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

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  • - [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

  • more definitively, we're turning to the patient evidence again. If parts of the

  • language system are critical for processing meaning in non-linguistic

  • representations, then aphasic individuals should have some difficulties with

  • nonverbal semantics. First, I want to share a quote with you from Tom Lubbock, a

  • former art critic at The Independent, who developed a tumor in the left temporal

  • lobe which eventually killed him. As the tumor progressed, and he was losing his

  • linguistic abilities, he was documenting his impressions of what it feels like to

  • lose the capacity to express yourself using verbal means.

  • So he wrote, "My language to describe things in the world is very small,

  • limited. My thoughts, when I look at the world, are vast, limitless, and normal,

  • same as they ever were. My experience of the world is not made less by lack of

  • language, but is essentially unchanged." I think this quote quite powerfully

  • highlights the separability of language and thought. So in work that I'm currently

  • collaborating on with Rosemary Varley and Nancy Kanwisher, we are evaluating the

  • global aphasics performance on a wide range of tasks, requiring you to process

  • meaning in nonverbal stimuli. So for example, can they distinguish between real

  • objects and novel objects that are matched for low-level visual properties? Can they

  • make plausibility judgments for visual events? What about events where

  • plausibility is conveyed simply by the prototypicality of the roles? So you can't

  • do this task by simply inferring that a watering can doesn't appear next to an egg

  • very frequently. Right? It seems like the data so far is suggesting that they indeed

  • seem fine on all of these tasks, and they laugh just like we do when they see these

  • pictures because they're sometimes a little funny. So they seem to process

  • these just fine. So this suggests, to me, that these kinds of tasks can be performed

  • without a functioning language system. So even if our language system stores some

  • abstract conceptual knowledge in some parts of it, it tells me at least that

  • that code must live somewhere else as well. So even if we lose our linguistic

  • way to encode this information, we can have access to it elsewhere.

  • So to conclude this part, fMRI in patients sudies converge suggesting that the front

  • temporal language system is not engaged in and is not needed for non-linguistic

  • cognition. Instead, it appears that these regions are highly specialized for

  • interpreting and generating linguistics signals. So just a couple minutes on what

  • this means. So given this highly selective response to language stimuli that we

  • observe, can we make some guesses already about what these regions actually do? I

  • think so. I think a plausible hypothesis is that this network houses our linguistic

  • knowledge, including our knowledge of the sounds of the language, the words, the

  • constraints on how sounds and words can combine with one another. Then essentially

  • the process of language interpretation is finding matches between the pieces of the

  • input that are getting into our language system and our previously stored

  • representations. Language production is just selecting the relevant subset of the

  • representations to then convey to our communication partner. This way . . . The

  • form that this knowledge takes is a huge question in linguistic psychology and

  • neuroscience. So one result I don't have time to discuss is that contra some

  • claims, it doesn't seem to be the case that syntactic processing is localized to

  • a particular part of this language system. It seems it's widely distributed across.

  • Anywhere throughout the system, you find sensitivity to both word-level meanings

  • and compositional aspects of language, which is much in line with all current

  • linguistic theorizing, which doesn't draw a short boundary between the lexicon and

  • grammar. So this way of thinking about the language system as a store of our language

  • knowledge makes it pretty clear that the system is probably not innate. In fact, it

  • must arise via experience with language as we accumulate this language store. It's

  • also presumably dynamic, changing all the time as we get more and more linguistic

  • input through our lifetimes. I assume that our language knowledge is plausibly

  • acquired with domain general statistical learning mechanisms, just like much other

  • knowledge. So what changed in our brains that allowed for the emergence of this

  • system? So one thing that changed is that our association cortices expanded. So

  • these are regions that are sensory and motor regions and include frontal,

  • temporal, and parietal regions. Okay. So these people have noted for a long time. I

  • think I'm kind of in the camp of people who think that our brains are not

  • categorically different in any way. They're just scaled-up versions of other

  • primate brains. I think there's quite good evidence for that. So how does the system

  • emerge? So I think one thing that was different

  • between us and chimps is that there is a protracted course to the brain development

  • in humans. So between birth and adulthood, our brains increase threefold, compared to

  • just twofold in chimps. It's a big difference. Basically this just makes us

  • exceptionally susceptible to environmental influences, and we can soak stuff up from

  • the environment very, very easily. So as our brains grow, we make more glial cells.

  • We make more synapses. Our axions continue to grow and become myelinated, and it's

  • basically tissue that's ready to soak up the regularity that we see in the world.

  • Of course, it comes at a cost. That's why we have totally useless babies that can't

  • do anything, but apparently somehow it was worth. . . The tradeoffs were worth it.

  • Okay. So the conclusions. We have this system. It's highly selective in its

  • responses. It presumably emerges over the course of our development and would enable

  • probably some combination of the expansion of these association cortices, where we

  • can store vast amounts of symbolic information and this protracted brain

  • development, which makes us great learners early on. Thank you.

  • [applause]

  • ♪ [music] ♪

  • - [Rachel] So I want to start this story actually in the 1800s. So in 1800, a young

  • physician named Itard decided to take on the task of teaching a young boy French.

  • Turns out that this young boy, who they think was between 10 and 12 years old, he

  • had no language. He'd been discovered running around in the woods, naked and

  • unable to communicate. Itard thought that this was a very important task because he

  • thought that civilization was based on the ability to empathize and also on language.

  • So he tried valiantly, for two years, to teach this young boy named Victor . . . He

  • named him Victor because over this two-year time span, the only language or

  • sounds that this boy could make was the French 'er', which sounds like Victor.

  • Hence, he had his name. But after two years, Victor was unable to speak any

  • French and comprehended very little French and primarily communicated with objects.

  • Then Itard wrote this up after two years. He wrote up his findings, and he said that

  • he thought that a major reason why Victor didn't learn French was because he was

  • simply too old. But of course, this was the 1800s, and he

  • didn't speculate as to what it was about being 10 years old with no language that

  • would prevent you from learning language if you had a daily tutor trying to teach

  • you French. There's an enormous amount of irony in this particular story, because

  • Itard was the house physician for the first school for the deaf in the entire

  • world. Here, we have a picture of it. The first school for the deaf was begun in

  • 1760 in Paris. At the time that Itard was teaching Victor, he was at the school with

  • all of these children who used sign language and all of these teachers who

  • used sign language. Nonetheless, it never occurred to him to try to teach Victor

  • sign language, but we can't blame him because in the 1800s, sign language was

  • not considered to be a language. In fact, that particular discovery and realization

  • wouldn't happen for another century and a half. So we can imagine why he didn't

  • teach Victor sign language. The question is: could Victor have actually learned

  • sign language if Itard had tried to teach it to him? So I'm going to try to answer

  • that question today through a series of studies. But before I talk about our

  • studies, I want to talk about something that all of the speakers who have preceded

  • me have talked about, which is that one thing that's . . . The defining

  • characteristic of language is that it's highly structured, and it's highly

  • structured at all of these multiple levels, so that speech sounds make up

  • words. Words make up words and phonemes. These words are strung together in

  • sentences with syntax, and the specific syntax helps us understand and produce

  • very specific kinds of meanings. Now one aspect of this language structure

  • is that humans have evolved to the point where children learn this structure

  • naturally. Nobody has to teach them. Nobody has to have a tutor to sit with

  • them for two years to teach them the structure of French or the structure of

  • English or the structure of ASL. Simply by being around people who use the language,

  • young children naturally acquire all of this multilevel and complicated structure.

  • That is to say all children do this if, in fact, they can access the language around

  • them, if they have normal hearing. They're born with normal hearing. They hear people

  • talk. Before you know it, they're talking themselves. But if children are born

  • profoundly deaf, they cannot hear the speech around them. We know that

  • lip-reading is insufficient to learn language because most of the speech sounds

  • are invisible. What happens to these children? If there's no sign language in

  • the environment, they can't learn a visual form of language either. So it happens

  • that there are large numbers of children who actually are like Victor, in the sense

  • that they grow up without language, without learning a language, but they are

  • like Victor-- they are unlike Victor, in that they weren't running around in the

  • woods, nude and having a very harsh life. So how does the lack of language in

  • childhood affect the ability to learn language? Or does it affect the ability to

  • learn language? This has been the focus of studies that we have been doing for many

  • years in our laboratory. Because we're using deaf children and sign language as a

  • means to model language acquisition and its effect on the brain, I think it's

  • appropriate that I talk a little bit about the kinds of stimuli we do and about

  • American Sign Language. So first of all, you should know that American Sign

  • Language, unlike many of the sign languages that have been discussed up to

  • this point, is a very sophisticated language. It's evolved clearly over 200

  • years. We might even say that American Sign Language evolved with the development

  • of the United States of America and spread as civilization went across, as white men

  • went across the continent. So American Sign Language has a phonological system, a

  • morphological system, syntax, and so forth. So for those of you who don't know

  • sign . . . I know there are many people here who do know sign. I want to show you

  • what some of this structure looks like. So I'm going to play you two video tapes. For

  • those of you who don't know sign, I would like you to guess which one is

  • syntactically structured. For those of you who do know sign, maybe you could keep the

  • answer to yourselves.

  • That's one. Here's two.

  • So how many think two? How many think one? Okay. For those of you who think two, I

  • captioned this. So this is really 'kon, dird, lun, blid, mackers, gancakes.'

  • Number one is a fully formed sentence with a subordinate clause.

  • The reason I'm showing you these two sentences is, for those of you who don't

  • know the language, you can't perceive or parse this particular structure. This is

  • what knowing a language is about. For those of you who know ASL and who know

  • this language, you know that the second example had all of these signs which were

  • non-signs, possible signs, but really just non-signs. This is part of what knowing

  • language is about. How do people learn this particular structure? The question

  • that we're interested in is: how does being a young child help people learn this

  • particular structure? So we did a series of experiments. When we started this work,

  • it wasn't even clear that age would make a difference in sign language acquisition.

  • Sign language is gestural. Sign language is mimetic. Maybe anybody can learn sign

  • language at any time in their lives. So in one experiment, what we did is we

  • recruited a number of people. This is in Canada, who were born deaf and who used

  • ASL. We asked them. We created an experiment where we had a set of sentences

  • that varied in complexity, and we showed them these ASL sentences, and we asked

  • them simply to point to a picture that reflected the meaning of the sentence that

  • they saw. We were quite struck by our findings. What you see here is that deaf

  • people who learned ASL from birth, from their parents, performed very, very well

  • on this task, in contrast to deaf people who were adults, who've been signing for

  • over 20 years performed at chance. So they had great difficulty understanding some of

  • these basic sentences in ASL. So this suggests that there are age of acquisition

  • effects for sign, as there are for spoken language. Everybody sort of . . . The word

  • on the street is it's much harder to learn a language if you're an adult than you're

  • a child. But what if it's something deeper than this? What if there's something about

  • learning a language in childhood that sets up the ability to learn language, that

  • creates the ability to learn language? So we did another experiment, also in Canada,

  • but we decided in order to test this particular hypothesis, we should switch

  • languages. So we're no longer testing ASL here. What we're testing here is English.

  • We devised an experiment in English, where we had a set of sentences, and some of

  • them were ungrammatical, and some of them were grammatical. This is a common kind of

  • task that psycholinguists use. Notice here that the people who were born

  • profoundly deaf and for whom ASL was a first language are near-native in English.

  • So this is a second language. So learning a language early, even though it's in

  • sign, helps people learn a second language. Notice also that they performed

  • . . . Their performance was indistinguishable from normally-hearing

  • people who had learned other languages at birth, German, Urdu, Spanish, and French.

  • So there seems to be that there's something about learning a language early

  • in life, regardless of whether it's sign language or spoken language, that actually

  • helps people learn more language. It's not simply learning language when one is

  • little. But as also part of this experiment, we tested a group of

  • individuals who had been signing for 20 years and had gone through the educational

  • system in Canada, who were born deaf. On this task, they performed at chance. On a

  • grammaticality judgment task, it's either yes or no. So it's at chance. So we see

  • that individuals who are deprived of language, who aren't able to learn

  • language at a young age, perform poorly on their primary language, sign language, and

  • they perform very poorly on a second language, which is ASL, and we see the

  • reverse. So there's something really special going on here about learning

  • language at an early age. What might this be? And might it be in the brain?

  • So in another set of studies, what we did is take this population that we had been

  • looking at, and we decided to neuro-image their language processing to see whether

  • this might give us some clues as to differences between first and

  • second-language learners and people who had language and people who did not have

  • language. So in this study, also done in Canada, with colleagues at the Montreal

  • Neurological Institute, we did fMRI. Maybe many of you have had MRIs. We showed the

  • subjects sentences, like the sentences that you saw, and we asked them to make

  • grammatical judgments on these sentences. We tested 22 people. They were all born

  • profoundly deaf. They all used American Sign Language as a primary language. They

  • had all gone through the educational system, but they ranged in the age at

  • which they were first able to acquire language. This is all the way from birth

  • up to age 14. So if the age at which you learn your first language doesn't make a

  • difference, then we should expect the neuro-processing patterns of all of these

  • individuals to be similar. If age of acquisition makes a difference, we should

  • see different patterns in the brain. In fact, this is what we have. This is what

  • we found. When we did the analysis, we found that there were seven regions in the

  • brain, primarily in the left hemisphere. As you know now, the left hemisphere has

  • areas that are responsible for language. One effect that we found was that in the

  • language regions of the left hemisphere, the earlier the person learned their first

  • language, the more activation we saw in the language hemisphere. However, the

  • older the person was when they learned their first language, the less activation

  • we got in the language areas of the brain. So if there's less activation, is there

  • something else going on here? We actually found a second effect, which we were not

  • expecting at all, which is in the back part of the brain, the posterior part of

  • the brain, in visual processing. This particular effect was that the longer the

  • person matured, the older the . . . Without language, the older they were when

  • they learned language, we found greater activation, more neural resources being

  • devoted to visual processing. So we see that here in this group of deaf signers,

  • we have two complementary reciprocal effects of when a child learns his or her

  • . . .when an individual learns their first language and what the brain seems to be

  • doing in terms of processing that language. So that for people who learned

  • language early in life, almost all of their neural resources are devoted to

  • processing the meaning and structure of that language. For people who learned

  • their first language later in life, more neural resources are devoted to just

  • trying, perhaps, to figure out what the signal was. Was this a word? Was it glum?

  • Or was it gleam? So we have this reciprocal relationship between perceptual

  • processing and language processing. This particular pattern is not unique to deaf

  • signers. There's work by Tim Brown and Shleger that showed that younger children

  • often have more posterior activation than older children. There are also some

  • clinical populations, such as autistic individuals, particularly those who have

  • low . . . whose language skills are not well-developed, will often show more

  • processing in the occipital lobe. So this is not a pattern that is unique to

  • deafness. So then the next question we had is

  • whether, in fact . . . How does language develop when an individual first starts to

  • learn it when they're much older, for example, when they're a teen? We have been

  • very fortunate to have followed five or six children in our laboratory who had no

  • language until they were 13 to 14 years of age, for a variety of reasons. Two of

  • these children are from the United States. These other children are from other

  • countries. Actually this particular circumstance, while we might think of it

  • as being very rare, is actually very common, particularly in underdeveloped

  • countries. So the way in which we have observed or analyzed language acquisition

  • is to use normal procedures that people use to study children's language

  • acquisition. We get a lot of spontaneous data from them, and we analyze it. So one

  • question we had is: if you're 13 years old, and you don't have a language system,

  • will you develop language like a baby? Or will you do something else? Because you

  • have a developed cognitive system. Will you jump in the middle of the task? How

  • will this progress? To answer this question, we need to look a little bit at

  • how normally-hearing children or deaf or hearing children develop language when

  • they are exposed to it as a young age. The major hallmark of children's language

  • acquisition is that they very quickly, as they're acquiring the grammar of their

  • language, their sentences get longer and longer.

  • The reasons their sentences get longer and longer is because they're learning all of

  • these . . .the morphology, the syntax. As they say ideas, as they're expressing

  • their ideas, they're better able to use grammar to express them. So these data

  • show the average length of children's expressions. Two of these children are

  • normally hearing and acquiring English, and two of these children are acquiring

  • ASL. So we see that, in fact, the teens that we have been following show no

  • increase in their language. They're able to learn language and put words together,

  • but, in fact, we don't see an increase in their grammar. In the last study, we

  • wanted to neuro-image these children. We wanted to see what are their brains doing

  • with the language that they have. So we used magnetoencephalography, which is a

  • different technique, which is complementary to the fMRI. What we did for

  • this is we studied their vocabulary, and we made stimuli that we knew that they

  • knew, words that were in their vocabulary, and that looked something like this. In

  • the first instance, the picture matched the sign. In the second instance, it

  • didn't. When that happens, the brain goes, "Uh-oh," and you get this N-400 response.

  • That's what we were localizing in the brain for these children. Because we're

  • using vocabulary that they have, we know that they knew these words. We asked them

  • to press buttons while they were doing this task, and we knew that they were

  • accurate. We didn't only test these children. We also tested control groups.

  • So some of these control groups are deaf. Some are hearing. Some are first language

  • learners, and some are second language learners.

  • The first panel shows the response of a group of normally hearing adults doing

  • this task while looking at pictures and listening to words. This is data that

  • Katie Travis used also to look at children's development, neural

  • development. The second panel, these are deaf adults who learned ASL from birth.

  • You can see that their processing is very much like the hearing adults who are

  • speaking English, primarily left hemisphere in the language areas, with

  • some support or help from the right hemisphere. Actually these patterns are

  • indistinguishable. Both the hearing adults and the deaf adults learned language from

  • birth, even though it was in a different form. What's this last panel? These are

  • college students who are normally hearing. They have been learning sign for about

  • three years, which is about the same amount of time that our cases were

  • learning language. So we see that responding on this task in speech in ASL,

  • whether it's a first language or a second language, so long as the subject had

  • language from birth, looks fairly similar. What about the cases? We were able to

  • neuro-image two cases, and you can see that their neural processing patterns are

  • quite different, and they look neither like second language learners, nor do they

  • look like deaf adults. These children have been signing for three years, and they had

  • no language before they started to learn how to sign. You can see primarily that

  • there's a huge response in the right hemisphere, in the occipital and parietal

  • areas. One of the subjects also shows some response in the language areas.

  • We can see that even though they're acquiring language, they're doing it in a

  • very different way, and their brains are responding very differently. So we see

  • that, in fact, there are huge effects of language environment on both the

  • development of language, but also how the brain processes language. So we see that

  • it seems to be that the human language capacity, both understanding language and

  • expressing language, but also the brain's ability to process language is very

  • dependent upon the baby's brain being exposed to or immersed in language from

  • birth. It's through this analyzing language and working on the data that it's

  • being fed that, in fact, I think the neural networks of language are being

  • created. So language is a skill that is not innate but emerges from the

  • interaction of the child with the environment, through linguistic

  • communication. That was probably the answer that Itard was looking for and

  • might be the reason why Victor did not acquire language. Thank you.

  • [applause]

  • ♪ [music] ♪

  • - [Edward] Okay. So I wanted to first thank the organizers really for this kind

  • invitation to join this cast of stellar thinkers about the biology and behavior of

  • language. I actually want to switch the title a little bit to something more

  • specific. I want to talk to you about organization, in particular a kind of

  • organization that I refer to as a taxonomy. The organization that I'm

  • actually really referring to is the organization of sound, in particular

  • speech sounds and how those are actually processed in a very important part of the

  • brain called the superior temporal gyrus, also known as Wernicke's area. The main

  • focus of my lab is actually to understand the basic transformation that occurs when

  • you have an acoustic stimulus and how it becomes transformed into phonetic units.

  • In other words, basically, how do we go from the physical stimulus that enters our

  • ears into one that's essentially a mental construct, one that's a linguistic one? To

  • basically ask the simple question, what is the structure of that kind of information

  • as it's processed in the brain? Now this actually turns out to be a very

  • complex problem because it's one that actually arises from many levels of

  • computation that occur in the ascending auditory system. As sounds actually come

  • through the ears, they go up through at least seven different synaptic connections

  • across many different parts of the brain, even bilaterally, to where they're

  • actually processed in the non-primary auditory cortex in the superior temporal

  • gyrus. What we know about this area from animal studies and non-human primates, for

  • example, is this is an area that no longer is tuned to basic low-level sound

  • features, like pure tones, pure frequencies, but in one that is actually

  • tuned to very broad, complex sounds. There have been very nice work in fMRI that's

  • actually demonstrated that this area is far more selective to complex sounds, like

  • speech, over non-speech sounds. So the basic question is not really about where

  • is this processing going on. The question is how. Okay. What is the structure of

  • information in this transformation that's going on? In particular, what kind of

  • linkages can we make between that physical stimulus and the internal one, which is a

  • phonetic one? For me, I think it's really important to acknowledge some really

  • important fundamental contributions that occur, that give us some insight and put

  • them in a very important perspective. For me, I think one of the most important

  • pieces of work that led to this work that I'm about to describe from our lab was

  • actually 25 years ago, using an approach that's actually far more complicated and

  • difficult to achieve than what we do in our own work.

  • This is using single unit and single neuron recordings that were recorded from

  • patients that were undergoing neurosurgical procedures for their

  • clinical routine care. This very extremely rare but precious opportunity to actually

  • record from certain brain areas while someone is actually recording-- listening

  • to speech. These are from my close colleague and mentor, Dr. Ojamin, who's in

  • the audience today. But why I think it's so important to acknowledge this work is a

  • lot of the clues about what I'm about to describe were actually seen 25 years ago.

  • This figure that's extracted from that paper, where they actually showed and

  • could record from single brain cells, called neurons, in the superior temporal

  • gyrus, that they were active and corresponding to very specific sounds. But

  • if you actually look at where those sounds are, they're not exactly corresponding to

  • the same exact, let's say, phonemes or the same exact sounds, but, in fact, they are

  • corresponding to a class of sounds. This was an observation that was made in this

  • paper. They thought perhaps this is some mention of phonetic category

  • representation there. But it wasn't that clear, actually, and there were a lot of

  • other really important observations that were made in that paper. Now from a

  • linguistic perspective, in thinking about how, behaviorally, we organize this

  • information in the brain, there's actually a wonderful way to approach it. It's not

  • perfect, but a very wonderful way to think about how languages across the world

  • actually share a similar and shared inventory of speech sounds, not all

  • completely the same. Each language has a different number, but they highly overlap.

  • The reason why they overlap is because they are produced by the same vocal tract.

  • This is essentially like a periodic table of sound elements for human language and

  • speech. So this table actually has two really important dimensions. The

  • horizontal dimension is actually one that we call the place of articulation. It's

  • referencing where in the vocal track these sounds are made. For example, bilabial

  • sounds, the 'P' and the 'B' require you to actually have a transient occlusion at the

  • lips, 'ba. ' You cannot make those sounds without that particular articulatory

  • movement. Whereas some of the other sounds, like a 'D' or a 'T', a 'da' a 'ta'

  • a 'da' we call alveolar because the front of the tongue tip is actually placed

  • against the teeth. So these are actually referencing where occlusions are occurring

  • in the vocal tract when we speak, and those actually correlate necessarily to

  • very specific acoustic signatures. The other dimension is what we call the manner

  • of articulation. So the manner of articulation is actually telling you a

  • little bit more about not so much where, but how in the vocal tract the

  • constrictions are made in order to produce those sounds. We have certain ones, like

  • plosives, where you have complete closure of the vocal tract and then a transient

  • release, other sounds where you have near-complete, like a fricative, like

  • 'sha,' 'za,' those sounds that we call fricative.

  • If you actually look at vowels, they actually have a similar structural

  • organization. There actually is something that actually references where in the

  • vocal tract, either the front, middle, or back, or the degree of open and closure.

  • So for both consonants and vowels, there actually is a structure that we know

  • about, linguistically and phonologically, about how these things are organized. I

  • think the thing that interests me is that, like I referenced before, that this is

  • something like a periodic table. There is something fundamental about these units to

  • our ability to perceive speech. These phonological representations are not

  • necessarily the ones that we think of as these letters that we call phonemes, but

  • actually groups of phonemes that share something in common, what we call

  • features. These are the members of small categories which combine to form the

  • speech sounds of human language. This became very attractive to me as a model of

  • something to look for in the brain because of . . . Essentially why it could be so

  • important is that languages actually do not vary without limit, but they actually

  • reflect some single or limited general pattern, which is actually rooted in both

  • the physical and cognitive capacities of the human brain, and I would add the vocal

  • tract. This is not a new kind of thinking, but it's one that has not been clearly

  • elucidated in terms of its biological mechanisms. So in order for us to get this

  • information, it requires a very special opportunity, the one where we can't

  • actually record directly from the brain. In many ways, this is actually a lot more

  • coarse than the kind of recordings that were done almost 25 years ago. These are

  • ones from electrode sensors that are placed on the brain in order to localize

  • seizures in patients that have epilepsy. In the seven to 10 days that they are

  • usually waiting to be localized, we have a very, again, precious opportunity to

  • actually have some of the participant, the patient volunteers, listen to natural,

  • continuous speech and look at those neural responses on these electrode recordings to

  • see how information is distributed in the superior temporal gyrus when they're

  • listening to these sounds. This gives you a sense of actually what that neural

  • activity pattern looks like. [audio sample]

  • - We're going to slow down that sentence a lot here.

  • - [audio sample] Ready tiger go to green five now.

  • - So you can see that the information is being processed in a very precise, both

  • spacial and temporal, manner in the brain. This is exactly the reason why this kind

  • of information has been elusive, because we do not currently have a method that

  • actually has both spacial and temporal resolution and, at the same time, covers

  • all of these areas simultaneously. So it's, again, in the context of these rare

  • opportunities with human patient volunteers that we can conduct this kind

  • of research. So the natural question is . . . Of course, now that I've shown you

  • that we can actually see a pattern in the brain, both that's temporally and

  • spatially specific, what actually happens when we try to deconstruct some of those

  • sound patterns from the brain? This just gives you an example, again, in the

  • superior temporal gyrus, where those sounds are activating the brain. An

  • example of the spectrogram for a given sentence, in this case, it's, "In what

  • eyes there were." The last part of that figure basically shows you that pattern

  • across different electrodes. It's not all happening in the same particular way. You

  • have very specific evoked responses that actually occur at different parts of the

  • superior temporal gyrus. I want to show you what happens when you

  • look at just one of those electrodes. If you look at the neural response of that

  • one particular electrode that's labeled e1, and you organize the neural response

  • by different phonemes, okay, you can actually see, again, on the vertical

  • access, starting with 'da,' 'ba,' 'ga,' 'ta,' 'ka.' You can see that this

  • electrode . . . Those hundreds or thousands of neurons that are under this

  • electrode are very selectively responsive to this set of sounds that we call

  • plosives. It's not one phoneme, but a category, and they share this feature that

  • we actually know, linguistically, to be called plosive. I can show you a series of

  • other electrodes. Electrode two has a very different kind of sensitivity. It's

  • showing you that it really likes those sounds 'sha,' 'za,' 'sa,' 'fa.' This

  • is an electrode that is, again, not tuned to one phoneme, but actually tuned to the

  • category of sibilant fricatives in linguistic jargon. We have another

  • electrode, e3, that is selective to low-back vowels, these "ah" based ones.

  • Another one that is a little bit more selective to high-fronted vowels, 'E.'

  • Even another electrode, e5, that is corresponding to nasal sounds.

  • So this is a very low-level description, but it's actually the first time we've

  • ever seen in this kind of principled way, obtained through very precise spatial and

  • temporal recordings, the ability to resolve phonetic feature selectivity at

  • single electrodes in the human brain. Now this is not enough. We need to really

  • address this issue of structure. That's one of the themes here. Are all of these

  • things just equally distributed as features? In the original thinking about

  • these things, you could have a binary list of features. It turns out that features,

  • in and of themselves, actually have structure and have relationships with one

  • another. So what we did, in order to look at that structure in the brain, we looked

  • at hundreds of electrodes that were recorded over a dozen patients. Each one

  • of those columns actually corresponds to one electrode and one particular superior

  • temporal gyrus in someone's brain. Like I just showed you before, the vertical axis

  • is actually how they're organized by different phonemes. What we did here was

  • we used a statistical method called hierarchical clustering. What hierarchical

  • clustering is used for is finding the patterns in this data. What the

  • hierarchical clustering showed us and sorted this data was that, in fact, there

  • is, indeed, structure in the brain's responses to human speech sounds, and it

  • looks like this. So we've organized the hierarchical

  • clustering as a function of a single electrode's, again, a single column's

  • selectivity to different phonemes, but we've also organized this clustering as a

  • population response across all of the electrodes and looking at that selectivity

  • for different phonemes. So we have two different axes that we're actually looking

  • at the brains large distributed response to speech sounds. We're using this method

  • which is what we call unsupervised, meaning we're not telling it any

  • linguistic information, or we're not organizing the data. We're just saying,

  • "Tell us how the brain is organizing this information." What we see from this is

  • that when we actually look at where this information is being organized, one of the

  • biggest divisions between different parts of different kinds of selectivity in the

  • brain are what we would call the difference between consonants and vowels

  • or really, actually, between obstruents and continuants, in linguistic jargon. But

  • within those different categories, you actually have sub-classification. So

  • within the consonants, you actually have a subdivision between plosives and

  • fricatives. Between the sonorants you actually have referencing for different

  • positions of the tongue, low back, low front, high front, different classes of

  • vowels and, in fact, nasal. So basically this is telling you that feature

  • selectivity in the brain is actually hierarchically structured. The second

  • thing is that instead of using phonemes in order to organize the responses, we

  • actually use features. So as an example, that term dorsal actually refers to the

  • tongue position when it's fairly back, like for 'G' 'K' sounds.

  • You can see that when we organize things by features, you have a much cleaner

  • delineation. The electrode responses seem to be much more tuned to phonetic features

  • shown below, as they are, compared to when you plot them as phonemes. Okay. So this

  • essentially disproves any idea that there is individual phoneme representation in

  • the brain, at least not one that's locally encoded, but tells you that the brain is

  • organized by its sensitivity to phonetic features. Now relating it to a phonetic

  • feature is the first step, and it's one that's really important because it's

  • referencing the one we know about from linguistics and the one that we know

  • behaviorally. But how do we connect this to the physical stimulus that's actually

  • coming through our ears? That's where we have to make a linkage to actually

  • something about the sound properties. Are these things truly abstract features that

  • are being picked up by the brain? Or actually, are they referencing specific

  • sound properties? Basically the answer is the latter. It's that what we're actually

  • seeing is sensitivity to particular spectral temporal features. In the top

  • row, I am showing you basically . . . When we look at the average tuning curves, the

  • frequency versus time, tuning curves for each one of those different

  • classifications for plosive fricatives, they're very similar to the acoustic

  • structure when you average those particular phonemes in the brain.

  • So what this means is the tuning that we're seeing that's corresponding to

  • phonetic features is, in fact, one that is tuned to high order acoustic spectral

  • temporal ones. The brain is selecting specific kind of acoustic information and

  • converting it into what we perceive as phonetic. In the interest of time, I'm

  • going to sort of skip more in-depth information about vowels and plosives and

  • how those are specifically encoded. But in summary, what we've found is that there's

  • actually a multidimensional feature space, actually, for speech sounds in the human

  • superior temporal gyrus. This feature space is organized in a way that actually

  • shows hierarchical structure. The hierarchical structure is fairly strongly

  • driven by the brain, in particular, this auditory cortex sensitivity to acoustic

  • differences, which are most signified actually in the manner of articulation

  • distinctions, linguistically. What's interesting about this is it actually does

  • correlate quite well with some known perceptual behavior. So I would like to

  • conclude there and acknowledge some of the really important people from my lab,

  • postdoctoral fellow Nima Mesgarani who did most of this work with one of our graduate

  • students, Connie Cheung. Thank you.

  • [applause]

  • ♪ [music] ♪

- [automated message] This UCSD TV program is presented by University of California

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CARTA: 言語はどのように進化するのか?脳の中の言語 (CARTA: How Language Evolves: Language in The Brain)

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    J.s. Chen に公開 2021 年 01 月 14 日
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