenery, okay?
A lot of different colored flowers and berries and things.
And, you know, and so there's lots of things of very bright colors, but they just don't
label the color in those cases.
And the reason probably, we don't know this, but we think probably what's going on here
is that what you do, why you label something is you need to talk to someone else about it.
And why do I need to talk about a color?
Well, if I have two things which are identical, and I want you to give me the one that's different,
and the only way it varies is color, then I invent a word which tells you, you know,
this is the one I want.
So I want the red sweater off the rack, not the green sweater, right?
There's two, and so those things will be identical, because these are things we made,
and they're dyed, and there's nothing different about them.
And so in industrialized society, we have, you know, everything we've got is pretty much
arbitrarily colored.
But if you go to a non-industrialized group, that's not true.
And so they don't, it's not like they're not interested in color.
If you bring bright colored things to them, they like them just like we like them.
Bright colors are great.
They're beautiful.
They are, but they just don't need to, no need to talk about them.
They don't have.
So probably color words is a good example of how language evolves from sort of function.
When you need to communicate the use of something.
I think so.
Then you kind of invent different variations.
And basically, you can imagine that the evolution of a language has to do with what the early
tribe's doing.
And like what they want to, what kind of problems are facing them.
And they're quickly figuring out how to efficiently communicate the solution to those problems,
whether it's aesthetic or functional, all that kind of stuff, running away from a mammoth
or whatever.
But, you know, it's, so I think what you're pointing to is that we don't have data on the
evolution of language because many languages have formed a long time ago.
So you don't get the chatter.
We have a little bit of like old English to modern English because there was a writing
system and we can see how, how old English looked.
So the word order changed, for instance, in old English to middle English to modern English.
And so it, you know, we could see things like that, but most languages don't even have
a writing system.
So of the 7,000 only, you know, a small subset of those have a writing system.
And even if they have a writing system, they, it's not a very modern writing system.
And so they don't have it.
Like, so we just basically have for Mandarin, for Chinese, we have a lot of, a lot of evidence
from, from, for a long time and for English and not for much else, not from in German
a little bit, but not for a whole lot of like long-term language evolution.
We don't have a lot.
We just have snapshots is what we've got of current languages.
Yeah.
You get an inkling of that from the rapid communication on certain platforms, like on Reddit, there's
different communities and they'll come up with different slang.
Usually from my perspective, German by a little bit of humor, um, or maybe mockery or whatever
it is, you know, just talking shit in different kinds of ways.
And, uh, you could see the evolution of language there because, um, I think a lot of things
on the internet, you don't want to be the boring mainstream.
So you like want to deviate from the proper way of talking.
And so you get a lot of deviation, like rapid deviation.
Then when communities collide, you get like, uh, just like you said, humans adapt to it
and you can see it through the lines of humor.
I mean, it's very difficult to study, but you can imagine like a hundred years from now,
well, if there's a new language born, for example, we'll get really high resolution data.
I mean, English changing, English changes all the time.
All languages change all the time.
So, you know, there's a famous, um, result about the Queen's English.
So the Queen, if you look at the Queen's vowels, the Queen's English is supposed to be, you
know, originally the proper way for the talk was sort of defined by whoever the Queen talked
or the King, whoever was in charge.
And, uh, and, and so if you look at the, how her vowels changed, uh, from when she first
became Queen in 1952 or 53, when she was coronated, the first, I mean, that's Queen Elizabeth who's
gone, who died recently, of course, uh, until, you know, 50 years later, her vowels changed,
her vowels shifted a lot.
And so that, you know, even in the sounds of British English in her, the way she was talking
was changing.
The vowels were changing slightly.
So that's just in the sounds, there's change.
I don't know what's, you know, we're, we're, I'm interested.
We're all interested in what's driving any of these changes.
The, the word order of English changed a lot over a thousand years, right?
So it used to look like German, you know, it looks, it used to be a verb final language
with case marking and it shifted to a verb medial language, a lot of contact.
So a lot of contact with French and it became a verb medial language with no case marking.
And so it became this, you know, verb, verb initially thing.
So, and so that's evolving.
It totally evolved.
And so it may very well, I mean, you know, it doesn't evolve maybe very much in 20 years
is maybe what you're talking about, but over 50 and a hundred years, things change a lot.
I think.
We'll now have good data on it, which is great.
That's for sure.
Can you talk to what is syntax and what is grammar?
So you wrote a book on syntax.
I did.
You were asking me before about what, you know, how do I figure out what a dependency structure
is?
I'd say the dependency structures aren't that hard generally.
I think it's a lot of agreement of what they are for almost any sentence in most languages.
I think people will agree on a lot of that.
There are other parameters in the mix such that some people think there's a more complicated
grammar than just a dependency structure.
And so, you know, like Noam Chomsky, he's the most famous linguist ever.
And he is famous for proposing a slightly more complicated syntax.
And so he invented phrase structure grammar.
So he's well known for many, many things.
But in the 50s and early 60s, like the late 50s, he was basically figuring out what's called
formal language theory.
So, and he figured out sort of a framework for figuring out how complicated language,
you know, a certain type of language might be, so-called phrase structure grammars of
language might be.
And so his idea was that maybe we can think about the complexity of a language by how complicated
the rules are, okay?
And the rules will look like this.
They will have a left-hand side and they'll have a right-hand side.
Something on the left-hand side will expand to the thing on the right-hand side.
So we'll say we'll start with an S, which is like the root, which is a sentence, okay?
And then we're going to expand to things like a noun phrase and a verb phrase is what he
would say, for instance, okay?
An S goes to an NP and a VP is a kind of a phrase structure rule.
And then we figure out what an NP is.
An NP is a determiner and a noun, for instance.
And a verb phrase is something else, is a verb and another noun phrase and another NP,
for instance.
Those are the rules of a very simple phrase structure, okay?
And so he proposed phrase structure grammar as a way to sort of cover human languages.
And then he actually figured out that, well, depending on the formalization of those grammars,
you might get more complicated or less complicated languages.
And so he said, well, these are things called context-free languages, that rule that he thought
human languages tend to be what he calls context-free languages.
But there are simpler languages, which are so-called regular languages, and they have a more constrained
form to the rules of the phrase structure of these particular rules.
So he basically discovered and kind of invented ways to describe the language.
And those are phrase structure, a human language.
And he was mostly interested in English initially in his work in the 50s.
So quick questions around all this.
So formal language theory is the big field of just studying language formally.
Yes, and it doesn't have to be human language there.
We can have computer languages, any kind of system which is generating some set of expressions
in a language.
And those could be like the statements in a computer language, for example.
So it could be that, or it could be human language.
So technically you can study programming languages.
Yes, and have been, heavily studied using this formalism.
There's a big field of programming languages within the formal language.
Okay.
And then phrase structure grammar is this idea that you can break down language into this
SNPVP type of thing.
It's a particular formalism for describing language.
Okay, so and Chomsky was the first one.
He's the one who figured that stuff out back in the 50s.
And that's equivalent, actually.
The context-free grammar is actually kind of equivalent in the sense that it generates
the same sentences as a dependency grammar would.
The dependency grammar is a little simpler in some way.
You just have a root, and it goes, like, we don't have any of these.
The rules are implicit, I guess.
We just have connections between words.
The phrase structure grammar is kind of a different way to think about the dependency grammar.
It's slightly more complicated, but it's kind of the same in some ways.
So to clarify, dependency grammar is the framework under which you see language,
and you make the case that this is a good way to describe language.
I think it's the—
That's correct.
And Noam Chomsky is watching.
This is very upset right now, so let's—
I'm just kidding.
But what's the difference between—
where's the place of disagreement between phrase structure grammar and dependency grammar?
They're very close.
So phrase structure grammar and dependency grammar aren't that far apart.
I like dependency grammar because it's more perspicuous, it's more transparent about
representing the connections between the words.
It's just a little harder to see in phrase structure grammar.
You know, the place where Chomsky sort of devolved or went off from this is he also thought there
was something called movement, okay?
And so—and that's where we disagree, okay?
That's the place where I would say we disagree.
And I mean, maybe we'll get into that later, but the idea is if you want to—do you want
me to explain that now?
I would love—can you explain movement?
Movement.
Okay, so Chomsky—
You're saying so many interesting things.
Yeah, yeah, yeah.
Okay, so here's the—movement is Chomsky basically sees English, and he says, okay,
I said, you know—we had that sentence earlier, like it was like two dogs entered the room.
Let's change it a little bit.
Say, two dogs will enter the room.
And he notices that, hey, English, if I want to make a question, a yes-no question from
that same sentence, I say, instead of two dogs will enter the room, I say, will two
dogs enter the room?
Okay, there's a different way to say the same idea, and it's like, well, the auxiliary
verb, that will thing, it's at the front as opposed to in the middle, okay?
And so—and he looked, you know, if you look at English, you see that that's true for all
those modal verbs, and for other kinds of auxiliary verbs in English, you always do
that.
You always put an auxiliary verb at the front, and when he saw that—so, you know, if I
say, I can win this bet, can I win this bet, right?
So I move a can to the front.
So, actually, that's a theory.
I just gave you a theory there.
He talks about it as movement.
That word in the declarative is the root—is the sort of default way to think about the
sentence, and you move the auxiliary verb to the front.
That's a movement theory, okay?
And he just thought that was just so obvious that it must be true, that there's nothing
more to say about that, that this is how auxiliary verbs work in English.
There's a movement rule such that you're moving—like, to get from the declarative to
the interrogative, you're moving the auxiliary to the front.
And it's a little more complicated as soon as you go to simple present and simple past,
because, you know, if I say, you know, John slept, you have to say, did John sleep, not
slept John, right?
And so you have to somehow get an auxiliary verb, and I guess underlyingly, it's like
slept is—it's a little more complicated than that, but that's his idea there's a
movement, okay?
And so a different way to think about that that isn't—I mean, then he ended up showing
later.
So he proposed this theory of grammar, which has movement.
There's other places where he thought there's movement, not just auxiliary verbs, but things
like the passive in English and things like questions, WH questions, a bunch of places where
he thought there's also movement going on.
And in each one of those, he thinks there's words—well, phrases and words are moving around
from one structure to another, which he called deep structure to surface structure.
I mean, there's like two different structures in his theory, okay?
There's a different way to think about this, which is there's no movement at all.
There's a lexical copying rule such that the word will or the word can, these auxiliary
verbs, they just have two forms, and one of them is the declarative, and one of them
is the interrogative.
And you basically have the declarative one, and, oh, I form the interrogative, or I can
form one from the other.
It doesn't matter which direction you go.
And I just have a new entry, which has the same meaning, which has a slightly different
argument structure.
Argument structure is just a fancy word for the ordering of the words.
And so if I say, you know, it was the dogs, two dogs can or will enter the room, there's
two forms of will.
One is will declarative, and then, okay, I've got my subject to the left, it comes before
me, and the verb comes after me in that one.
And then the will interrogative is like, oh, I go first.
Interrogative, will is first, and then I have the subject immediately after, and then the
verb after that.
And so you just, you can just generate from one of those words another word with a slightly
different argument structure, with different ordering.
And these are just lexical copies.
And they're just...
They're not necessarily moving from one to another.
There's no movement.
There's a romantic notion that you have like one main way to use a word, and then you could
move it around.
Right, right.
Which is essentially what movement is implying.
Yeah, but that's the lexical copying is similar.
So then we do lexical copying for that same idea, that maybe the declarative is the source,
and then we can copy it.
And so an advantage, there's multiple advantages of the lexical copying story.
It's not my story.
This is like Ivan Sog, linguists, a bunch of linguists have been proposing these stories
as well, you know, in tandem with the movement story.
Okay, you know, Ivan Sog died a while ago, but he was one of the proponents of the non-movement
of the lexical copying story.
And so that is that a great advantage is, well, Chomsky really famously in 1971 showed that
the movement story leads to learnability problems.
It leads to problems for how language is learned.
It's really, really hard to figure out what the underlying structure of a language is if
you have both phrase structure and movement, it's like really hard to figure out what came
from what.
There's like a lot of possibilities there.
If you don't have that problem, the learning problem gets a lot easier.
Just say there's lexical copies.
Yeah, yeah, yeah.
When we say the learning problem, do you mean like humans learning a new language?
Yeah, just learning English.
So baby is lying around listening to the crib, listening to me talk, and, you know, how are
they learning English?
Or, you know, maybe it's a two-year-old who's learning, you know, interrogatives and stuff
or one, you know, how are they doing that?
Are they doing it from, like, are they figuring out or, like, you know, so Chomsky said it's
impossible to figure it out, actually.
He said it's actually impossible, not hard, but impossible.
And therefore, that's where universal grammar comes from, is that it has to be built in.
And so what they're learning is that there's some built-in, movement is built in in his story,
is absolutely part of your language module, and then you are, you're just setting parameters.
You're set, depending on English, it's just sort of a variant of the universal grammar,
and you're figuring out, oh, which orders does English do these things?
That's, the non-movement story doesn't have this.
It's, like, much more bottom-up.
You're learning rules.
You're learning rules one by one, and, oh, there's, this word is connected to that word.
A great advantage, another advantage, it's learnable, another advantage of it is that
it predicts that not all auxiliaries might move.
Like, it might depend on the word, depending on whether you, and that turns out to be true.
So there's words that don't really work as auxiliary, they work in declarative and not
in interrogative.
So I can say, I'll give you the opposite first.
I can say, aren't I invited to the party, okay?
And that's an interrogative form, but it's not from, I aren't invited to the party.
There is no I aren't, right?
So that's interrogative only.
And then we also have forms like ought, I ought to do this.
And I guess some British, old British people can say...
Ought I.
Exactly.
It doesn't sound right, does it?
For me, it sounds ridiculous.
I don't even think ought is great, but, I mean, I totally recognize I ought to.
It's not too bad, actually.
I can say ought to do this.
That sounds pretty good.
Yeah.
If I'm trying to sound sophisticated, maybe.
I don't know.
It just sounds completely out to me.
Ought I.
Yeah.
Anyway, so there are variants here.
And a lot of these words just work in one versus the other.
And that's like fine under the lexical copying story.
It's like, well, you just learn the usage.
Whatever the usage is, is what you do with this word.
But it doesn't, it's a little bit harder in the movement story.
The movement story, like that's an advantage, I think, of lexical copying.
In all these different places, there's all these usage variants, which make the movement story a little bit harder to work.
So one of the main divisions here is the movement story versus the lexical copying story.
That has to do about the auxiliary warts and so on.
But rewind to the phrase structure grammar versus dependency grammar.
Those are equivalent in some sense in that for any dependency grammar, I can generate a phrase structure grammar, which generates exactly the same sentences.
I just like the dependency grammar formalism because it makes something really salient, which is the length of dependencies between words, which isn't so obvious in the phrase structure.
In the phrase structure, it's just kind of hard to see.
It's in there.
It's just very, very, it's opaque.
Technically, I think phrase structure grammar is mappable to dependency grammar.
And vice versa.
And vice versa.
Yeah, yeah.
But there's like these like little labels, S and PVP.
Yeah.
For a particular dependency grammar, you can make a phrase structure grammar, which generates exactly those same sentences and vice versa.
But there are many phrase structure grammars, which you can't really make a dependency grammar.
I mean, you can do a lot more in a phrase structure grammar, but you get many more of these extra nodes, basically.
You can have more structure in there.
And some people like that.
And maybe there's value to that.
I don't like it.
Well, for you, so we should clarify.
So dependency grammar is just, well, one word depends on only one other word.
And you form these trees.
And that makes, it really puts priority on those dependencies, just like as a tree that you can then measure the distance of the dependency from one word to the other.
They can then map to the cognitive processing of the sentences, how easy it is to understand and all that kind of stuff.
So it just puts the focus on just like the mathematical distance of dependence between words.
So like it's just a different focus.
Absolutely.
Just continue on a thread of Chomsky because it's really interesting.
Because as you're discussing disagreement, to the degree there's disagreement, you're also telling the history of the study of language, which is really awesome.
So you mentioned context-free versus regular.
Does that distinction come into play for dependency grammars?
No.
Not at all.
I mean, regular languages are too simple for human languages.
They are, it's a part of the hierarchy.
But human languages are, in the phrase structure world, are definitely, they're at least context-free.
Maybe a little bit more, a little bit harder than that.
So there's something called context-sensitive as well, where you can have, like this is just the formal language description.
In a context-free grammar, you have one, this is like a bunch of like formal language theory we're doing here.
I love it.
Okay.
So you have a left-hand side category and you're expanding to anything on the right.
That's a context-free.
So like the idea is that that category on the left expands in independent of context to those things, whatever there are on the right.
It doesn't matter what.
And a context-sensitive says, okay, I actually have more than one thing on the left.
I can tell you only in this context.
You know, maybe you have like a left and a right context or just a left context or a right context.
I have two or more stuff on the left tells you how to expand those things in that way.
Okay.
So it's context-sensitive.
A regular language is just more constrained.
And so it doesn't allow anything on the right.
It allows very – basically it's one very complicated rule is kind of what a regular language is.
And so it doesn't have any – I was going to say long-distance dependencies.
It doesn't allow recursion, for instance.
There's no recursion.
Yeah.
Recursion is where you – which is human languages have recursion.
They have embedding and you can't – well, it doesn't allow center-embedded recursion, which human languages have, which is what –
Center-embedded recursion.
So within a sentence, within a sentence.
Yeah, within a sentence.
So here we're going to get to that.
But, you know, the formal language stuff is a little aside.
Chomsky wasn't proposing it for human languages even.
He was just pointing out that human languages are context-free.
And then he was most – for human – because that was kind of stuff we did for formal languages.
And what he was most interested in was human language.
And that's like the movement is where we – where he sort of set off on the – I would say – a very interesting but wrong foot.
It was kind of interesting – it's a very – I agree, it's a very interesting history.
So there's this – so he proposed this multiple theories in 57 and then 65.
They all have this framework, though, was phrase structure plus movement.
Different versions of the phrase structure and the movement in the 57 – these are the most famous original bits of Chomsky's work.
And then in 71 is when he figured out that those lead to learning problems.
That there's cases where a kid could never figure out which rule – which set of rules was intended.
And so – and then he said, well, that means it's innate.
It's kind of interesting.
He just really thought the movement was just so obviously true that he couldn't – he didn't even entertain giving it up.
It's just obvious – that's obviously right.
And it was later where people figured out that there's all these like subtle ways in which things would – which look like generalizations aren't generalizations and they – across the category.
They're word-specific and they have – and they kind of work, but they don't work across various other words in the category.
And so it's easier to just think of these things as lexical copies.
And I think he was very obsessed – I don't know, I'm guessing – that he just – he really wanted this story to be simple in some sense.
And language is a little more complicated in some sense.
You know, he didn't like words.
He never talks about words.
He likes to talk about combinations of words.
And words are – you know, look up a dictionary.
There's 50 senses for a common word, right?
The word take will have 30 or 40 senses in it.
So there'll be many different senses for common words.
And he just doesn't think about that.
It doesn't think that's language.
I think he doesn't think that's language.
He thinks that words are distinct from combinations of words.
I think they're the same.
If you look at my brain in the scanner while I'm listening to a language I understand and you compare, I can localize my language network in a few minutes, in like 15 minutes.
And what you do is I listen to a language I know, I listen to, you know, maybe some language I don't know, or I listen to muffled speech, or I read sentences, and I read non-words.
Like, I can do anything like this.
Anything that's sort of really like English and anything that's not very like English.
So I've got something like it and not, and I've got a control.
And the voxels, which is just, you know, the 3D pixels in my brain that are responding most, is a language area.
And that's this left-lateralized area in my head.
And wherever I look in that network, if you look for the combinations versus the words, it's everywhere.
It's the same.
That's fascinating.
And so it's like hard to find.
There are no areas that we know.
I mean, that's, it's a little overstated right now.
At this point, the technology isn't great.
It's not bad, but we have the best, the best way to figure out what's going on in my brain when I'm listening or reading language is to use fMRI, functional magnetic resonance imaging.
And that's a very good localization method.
So I can figure out where exactly these signals are coming from, pretty, you know, down to, you know, millimeters, you know, cubic millimeters or smaller.
Okay, very small.
We can figure those out very well.
The problem is the when, okay?
It's measuring oxygen, okay?
And oxygen takes a little while to get to those cells.
And so it takes on the order of seconds.
So I talk fast.
I probably listen fast.
And I can probably understand things really fast.
So a lot of stuff happens in two seconds.
And so to say that we know what's going on, that the words right now in that network, our best guess is that whole network is doing something similar.
But maybe different parts of that network are doing different things.
And that's probably the case.
We just don't have very good methods to figure that out, right, at this moment.
And so.
Since we're kind of talking about the history of the study of language, what other interesting disagreements, and you're both at MIT or at war for a long time, what kind of interesting disagreements there, tension of ideas are there between you and Noam Chomsky?
And we should say that Noam was in the linguistics department, and you're, I guess for a time, were affiliated there, but primarily a brain and cognitive science department, which is another way of studying language.
And you've been talking about fMRI.
So, like, what, is there something else interesting to bring to the surface about the disagreement between the two of you or other people in the discipline?
Yeah, I mean, I've been at MIT for 31 years, since 1993, and he, Chomsky's been there much longer.
So, I met him, I knew him, I met him when I first got there, I guess, and we would interact every now and then.
I'd say that, so, I'd say our biggest difference is our methods.
And so, that's the biggest difference between me and Noam, is that I gather data from people.
I do experiments with people, and I gather corpus data, whatever corpus data is available, and we do quantitative methods to evaluate any kind of hypothesis we have.
He just doesn't do that.
So, you know, he has never once been associated with any experiment or corpus work ever.
And so, it's all thought experiments.
It's his own intuitions.
So, I just don't think that's the way to do things.
That's a, you know, cross the street, they're across the street from us kind of difference between brain and cog sci and linguistics.
I mean, not all linguists, some of the linguists, depending on what you do, more speech-oriented, they do more quantitative stuff.
But in the meaning, words and, well, it's combinations of words, syntax, semantics, they tend not to do experiments and corpus analyses.
So, on the linguistic side, probably, well, but the method is a symptom of a bigger approach, which is sort of a psychology philosophy side on Noam.
And for you, it's more sort of data-driven, sort of almost like mathematical approach.
Yeah, I mean, I'm a psychologist, so I would say we're in psychology.
You know, brain and cognitive science is MIT's old psychology department.
It was the psychology department up until 1985, and it became the brain and cognitive science department.
And so, I mean, my training is math and computer science, but I'm a psychologist.
I mean, I don't know what I am.
So, data-driven psychologist.
Yeah, yeah, yeah.
You are.
I am what I am, but I'm happy to be called a linguist, I'm happy to be called a computer scientist, I'm happy to be called a psychologist, any of those things.
But in the actual, like, how that manifests itself outside of the methodology is, like, these differences, these subtle differences about the movement story versus the lexical copy story.
Yeah, those are theories, right?
So, the theories are, but I think the reason we differ in part is because of how we evaluate the theories.
And so, I evaluate theories quantitatively, and Gnome doesn't.
Got it.
Okay, well, let's explore the theories that you explore in your book.
Let's return to this dependency grammar framework of looking at language.
What's a good justification why the dependency grammar framework is a good way to explain language?
What's your intuition?
So, the reason I like dependency grammar, as I've said before, is that it's very transparent about its representation of distance between words.
So, it's like, all it is, is you've got a bunch of words, you're connecting them together to make a sentence.
And a really neat insight, which turns out to be true, is that the further apart the pair of words are that you're connecting, the harder it is to do the production, the harder it is to do the comprehension.
It's harder to produce, it's harder to understand when the words are far apart.
When they're close together, it's easy to produce, and it's easy to comprehend.
Let me give you an example, okay?
So, we have, in any language, we have mostly local connections between words, but they're abstract.
The connections are abstract.
They're between categories of words.
And so, you can always make things further apart if you put your, if you add modification, for example, after a noun.
So, a noun in English comes before a verb, the subject noun comes before a verb, and then there's an object after, for example.
So, I can say what I said before, you know, the dog entered the room or something like that.
So, I can modify dog.
If I say something more about dog after it, then what I'm doing is, indirectly, I'm lengthening the dependence between dog and entered by adding more stuff to it.
And so, I just make, just to make it explicit here, if I say, the boy who the cat scratched cried.
We're going to have a mean cat here.
And so, what I've got here is, the boy cried would be a very short, simple sentence.
And I just told you something about the boy, and I told you it was the boy who the cat scratched, okay?
So, the cry is connected to the boy.
The cry at the end is connected to the boy in the beginning.
Right.
And so, I can do that.
And I can say that.
That's a perfectly fine English sentence.
And I can say, the cat which the dog chased ran away or something.
Okay.
I can do that.
But it's really hard now.
I've got, you know, whatever I have here.
I have the boy who the cat.
Now, let's say I try to modify cat, okay?
The boy who the cat which the dog chased scratched ran away.
Oh, my God.
That's hard, right?
I can, I'm sort of just working that through in my head, how to produce and how to, and
it's really very, just horrendous to understand.
It's not so bad.
At least I've got intonation there to sort of mark the boundaries and stuff.
But it's, that's really complicated.
That's sort of English in a way.
I mean, that follows the rules of English.
But, so what's interesting about that is, is that what I'm doing is nesting dependencies
there.
I'm putting one, I've got a subject connected to a verb there.
And then I'm modifying that with a clause, another clause, which happens to have a subject and
a verb relation.
I'm trying to do that again on the second one.
And what that does is it lengthens out the dependence, multiple dependents actually get
lengthened out there.
The dependencies get longer, longer, and the outside ones get long.
And even the ones in between get kind of long.
And, and, and you just, so what's fascinating is that that's bad.
That's really horrendous in English, but that's horrendous in any language.
And so in, in no matter what language you look at, if you do just figure out some structure
where I'm going to have some modification following some head, which is connected to
some later head, and I do it again, it won't be good.
It guaranteed, like 100% that will be uninterpretable in that language in the same way that was uninterpretable
in English.
Let's just clarify, the distance of the dependencies is whenever the boy cried, there's a dependence
between two words, and then you're counting the number of what, morphemes between them?
That's a good question.
I'll just say words.
Your words are morphemes between.
We don't know that.
Actually, that's a very good question.
What is the distance metric?
But let's just say it's words, sure.
Okay, so that, and you're saying the longer the distance of that dependence, the more,
no matter the language, except legalese, even legalese, okay, we'll talk about it.
We'll get to that.
Okay, okay, okay.
But that, the people will be very upset that speak that language, not upset, but they'll
either not understand it, or they'll be like, this is, they'll, their brain will be working
in overtime.
Yeah.
They will have a hard time either producing or comprehending it.
They might tell you that's not their language.
You know, it's sort of the language.
I mean, it's following their, like, they'll agree with each of those pieces as part of
the language, but somehow that combination will be very, very difficult to produce and
understand.
Is that a chicken or the egg issue here?
So, like, is...
Well, I'm giving you an explanation.
Right.
So, the, well, I mean, and then there's, I'm giving you two kinds of explanations.
I'm telling you that center embedding, that's nesting, those are the same, those are
synonyms for the same concept here.
And the explanation for why, those are always hard.
Centrum embedding and nesting are always hard.
And I gave you an explanation for why they might be hard, which is long distance connections.
There's a, when you do centrum embedding, when you do nesting, you always have long distance
connections between the dependents.
You just, and so that's not necessarily the right explanation.
It just happens.
I can go through reasons why that's probably a good explanation.
And it's not really just about one of them.
So, probably it's a pair of them or something of these dependents that you get long that drives
you to like be really confused in that case.
And so, what the behavioral consequence there, I mean, this is kind of methods, like how do
we get at this?
You could try to do experiments to get people to produce these things.
They're going to have a hard time producing them.
You can try to do experiments to get them to understand them and see how well they understand
them, can they understand them.
Another method you can do is give people partial materials and ask them to complete them,
you know, those centrum bedded materials and they'll fail.
So, I've done that.
I've done all these kinds of things.
So, wait a minute.
So, central bedding meaning like you take a normal sentence like boy cried and inject a
bunch of crap in the middle.
Yes.
That separates the boy and the cried.
Yes.
Okay.
That's central bedding.
And nesting is on top of that.
No, no.
Nesting is the same thing.
Centrum bedding, those are totally equivalent terms.
I'm sorry.
I sometimes use one and sometimes use the other.
Got it.
Got it.
Totally equivalent.
Anything different.
Got it.
And then what you're saying is there's a bunch of different kinds of experiments you can
do.
I mean, I like the understanding one is like have more embedding, more central bedding.
Is it easier or harder to understand?
But then you have to measure the level of understanding, I guess.
Yeah.
Yeah, you could.
I mean, there's multiple ways to do that.
I mean, there's the simplest ways.
Just ask people, how good is it sound?
How natural is the sound?
That's a very blunt but very good measure.
It's very, very reliable.
People will do the same thing.
And so it's like, I don't know what it means exactly, but it's doing something such that
we're measuring something about the confusion, the difficulty associated with those.
And those are giving you a signal.
That's why you can say that.
Yeah.
Yeah.
Very strong.
What about the completion of the central bed?
So if you give them a partial sentence, say I say the book, which the author, who,
and I ask you to now finish that off for me.
I mean, either say it.
Yeah, yeah, but you can just say it's written in front of you and you can just type and have
as much time as you want.
They will, even though that one's not too hard, right?
So if I say it's like the book, it's like, oh, the book, which the author who I met wrote
was good.
You know, that's a very simple completion for that.
If I give that completion online somewhere to a, you know, a crowdsourcing platform and
ask people to complete that, they will miss off of a verb very regularly, like half the
time, maybe two thirds of the time.
They'll say, they'll just leave off one of those verb phrases, even with that simple,
so to say the book, which the author who, and they'll say was, they won't have, you
need three verbs, right?
I need three verbs or who I met wrote was good.
And they'll give me two.
They'll say who was, who was famous was good or something like that.
They'll just give me two.
And, and, and that that'll happen about 60% of the time.
So 40%, maybe 30, they'll do it correctly, correctly, meaning they'll do a three verb
phrase.
I don't know what's correct or not.
You know, it, this is hard.
It's a hard task.
Yeah.
I can actually, I'm struggling with it in my head.
Yeah.
Well, it's, it's easier when you, when you stare at it.
If you look, it's a little easier than listening is pretty tough because you have to, because
there's no trace of it.
You have to remember the words that I'm saying, which is very hard auditorily.
We wouldn't do it this way.
We do it written.
You can look at it and figure it out.
But it's easier in many dimensions in some ways, depending on the person.
It's easier to gather written data for, I mean, most sort of psycho, I work in psycholinguistics,
right?
Psychology of language and stuff.
And so a lot of our work is based on written stuff because it's so easy to gather data from
people doing written kinds of tasks.
Spoken tasks are just more complicated to administer and analyze because people do weird things when
they speak and it's harder to analyze what they do, but they, they, they generally point
to the same kinds of things.
So, okay.
So the, the universal theory of language.
Yeah.
By Ted Gibson is that you can form dependency.
You can form trees for many sentences.
That's right.
You can measure the distance in some way of those dependencies.
And then you can say that, uh, most languages have very short dependencies.
All languages.
All languages.
All languages have short dependencies.
You can actually measure that.
So, uh, I, I, an ex-student of mine, this guy is at, um, University of California, Irvine,
Richard Futrell did a thing a bunch of years ago now where he looked at all the languages
we could look at, which was about 40 initially.
And now I think there's about 60 for which there are dependency structures.
Like you, so they're meaning there's got to be like a big text, a bunch of texts, which
have been parsed for the dependency structures.
And there's about 60 of those which have been parsed that way.
And for all of those, um, you can, what, what he did was take any, um, any sentence in
one of those languages and, uh, and you can do the dependency structure and then start
at the root.
We were, we're talking about dependency structures.
That's pretty easy now.
And, and he's trying to figure out what a control way you might say the same sentence is
in that language.
And so what he did is just like, all right, there's a root and it has, let's say as a
sentence is, um, let's go back to, you know, two dogs entered the room.
And so entered is the root and entered has, um, two dependents.
It's got dogs and it has room.
Okay.
And what he does is like, let's scramble that order.
That's three things, the root and the, the, the head and the two dependents and into some
random order, just random.
And then just do that for all the dependents down the, for it.
So now look, do it for the and whatever was two and dogs and for, uh, and room.
And, and that's, you know, that's not a, it's a very short sentence.
When sentences get longer and you have more dependents, there's more scrambling that's
possible.
And what he found what, so that, so, so that that's one, you can figure out one scrambling
for that sentence.
He did this like a hundred times for every sentence in every corp, in every one of these texts,
every corpus.
And, and then he just compared the dependency lengths in those random scramblings to what
actually happened, what, what the, the English or the French or the German was in the original
language or Chinese or what all these like 80, no 60 languages.
Okay.
And, and the dependency lengths are always shorter in the real language compared to the, to
these, this kind of a control.
And there's another, he, it's a, it's a little more rigid, his control.
So the, the way I described it, you could have crossed dependencies, like by scrambling
that way, you could scramble in any way at all.
Languages don't do that.
They tend not to cross dependencies very much.
Like, so the dependency structure, they just, they tend to keep things non-crossed and there's
a, you know, like there's a technical term, they call that projective, but it's just non-crossed
is all that is projective.
And so if you just constrain the, the scrambling so that it only gives you projective sort of
non-crossed is the same thing holds.
So it's, so the, you still, still human languages are much shorter than these, this kind of
a control.
So there's like, what it means is that, that, that we're in every language, we're trying
to put things close in relative to this kind of a control.
Like there, it doesn't matter about the word order.
Some of these are verb final.
Some of them is a verb medial, like English, and some are even verb initial.
There are a few languages of the world, which have VSO, word order, verb, subject, object
languages.
I haven't talked about those.
It's like 10% of the...
And even, even in those languages, it's still short dependencies.
Short dependencies is rules.
Okay.
So how, what, what, what are some possible explanations for that?
For why, why languages have evolved that way?
So that, that's one of the, I suppose, disagreements you might have with Chomsky.
So you consider the evolution of language in, in terms of information theory.
Yeah.
And for you, the purpose of language is ease of communication, right?
And processing.
That's right.
That's right.
So, I mean, the, the story here is just about communication.
It is just about production, really.
It's about ease of production is the story.
When you say production, can you, can you...
Oh, I just mean ease of language production.
It's easier for me to say things when the, what I'm doing, whenever I'm talking to you,
is somehow I'm formulating some idea in my head and I'm putting these words together.
And it's easier for me to do that, to put, to say something where the words are close,
closely connected in a dependency, as opposed to separated, like by putting something in
between and over and over again.
I, it's just hard for me to keep that in my head.
Like that's, that's the whole story.
Like the story is basically, it's like the dependency grammar sort of gives that to you,
like just like long, long as bad, short as good.
It's like easy to keep in mind because you have to keep it in mind for, probably for production,
probably, you know, probably matters in comprehension as well.
Like also matters in comprehension.
It's on both sides of the production and the...
But I would guess it's probably evolved for production.
Like it's about producing.
It's about what's easier for me to say that ends up being easier for you also.
That's very hard to disentangle this idea of who is it for?
Is it for me, the speaker, or is it for you, the listener?
I mean, part of my language is for you.
Like the way I talk to you is going to be different from how I talk to different people.
So I'm, I'm definitely angling what I'm saying to who I'm saying, right?
It's not like I'm just talking the same way to every single person.
And so I am sensitive to my audience, but how, does that, does that, you know, work itself
out in the, in the dependency length differences?
I don't know.
Maybe that's about just the words, that part, you know, which words I select.
My initial intuition is that you optimize language for the audience.
Yeah.
But it's both.
It's just kind of like messing with my head a little bit to say that some of the optimization
might be, it may be the primary objective of the optimization might be the ease of production.
Yeah.
We have different senses, I guess.
I'm, I'm like very selfish.
And you're like, I'm like, I think it's like, it's all about me.
I'm like, I'm just doing what's easiest for me at all times.
I don't want to, I'm like, I'll, I mean, but I have to, of course, choose the words that
I think you're going to know.
I'm not going to choose words you don't know.
In fact, I'm going to fix that when I, you know, so there it's about, but, but maybe
for, for the syntax, for the combinations, it's just about me.
I feel like it's, I don't know though.
It's very hard.
Wait, wait, wait, wait, but the purpose of communication is to be understood, is to
convince others and so on.
So like the selfish thing is to be understood.
Okay.
It's about the listener.
It's a little circular there too then.
Okay.
Right.
I mean, like the ease of production.
Helps, helps me be understood then.
I don't think it's circular.
No, I think the primary, I think the primary objective is to be understood, is about the
listener.
Because otherwise, if you're optimizing for the ease of production, then you're, you're
not going to have any of the interesting complexity of language.
Like you're trying to like explain.
Well, let's control for what it is I want to say.
Like I, I'm saying let's control for the thing, the message.
Control for the message.
But that means the message needs to be understood.
That's the goal.
Oh, but that's the meaning.
So I'm still talking about the form, just the form of the meaning.
How do I frame the form of the meaning is all I'm talking about.
You're talking about a harder thing, I think.
It's like, how am I like trying to change the meaning?
Let's, let's keep the meaning constant.
Like which, if you keep the meaning constant, how can I phrase whatever it is I need to say?
Like I got to pick the right words and I'm going to pick the order so that it's, so it's
easy for me.
You know, that's, that's, that's, that's what I think is probably like.
I think I'm still tying meaning and form.
Yeah.
Together in my head, but you're saying if you keep the meaning of what you're saying
constant, what the optimization, yeah, it could be the primary objective of that optimization
is the, for production.
That's interesting.
I'm struggling to keep constant the meaning.
It's just so, I mean, I'm, I'm such a, I'm a human, right?
So for me, the form without having introspected on this, the form and the meaning are tied together.
Like deeply.
Because I'm a human.
Like for, for me when I'm speaking, because I haven't thought about language, like in
a rigorous way about the form of language.
But look, for any event, there's, there's an, an unbounded, I don't, I don't want to say
infinite, but sort of unbounded, you know, ways of that I might communicate that same
event.
This two dogs entered a room, I can say in many, many different ways.
I can say, hey, there's two dogs.
They entered the room.
Hey, the room was entered by something.
The thing that was entered was two dogs.
I mean, there's, I mean, it's kind of awkward and weird and stuff, but those are all similar
messages with different forms, different ways I might frame.
And of course, I use the same words there all the time.
I could have referred to the dogs as, you know, a Dalmatian and a poodle or something.
You know, I, I could have been more specific or less specific about what they are.
And I could have said, been more abstract about, about, about the number.
There's like, so I, like, I'm trying to keep the meaning, which is this event constant.
And then how am I going to describe that to get that to you?
It kind of depends on what you need to know, right?
And what I think you need to know, but I'm like trying to, let's get control for all
that stuff and not, and I'm just like choosing about, I'm doing something simpler
than you're doing, which is just forms.
Yes.
Just words.
So to you specifying the species, the, the breed of dog and whether they're cute or not
is changing the meaning.
That might be.
Yeah.
Yeah.
That would be changing.
Oh, that would be changing the meaning for sure.
Right.
So you're just, yeah.
Yeah.
Yeah.
That's changing the meaning.
But say, even if we keep that constant, we can still talk about what's easier or hard
for me, right?
The listener and the, and the, right.
Which phrase structures I use, which combinations, which, you know.
This is so fascinating and just like a, a really powerful window into human language.
But I wonder still throughout this, how vast the gap between meaning and form.
I just, I just have this like maybe romanticized notion that they're close together, that they
evolve close, like hand in hand, that you can't just simply optimize for one without the
other being in the room with us.
Like it's, well, it's kind of like an iceberg.
Form is the tip of the iceberg and the rest, the, the meaning is the iceberg, but you can't
like separate.
But I think that's why these large language models are so successful is because they're
good at form and form isn't that hard in some sense.
And meaning is tough still.
And that's why they're not, they're, you know, they don't understand what they're doing.
We're going to talk about that later, maybe, but like we can distinguish in our, forget
about large language models, like humans, where maybe you'll talk about that later too,
is like the difference between language, which is a communication system and thinking, which
is meaning.
So language is a communication system for the meaning.
It's not the meaning.
And so that's why, I mean, that, and there's a lot of interesting evidence we can talk about
relevant, relevant to that.
Well, I mean, that's a really interesting question.
What is the difference, what is the difference between language, written, communicated versus
thought?
What to you is the difference between them?
Well, you or anyone has to think of a task, which they think is, is a good thinking task.
And there's lots and lots of tasks, which should be good thinking tasks.
And whatever those tasks are, let's say it's, you know, playing chess or that's a good thinking
task or playing some game or doing some complex puzzles, maybe, maybe remembering some digits
that's thinking, remembering some, a lot of different tasks we might think, maybe just
listening to music is thinking, or there's a lot of different tasks we might think of
as thinking.
There's a woman in my department, F. Federenko, and she's done a lot of work on this question
about what's the connection between language and thought.
And so she uses, I was referring earlier to MRI, fMRI, that's her primary method.
And so she has been really fascinated by this question about whether, what languages, okay?
And so, as I mentioned earlier, you can localize my language area, your language area in a few
minutes, okay?
In like 15 minutes, I can listen to language, listen to non-language or backward speech or
something, and we'll find areas, left-lateralized network in my head, which is especially, which
is very sensitive to language, as opposed to whatever that control was, okay?
Can you specify what you mean by language, like communicated language?
Just sentences.
You know, I'm listening to English of any kind, story, or I can read sentences, anything
at all that I understand, if I understand it, then it'll activate my language network.
So right now, my language network is going like crazy when I'm talking and when I'm listening
to you, because we're both, we're communicating.
And that's pretty stable.
Yeah, it's incredibly stable.
So I've, I happen to be married to this woman at Fedorenko, and so I've been scanned by her
over and over and over since 2007 or 2006 or something.
And so my language network is exactly the same, you know, like a month ago as it was back in
2007.
It's amazingly stable.
It's astounding.
It's a really fundamentally cool thing.
And so my language network is, it's like my face, okay?
It's not changing much over time inside my head.
Can I ask a quick question?
Sorry, it's a small tangent.
At which point in the, as you grow up from baby to adult, does it stabilize?
We don't know.
Like that's a, that's a very hard question.
They're working on that right now because of the problem scanning little kids, like doing
the, trying to do local, trying to do the localization on little children in this scanner,
right?
You're lying in the fMRI scan.
That's the best way to figure out where something's going on inside our brains.
And the scanner is loud and you're in this tiny little area, you're claustrophobic and
it doesn't bother me at all.
I can go to sleep in there, but some people are bothered by it and little kids don't really
like it and they don't like to lie still.
And you have to be really still because you can move around, that's, that messes up the
coordinates of where, where everything is.
And so, you know, try to get, you know, your question is how and when are language developing?
You know, how, when, how does this left lateralized system come to play?
You know, and it's really hard to get a two-year-old to do this task, but you can maybe, where they're
starting to get three and four and five-year-olds to do this task for short periods.
And it looks like it's there pretty early.
So clearly when you lead up to like a baby's first words before that, there's a lot of
fascinating turmoil going on about like figuring out like, what are, what are these people saying?
And you're trying to like make sense, how does that connect to the world and all that
kind of stuff.
Yeah.
That might be just fascinating development that's happening there.
That's hard to introspect.
But anyway, you.
Anyway, we're back to the scanner and I can find my network in 15 minutes and now we can
ask, we can ask, find my network, find yours, find, you know, 20 other people do this task
and we can do some other tasks.
Anything else you think is thinking of some other thing.
I can do a spatial memory task.
I can do a music perception task.
I can do programming task if I program.
Okay.
I can do where I can like understand computer programs and none of those tasks will tap the
language network at all.
Like at all.
There's no overlap.
They do.
They're, they're highly activated in other parts of the brain.
There's a, there's a bilateral network, which I think she tends to call the multiple demands
network, which does anything kind of hard.
And so anything that's kind of difficult in some ways will activate that multiple demands
network.
I mean, music will be in some music area, you know, there's music specific kinds of areas.
And so, but there, but, but none of them are activating the language area at all, unless
there's words.
Like, so if you have music and there's a song and you can hear the words, then, then, then
you get the language area.
Are we talking about speaking and listening, but are, or are we also talking about reading?
This is all comprehension of any kind.
And so, so.
That is fascinating.
So what this, this, this network doesn't make any difference if it's written or spoken.
So the, the, the language, the thing that she calls, Fedorenko calls the language network
is this high level language.
So it's not about the spoken, the spoken language, and it's not about the written language.
It's about either one of them.
And so we're, so when you do speech, you're sort of listening, you're, you either, you listen
to speech and you, you subtract away some language you don't understand.
And so which, and, or you subtract away back, backward speech, which signs, sounds like
speech, but it isn't.
And, and then, so you, you take away the sound part altogether.
And so, and then if you do written, you get exactly the same network.
So for just reading the language versus reading sort of nonsense words or something like that,
you'll find exactly the same network.
And so it's just about high level, um, the comprehension of language.
Yeah.
In this case, and, and the same thing happened, production is a little harder to run the scanner,
but the same thing happens in production.
You get the same network.
So production is a little harder, right?
You have to figure out how do you run a task, you know, in the network such that you're doing
some kind of production.
And I can't remember what, they've done a bunch of different kinds of tasks there where
you get people to, you know, produce things.
Yeah.
Figure out how to produce.
And the same network goes on there.
It's actually the same place.
And so if you, wait, wait, so if you read random words.
Yeah.
If you read things like, um.
Like gibberish.
Yeah.
Yeah.
Lewis Carroll's Twas Brillig, Jabberwocky, right?
They call that Jabberwocky speech.
The network doesn't get activated.
Not as much.
There are words in there.
Yeah.
Because it's still, it's got it.
There's function words and stuff.
So it's lower activation.
That's fascinating.
Yeah.
Yeah.
So there's like, basically the more language like it is, the higher it goes in the language
network.
And that network is there from when you speak, from as soon as you learn language.
And, and it's, it's there, like you speak multiple languages, the same network is going
for your multiple languages.
So you speak English, you speak Russian, then the, both of them are hitting that same network.
If you, if you're affluent in those languages.
So programming.
Not at all.
Isn't that amazing?
Even if you're a really good programmer, that is not a human language.
It's just not conveying the same information.
And so it is not in the language network.
And so.
That is mind blowing as I think.
That's pretty cool.
That's weird.
It is amazing.
That's really weird.
So that's like one set of day.
This is hers, like shows that what you might think is thinking is, is not language.
Language is just the, just, just this conventionalized system that we've worked out in, in human
languages.
Oh, another fascinating little bit tidbit is that even if they're these constructed languages
like Klingon or, um, I don't know the languages from Game of Thrones.
I'm sorry.
I don't remember those languages.
There's a lot of people offended right now.
There's people that speak those languages.
They, they, they really speak those languages because the people that wrote the languages
for the shows, um, they did an amazing job of constructing something like a human language.
And those, that, that lights up the language area.
That's like, because they can speak, you know, pretty much arbitrary thoughts in a human
language.
It's not a, it's a constructed human language and it probably it's related to human languages
because the people that were constructing them was, we're making them like human languages
in various ways, but it also activates the same network.
Which is pretty, really cool.
Anyway.
Sorry to go into a place where you may be a little bit philosophical, but is it possible
that this area of the brain is doing some kind of translation into a deeper set of almost
like, uh, concepts?
I mean, so it has to be doing.
So it's doing in communication, right?
It is translating from thought, whatever that is, it's more abstract and it's doing that.
That's what it's doing.
Like it is, that, that is kind of what it is doing.
It's like, it's like kind of a meaning network, I guess.
Yeah.
Like a translation network.
Yeah.
But I wonder what is at the core, at the bottom of it, like what are thoughts?
Are they, are thoughts, to me like, thoughts and words, are they neighbors or are, is it
one turtle sitting on top of the other?
Meaning like, is there a deep set of concepts that we...
Well, there's connections right between the, what, what, what these things mean.
And then there's probably other, other parts of the brain that, what these things mean.
And so, you know, when I'm talking about whatever it is I want to talk about, if it's some,
it'll be represented somewhere else.
That, that knowledge of whatever that is will be represented somewhere else.
Well, I wonder if there's like some stable, nicely compressed encoding of meanings.
I don't know.
That's separate from language.
That link, you know, I guess, I guess the implication here is that, that we don't think in language.
That's correct.
Isn't that cool?
And, and, and that's so interesting.
So people, I mean, this is like hard to do experiments on, but there is this idea of an
inner voice and a lot of people have an inner voice.
And so if you do a poll on the internet and ask if you, you hear yourself talking when
you're just thinking or whatever, you know, about 70 or 80% of people will say,
yes, most people have an inner voice.
I don't.
And so I always find this strange when, so when people talk about an inner voice, I always
thought this was a metaphor and they hear, I'm, I know most of you, whoever's listening
to this thinks I'm crazy now because I'm, I don't have an inner voice and I, I just don't
know what you're listening to.
I just, it sounds so kind of annoying to me, but that to have this voice going on while
you're, while you're thinking, but I guess most people have that and I don't have
that and I don't, we don't really know what that connects to.
I wonder if the inner voice activates that same network.
I wonder.
I don't, I don't know.
I don't know.
I mean, this could be speechy, right?
So that's like the, you hear, do you have an inner voice?
I don't think so.
Oh.
A lot of people have this sense that they hear other people, they hear themselves and
then say they read someone's email.
I've heard people tell me that they hear that other person's voice when they read other
people's emails.
And I'm like, wow, that sounds so disruptive.
I do think I like vocalize what I'm reading, but I don't think I hear a voice.
Well, that's, you probably don't have an inner voice.
Yeah, I don't think I have an inner voice.
People have an inner voice.
People have this strong percept of hearing sound in their heads when they're just thinking.
I refuse to believe that's the majority of people.
Majority.
Absolutely.
What?
It's like two thirds or three quarters.
It's a lot.
I would never ask class.
And I went internet, they always say that.
So you're in a minority.
It could be a self-report flaw.
It could be.
You know, when I'm reading inside my head, I'm kind of like saying the words, which is
probably the wrong way to read, but I don't hear a voice.
There's no precept of a voice.
I refuse to believe the majority of people have it.
Anyway, it's a fascinating, the human brain is fascinating, but it still blew my mind that
that language does appear.
Comprehension does appear to be separate from thinking.
So that's one set.
One set of data from Fedorenko's group is that no matter what task you do, if it doesn't have
words and combinations of words in it, then it won't light up the language network.
You know, it'll be active somewhere else, but not there.
So that's one.
And then this other piece of evidence relevant to that question is that it turns out there
are these group of people who've had a massive stroke on the left side and wiped out their
language network.
And as long as they didn't wipe out everything on the right as well, in that case, they wouldn't
be, you know, cognitively functionable.
But if they just wiped out language, which is pretty tough to do because it's very expansive
on the left, but if they have, then there are these, there's patients like this, so-called
global aphasics, who can do any task just fine, but not language.
They can't talk to them.
I mean, they don't understand you.
They can't speak.
They can't write.
They can't read.
But they can do, they can play chess.
They can drive their cars.
They can do all kinds of other stuff, you know, do math.
So math is not in the language area, for instance.
You do arithmetic and stuff.
That's not in the language area.
It's got symbols.
So people sort of confuse some kind of symbolic processing with language, and symbolic processing
is not the same.
So there are symbols, and they have meaning, but it's not language.
It's not a, you know, conventionalized language system.
And so math isn't there.
And so they can do math.
They do just as well as their control, age-match controls, and all these tasks.
This is Rosemary Varley over in University College London, who has a bunch of patients who
she's shown us, that they're just...
So that sort of combination suggests that language isn't necessary for thinking.
It doesn't mean you can't think in language.
You could think in language, because language allows a lot of expression, but it's just,
you don't need it for thinking.
It suggests that language is separate, is a separate system.
This is kind of blowing my mind right now.
It's cool, isn't it?
I'm trying to load that in.
Because it has implications for large language models.
It sure does.
And they've been working on that.
Well, let's take a stroll there.
You wrote that the best current theories of human language are arguably large language
models.
So this has to do with form.
It's kind of a big theory.
And, but the reason it's arguably the best is that it does the best at predicting what's
English, for instance.
It's incredibly good, you know, better than any other theory.
It's so, you know, but, you know, we don't, you know, there's, it's not sort of, there's
not enough detail.
Well, it's opaque.
Like, there's not, you don't know what's going on.
You don't know what's going on.
It's another black box.
But I think it's, you know, it is a theory.
What's your definition of a theory?
Because it's a gigantic, it's a gigantic black box with, you know, a very large number
of parameters controlling it.
To me, theory usually requires a simplicity, right?
Well, I don't know.
Maybe I'm just being loose there.
I think it's a, it's not a, it's not a great theory, but it's a theory.
It's a good theory in one sense, in that it covers all the data.
Like, anything you want to say in English, it does.
And so that's why it's, that's how it's arguably the best, is that no other theory is as good
as a large language model in predicting exactly what's good and what's bad in English.
You know, now you're saying, is it a good theory?
Well, probably not, you know, because I want a smaller theory than that.
It's too big.
I agree.
You could probably construct mechanism by which it can generate a simple explanation
of a particular language, like a set of rules, something like a, it could generate a dependency
grammar for a language, right?
Yes.
You could probably, you could probably just ask it about itself.
Well, you know, that's, I mean, that presumes, and there's some evidence for this, that some
large language models are implementing something like dependency grammar inside them.
And so there's work from a guy called Chris Manning and colleagues over at Stanford in natural
language.
And they looked at, I don't know how many large language model types, but certainly Bert and
some others where, and where, where you do some kind of fancy math to figure out exactly
what the sort of, what, what kind of abstractions of representations are going on.
And they, and they were saying, it does look like dependency structure is, is what they're
constructing.
It doesn't like, so it's actually a very, very good map.
So it's kind of a, they are constructing something like that.
Does it mean that, you know, that they're using that for meaning?
I mean, probably, but we don't know.
You write that the kinds of theories of language that LLMs are closest to are called construction
based theories.
Can you explain what construction based theories are?
It's just a general theory of language such that there's a form and a meaning pair for,
for lots of pieces of the language.
And so it's, it's, it's primarily usage based is a construction grammar.
It's just, it's trying to deal with the things that people actually say, actually say and
actually write.
And so that's, it's a usage based idea.
And what's a construction?
A construction is either a simple word.
So if, of like a morpheme plus its meaning or a combination of words, it's basically
combinations of words, like the, the rules.
So, but it's, it's, it's, it's unspecified as to what the form of the grammar is under,
underlyingly.
And so I, I would, I, I would argue that the dependency grammar is maybe the, the right
form to use for the types of construction grammar.
Construction grammar typically isn't kind of formalized quite.
And so maybe the formalization, a formalization of that, it might be in dependency grammar.
I mean, I, I would think so, but I mean, it's up to people, other researchers in that area,
if they agree or not.
So do you think that large language models understand language or they mimicking language?
I guess the deeper question there is, are they just understanding the surface form or
do they understand something deeper about the meaning that then generates the form?
I mean, I would argue they're doing the form.
They're doing the form and doing it really, really well.
And are they doing the meaning?
No, probably not.
I mean, there's lots of these examples from various groups showing that they can be tricked
in all kinds of ways.
They really don't understand the, the meaning of what's going on.
And so there's a lot of examples that he and other groups have given, which just, which
show they don't really understand what's going on.
So, you know, the Monty Hall problem is this silly problem, right?
Where, you know, if you have three door that it's, it's, let's make a deal is this old
game show and there's three doors and there's a prize behind one and there's some junk prizes
behind the other two and you're trying to select one.
And if you, you know, he knows Monty, he knows where the target item is.
The good thing, he knows everything is back there and you're supposed to, he gives you
a choice.
You choose one of the three and then he opens one of the doors and it's some junk prize.
And then the question is, should you trade to get the other one?
And the answer is yes, you should trade because he knew which ones you could turn around.
And so now the odds are two thirds.
Okay.
And then if you just change that a little bit to the large language model, the large language
model has seen that, that, that explanation so many times that it just, if you change the
story, it's a little bit, but it'd make it sound like it's the Monty Hall problem, but
it's not.
You just say, oh, there's three doors and one behind them is a good prize and there's
two bad doors.
I happen to know it's behind door number one.
The good prize, the car is behind door number one.
So I'm going to choose door number one.
Monty Hall opens door number three and shows me nothing there.
Should I trade for door number two?
Even though I know the good prize is in door number one.
And then the large language model say, yes, you should trade because it's, it's, it just
goes through the, the, the, the, the forms that it's seen before so many times on these
cases where it's, yes, you should trade because, you know, your odds have shifted from one
in three now to two out of three to being that thing.
It doesn't have any way to remember that actually you have a hundred percent probability behind
that door number one.
You know that that's not part of the, of the, the scheme that it's seen hundreds and hundreds
of times before.
And so you can't, you can't, even if you try to explain to it that it's wrong, that they
can't do that.
It'll just keep giving you back the, the, the problem.
But it's also possible the larger language model will be aware of the fact that there's
sometimes over-representation of a, of a particular kind of formulation.
And it's easy to get tricked by that.
And so you could see if they get larger and larger models be a little bit more skeptical.
So you see a over-representation.
So like you, it just feels like form can, training on form can go really far in terms
of being able to generate things that look like the thing understands deeply the underlying
world, world model of the kind of mathematical world, physical world, psychological world that
would generate these kinds of sentences.
It just feels like you're creeping close to the meaning part, easily fooled, all this kind
of stuff, but that's humans too.
So it just seems really impressive how often it seems like it understands concepts.
I mean, you don't have to convince me of that.
I'm, I am very, very impressed, but does it, does it do, I mean, you're, you're giving a
possible world where maybe someone's going to train some other versions such that it'll
be somehow abstracting away from types of forms.
I mean, I don't think that's happened.
And so.
Well, no, no, no, no.
I'm not saying that.
I think when you just look at anecdotal examples and just showing a large number of them where
it doesn't seem to understand and it's easily fooled, that does not seem like a scientific
data driven, like analysis of like how many places is a damn impressive in terms of meaning
and understanding and how many places is easily fooled.
And like.
That's not the inference.
Yeah.
So I don't want to make that.
The inference I don't, I wouldn't want to make was that inference.
The inference I'm trying to push is just that, is it, is it like humans here?
It's probably not like humans here.
It's different.
So humans don't make that error.
If you explain that to them, they're not going to make that error.
You know, they don't make that error.
And so that's something, it's doing something different from humans that they're doing in
that case.
Well, what's the mechanism by which humans figure out that it's an error?
I'm just saying the error there is like, if I explain to you there's a hundred percent
chance that the cars behind this case, this door, will you, do you want to trade?
People say no, but this thing will say yes, because it's so true.
That trick, it's so wound up on the form that it's, that's an error that a human doesn't
make, which is kind of interesting.
Less likely to make, I should say.
Yeah, less likely.
Because like humans are very...
Oh, yeah.
I mean, you're asking, you know, you're asking humans to, you're asking a system to understand
a hundred percent, like you're asking some mathematical concepts.
And so like...
Look, the places where large language models are, the form is amazing.
So let's go back to nested structures, center embedded structures.
Okay.
If you ask a human to complete those, they can't do it.
Neither can a large language model.
They're just like humans in that.
If you ask, if I ask a large language model...
That's so fascinating, by the way.
The central embedding?
Yeah, the central embedding is struggles with...
Just like humans.
Exactly like humans.
Exactly the same way as humans.
And that's not trained.
So they do exactly...
So that is a similarity.
So, but then it's, that's not meaning, right?
This is form.
But when we get into meaning, this is where they get kind of messed up.
When you start to saying, oh, what's behind this door?
Oh, it's, you know, this is the thing I want.
Humans don't mess that up as much.
You know, here, the form is, it's just like, the form of the match is amazing, similar,
without being trained to do that.
I mean, it's trained in the sense that it's getting lots of data, which is just like human
data, but it's not being trained on, you know, bad sentences and being told what's
bad.
It just can't do those.
It'll actually say things like, those are too hard for me to complete or something,
which is kind of interesting, actually.
Kind of, how does it know that?
I don't know.
But it really often doesn't just complete, it very often says stuff that's true.
And sometimes says stuff that's not true.
And almost always the form is great.
Yeah.
But it's still very surprising that with really great form, it's able to generate a lot of
things that are true, based on what it's trained on and so on.
Yes, yes, yes.
So it's not just, it's not just form that is generating, it's mimicking true statements.
That's right, that's right.
From the internet.
I guess, I guess the underlying idea there is that on the internet, truth is overrepresented
versus falsehoods.
I think that's probably right.
Yeah.
So, but the fundamental thing it's trained on, you're saying, is just form.
I think so.
Yeah.
Yeah, I think so.
Well, that's a sad, if that's, to me that's still a little bit of an open question.
I'd probably lean agreeing with you, especially now you've just blown my mind that there's
a separate module in the brain for language versus thinking.
Maybe there's a fundamental part missing from the large language model approach that lacks
the thinking, the reasoning capability.
Yeah, that's what this group argues.
So the same group, Fedorenko's group, has a recent paper arguing exactly that.
There's a guy called Kyle Mahowal, who's here in Austin, Texas, actually.
He's an old student of mine, but he's a faculty in linguistics at Texas, and he was the first
author on that.
That's fascinating.
Still, to me, an open question.
Yeah.
What do you have the interesting limits of LLMs?
You know, I don't see any limits to their form.
Their form is perfect.
Impressive.
Yeah, yeah, yeah.
It's pretty much, I mean, it's close to being.
Well, you said ability to complete central embeddings.
Yeah.
It's just the same as humans.
It seems the same.
But that's not perfect, right?
It should be able to.
That's good.
No, but I want it to be like humans.
I'm trying to, I want a model of humans.
Oh, wait, wait, wait.
Oh, so perfect is as close to humans as possible.
I got it.
Yeah.
But you should be able to, if you're not human, you're like, you're superhuman, you
should be able to complete central embedded sentences, right?
I mean, that's the mechanism is if it's modeling something, I think it's kind of really interesting
that it can't.
That it's really interesting.
That it's more like, like I think it's potentially underlyingly modeling something like what the
the way the form is processed.
The form of human language.
Yeah.
The way that you-
And how humans process the language.
Yes.
Yes.
I think that's plausible.
And how they generate language.
Process language and generate language.
That's fascinating.
Yeah.
So in that sense, they're perfect.
If we can just linger on the center embedding thing, that's hard for LLMs to produce and
that seems really impressive because that's hard for humans to produce.
And how does that connect to the thing we've been talking about before, which is the dependency
grammar framework in which you view language and the finding that short dependencies seem
to be a universal part of language.
So why is it hard to complete center embeddings?
So what I like about dependency grammar is it makes the cognitive cost associated with
longer distance connections very transparent.
Basically, it turns out there is a cost associated with producing and comprehending connections between
words which are just not beside each other.
The further apart they are, the worse it is, according to, well, we can measure that.
And there is a cost associated with that.
Can you just linger on what do you mean by cognitive cost and how do you measure it?
Oh, well, you can measure it in a lot of ways.
The simplest is just asking people to say whether, you know, how good a sentence sounds.
We just ask.
That's one way to measure.
And you can try to, like, triangulate then across sentences and across structures to try
to figure out what the source of that is.
You can look at reading times in controlled materials, you know, in certain kinds of materials
when the, and then we can, like, measure the dependency distances there.
We can, there's a recent study which looked at, we're talking about the brain here.
We could look at the language network, okay?
We could look at the language network and we could look at the activation in the language
network and how big the activation is depending on the length of the dependencies.
And it turns out in just random sentences that you're listening to, if you're listening to,
so it turns out there are people listening to stories here.
And the bigger, the longer the dependency is, the stronger the activation in the language
network.
And so there's some measure, there's a different, there's a bunch of different measures we could
do.
That's a kind of a neat measure, actually, of actual activation in the brain.
So that you can somehow in different ways convert it to a number.
I wonder if there's a beautiful equation connecting cognitive costs and length of dependency.
E equals MC squared kind of thing.
Yeah, it's complicated, but probably it's doable.
I would guess it's doable.
You know, I tried to do that a while ago and I was reasonably successful, but for some
reason I stopped working on that.
I agree with you that it would be nice to figure out.
So there's, like, some way to figure out the cost.
I mean, it's complicated.
Another issue you raised before was, like, how do you measure distance?
Is it words?
It probably isn't, is part of the problem, is that some words matter more than others.
And probably, you know, meaning, like, nouns might matter depending, and then it maybe depends
on which kind of noun.
Is it a noun we've already introduced or a noun that's already been mentioned?
Is it a pronoun versus a name?
Like, all these things probably matter.
So probably the simplest thing to do is just, like, oh, let's forget about all that and just
think about words or morphemes.
For sure, but there might be, like, there might be some insight in the kind of function
that fits the data, meaning, like, a quadratic, like, what?
I think it's an exponential.
So we think it's probably an exponential such that the longer the distance, the less it
matters.
And so then it's the sum of those is my, that was our best guess a while ago.
So you've got a bunch of dependencies.
If you've got a bunch of them that are being connected at some point, that's, like, at the
ends of those, the cost is some exponential function of those, is my guess.
But because the reason it's probably an exponential is, like, it's not just the distance between
two words.
Because I can make a very, very long subject verb dependency by adding lots and lots of
noun phrases and prepositional phrases, and it doesn't matter too much.
It's when you do nested, when I have multiple of these, then things go really bad, go south.
Probably somehow connected to working memory or something like this.
Yeah, that's probably a function of the memory here, is the access, is trying to find those
earlier things.
It's kind of hard to figure out what was referred to earlier.
Those are those connections.
That's the sort of notion of working, as opposed to a storage-y thing, but trying to connect,
retrieve those earlier words, depending on what was in between.
And then we're talking about interference of similar things in between.
That's, the right theory probably has that kind of notion in it, is interference of similar.
And so I'm dealing with an abstraction over the right theory, which is just, you know,
let's count words, it's not right, but it's close.
And then maybe you're right, though, there's some sort of an exponential or something to
figure out the total so we can figure out a function for any given sentence in any given
language.
But, you know, it's funny, you know, people haven't done that too much, which I do think
is, I'm interested that you find that interesting.
I really find that interesting.
And a lot of people haven't found it interesting.
And I don't know why I haven't got people to want to work on that.
I really like that, too.
No, that's a, that's a, that's a beautiful, and the underlying idea is beautiful, that there's
a cognitive cost that correlates with the length of dependency.
And it just, it feels like it's a deep, I mean, language is so fundamental to the human
experience.
And this is a nice, clean theory of language, where it's like, wow, okay, so like, we like
our words close together.
Yeah.
Dependent words close together.
Yeah.
That's why I like it, too.
It's so simple.
Yeah, the simplicity of the theory, man.
And yet it explains some very complicated phenomena.
If you, if I write these very complicated sentences, it's kind of hard to know why they're so hard.
And you can like, oh, nail it down.
I can do, I can give you a math formula for why each one of them is bad and where.
And that's kind of cool.
I think that's very neat.
Have you gone through the process?
Is there like a, you take a piece of text and then simplify, sort of like there's an average
length of dependency, and then you like, you know, reduce it and see,
comprehension on the entire, not just a single sentence, but like, you know, you go from
James Joyce to Hemingway or something.
No, no, simple answer is no, that does, there's probably things you can do in that, in that
kind of direction.
That's fun.
We might, you know, we're going to talk about legalese at some point.
Yes.
And so maybe we'll talk about that kind of thinking with applied to legalese.
Let's talk about legalese, because you mentioned that as an exception.
We just take it tangent upon tangent.
That's an interesting one.
You give it as an exception.
It's an exception.
Uh, that you say that most natural languages, as we've been talking about, have local dependencies,
with one exception, legalese.
That's right.
So what is legalese, first of all?
Oh, well, legalese is what you think it is.
It's just any legal language.
I mean, like, I actually don't know very little about the kind of language that lawyers use.
So I'm just talking about language in laws and language in contracts.
Got it.
So the stuff that you have to run into, we have to run into every other day or every
day.
Uh, and you skip over because it reads poorly and, or, you know, partly it's just long,
right?
There's a lot of texts there that we don't really want to know about.
And so, but the, the thing I'm interested in, so I, I've been working with, um, this guy
called Eric Martinez, who is a, um, he was a lawyer who was taking my class.
I was teaching a psycholinguistics lab class and I haven't been teaching it for a long
time at MIT and he's a, he was a law student at Harvard and he took the class because he
had done some linguistics as an undergrad and he was interested in the problem of why
legalese sounds hard to understand, you know, why, and so why is it hard to understand and
why do they write that way?
If it is hard to understand, it seems apparent that it's hard to understand.
The question is, why is it?
And so we, we didn't know, and, uh, we did, uh, an evaluation of a bunch of contracts.
Actually, we just took a bunch of random contracts because I don't know, you know, there's contracts
and laws might not be exactly the same, but, uh, contracts are kind of the things that most
people have to deal with most of the time.
And so that's kind of the most common thing that humans have, like humans, that, that adults
in our industrialized society have to deal with a lot.
And so, so that's what we, we pulled and we, we didn't know what was hard about them,
but it turns out that the way they're written is, is very center embedded, has nested structures
in them.
So it has low frequency words as well.
That's not surprising.
Lots of texts have low, it does have surprising, a slightly lower frequency words than other
kinds of control texts, even sort of academic texts.
Legalese is even worse.
It is the worst that, that we were able to find.
You just, you just reveal the game that lawyers are playing.
They're optimizing a different, well.
You know, it's interesting.
That's a, that's a, now you're getting at why, and so, and I don't think, so now you're
saying it's, they're doing intentionally.
I don't think they're doing intentionally.
But let's, let's, let's, um, let's get to this.
It's an emergent phenomenon.
Okay.
Yeah, yeah, yeah.
We'll get to that.
We'll get to that.
And so, but, but we wanted to see why, so we see what first as opposed, so like, because
it turns out that we're not the first to observe that legalese is weird.
Like, back to, uh, Nixon had a plain language act in 1970, and, and Obama had one.
And, uh, boy, a lot of these, you know, a lot of presidents have said, oh, we've got
to simplify legal language, must simplify it.
But if you don't know how it's complicated, it's not easy to simplify it.
You need to know what it is you're supposed to do before you can fix it, right?
And so you need to, like, you need a psycholinguist to analyze the text and see what's wrong with
it before you can, like, fix it.
You don't know how to fix it.
How am I supposed to fix something?
I don't know what's wrong with it.
And so what we did was just, that's what we did.
We figured out, well, let's, okay, we just took a bunch of contracts, had people, uh,
and we, and we encoded them for the, the, it's a bunch of features.
And so another feature that people, one of them was centrum bedding.
And so, uh, that is like basically how often a, um, a clause would, would, would intervene
between a subject and a verb, for example, that's one kind of a centrum bedding of a
clause.
Okay.
And, um, turns out they're massively centrum bedded.
Like, so I think in random contracts and in random laws, I think you get about 70% or
80, something like 70% of sentences have a centrum bedded clause in them, which is insanely
high.
If you go to any other text, it's down to 20% or something.
It's, it's, it's so much higher than the, any control you can think of, including you
think, oh, people think, oh, technical, um, academic texts.
No, people don't write centrum bedded sentences in, in technical academic texts.
I mean, they do a little bit, but much, it's, it's on the 20%, 30% realm as opposed to 70.
And so, and so there's that, and, and there's low frequency words.
And then people, oh, maybe it's passive.
People don't like the passive.
Passive, for some reason, the passive voice in English has a bad rap, and I'm not really
sure where that comes from.
Um, and, and, and there is a lot of passive in, uh, the, there's much more passive voice
in the, in the, uh, in legalese than there is in other texts.
And the passive voice accounts for some of the low frequency words.
No, no, no, no, no.
Those are separate.
Those are separate.
Oh, so passive voice sucks.
There's three, these are three.
Low frequency words sucks.
Well, sucks is different.
So these are different.
That's a judgment, I'm passive.
Yeah, yeah, yeah.
Passive, drop the judgment.
It's just like, these are frequent.
These are things which happen in legalese texts.
Then we can ask, the dependent measure is like, how well you understand those things with those
features, okay?
And so then, and it turns out the passive makes no difference.
So it has a zero effect on your comprehension ability, on your recall ability.
No, nothing at all.
That has no effect.
Your, the, the words matter a little bit.
They do, low frequency words are going to hurt you in recall and understanding.
But what really, what really hurts is the center embedding.
That kills you.
That is like, that slows people down.
That makes them, that makes them very poor at understanding.
That makes them, uh, they, they, they can't recall what was said as well, nearly as well.
And we, we did this not only on lay people, we did it on a lot of lay people.
We ran it on a hundred lawyers.
We recruited lawyers from a, from a wide range of, of, um, sort of different levels of law
firms and stuff.
And they have the same pattern.
So they also, like, why, when, when, when they did this, I did not know what happened.
I thought maybe they could process, they're used to legalese.
They can process it just as well as if it was normal.
No, no, they, they, they're much better than lay people.
So they're much, like, they can much better recall, much better understanding, but they
have the same main effects as, as, as, as lay people, as lay people, exactly the same.
So they also much prefer the non-center.
So we, we, we, oh, we constructed non-center embedded versions of each of these.
We constructed versions which have, um, higher frequency words in those places.
And we, we did, we un, un, un, un-passivized.
We turned them into active versions.
The, the passive active made no difference.
The words made a little difference.
And, uh, un-center embedding makes, makes big differences in all the populations.
Un-center embedding.
How hard is that process, by the way?
It's not very hard.
I'm so sorry, don't question.
But how hard is it to detect center embedding?
Oh, easy, easy to detect.
That's just easier to parse.
You're just looking at long dependencies, or is there a real?
You can just, you can, so there's automatic parsers for English, which are pretty good.
Very, very good.
And they can detect center embedding.
Oh, yeah, very good.
Or, I guess, nesting.
Perfectly.
Yeah, pretty much.
So you, you're not just looking for long dependencies.
You're just literally looking for center embedding.
Yeah, we are in this case, in these cases.
But long dependencies, they're highly correlated, these things, to this.
So, like, a center embedding is a big bomb you throw inside of a sentence that just blows
up the, that makes super.
Can I read a sentence for you from these things?
Sure.
I see.
I mean, this is just, like, one of the things that, this is just typical.
My eyes might glaze over in mid-sentence.
No, I understand that.
I mean, legalese is hard.
This is a go.
It goes, in the event that any payment or benefit by the company, all such payments
and benefits, including the payments and benefits under section 3a hereof, being here and after
referred to as a total payment, would be subject to the excise tax, then the cash severance
payments shall be reduced.
So that's something we pulled from a regular text, from a contract.
And the center embedded bit there is just, for some reason, there's a definition.
They throw the definition of what payments and benefits are in between the subject and
the verb.
How about don't do that?
How about put the definition somewhere else, as opposed to in the middle of the sentence?
And so that's very, very common, by the way.
That's what happens.
You just throw your definitions, you use a word, a couple words, and then you define
it, and then you continue the sentence.
Like, just don't write like that.
And you ask, so then we asked lawyers.
We thought, oh, maybe lawyers like this.
Lawyers don't like this.
They don't like this.
They don't want to write like this.
We asked them to rate materials which are with the same meaning, with un-center embedded
and center embedded, and they much preferred the un-center embedded versions.
On the comprehension, on the reading side.
Yeah, and we asked them, would you hire someone who writes like this or this?
We asked them all kinds of questions, and they always preferred the less complicated
version, all of them.
So I don't even think they want it this way.
Yeah, but how did it happen?
How did it happen?
That's a very good question.
And the answer is, I still don't know, but...
I have some theories.
Well, our best theory at the moment is that there's actually some kind of a performative
meaning in the center embedding in the style which tells you it's legalese.
We think that that's the kind of a style which tells you it's legalese.
Like, that's a reasonable guess.
And maybe it's just...
So, for instance, if you're...
Like, it's like a magic spell.
So we kind of call this the magic spell hypothesis.
So when you tell someone to put a magic spell on someone, what do you do?
People know what a magic spell is, and they do a lot of rhyming.
You know, that's kind of what people will tend to do.
They'll do rhyming, and they'll do sort of like some kind of poetry kind of thing.
Yeah, abracadabra type of thing.
Yeah, and maybe that's...
There's a syntactic sort of reflex here of a magic spell, which is center embedding.
And so that's like, oh, it's trying to like tell you this is something which is true,
which is what the goal of law is, right?
It's telling you something that we want you to believe is certainly true, right?
That's what legal contracts are trying to enforce on you, right?
And so maybe that's like a form which has...
This is like an abstract, very abstract form, centrum embedding,
which has a meaning associated with it.
Well, don't you think there's an incentive for lawyers to generate things that are hard to understand?
That was one of our working hypotheses.
We just couldn't find any evidence of that.
No, lawyers also don't understand it.
You're creating space while you...
I mean, you ask in a communist Soviet Union, the individual members,
their self-report is not going to correctly reflect what is broken about the gigantic bureaucracy
that leads to Chernobyl or something like this.
I think the incentives under which you operate are not always transparent to the members within that system.
So it just feels like a strange coincidence that there is benefit if you just zoom out and look at the system
as opposed to asking individual lawyers that making something hard to understand is going to make a lot of people money.
You're going to need a lawyer to figure that out, I guess, from the perspective of the individual.
But then that could be the performative aspect.
It could be as opposed to the incentive-driven to be complicated.
It could be performative to where we lawyers speak in this sophisticated way and you regular humans don't understand it,
so you need to hire a lawyer.
Yeah, I don't know which one it is, but it's suspicious.
Suspicious that it's hard to understand and that everybody's eyes glaze over and they don't read.
I'm suspicious as well.
I'm still suspicious.
And I hear what you're saying.
It could be kind of, you know, no individual and even average of individuals.
It could just be a few bad apples in a way which are driving the effect in some way.
Influential bad apples.
Yeah.
At the sort of, that everybody looks up to or whatever.
They're like central figures in how, you know.
But it is kind of interesting that among our hundred lawyers, they did not share that.
They didn't want this.
That's fascinating.
They really didn't like it.
And so it gave us hope.
And they weren't better than regular people at comprehending it.
Or they were on average better.
But they had the same difference.
The same difference.
Exact same difference.
So they, but I, they wanted it fixed.
So they, they also, and so that, that gave us hope that because it actually isn't very
hard to construct a material which is un-center embedded and has the same meaning, it's not
very hard to do.
Just basically in that situation, you're just putting definitions outside of the subject
verb relation in that particular example.
And that's kind of, that's pretty general.
What they're doing is just throwing stuff in there, which you didn't have to put in
there.
There's extra words involved.
Typically, you may need a few extra words sort of to refer to the things that you're
defining outside in some way.
Because if you only use it in that one sentence, then there's no reason to introduce extra,
extra terms.
But so we might have a few more words, but it'll be easier to understand.
And so, I mean, I, I have hope that now that maybe, maybe we can make legalese less, less
convoluted in this way.
So maybe the, the next president of the United States can, instead of saying generic things,
say, I ban center, center embeddings and make Ted the, the language czar of the United
States.
Eric Martinez is the guy you should really put in there.
Yeah, yeah, yeah.
I mean, but center embeddings are the, the, the bad thing to have.
That's right.
Yeah.
So if you get rid of that.
That'll do a lot of it.
That'll fix a lot.
That's fascinating.
Yeah.
That is so fascinating.
Yeah.
And it is really fascinating on many fronts that humans are just not able to deal with
this kind of thing.
And that language, because of that evolved in the way it did.
It's fascinating.
So one of the mathematical formulations you have when talking about languages communication
is this idea of noisy channels.
What's a noisy channel?
So that's about communication.
And so this is going back to Shannon.
So Shannon, Claude Shannon was a student at MIT in the 40s.
And so he wrote this very influential piece of work about communication theory or information
theory.
And he was interested in human language, actually.
He was trying to, he was interested in this problem of communication, of getting a message
from my head to your head.
And so he was concerned or interested in what was a robust way to do that.
And so that assuming we both speak the same language, we both already speak English, whatever
the language is, we speak that.
What is a way that I can say the language so that it's most likely to get the signal that
I want to you?
And so, and then the problem there in the communication is the noisy channel, is that
there's, I make, there's a lot of noise in the system.
I don't speak perfectly.
I make errors.
That's noise.
There's background noise.
You know, you know that.
Like a literal.
Literal background noise.
There is like white noise in the background or some other kind of noise.
There's some speaking going on that you're just, you're at a party.
That's background noise.
You're trying to hear someone, it's hard to understand them because there's all this
other stuff going on in the background.
And then there's noise on the communication, on the receiver side.
So that you have some problem maybe understanding me for stuff that's just internal to you in
some way.
So you've got some other problems, whatever, with understanding for whatever reasons.
Maybe you're, maybe you've had too much to drink.
You know, who knows why you're not able to pay attention to the signal.
So that's the noisy channel.
And so, so that language, if it's a communication system, we are trying to optimize in some sense
the, the passing of the message from one side to the other.
And so it, I mean, one idea is that maybe, you know, aspects of like word order, for example,
might've optimized in some way to, to make language a little more easy to be passed from
speaker to listener.
And so Shannon's the guy that did this stuff way back in the forties, you know, it's very
interesting, you know, historically he was interested in working in linguistics.
He was at MIT and he did, this was his master's thesis of all things.
You know, it's crazy how much, how much he did for his master's thesis in 1948, I think,
or 49 or something.
And, and he wanted to keep working in language and it just wasn't a popular communication
as a, as a reason, a source for what language was, wasn't popular at the time.
So Chomsky was becoming, it was moving in there.
He was, and he just wasn't able to get a handle there, I think.
And so, and so he moved to Bell Haps and worked on communication from a mathematical point
of view and was, you know, did all kinds of amazing work.
And so he's just.
More on the signal side versus like the language side.
Yeah.
Yeah.
It would have been interesting to see if you pursued the language side.
Yeah.
That's really interesting.
Yeah.
He was interested in that.
His examples in the forties are, are, are kind of like, they're like, very language
like, like things.
Yeah.
We can kind of show that there's a noisy channel process going on in when you're listening
to me, you know, you're, you can often sort of guess what I meant by what I, you know,
what you think I meant, given what I said.
And I, I mean, with respect to sort of why language looks the way it does, we might, there
might be sort of, as I said, I alluded to, there might be ways in which word orders is
somewhat optimized for, for, because of the noisy channel in some way.
I mean, that's really cool to sort of model if you don't hear certain parts of a sentence
or have some probability of missing that part.
Like how do you construct a language that's resilient to that?
That's somewhat robust to that.
Yeah.
That's the idea.
And then you're, you're kind of saying like the word order and the syntax of language,
the dependency length are all helpful.
Yeah.
Well, the dependency length is, is really about memory, right?
I think that's like about sort of what's easier or harder to produce in some way.
And these other ideas are about sort of robustness to communication.
So the problem of potential loss of loss of signal due to noise.
It's so that there may be aspects of word order, which is somewhat optimized for that.
And, and, you know, we have this one guess in that direction.
And these are kind of just so stories.
I have to be, you know, pretty frank.
They're not like, I can't show this is true.
All we can do is like, look at the current languages of the world.
This is like, we can't sort of see how languages change or anything because we've got these
snapshots of a few, you know, a hundred or a few thousand languages.
We don't have, we don't really, we can't do the right kinds of modifications to test
these things experimentally.
And so, you know, so just take that, this with a grain of salt.
Okay.
From here, this, this stuff, the dependency stuff I can, I'm much more solid on.
I'm like, here's what the lengths are and here's, and here's what's hard.
Here's what's easy.
And this is a reasonable structure.
I think I'm pretty reasonable.
Here's like, why, you know, why does the word order look the way it does is we're now into
shaky territory, but it's kind of cool.
But we're talking about, just to be clear, we're talking about maybe just actually the
sounds of communication.
Like you and I are sitting in the bar, it's very loud.
And you, you model with a noisy channel, the loudness, the noise, and we have the signal
that's coming across that, and you're saying word order might have something to do with
optimizing that.
Yes.
When there's presence of noise.
Yes.
Yeah.
I mean, it's really interesting.
I mean, to me, it's interesting how much you can load into the noisy channel.
Like how much can you bake in?
You said like, you know, cognitive load on the receiver end.
We think that those are, there's three, at least three different kinds of things going
on there.
And we probably don't want to treat them all as the same.
And so I think that you, you know, the right model, a better model of a noisy channel would
treat, would have three different sources of noise, which, because, which are background
noise, you know, speaker, speaker inherent noise and listener inherent noise.
And those are not the, those are all different things.
Sure.
But then underneath it, there's a million other subsets.
Oh yeah.
Like what?
That's true.
On the receiving, I mean, I just mentioned cognitive load on both sides.
Then there's like a speaking, a speech impediments or just everything, a worldview.
I mean, on the meaning, we start to creep into the meaning realm of like, we have different
worldviews.
Well, how about just form still though?
Like just, just what language you know, like, so how well you know the language.
And so if it's second language for you versus first language and in how maybe what other
languages, you know, these are still just form stuff.
And that's like potentially very informative and, and, you know, how old you are, these
things probably matter, right?
So like a child learning a language is, is a, you know, as a noisy representation of English
grammar, uh, you know, depending on how old they are.
So maybe when they're six, they're perfectly formed.
But, uh, you mentioned one of the things is like a way to measure the, the, a language
is learning problems.
So like, what's the correlation between everything we've been talking about and how easy it is
to learn a language?
So is, is, uh, like, uh, short dependencies correlated to ability to learn a language?
Is there some kind of, or like the dependency grammar, is there some kind of connection there?
How easy it is to learn?
Yeah.
Well, all the languages in the world's language, none is right now we know is any better than
any other with respect to sort of optimizing dependency lengths, for example, they're all
kind of do it, do it well.
They all keep low.
It's so that I think of every human language is some kind of an opposite, sort of an optimization
problem, a complex optimization problem to this communication problem.
And so they've like, they've solved it.
You know, they're just sort of noisy solutions to this problem of communication.
There's just so many ways you can do this.
So they're not optimized for learning.
They're probably less for communication.
And, and learning.
So yes, one of the factors which is, yeah, so learning is messing this up a bit.
And so, so for example, if it were just about minimizing dependency lengths, and, and that
was all that matters, you know, then we, you know, so then, then we might find grammars
which didn't have regularity in their rules, but languages always have regularity in their
rules.
So, so what I mean by that is that if, if I wanted to say something to you in the, in
the optimal way to say it was, it would really matter to me.
All that mattered was keeping the dependencies as close together as possible.
Then I, then I would have a very lax set of phrase structure or dependency rules.
I wouldn't have very many of those.
I would have very little of that.
And I would just put the words as close, the things that refer to the things that are
connected right beside each other.
But we don't do that.
Like there, like there are word order rules, right?
So they're very, and depending on the language, they're more and less strict, right?
So you speak Russian, they're less strict than English.
English is very rigid word order rules.
We order things in a very particular way.
And, and so why do we do that?
Like, that's probably not about communication.
That's probably about learning.
I mean, then we're talking about learning.
It's probably easier to learn regular, regular things, things which are very predictable and
easy to.
So, so that's, that's probably about learning is my, is our guess.
Cause that can't be about communication.
Can it be just noise?
Can it be just the, the messiness of the development of a language?
Well, if it were just a communication, then we, we should have languages which have very,
very free word order.
And we don't have that.
We have freer, but not free.
Like there's always.
Well, no, but what I mean by noise is like cultural, like sticky cultural things.
Like the way, the way you communicate, just there's a stickiness to it, that it's, it's
an imperfect, it's a noisy, it's stochastic.
Yeah.
The, the, the function over which you're optimizing is very noisy.
Yeah.
So, because I don't, it feels weird to say that learning is part of the objective function
because some languages are way harder to learn than others.
Right.
Or is that, that's not true.
That's interesting.
I mean, that's the public perception, right?
Yes, that's true.
For a second language.
For a second language.
But that depends on what you started with, right?
So, so it's, it really depends on how close that second language is to the first language
you've got.
And so, yes, it's very, very hard to learn Arabic if you've started with English or it's
harder to, you know, harder to learn Japanese or, or if you've started with English or Chinese,
I think is the worst in the, there's like Defense Language Institute in the, in the United
States has like a list of, of, of how hard it is to learn what language from English.
I think Chinese is the worst.
But this is the second language, you're saying babies don't care.
No.
There's no evidence that there's anything harder, easier about any baby, any language learned
like, by three or four, they speak that language.
And so there's no evidence of any, anything harder, easier about any human language.
They're all kind of equal.
To what degree is language, this is returning to Chomsky a little bit, is, is innate.
You said that for Chomsky, he used the idea that language is, some aspects of language
are innate to explain away certain things that are observed.
But to, how much are we born with language at the core of our mind, brain?
I mean, I, I, you know, the answer is I don't know, of course, but the, I mean, I, I like
to, I'm an engineer at heart, I guess, and I sort of think it's fine to postulate that
a lot of it's learned.
And so I, I'm guessing that a lot of it's learned.
So I think the reason Chomsky went with innateness is because he, he, he hypothesized movement
in his grammar.
He was interested in grammar and movement's hard to learn.
I think he's right.
Movement is a hard, it's a hard thing to learn, to learn these two things together and how
they interact.
And there's like a lot of ways in which you might generate exactly the same sentences
and it's like really hard.
And so he's like, oh, I guess it's learned.
Sorry, so I guess it's not learned, it's innate.
And if you just throw out the movement and just think about that in a different way, you know,
then you, you get some messiness, but the messiness is human language, which it actually
fits better.
It's a, that messiness isn't a problem.
It's actually a, it's a valuable asset of, of, of the theory.
And so, so I think I don't really see a reason to postulate much innate structure.
And that's kind of why I think these large language models are learning so well is because
I think you can learn the form, the forms of human language from the input.
I think that's like, it's likely to be true.
So that part of the brain that lights up when you're doing all the comprehension, that
could be learned.
That could be just, you don't need, you don't need any.
It doesn't have to be innate.
So like lots of stuff is modular in the brain that's learned.
It doesn't have to, you know, so there's something called the visual word form area in the back.
And so it's in the back of your head near the, you know, the visual cortex.
And that is very specialized language, sorry, very specialized brain area, which does visual
word processing if you read, if you're a reader.
Okay.
If you don't read, you don't have it.
Okay.
Guess what?
You spend some time learning to read and you develop that, that brain area, which does exactly
that.
And so these, the modularization is not evidence for innateness.
So the modularization of a language area doesn't mean we're born with it.
We could have easily learned that.
We might've been born with it.
We just don't know at this point.
We might very well have been born with this left lateralized area.
I mean, there's like a lot of other interesting components here, features of this kind of
argument.
So some people get a stroke or something goes really wrong on the left side, where the language
area would be.
And that, and that isn't there.
It's not, not available.
And it develops just fine on the right.
So it's no, so it's not about the left.
It goes to the left.
Like, this is a very interesting question.
It's like, why is the, why are any of the brain areas the way that they are?
And how, how, how did they come to be that way?
And, uh, you know, there's these natural experiments, which happen where people get these, you know,
strange events in their brains at very young ages, which wipe out sections of their
brain and, and they behave totally normally and no one knows anything was wrong.
And we find out later, because they happen to be accidentally scanned for some reason.
It's like, what, what happened to your left hemisphere?
It's missing.
There's not many people have missed their whole left hemisphere, but they'll be missing
some other section of their left or their right.
And they behave absolutely normally, would never know.
So that's like a very interesting, you know, current research.
You know, this is another project that this person, M Fedorenko, is working on.
And she's got all these people contacting her because she's scanned some people who have
been missing sections.
One person missing, missed a section of her brain and was scanned in her lab.
And she, and she happened to be a writer for the New York Times.
And there was an article in the New York Times about, about the, uh, just about the scanning
procedure and, and about what might be learned about, by sort of the general process of MRI
in language, in not necessarily language.
And, and because she's writing for the New York Times, then she, there's all these people
started writing to her who also have similar, similar kinds of deficits because they've
been, you know, accidentally, you know, scanned for some reason and, uh, and found out they're
missing some section.
And they, and they say, they volunteer to be scanned.
These are natural experiments.
Natural experiments.
They're kind of messy, but natural experiments.
It's kind of cool.
She calls them interesting brains.
The first few hours, days, months of human life are fascinating.
It's like, uh, well, inside the womb, actually like that development, that machinery, whatever
that is, seems to create powerful humans that are able to speak, comprehend, think all that
kind of stuff, no matter what happens, not no matter what, but robust to the different
ways that, um, um, the, the, the brain might be damaged and so on.
That's, that's really, that's really interesting.
But, uh, what, what would Chomsky say about the fact, the thing you're saying now that language
is, is, seems to be happening separate from thought?
Because as far as I understand, maybe you can correct me, he thought that language underpins
a thought.
He thinks so.
I don't know what he'd say.
He would be surprised.
Because for him, the idea is that language is a sort of the foundation of thought.
That's right.
Absolutely.
And it's pretty, uh, mind-blowing to think that it could be completely separate from thought.
That's right.
But, so, you know, he's basically a philosopher, philosopher of language in a way, thinking
about these things.
It's a fine thought.
You can't test it in his methods.
You can't do a thought experiment to figure that out.
You need a scanner.
You need brain damaged people.
You need something.
You need ways to measure that.
And that's what, you know, fMRI offers as a, and, and, you know, patients are a little
messier.
FMRI is pretty unambiguous, I'd say.
It's like very unambiguous.
There's no way to say that the language network is doing any of these tasks.
There's, like, you should look at those data.
It's like, there's no chance that you can say that they're, those networks are overlapping.
They're not overlapping.
They're just like completely different.
And, and so, uh, you know, so the, the, you know, you can always make, you know, it's only
two people, it's four people or something for the, for the patients.
And there's something special about them.
We don't know, but these are just random people and, and with lots of them and you find
always the same effects and it's very robust, I'd say.
What's a fascinating effect.
Uh, what's the, you mentioned Bolivia.
Uh, what's the connection between culture and language?
Uh, you, you've, uh, you've also mentioned that, you know, much of our study of language
comes from, uh, W-E-I-R-D, weird people, Western educated, industrialized, rich, and democratic.
So when you study like remote cultures, such as, uh, around the Amazon jungle, what can you
learn about language?
So, uh, that term weird is, uh, from Joe Henrich.
He's at, uh, Harvard.
He's a Harvard evolutionary biologist.
And so he works on lots of different topics.
And, uh, he basically was pushing that observation that we should be careful about the inferences
we want to make when we're talking in psychology or social, uh, yeah, mostly in psychology, I
guess, about humans.
If we're talking about, you know, undergrads at MIT and Harvard, those aren't the same,
right?
These aren't the same things.
And so if you want to make inferences about language, for instance, you, there's a lot
of very, a lot of other kinds of languages in the world than English and French and Chinese,
you know?
And so maybe in, for, for, for language, we care about how culture, because cultures can
be very, I mean, of course, English and Chinese cultures are very different, but, you know,
hunter-gatherers are much more different in, in some ways.
And so, you know, if culture has an effect on what language is, then we kind of want to
look there as well as looking.
It's not like the industrialized cultures aren't interesting.
Of course they are.
But we want to look at non-industrialized cultures as well.
And so I worked with two.
I've worked with the Chimani, which are in, um, Bolivia and Amazon, both in the Amazon,
these cases.
And there are so-called farmer foragers, which is not hunter-gatherers.
Um, it's sort of one up from hunter-gatherers in that they do a little bit of farming as
well.
A lot of hunting as well, but a little bit of farming and the, and the kind of farming
they do is the kind of farming that I might do if I ever were to grow like tomatoes or
something in my backyard.
It's, it's that, it's not like, so it's not like big field farming.
It's just a farming for a family, a few things you do that.
And so that's what, that's the kind of farming they do.
Um, and, uh, the other group I've worked with are the Piraha, which are in, uh, also in
the Amazon and happened to be in Brazil.
And that's with, um, a guy called Dan Everett, who is a, um, linguist anthropologist who actually
lived and worked in the, I mean, he was a missionary actually, initially back in the
seventies, working with, trying to translate languages so they could teach them the Bible,
teach them Christianity.
What can you say about that?
Yeah, so the two groups I've worked with, the Chimani and the Piraha are both isolate
languages, meaning there's no known connected languages at all.
They're just like on their own.
Yeah, there's a lot of those.
And, and most of the isolates occur in, in the, in the Amazon or in Papua New Guinea and
these, these places where the world has sort of stayed still for a long enough.
And there have, like, so there, there aren't earthquakes, there aren't, um, uh, well, certainly
no earthquakes in the Amazon jungle and, and, and, uh, the climate isn't bad.
So you don't have droughts.
And so, you know, in Africa, you've got a lot of moving of people because there's drought
problems.
And so, so they get a lot of language contact when you have, when people have to, if you've
got to move because you're, you've got no water, then you've got to get going.
And then, uh, then you run into contact with other, other tribes, other groups in, in the
Amazon, that's not the case.
And so people can stay there for hundreds and hundreds and probably thousands of years,
I guess.
And so these groups have, and the Chimani and the Piraha are both isolates in that, and
they can just, I guess they've just lived there for ages and ages with minimal contact
with other outside groups.
Um, and so, I, I mean, I'm interested in them because they are, I mean, I, you know, I, in
these cases, I'm interested in their words.
I mean, I would love to study their syntax, their orders of words, but I'm mostly just interested
in how languages, you know, are connected to, um, their, their cultures in this way.
And so with the Piraha, sort of most interesting, I was working, I was working on number there,
number information.
And so the, the basic idea is I think language is invented, right?
That's what I get from the words here is that I think language is invented.
We talked about color earlier.
It's the same idea.
So that what you need to talk about with someone else is what you're going to invent words for.
Okay.
And so we invent labels for colors that I need, not that I, that I can see, but that,
but the things I need to tell you about so that I can get objects from you or get you
to give me the right objects.
And I just don't need a word for teal or, or a word for aquamarine in, in the, in the
Amazon jungle for the most part, because I don't have two things which differ on those
colors.
I just don't have that.
And so, and so numbers are really another fascinating source of information here where
you might, you know, naively, I certainly thought that all humans would have words for exact
counting and the Piraha don't.
Okay.
So they don't have any words for even one.
There's not a word for one in their, in their language.
And so there's certainly not a word for two, three, or four.
So, so that kind of blows people's minds off.
Yeah.
That's blowing my mind.
That's pretty weird.
How are you, how are you going to ask, I want two of those?
You just don't.
And so that's just not a thing you can possibly ask in the Piraha.
It's not possible.
That is, there's no words for that.
So here's how we found this out.
Okay.
So, so it was thought to be a one, two, many language.
There are three words for quantifiers for, for, for, for sets, but, um, and people had
thought that those meant one, two, and many, uh, but what they really mean is few, some,
and many.
Many is correct.
It's few, some, and many.
And so, and so the way we figured this out, uh, and, uh, this is kind of cool is that,
um, we gave people, uh, we had a set of objects.
Okay.
And these were having to be spools of thread.
It doesn't really matter what they are.
Identical objects.
And, and, and, and when I sort of start off here, I just give, you know, give you one
of those and say, what's that?
Okay.
So you're a piano hall speaker and you tell me what it is.
And, and then I give you two and say, what's that?
And, and nothing's changing in the set except for the number.
Okay.
And then I just ask you to label these things.
And we just do this for a bunch of different people.
And it, and frankly, it's a, I, I did this task and it's a weird, it's a little bit weird.
So you say, say the word that they thought that we thought was one, it's few, but for the
first one, and then maybe they say few, or maybe they say some for the second, and then
for the third or the fourth, they start using the word many for the set.
And then five, six, seven, eight, I go all the way to 10 and it's always the same word.
And they look at me like I'm stupid because they told me what the word was for six, seven,
eight, and I'm going to continue asking them at nine and 10.
I'm like, I'm sorry.
I just, I just, they understand that I want to know their language.
That's the point of the task is like, I'm trying to learn their language.
And so that's okay.
But it does seem like I'm a little slow because I, they already told me what the word for many
was, five, six, seven, and I keep asking.
So it's a little funny to do this task over and over.
We did this with a guy called, Dan was the, our translator.
He's the only one who really speaks Piraha fluently.
He's a good bilingual for a bunch of languages, but also in English and Piraha.
And then a guy called Mike Frank was also a student with me down there.
He, he and I did these things.
And so you do that.
Okay.
And everyone does the same thing.
All, all, all, you know, we asked like 10 people and they all do exactly the same labeling
for one up.
And then we just do the same thing down on like random order.
Actually, we do some of them up, some of them down first.
Okay.
And so we do, instead of one to 10, we do 10 down to one.
And so, so I give them 10, nine, at eight, they start saying the word for some.
And then at down, when you get to four, everyone is saying the word for few, which we thought
was one.
So it's like, it's the context determined what word, what, what, what that quantifier
they used was.
And so it's not a count word.
They're not, they're not count words.
They're, they're just approximate words.
And they're going to be noisy when you interview a bunch of people, the, what the definition
of few, and there's going to be a threshold in the context.
Yeah.
Yeah.
Yeah.
I don't know what that means.
That's, that's going to be 10 on the context.
I think it's true in English too, right?
If you ask an English person what a few is, I mean, that's dependent completely on the
context.
And that might actually be at first hard to discover.
Yeah.
Because for a lot of people, the jump from one to two will be few, right?
So it's a jump.
Yeah.
It might be, it might still be there.
Yeah.
I mean, that's fascinating.
That's fascinating that numbers don't present themselves.
Yeah.
So the words aren't there.
And then, and so then we do these other things.
Well, if, if they don't have the words, can they do exact matching kinds of tasks?
Can they even do those tasks?
And, and, and the answer is sort of yes and no.
And so, yes, they can do them.
So here's the tasks that we did.
We, we put out those spools of thread again.
Okay.
So I'm going to put like three out here and then we gave them some objects and those
happened to be uninflated red balloons.
It doesn't really matter what they are.
It's just, they're a bunch of exactly the same thing.
And it was easy to put down right next to these spools of thread.
And so then I put out three of these and your task was to just put one against each of my
three things and they could do that perfectly.
So, I mean, I would actually do that.
It was a very easy task to explain to them because I have, I did this with this guy,
Mike Frank, and he would be my, I'd be the experimenter telling him to do this and showing
him to do this.
And then we just like, just do what he did.
You'll copy him.
All we had to, I didn't have to speak pure aha, except for know what copy him, like do
what he did is like all we had to be able to say.
And then they would do that just perfectly.
And so we'd move it up.
We'd do some sort of random number of items up to 10 and they basically do perfectly on
that.
They never get that wrong.
I mean, that's not a counting task, right?
That is just a match.
You just put one against, it doesn't matter how many, I don't need to know how many there
are there to do that correctly.
And they would make mistakes, but very, very few and no more than MIT undergrads.
I'm just going to say, like there's no, these are low stakes.
So, you know, you make mistakes.
Counting is not required to complete the matching.
That's right.
Not, not at all.
Okay.
And so, and so that's our control.
And this guy had gone down there before and said that they couldn't do this task, but
I just don't know what he did wrong there because they can do this task perfectly well.
And, you know, I can train my dog to do this task.
So of course they can do this task.
And so, you know, it's not a hard task, but the other task that was sort of more interesting
is like, so then we do a bunch of tasks where you need some way to encode the set.
And so like one of them is just, I just put a opaque sheet in front of the things.
I put down a bunch, a set of these things and I put an opaque sheet down.
And so you can't see them anymore.
And I tell you, do the same thing you were doing before, right?
You know, and it's easy if it's two or three, it's very easy.
But if I don't have the words for eight, it's a little harder, like maybe, you know, with
practice, well, no.
Because you have to count.
For us, it's easy because we just count them.
It's just so easy to count them.
But they don't, they can't count them because they don't count.
They don't have words for this thing.
And so they would do approximate.
It's totally fascinating.
So they would get them approximately right, you know, after four or five, you know, because
you can, basically you always get four right, three or four, that looks, that's something
we can visually see.
But after that, you kind of have, it's an approximate number.
And so then, and there's a bunch of tasks we did and they all failed as, I mean, failed.
They did approximate after five on all those tasks.
And it kind of shows that the words, you kind of need the words, you know, to be able to
do these kinds of tasks.
There's a little bit of a chicken and egg thing there, because if you don't have the words,
then maybe they'll limit you in the kind of, like a little baby Einstein there, won't be
able to come up with a counting task.
You know what I mean?
Like the ability to count enables you to come up with interesting things probably.
So yes, you develop counting because you need it.
But then once you have counting, you can probably come up with a bunch of different inventions.
Like how to, I don't know, what kind of thing.
They do matching really well for building purposes, building some kind of hut or something
like this.
So it's interesting that language is a limiter on what you're able to do.
Yeah.
Here's language is just, is the words.
Here is the words.
Like the words for exact count is the limiting factor here.
They just don't have them.
Yeah.
Yeah.
Well, that's what I mean.
Yeah.
The, the, that limit is also a limit on the society of what they're able to build.
That's going to be true.
Yeah.
So it's probably, I mean, we don't know, this is one of those problems with the snapshot
of just current languages is that we don't know what causes a culture to discover slash
invent accounting system.
But the hypothesis is the guess out there is something to do with farming.
So if you have a bunch of goats and you want to keep track of them and you have saved 17
goats and you go to bed at night and you get up in the morning, boy, it's easier to have
a count system to do that.
You know, if I have, that's an abstract, abstraction over a set.
So they don't have, like people often ask me when I talk, tell them about this kind of
work and they say, well, don't these, don't they have kids?
Don't they have a lot of children?
I'm like, yeah, they have a lot of children.
And they do, they often have families of three or four or five kids and they go, well, don't
they need the numbers to keep track of their kids?
And I always ask the person who says this, like, do you have children?
And the answer is always no, because that's not how you keep track of your kids.
You care about their identities.
It's very important to me when I go, I think I have five children.
It's, it's, it doesn't matter which, yeah, it matters which five.
It's like, if you replaced one with someone else, I would, I would care.
A goat, maybe not, right?
That's the kind of point.
It's an abstraction.
Something that looks very similar to the one wouldn't matter to me, probably.
But if you care about goats, you're going to know them actually individually also.
Yeah, you will.
I mean, cows and goats, if it's a source of food and milk and all that kind of stuff,
you're going to actually really do.
You're absolutely right.
But, but I'm saying it is an abstraction such that you don't have to care about their
identities to do this thing fast.
That's, that's the hypothesis, not mine.
From anthropologists as they're guessing about where words for counting came from is from
farming, maybe.
Yeah.
Do you have a sense why universal languages like Esperanto have not taken off?
Like, why do we have all these different languages?
Yeah, yeah.
Well, my guess is that the function of a language is to do something in a community.
And, and, I mean, unless there's some function to that language in the community, it's, it's
not going to survive.
It's not going to be useful.
So here's a great example.
So what I'm, like, language death is super common, okay?
Languages are dying all around the world.
And here's how, here's why they're dying.
And it's like, yeah, I see this in, you know, in, it's not happening right now in either
the Chimani or the, or the Piraham, but it probably will.
And so there's a neighboring group called Mositan, which is, I said that it's a isolate.
It's actually, there's a dual.
There's two of them, okay?
So it's actually, there's two languages which are really close, which are Mositan and Chimani,
which are unrelated to anything else.
And Mositan is unlike Chimani in that it has a lot of contact with Spanish and it's dying.
So that language is dying.
The reason it's dying is there's not a lot of value for the local people in their native
language.
So there's much more value in knowing Spanish, like, because they want to feed their families.
And how do you feed your family?
You learn Spanish so you can make money, so you can get a job and do these things and
then you can, and then you make money.
And so they want Spanish things.
They want, and so, so Mositan is in danger and is dying.
And that's normal.
And so basically the problem is that people, the reason we learn language is to communicate
and we need to, we use it to make money and to do whatever it is to feed our families.
And if that's not happening, then it won't take off.
It's not like a game or something.
This is like something we use, like, why is English so popular?
It's not because it's an easy language to learn.
Maybe it is.
I don't really know.
But that's not why it's popular.
But because it's a gigantic, the United States is a gigantic economy and therefore-
Yeah, yeah, it's big economies that do this.
It's all it is.
It's all about money and that's what, and so, you know, there's a motivation to learn
Mandarin.
There's a motivation to learn Spanish.
There's a motivation to learn English.
These languages are very valuable to know because there's so, so many speakers all over
the world.
That's fascinating.
There's less of a value economically.
It's like kind of what drives this.
It's not about, it's not a, you know, it's not just for fun.
I mean, there are these groups that do want to learn language just for language's sake
and they want, and then, and there's something, you know, to that.
But those are rare, those are rarities in general.
Those are a few small groups that do that.
Not, most people don't do that.
Well, if that was a primary driver, then everybody was speaking English or speaking one language.
There's also a tension.
That's happening.
Not, well, well.
We're moving towards fewer and fewer languages.
We are.
I wonder if, you're right.
Maybe, maybe, you know, this is slow, but maybe that's where we're moving.
But there is a tension.
You're saying a language that the fringes, but if you look at geopolitics and superpowers,
it does seem that there's another thing in tension, which is a language is a national
identity sometimes.
Oh, yeah.
For certain nations.
I mean, that's the war in Ukraine, language, Ukrainian language is a symbol of that war
in many ways, like a country fighting for its own identity.
So it's not merely the convenience.
I mean, those two things are a tension, is the convenience of trade and the economics
and be able to communicate with neighboring countries and trade more efficiently with neighboring
countries, all that kind of stuff, but also identity of the group.
That's right.
I completely agree.
Because language is the way, for every community, like dialects that emerge are a kind of identity
for people.
Yeah.
And sometimes a way for people to say F you to the more powerful people.
Yeah.
And it's interesting.
So in that way, language can't be used as that tool.
Yeah, I completely agree.
And there's a lot of work to try to create that identity so people want to do that.
You know, as a cognitive scientist and language expert, I hope that continues because I don't
want languages to die.
I want languages to survive because they're so interesting for so many reasons.
But I mean, I find them fascinating just for the language part, but I think there's a lot
of connections to culture as well, which is also very important.
Do you have hope for machine translation that can break down the barriers of language?
So while all these different diverse languages exist, I guess there's many ways of asking
this question, but basically how hard is it to translate in an automated way from one
language to another?
There's going to be cases where it's going to be really hard, right?
So there are concepts that are in one language and not in another.
Like the most extreme kinds of cases are these cases of number information.
So good luck translating a lot of English into Piraha.
It's just impossible.
There's no way to do it because there are no words for these concepts that we're talking
about.
There's probably the flip side, right?
There's probably stuff in Piraha which is going to be hard to translate into English on
the other side.
And so I just don't know what those concepts are.
I mean, you know, the space, the world space is a little, is different from my world space.
And so I don't know what, like, so that the things they talk about, things are, you know,
it's going to have to do with their life as opposed to, you know, my industrial life, which
is going to be different.
And so there's going to be problems like that always.
You know, there's like, it's not, maybe it's not so bad in the case of some of these spaces
and maybe it's going to be harder than others.
And so it's pretty bad in number.
It's like, you know, extreme, I'd say, in the number space, you know, exact number space.
But in the color dimension, right?
So that's not so bad.
There's, I mean, but it's a problem that you don't have ways to talk about the concepts.
And there might be entire concepts that are missing.
So to you, it's more about the space of concept versus the space of form.
Like form, you can probably map.
Yes.
Yeah.
But so you were talking earlier about translation and about how translations, you know, there's
good and bad translations.
I mean, now we're talking about translations of form, right?
So what makes writing good, right?
There's a music to the form.
Right.
It's not just the content.
It's, you know, it's how it's written.
And translating that, I, you know, I, you know, that's, that sounds difficult.
So we should, we should say that there is like, I don't hesitate to say meaning, but there's
a music and a rhythm to the form when you look at the broad picture, like the difference
between Dostoevsky and Tolstoy or Hemingway, Bukowski, James Joyce, like I mentioned, there's
a beat to it.
There's an edge to it that it's like is in the form.
We can probably get measures of those.
Yeah.
I, I, I don't know.
That's interesting.
I'm optimistic that we could get measures of those things.
And so maybe that's mappable.
Translatable.
I don't know.
I don't know though.
I have not worked on that.
I would love to see.
That sounds totally fascinating.
Translation to, I mean, Hemingway is probably the lowest, I would love to do, see different
authors, but the average per sentence dependency length for Hemingway is probably the shortest.
Huh, huh.
That's your sense, huh?
It's simple sentences with short, short, yeah, yeah, yeah, yeah, yeah.
I mean, that's when, if you have really long sentences, even if they don't have center
embedding, like.
They can have longer connections, yeah.
They can have longer connections.
They don't have to, right?
You can't have a long, long sentence with a bunch of local words, yeah.
Yeah.
But it is much more likely to have the possibility of long dependencies with long sentences, yeah.
I met a guy named Azaraskin who does a lot of cool stuff, really brilliant, works with
Tristan Harris and a bunch of stuff.
But he was talking to me about communicating with animals.
He co-founded Earth Species Project where you're trying to find the common language between
whales, crows, and humans.
And he was saying that there is a, there's a lot of promising work that even though the
signals are very different.
Right.
Like the actual, like, if you have embeddings of the languages, they're actually trying to
communicate similar type things.
And is there something you can comment on that?
Like where, is there promise to that in everything you've seen in different cultures, especially
like remote cultures, that this is a possibility?
Or no, that we can talk to whales?
I would say yes.
I think it's not crazy at all.
I think it's quite reasonable.
There's this sort of weird view, well, odd view, I think, that to think that human language
is somehow special.
I mean, it is, maybe it is.
We can certainly do more than any of the other species, you know, and so, and maybe, maybe
our language system is part of that.
It's possible.
But people do, have often talked about how human, like Chomsky, in fact, has talked about
how human only, only human language has, you know, this, you know, this, this compositionality
thing that he thinks is sort of key in language.
And it's, the problem with that argument is he doesn't speak whale.
And he doesn't speak crow and he doesn't speak monkey, you know, he's like, they say things
like, well, they're making a bunch of grunts and squeaks.
And, and, and that, the reasoning is like, that's bad reasoning.
Like, you know, I'm pretty sure if you asked a whale what we're saying, they'd say, well,
I'm making a bunch of weird noises.
And so it's like, this is a very odd reasoning to, to be making that human language is special
because we're the only ones who have human language.
I'm like, well, we don't know what those other, we just don't, we can't talk to them
yet.
And so there's probably a signal in there and it might very well be something complicated
like human language.
I mean, sure, with a small brain in, in, in lower, in lower species, there's probably
not a very good communication system, but in these higher, higher species where you have,
you know, what seems to be, you know, abilities to communicate something, there might very well
be a lot more signal there than we're, than we might have otherwise thought.
But, but also if we have a lot of intellectual humility here, there's somebody formerly from
MIT, Neri Oxman, who I admire very much, has talked a lot about, has worked on communicating
with plants.
So like, yes, the signal there is even less than, but like, it's not out of the realm of
possibility that all nature has a way of communicating and it's a very different language, but they do
develop a kind of language through the chemistry, through some way of communicating with each
other.
And if you have enough humility about that possibility, I think you can, I think it would
be a very interesting, in a few decades, maybe centuries, hopefully not, a humbling possibility
of being able to communicate, not just between humans effectively, but between all of living
things on earth.
Well, I mean, I think some of them are not going to have much interesting to say, but
some of them will.
We don't know.
We certainly don't know.
I think.
I, I think if we're humble, there could be some interesting trees out there.
Well, they're probably talking to other trees, right?
They're not talking to us.
And so to the extent they're talking, they're saying something interesting to some other,
you know, you know, conspecific as opposed to us, right?
And so there probably is, there may be some signal there.
I, I, you know, so there are people out there, actually it's pretty common to say that language,
that human language is special and different from any other animal communication system.
And I, I, I just, I just don't think the evidence is there for that claim.
I think it's not obvious.
You know, I, we just don't know what, what, because we, we, we don't speak these other
communication systems until we get better.
You know, I, I do think there's, there are people working on that, as you pointed out,
the people working on whale speak, for instance, like that's really fascinating.
Let me ask you a wild out there sci-fi question.
If we make contact with an intelligent alien civilization and you get to meet them,
how hard do you think, like how surprised would you be about their way of communicating?
Do you think it would be recognizable?
Maybe there's some parallels here to when you go to the remote tribes.
I mean, I would want Dan Everett with me.
He is like amazing at learning foreign languages.
And so he, like, this is an amazing feat, right?
To be able to go, this is a language, Piraha, which has no translators before him.
I mean, there were, he was a missionary.
Well, there was a guy that had been there before, but he wasn't very good.
And so he learned the language far better than anyone else had learned before him.
He's like good at, he's just a, he's a very social person.
I think that's a big part of it is being able to interact.
So I don't know, it kind of depends on these, this species from outer space, how much they
want to talk to us.
Is there something you can say about the process he follows?
Like what, how do you show up to a tribe and socialize?
I mean, I guess colors and counting is one of the most basic things to figure out.
Yeah.
You start that.
You actually start with like objects.
Yes.
And just say, you know, just throw a stick down and say stick.
And then you say, what do you call this?
And they do this feature.
And then they'll say the word, whatever.
And he says, the standard thing to do is to throw two sticks, two sticks.
And then, you know, he learned pretty quick that there weren't any count words in this
language because they didn't know this wasn't interesting.
I mean, it was kind of weird.
They'd say some or something, the same word over and over again.
But that is a standard thing.
You just like try to, but you have to be pretty out there socially, like willing to
talk to random people, which these are, you know, really very different people from you.
And he was, and he's, he's very social.
And so I think that's a big part of this is like, that's how, you know, a lot of people
know a lot of languages is they're willing to talk to other people.
That's a tough one where you just show up knowing nothing.
Yeah.
Oh God.
That's a beautiful, it's beautiful that humans are able to connect in that way.
Yeah.
Yeah.
You've had an incredible career exploring this fascinating topic.
What advice would you give to young people about how to have a career like that or a
life that they can be proud of?
When you see something interesting, just go and do it.
Like I do, I do that.
Like that's something I do, which is kind of unusual for most people.
So like when I saw the Piraha, like if Piraha was available to go and visit, I was like, yes,
yes, I'll go.
And then when we couldn't go back, we had some trouble with the Brazilian,
Brazilian government.
There's some corrupt people there.
It was very difficult to get, go back in there.
And so I was like, all right, I got to find another group.
And so we searched around and we were able to find the Chimani because I wanted to keep
working on this kind of problem.
And so we found the Chimani and just go there.
I didn't really have, we didn't have contact.
We had a little bit of contact and brought someone.
And that was, you know, we just, you just kind of just try things.
I say it's like a lot of that's just like ambition, just try to do something that other
people haven't done.
Just give it a shot is what I, I mean, I do that all the time.
I don't know.
I love it.
And I love the fact that your pursuit of fun has landed you here talking to me.
This was an incredible conversation that you're, you're, you're just a fascinating
human being.
Thank you for taking a journey through human language with me today.
This is awesome.
Thank you very much, Alexis.
It's been a pleasure.
Thanks for listening to this conversation with Edward Gibson to support this podcast.
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And now let me leave you with some words from Wittgenstein.
The limits of my language mean the limits of my world.
Thank you for listening and hope to see you next time.