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- [male] We are the paradoxical ape, bi-pedal, naked, large brain, long the

master of fire, tools, and language, but still trying to understand ourselves.

Aware that death is inevitable, yet filled with optimism. We grow up slowly. We hand

down knowledge. We empathize and deceive. We shape the future from our shared

understanding of the past. CARTA brings together experts from diverse disciplines

to exchange insights on who we are and how we got here. An exploration made possible

by the generosity of humans like you.

♪ [music] ♪

- [Evelina] Thanks very much for having me here. So today, I will tell you about the

language system and some of its properties that may bear on the question of how this

system came about. I will begin by defining the scope of my inquiry, because

people mean many different things by language. I will then present you some

evidence, suggesting that our language system is highly specialized for language.

Finally I will speculate a bit on the evolutionary origins of language.

So linguistic input comes in through our ears or our eyes. Once it's been analyzed

perceptually, we interpret it by linking the incoming representations toward our

stored language knowledge. This knowledge, of course, is also used for generating

utterances during language production. Once we've generated an utterance in our

heads, we can send it down to our articulation system. The part that I focus

on is this kind of high-level component of language processing.

This cartoon shows you the approximate locations of the brain regions that

support speech perception, shown in yellow, visual letter and word recognition

in green. This region is known as the visual word-form area. Articulation in

pink and high-level language processing in red.

What differentiates the high-level language processing regions from the

perceptual and articulation regions is that they're sensitive to the

meaningfulness of the signal. So for example, the speech regions will respond

just as strongly to foreign speech as they respond to meaningful speech in our own

language. The visual word-form area responds just as much to a string of

consonants as it does to real words. The articulation regions can be driven to a

full extent by people producing meaningless sequences of syllables.

But in contrast, the high-level language-processing system or network, or

sometimes I refer to it just as the language network for short, cares deeply

about whether the linguistics signal is meaningful or not.

In fact, the easiest way to find this system in the brain is to contrast

responses to meaningful language stimuli, like words, phrases, or sentences. Some

control conditions like linguistically degraded stimuli.

The contrast I use most frequently is between sentences and sequences of

non-words. A key methodological innovation that laid the foundation for much of my

work was the development of tools that enable us to define the regions of the

language network, functionally at the individual subject level, using contrasts

like these. Here, I'm showing you sample language regions in three individual

brains. This so-called functional localization approach has two advantages.

One, it circumvents the need to average brains together, which is what's done in

the common approach, and it's a very difficult thing to do because brains are

quite different across people. Instead, in this approach, we can just average the

signals that we extract from these key regions of interest.

The second advantage is that it allows us to establish a cumulative research

enterprise, which I think we all agree is important in science, because comparing

results across studies and labs is quite straightforward if we're confident that

we're referring to the same brain regions across different studies. This is just

hard or impossible to do in the traditional approach, which relies on very

coarse anatomical landmarks, like the inferior frontal gyrus or the superior

temporal sulcus, which span many centimeters of cortex and are just not at

the right level of analysis. So what drives me and my work is the

desire to understand the nature of our language knowledge and the computations

that mediate language comprehension and production. However, these questions are

hard, especially given the lack of animal models for language. So for now, I settle

on more tractable questions. For example, one, what is the relationship between the

language system and the rest of human cognition? Language didn't evolve, and it

doesn't exist in isolation from other evolutionarily older systems, which

include the memory and attention mechanisms, the visual and the motor

systems, the system that supports social cognition, and so on. That means that we

just can't study language as an isolated system. A lot of my research effort is

aimed at trying to figure out how language fits with the rest of our mind and brain.

The second question delves inside the language system, asking, "What does its

internal architecture look like?" It encompasses questions like, "What are the

component parts of the language system? And what is the division of labor among

them, in space and time?" Of course, both of those questions

ultimately should constrain the space of possibilities for how language actually

works. So they place constraints on both the data structures that underlie language

and the computations that are likely performed by the regions of the system.

Today, I focus on the first of these questions. Okay. So now onto some

evidence. So the relationship between language and the rest of the mind and

brain has been long debated, and the literature actually is quite abundant with

claims that language makes use of the same machinery that we use for performing other

cognitive tasks, including arithmetic processing, various kinds of executive

function tasks, perceiving music, perceiving actions, abstract conceptual

processing, and so on. I will argue that these claims are not

supported by the evidence. Two kinds of evidence speak to the relationship between

language and other cognitive systems. There is brain-imaging studies,

brain-imaging evidence, and investigations of patients with brain damage. In fMRI

studies, we do something very simple. We find our regions that seem to respond a

lot to language, and then we ask how do they respond to other various

non-linguistic tasks. If they don't show much of a response, then we can conclude

that these regions are not engaged during those tasks. In the patient studies, we

can evaluate non-linguistic abilities in individuals who don't have a functioning

language system. If they perform well, we can conclude that the language system is

not necessary for performing those various non-linguistic tasks. So starting with the

fMRI evidence, I will show you responses in the language regions to arithmetic,

executive function tasks, and music perception today. So here are two sample

regions, the regions of the inferior frontal cortex around Broca's area and the

region in the posterior middle temporal gyrus, but the rest of the regions of this

network look similar in their profiles. The region on the top is kind of in and

around this region known as Broca's area, except I don't use that term because I

don't think it's a very useful term. In black and gray, I show you the responses

to the two localizer conditions, sentences and non-words. These are estimated in data

that's not used for defining these regions. So we divide the data in half,

use half of the data to define the regions and the other half to quantify their

responses. I will now show you how these regions respond when people are asked to

do some simple arithmetic additions, perform a series of executive function

tasks, like for example, hold a set of locations in spacial memory, spacial

locations in working memory, or perform this classic flanker task, or listen to

various musical stimuli. For arithmetic and various executive tasks, we included a

harder and an easier condition, because we wanted to make sure that we can identify

regions that are classically associated with performing these tasks, which is

typically done by contrasting a harder and an easier condition of a task.

So I'll show you now, in different colors, responses to these various tasks, starting

with the region on the lower part of the screen. So we find that this region

doesn't respond during arithmetic processing, doesn't respond during working

memory, doesn't respond during cognitive control tasks, and doesn't respond during

music perception. Quite strikingly to me at the time, a very similar profile is

observed around this region, which is smack in the middle of so-called Broca's

area, which appears to be incredibly selective in its response for language.

Know that it's not just the case that these, for example, demanding tasks fail

to show a hard versus an easy difference. They respond pretty much at or below

fixation baseline when people are engaged in these tasks. So that basically tells

you that these language regions work as much when you're doing a bunch of math in

your head or hold information in working memory, as what you're doing when you're

looking at a blank screen. So they really do not care. So of course, to interpret

the lack of the response in these language regions, you want to make sure that these

tasks activate the brain somewhere else. Otherwise, you may have really bad tasks

that you don't want to use. Indeed, they do.

So here, I'll show you activations for the executive function tasks, but music also

robustly activates the brain outside of the language system. So here are two

sample regions, one in the right frontal cortex, one in the left parietal cortex.

You see the profiles of response are quite different from the language regions. For

each task, we see robust responses, but also a stronger response to the harder

than the easier condition across these various domains. These regions turn out to

be part of this bilateral frontal parietal network, which is known in the literature

by many names, including the cognitive control network or the multiple demand

system, the latter term advanced by John Duncan, who wanted to highlight the notion

that these regions are driven by many different kinds of cognitive demands. So

these regions appeared to be sensitive to effort across tasks, and their activity

has been linked to a variety of goal-directed behaviors. Interestingly if

you look at the responses of these regions to our language localizer conditions, we

find exactly the opposite of what we find in the language regions. They respond less

to sentences than sequences of non-words, presumably because processing sentences

requires less effort, but clearly this highlights, again, the language and the

cognitive control system are clearly functionally distinct.

Moreover, damage to the regions of the multiple demand network has been shown to

lead to decreases in fluid intelligence. So Alex Woolgar reported a strong

relationship between the amount of tissue loss in frontal and parietal cortices and

a measure of IQ. This is not true for tissue loss in the temporal lobes. It's

quite striking. You can actually calculate for this many cubic centimeters of loss in

the MD system, you lose so many IQ points. It's a strong, clear relationship. So this

system is clearly an important part of the cognitive arsenal of humans because the

ability to think flexibly and abstractly and to solve new problems are exactly . .

. These are the kinds of abilities that IQ tests aim to measure, are considered kind

of one of the hallmarks of human cognition. Okay. So as I mentioned, the

complementary approach for addressing questions about language specificity and

relationship to other mental functions is to examine cognitive abilities in

individuals who lack a properly functioning language system. Most telling

are cases of global aphasia. So this is a severe disorder which affects pretty much

the entire front temporal language system, typically due to a large stroke in the

middle cerebral artery and lead to profound deficits in comprehension and

production. Rosemary Varley at UCL has been studying this population for a few

years now. With her colleagues, she has shown that

actually these patients seem to have preserved abilities across many, many

domains. So she showed that they have in-tact arithmetic abilities. They can

reason causally. They have good nonverbal social skills. They can navigate in the

world. They can perceive music and so on and so forth. Of course, these findings

are then consistent with the kind of picture that emerges in our work in fMRI.

Let's consider another important non-linguistic capacity, which a lot of

people often bring up when I tell them about this work. How about the ability to

extract meaning from non-linguistic stimuli? Right? So given that our language

regions are so sensitive to meaning, we can ask how much of that response is due

to the activation of some kind of abstract, conceptual representation that

language may elicit, rather than something more language-specific, a semantic

representation type. So to ask these questions, we can look at how language

regions respond to nonverbal, meaningful representations. In one study, we had

people look at events like this or the sentence-level descriptions of them, and

either we had them do kind of a high-level semantic judgment test, like decide

whether the event is plausible, or do a very demanding perceptual control task.

Basically what you find here is, again, the black and gray are responses to the

localizer conditions. So in red, as you would expect, you find strong responses to

this and to the condition where people see sentences and make semantic judgments on

them. So what happens when people make semantic judgments on pictures? We find

that some regions don't care at all about those conditions, and other regions show

reliable responses, but they're much weaker than those elicited by the

meaningful sentence condition. So could it be that some of our language regions are

actually abstract semantic regions? Perhaps. But for now, keep in mind that

the response to the sentence-meaning condition is twice stronger, and it is

also possible that participants may be activating linguistic representations to

some extent when they encounter meaningful visual stimuli. So to answer this question