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more. This

1:03

program is sponsored by the

1:05

Cauvly Prize, which honors scientists

1:07

for breakthroughs in astrophysics, nanoscience,

1:10

and neuroscience. The

1:12

Cauvly Prize is a partnership among

1:14

the Norwegian Academy of Science and

1:16

Letters, the Norwegian Ministry of Education

1:18

and Research, and the

1:20

U.S.-based Cauvly Foundation in Los

1:22

Angeles, California. I'm

1:29

Alan Alder, and this is Clear

1:31

and Vivid, conversations about

1:34

connecting and communicating. This

1:40

morning, June 12th, the winners of the 2024 Cauvly Prize

1:42

were announced.

1:45

There are eight winners in all, and in this

1:47

episode of Clear and Vivid, I'll

1:49

be talking with two of them. David

1:51

Charbonneau is an astrophysicist searching

1:53

for planets around other suns,

1:56

and he's a pioneer in developing a way

1:58

to discover if these any- exoplanets may

2:01

support life. Doris

2:03

Tso is a neuroscientist building on

2:05

her work exploring the brain's ability

2:07

to recognize faces in

2:10

order to understand how we recognize anything,

2:12

including, as we'll find out,

2:15

fire hydrants. First,

2:17

my conversation with David Charbonneau. You

2:21

know, I'm very interested in

2:23

how excitement spreads among scientists

2:26

and propels science forward. Sarah

2:28

Seager has been a

2:30

guest on our show and she's your

2:32

fellow cavalry laureate. Yes. And

2:34

I heard a story in which her

2:36

excitement propelled you on your life's work.

2:39

It's absolutely true. Sarah and I

2:41

knew each other since

2:44

before I was an astronomer. She

2:46

knew me when I was a

2:48

teenager and I had

2:50

just started at the University of Toronto and

2:52

we met because we were both very interested

2:54

in hiking and canoeing in

2:57

the Canadian wilderness. And

2:59

so we would organize trips for other

3:02

students at the university who were interested in

3:05

going out and exploring on the weekend. And

3:08

then I learned she was interested in

3:10

mathematics and physics. And then

3:12

several years later, she went off to graduate school

3:15

in the United States at Harvard and

3:18

she wrote and said, gosh, this

3:20

is a great program. You should apply here

3:22

when you graduate. And so I did.

3:24

And it's been a really nice

3:26

connection because she shifted into exoplanets

3:29

and I moved into the field of

3:31

exoplanets which was a completely new

3:33

field. And she works

3:35

on the theoretical side. She makes

3:37

predictions and calculations and

3:40

I'm very much on the observational side trying

3:42

to measure and test some of those theories.

3:46

So exoplanets, these planets that

3:49

are outside of our solar system

3:51

and orbiting other stars, they're

3:53

hard to detect, right? They're very small

3:55

and very far away. Yes,

3:58

the trick with trying to... find a planet around

4:01

another star is that, of course, the star puts

4:03

out an enormous amount of light and the planet

4:05

doesn't put out much at all. And

4:08

so the big realization was to

4:10

use the star as an ally

4:13

instead. And we look at

4:15

the light of the star, and if

4:17

the planet happens to pass in front of the

4:19

star every time that it makes an orbit, it

4:21

blocks some of that light, and that's relatively easy

4:23

to measure. So that's when

4:26

I was a graduate student, I

4:28

was the first person to make that measurement to see one

4:30

of these eclipses, which we

4:33

now call transits. So

4:35

Sarah's excitement transferred to you and

4:37

you right away found your first

4:39

one. That's amazing. Yeah,

4:41

it was a big step forward in terms

4:43

of our understanding of these worlds, because for

4:46

the first time, we could

4:49

learn both their mass and

4:51

their size. And if you have a sphere, if

4:53

you have a planet and you know how much

4:55

it weighs and how big it is, you can

4:58

calculate its density. And of course, that's

5:00

your first guess at what it's actually made of.

5:02

And so that particular planet had

5:05

a very low density and we learned it was

5:07

a gas giant, it was similar to Jupiter. But

5:11

since then, this technique has been

5:13

used to discover and characterize thousands

5:15

of planets. And of course,

5:17

the measurements have gotten much, much better. And so now

5:19

we're able to do this regularly for

5:21

Earth-sized planets and really see that indeed they're

5:23

made of rock and iron, just like our

5:25

own planet. What

5:28

we realized after we had a transiting

5:30

planet was that

5:32

we could use the light from the

5:34

star to probe the planetary atmosphere. And

5:37

so we applied

5:40

for and we're awarded time on

5:42

the Hubble Space Telescope, because of

5:44

course, we want to be free of our own atmosphere if

5:46

we're trying to study the atmosphere of another planet. We want

5:49

to be looking up for our own messy atmosphere. There's

5:51

enough going on there already. You say,

5:53

well, look at all that oxygen there

5:55

and it's large. Well, yeah, that's right.

5:57

Now, for your jumping ahead. That's

6:00

what we want to do soon. But for

6:02

the first planet, we

6:05

waited for it to pass in front of its star.

6:08

And then we looked very carefully with the

6:10

Hubble Space Telescope. And we were

6:12

able to basically take spectra

6:14

of the star, so measure how bright

6:17

the star is in different colors. And

6:19

of course, different atoms and molecules basically

6:23

leave a fingerprint. They

6:25

block very specific colors of

6:27

light. And

6:30

that method, that idea of

6:32

letting the planet pass in front of the star and

6:34

studying what's in its

6:36

atmosphere by studying how the light

6:38

is transmitted through the planetary atmosphere,

6:40

that I think really is our

6:43

first big shot to go

6:45

and look for biosignatures, to go and

6:47

look for things like oxygen, which

6:50

are actually made by life on those

6:52

planets, and maybe infer the presence of

6:54

life indirectly by that

6:56

method. Is there any other reason

6:58

oxygen would be in the atmosphere other

7:00

than life put it there? Oh,

7:03

gosh, yeah. There's been a lot of work

7:05

thinking, if

7:07

you detect oxygen, can you really

7:09

say that it's due to life? And

7:13

one idea is, OK, maybe if there's a

7:15

lot of water on the planet and

7:17

the planet's very close to its star, then

7:20

the UV radiation from the star

7:22

will break apart the water. And as you know,

7:24

water is made from hydrogen and oxygen. And so

7:26

you would make a lot of oxygen gas that

7:28

way. So

7:31

there's a lot of work done

7:33

to think about these false positives.

7:36

And I think what we've learned as

7:38

a community from those studies is that

7:40

just detecting oxygen isn't enough. But

7:43

if you were able to detect

7:45

that there was oxygen, there

7:47

was carbon dioxide and methane, and

7:50

measure the relative amounts of other

7:52

molecules at the same time, then

7:55

really the only plausible explanation

7:57

would be life, in particular

7:59

would be photosynthesis. Because all

8:01

of those together. All those together, that's...

8:03

Kind of signature of life. Exactly,

8:05

exactly. And so that's really where the community

8:08

is headed now. On

8:10

a maybe a 10-year or 15-year timescale to

8:12

really go after trying to measure all of

8:15

those things together and really look

8:17

for life on

8:19

a distant world through the detection of

8:21

these biosignatures. I take it

8:23

from what you're saying now, there hasn't been a planet

8:25

found yet. Would

8:28

that combination, is that what you're saying? Exactly.

8:30

So no one has yet

8:33

detected molecular oxygen in

8:35

the atmosphere of a planet

8:37

orbiting of a star. Let alone

8:39

seen it in combination with these other

8:41

gases that we know are present on

8:43

the Earth. But we know

8:46

how to do it. We know exactly what

8:48

colors of light you would have to look

8:50

for and how big a telescope you would

8:52

need. And so we have a plan

8:54

in the works as a community about how to

8:56

do it. A big part of my work

8:58

is thinking about and building

9:01

telescopes to discover these planets that in

9:04

the future we will want to go

9:06

and characterize. And so a

9:09

project that I have put a lot of

9:11

time into over the last decade is

9:13

called the Mirth Project. And the reason

9:15

we call it the Mirth Project is because...

9:19

Okay, it makes us happy. So Mirth. It's

9:21

Mirth, yeah. Also, we're

9:23

looking for Earth-like planets orbiting

9:26

M dwarf stars, which just means small

9:28

stars, and that makes it easier. So

9:31

go into a little bit why you're infatuated

9:33

with dwarf stars, M dwarf stars. Why are

9:35

they a good place to look? M

9:38

dwarfs are a great place to look for

9:40

a few reasons. These stars are about 10%

9:42

or 20% the size of the sun in

9:45

their diameter. So

9:48

when I was in high school and I was first getting

9:50

really interested in astronomy, I was told

9:52

a big lie, which is the sun

9:54

is an average star. And that's not

9:56

at all true. Most stars in the galaxy

9:58

are much, much smaller. and

10:00

less massive than the Sun, they are these M-dwarfs.

10:03

So one reason to go after these red dwarfs

10:05

or M-dwarfs is because of their size, it makes

10:07

the job easier. If we can detect them in

10:09

front of these small stars, then the signal is

10:11

much bigger because the planet

10:14

blocks more of the surface of the

10:16

star. The other one is that they're

10:18

much, much more numerous. So if you

10:20

draw a bubble around the Sun and

10:23

you go out to say, oh, I don't know, 30

10:26

light years or so, so that sounds really far

10:29

away, maybe that's close for an astronomer, that's just

10:31

the local neighbors, and you count up

10:33

all the stars in that bubble, there

10:35

are about 20 stars that are like the

10:37

Sun, and in that same

10:39

volume of space, there are 250 M-dwarfs. Then

10:44

almost certainly the closest planets to

10:46

us orbit those kind

10:49

of stars and not Sun-like stars.

10:51

I think there is some hint

10:53

that these, what we call terrestrial

10:55

Earth-like planets, have atmospheres, but actually

10:58

that's debated. We don't really

11:00

know for sure. There's not a consensus in the scientific

11:02

community. Day to

11:04

day, people are getting new data, analyzing

11:06

that data and going to conferences and debating about

11:08

whether they really have atmospheres. I

11:11

think we'll know the answer in a year or

11:13

two. So you're catching us at this really special

11:15

moment when we're just learning for the first time,

11:17

gosh, we now know for sure that there's rocky

11:19

planets out there and there's planets that

11:22

have the same temperature as the Earth, but do they

11:24

have atmospheres? We think that atmosphere is essential for life

11:26

as we know it. That's

11:28

what everybody's working on. It's a very exciting

11:31

moment in astronomy. You

11:33

know, I wonder about that phrase, life

11:35

is we know it. And I

11:37

get the impression that you're pretty liberal about

11:40

how far away from our kind of life you're

11:43

willing to go to find what

11:45

you could call life. Doesn't

11:47

have to be exactly what we know here. Is

11:50

that right? Yeah, when I'm thinking about

11:52

designing experiments to look for life in the

11:55

universe, we want to

11:57

cast the widest possible net. And

11:59

so. So, you know, one approach

12:01

is to look for signals from

12:03

intelligent civilizations, right, to what we

12:05

do, what we call SETI, looking

12:08

for radio signals or lasers

12:11

from other civilizations. And

12:13

that would be very, very similar to life on the Earth.

12:15

That would be tremendously exciting. What I'm

12:17

interested in is really stepping back and

12:19

saying, what are all the different paths,

12:21

all the different chemistries that we could

12:23

imagine that life would possibly take the

12:26

form of, and then thinking about

12:28

how do we design a set of experiments

12:30

generally to do that. I

12:36

was wondering about our own planet,

12:38

which for, I don't know, a couple

12:40

of billion years, as I understand it, there

12:43

was no oxygen involved. There was

12:45

life that was anaerobic. If

12:47

there hadn't been that transition

12:49

to aerobic life and

12:52

the anaerobic creatures

12:54

persisted, they might

12:56

have evolved into something more complex, no,

12:59

and interesting to us, although not at all like

13:01

us. I think one of the

13:03

most magical parts of astronomy is

13:05

the ability to travel through time. And

13:08

what I mean is when we look out into

13:10

the galaxy, we see all of these stars and

13:13

they all have planets, but

13:15

those stars have all been born

13:17

at different ages. So we can

13:19

find stars that are the same

13:21

age as the Sun. Okay,

13:24

there are piers, but we can find

13:26

stars that are recently born

13:29

and we can find stars that are much, much older than

13:31

the Sun. We can find stars that are 10 billion years

13:33

old. So they're twice as old as the Sun and the

13:35

Earth. And then

13:38

can you imagine doing the experiment

13:40

where now you're going to go

13:42

and study the atmospheres of all

13:44

of those planets and really trace

13:46

out, yeah, do most planets have

13:48

this pause, the sort of two

13:50

billion years where there might be

13:52

life on them, but it's anaerobic

13:54

and it's not producing oxygen. And

13:56

then at some point you have photosynthesis really

13:58

build up this critical mass. oxygen in the

14:01

atmosphere and then more complicated life forms

14:03

take place. And we could actually do

14:06

that experiment by finding planets around stars

14:08

of different ages. And

14:10

yes, it certainly looks like on the Earth, you

14:12

know, there was one set of organisms that worked

14:15

tirelessly for a very long time

14:17

until finally that oxygen was

14:20

built up. And that oxygen was probably toxic

14:22

to a lot of the organisms that

14:24

were present at the time. But then,

14:26

of course, other organisms moved into that

14:28

new space because

14:30

oxygen is great for in terms of how

14:33

organisms manipulate energy. And you could have multicellular

14:35

organisms and you could have larger plants and

14:37

animals and so on. So I think that

14:40

changes in the chemistry of the atmosphere do

14:42

very much affect the kind of organisms that

14:44

you get downstream. But the point is, as

14:46

astronomers, you could even imagine tracking that. And

14:49

I just that would be such an exciting

14:51

experiment to plan with powerful telescopes. What

14:54

do you think about the probability of

14:56

life throughout the universe? You can only see,

14:59

and you've only studied, a relatively small

15:02

part of it. Well, what gives you any

15:05

confidence that the place is full of

15:07

life? So here I

15:09

differ with a lot of my colleagues. I think

15:11

when I talk to other

15:13

astronomers around the world, most

15:16

of them seem to

15:19

be of the opinion that life

15:21

is present elsewhere in the universe.

15:24

But I'm not so sure. I am

15:27

more skeptical. I really need

15:29

to measure things to see that they're true.

15:31

Okay, that's how I come at science. So,

15:33

you know, the universe appears to be infinite.

15:35

And so there may be extremely distant galaxies,

15:38

and in those galaxies, there's planets with life.

15:40

But that's not a testable hypothesis. What I'm

15:42

really interested in figuring out is, you

15:45

know, within a few hundred light years of the Sun,

15:47

do those planets have life? Because, you know,

15:50

once you go and look at the closest

15:53

Earth-like planet outside the solar

15:55

system, right away, you have an important data point,

15:57

right? Maybe you get life all the time. 100%

16:00

of the time, life is extremely robust. And so you just

16:02

have to look at one other planet to know whether that's

16:04

true. And then you look at 10 of them

16:07

and you've learned, okay, well, it's less than

16:09

10% of the time. Or

16:11

you look at 100, it's less than 1% of the time. So

16:14

even just looking at a relatively small

16:16

number of planets would give us

16:18

a huge insight into

16:20

how improbable were the events that went down

16:22

on planet Earth 4 1,500,000,000 years ago. That's

16:26

why I'm really excited about that large telescope

16:28

that the community and NASA and everybody is

16:31

working towards. And so we have an understanding

16:33

of how big that telescope is, what

16:35

its capabilities need to be, and of course what the cost

16:37

would be. And that idea,

16:40

that's called the habitable world's observatory.

16:42

And so we think, we hope

16:44

that the next big mission that

16:47

NASA is going to work on,

16:49

okay, and start building and maybe

16:51

launch on a time scale of 10 or 15 years

16:54

is this facility. And it would

16:56

be a big telescope in space. It might

16:58

be about, oh, you know,

17:01

six meters across. So maybe about 20 feet

17:03

across, which is really big

17:05

for a space telescope. It would

17:07

have to be sensitive to specific wavelengths

17:09

and it would be looking for light

17:11

reflecting off of these Earth-like

17:13

planets and really allow us to look for

17:16

molecules such as oxygen. And

17:19

that would take considerable resources, which

17:22

prompts me to ask you a question

17:24

similar to the one we started with

17:26

about the usefulness of excitement and how

17:28

it propels things. I think

17:30

I've heard you say something roughly like, this

17:33

work that you're doing doesn't

17:35

solve problems here on Earth and

17:38

yet it attracts our wonder. What do you

17:40

suppose that is? What is that wonder and

17:42

excitement born of? Yeah,

17:45

I think that really gets to the heart

17:47

of why people

17:49

are drawn to science and drawn

17:51

to astronomy from

17:53

even the very youngest ages. You know, you

17:55

talk to five-year-old kids and

17:58

they're passionate about questions. And

18:00

then I go and I

18:02

talk to my dad and he's in his 80s and

18:04

he's asking the same questions. And

18:06

why is that? And it is

18:09

that sense of curiosity and wonder. And

18:11

I do think science is incredibly important

18:14

for coming up with practical

18:16

solutions for real problems

18:18

that we all face in

18:20

terms of technology, certainly

18:23

in terms of medicine, in terms

18:25

of really dealing with the scourge of disease

18:27

like cancer. Okay, that's a really important role

18:29

of science. But that's not the

18:32

only thing science does. There is this

18:34

other huge element of science which has

18:37

to do with just understanding who we

18:39

are as humans and our place in

18:42

the physical universe. And

18:45

I think the great contributions of

18:47

astronomy to human thought are

18:50

showing us that our lifetimes, if

18:53

you live decades, that your

18:56

lifetime is incredibly short compared to the

18:58

age of the universe. The

19:00

universe has been around for 14 billion years and

19:02

it looks like it will go on forever and

19:04

ever. And we're just here

19:06

for this little sliver of time. And

19:09

I think that gives us an enormous perspective.

19:11

The same is the physical size of the

19:13

universe, right? We've learned how big the universe

19:15

is and how small we are as people

19:17

in that space. And then

19:19

the study of life in the universe, I

19:21

think that also will very much put us

19:23

in perspective. Are we one of many, many

19:25

civilizations throughout the galaxy or are

19:27

we not? Are we really alone and unique in some

19:29

special way? And that's always

19:32

been the role of astronomy for hundreds of

19:34

years. And we just right now we're

19:36

live at this very special moment where

19:38

I think we're going to add

19:40

a very, very important part to that

19:42

puzzle, namely with how common life is

19:45

in the universe. Well

19:47

I'm eager to watch your progress as you find

19:50

the pieces to the puzzle. Thank

19:52

you for doing that puzzle work and

19:54

thank you for being with us today. I really

19:56

appreciate it. Oh, it's been my pleasure.

19:58

I just have absolutely. adored working

20:01

as an astronomer with all of the young

20:04

scientists and students over

20:06

several decades now. And I just can't

20:08

wait to see what they discover, what

20:10

we all get to discover in the

20:13

next couple of years. That's

20:15

great. Thank

20:17

you, David. When

20:22

we come back from our break, we'll find

20:24

out how a young girl growing up in

20:26

China during the Cultural Revolution was

20:28

inspired to become a neuroscientist while

20:31

correcting her father's English grammar. Our

20:37

program is sponsored by the

20:40

Kavli Prize, which honors scientists

20:42

for breakthroughs in astrophysics, nanoscience,

20:44

and neuroscience that transform

20:46

our understanding of the very big,

20:49

the very small, and the

20:51

very complex. From

20:53

scientific breakthroughs like the discovery

20:55

of CRISPR-Cas9 and the detection

20:57

of gravitational waves to

21:00

inventing new fields of research, Kavli

21:02

Prize Laureates pushed the limits of what

21:04

we know and advanced science in ways

21:07

that could not have been imagined. The

21:10

Kavli Prize is a partnership among

21:12

the Norwegian Academy of Science and

21:14

Letters, the Norwegian Ministry of Education

21:16

and Research, and the

21:18

U.S.-based Kavli Foundation in Los

21:20

Angeles, California. It's

21:23

one thing falling in love with a

21:25

house, picturing yourself moving in and calling

21:28

it home, and quite another navigating the

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world of price negotiating, mortgage lenders, and

21:32

finding the budget that works best for

21:34

you. An agent who's a realtor can

21:36

make understanding that world easier. Realtors

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have the expertise, access to proprietary data,

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and tools to help you get from

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imagining living somewhere to actually doing it.

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That's the kind of help we can provide. Because

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that's who we are. Realtors are members of

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the National Association of Realtors. for

22:00

Convely Prizes. The prize

22:02

awarded to Doris Tseo cites

22:04

her work studying the cells in the

22:07

brain that are dedicated to identifying faces.

22:11

This is really interesting work you've done,

22:14

and congratulations on the prize. Thank you so

22:16

much. You were born in

22:18

China and spent a lot of your

22:20

youth in the library reading

22:22

all day long in the library. Why were

22:25

you in the library all day long? Oh,

22:27

it was because my parents simply didn't

22:29

have money to hire a babysitter. So

22:31

this was very cheap child care. And

22:35

your dad taught himself or

22:37

learned somehow about mathematics and

22:40

AI and various aspects of

22:42

science at night after finishing

22:45

logging all day long. Yeah,

22:47

he was really self-made. He knew

22:49

that the cultural revolution wouldn't last

22:51

forever, and he always dreamed

22:53

that he would be able to escape.

22:55

And when it was over, intellectual

22:58

pursuits would be valued again. And he

23:01

was just extremely curious about the world.

23:03

And it's amazing how much you can

23:06

learn, even when you're in the fourth

23:08

submenture with some books. I

23:10

was really interested to see what an

23:12

effect your father's intellectual life has had

23:14

on yours when your whole family was

23:16

in the US. And I think

23:19

you were at Cal Tech, and you

23:21

went on a camping trip, and

23:23

you were proofreading a paper of his

23:25

to help correct any mistakes in English.

23:28

What did you get from that paper, aside

23:30

from the fun of correcting your dad's grammar?

23:33

Yeah, it introduced me to this

23:35

idea that the

23:37

brain can compute

23:39

by somehow mirroring

23:42

what happens in the world. And that idea, you

23:44

know, at the time I was like a sophomore

23:46

in college, I felt like I was the only

23:48

person, me and my dad, we're the only person

23:51

who knew about this idea. And

23:53

then later on, I kind of learned that

23:55

many people have had this idea. It's actually

23:57

one dominant way of thinking about how the

23:59

brain works. It's very closely

24:01

related to this idea of predictive coding

24:03

that's actually used now to train chat

24:05

GPT. It's such a

24:07

powerful idea. But back then, I just

24:09

felt so happy. I

24:12

just discovered this in the course of correcting

24:14

my dad's English. Then

24:18

you took that idea further. I

24:21

think you were working with

24:23

Nancy Kanwisher, a fellow laureate,

24:25

and you were discovering face

24:27

patches, patches in the brain

24:29

that recognize elements of the face or the

24:31

whole face. How does that work? What's a

24:33

face patch? Yeah. So

24:35

a face patch is a

24:38

piece of cortex where

24:40

all the cells are dedicated to

24:43

processing faces. Indeed,

24:45

Nancy was the first to discover this.

24:48

She wrote this landmark paper in 1997. It's

24:51

the most cited paper of all time in

24:53

general neuroscience, where she scanned a

24:55

bunch of humans, put them in an fMRI scanner

24:57

and asked, is there any part of the brain

24:59

where there's more blood flow when you look at

25:01

pictures of faces than when you look at pictures

25:03

of other things? I just was

25:05

introduced the idea of face patches from her

25:08

paper. She actually reported that there were multiple

25:10

of these face hairs. There wasn't just one.

25:13

Her paper emphasized one of them because it

25:15

was the most reproducible across all the humans.

25:17

But actually, most of the people had multiple

25:19

of these face hairs. When we scanned monkeys,

25:22

we found that they had

25:24

six of these face patches. Each

25:26

patch is performing a different step and

25:28

they're working together to build a perception

25:31

of a face. So how many

25:33

aspects of the face are being checked out

25:35

to give you recognition? I've heard somewhere with

25:37

something like 50. You

25:41

can change a face in all these different

25:43

dimensions. What

25:45

we found was that the cells in these

25:47

face patches were sensitive to

25:49

change in these dimensions, meaning that

25:51

they would change their electrical firing

25:55

as you change the dimension. If you make

25:57

the eyes bigger in particular, over 70

25:59

percent, of the cells would start

26:02

firing more strongly. So we're extremely sensitive to the

26:04

size of the eyes. Sighs of the eye,

26:06

how far apart they are about that? Yeah, exactly

26:08

how far apart they are, how thick the

26:10

hair is. So there were so

26:12

many different dimensions that the cells seem to care

26:14

about. And I think it's actually more

26:16

than 50. But with 50

26:18

dimensions, it turns out that you can

26:20

produce a good likeness of anyone's face.

26:23

Like I can describe

26:25

your face, Alan, with just

26:27

50 dimensions. And it's not going

26:29

to look perfect. It's not going to capture all the details.

26:31

But I'll be able to recognize it. And

26:34

the cells definitely are able to code all 50

26:36

of these dimensions. And we think more. So it's

26:38

a very high dimensional space that the cells

26:40

are coding. I've read a couple of

26:43

years ago that a single cell

26:45

in the brain could recognize the

26:47

face. Is that true? I

26:50

think you're referring to this famous

26:53

Jennifer Aniston cell. This

26:55

was a cell that responded to

26:57

the picture of Jennifer Aniston, but

26:59

not to picture Brad Pitt. And

27:02

so this was reported to be

27:04

a cell that was

27:06

just responsible for representing her. I

27:10

think the cell was actually the most

27:12

misunderstood cell in all of neuroscience, because

27:14

it's such a famous neuron. It's probably

27:16

the most famous neuron ever.

27:19

In fact, one colleague has joked that

27:21

he probably has a cell in his

27:23

brain for representing the Jennifer Aniston cell.

27:27

But the reason I

27:29

say it's misunderstood is that if you

27:32

look in the supplementary information for that

27:34

paper, it turns out that that cell

27:36

also responded to the picture of Lisa

27:38

Kudrow, who is another

27:40

actor on Friends. So I

27:42

think the corrected interpretation of that cell is

27:45

that it wasn't just coding this

27:47

one person, Jennifer Aniston, but it was coding

27:49

this concept of Friends. And just

27:51

like all of memory, it's part

27:54

of an associative network. I don't

27:56

believe there's any cell in the

27:58

brain that's only responsible. to

28:01

one single person space. So in

28:03

a way, there's a network of

28:05

cells that have developed in

28:07

response to aspects of friends. Yes.

28:10

And Jennifer Aniston is an important part of that.

28:13

Yeah, that's very interesting because that sort of throws

28:15

light on what I wanted to ask

28:18

you about is, how does the code

28:20

work? How do these various

28:23

cells in different parts of the brain, and

28:25

from one patch to another, how

28:28

do they cooperate to give you a

28:30

face that you can remember? Yeah,

28:32

I mean, that's a huge question. I can't,

28:35

we certainly haven't solved that question. How

28:37

exactly do they cooperate? So

28:39

we have hypothesis about what they're

28:41

doing. For example, as you

28:44

go anterior in the brain, as you go

28:46

closer from the back to the front of

28:48

the brain, the processing

28:50

becomes more abstract and complex. So

28:53

the back of your brain is like where your early visual cortex is, and

28:56

the very front of your brain is your frontal

28:58

lobe that's responsible for extremely high-level decision making. So

29:01

anyways, within this face patch system, as you move

29:03

anterior, what you find is that the

29:06

different patches, their processing becomes more

29:09

and more abstract. So in the

29:11

most posterior patch, the cells care about specific

29:13

features like the eye, and

29:16

then as you go anterior, in the

29:18

most anterior patch, they actually can respond to

29:20

the head orientation. So a face can be

29:22

looking straight at you or looking up or

29:24

looking to the sideways, and the cell will

29:26

respond the same way. And

29:28

we think that's one way that the cells are

29:31

building up this perceptive face. So in an earlier

29:34

area, you might have eight different cells for

29:37

representing the eight different views of a person, and

29:39

then in the most anterior area, those eight

29:41

cells converge on a single cell that represents

29:44

that person invariant to how they're looking. This

29:46

may be a question more

29:48

for Nancy Kanwisher. It's my

29:50

own personal response to

29:53

faces. I have

29:55

prosopagnosia, which is

29:57

face blindness. When I tell people I... I

30:00

could have had dinner with somebody the

30:02

night before and meet them the next day, and

30:05

I don't know who they are. And

30:08

then as I apologize, I say, I'm

30:10

really sorry I have face blindness. And

30:12

they say something like, oh yeah, I have that too. Not

30:16

knowing that it's a real condition. What's

30:18

gone wrong in my brain? Yeah,

30:21

so there's different types of

30:23

prasipagnosia. If you are

30:26

fortunate enough to have a lesion and suffer

30:28

a stroke in your temporal lobe and just

30:30

lose your face area,

30:32

then you definitely will become prasipagnosic. But

30:35

in your case, obviously, that's not the

30:37

case. So there's also this syndrome called

30:41

developmental prasipagnosia. So

30:44

we'd have to put you in fMRI scanner to know

30:47

for sure. But I find

30:49

it very interesting that you're prasipagnosic because I've

30:51

read that Brad Pitt is also prasipagnosic. And

30:54

yeah, I wonder if that you're

30:56

both actors. Maybe there's a higher

30:58

percentage among actors. You wonder where you

31:00

think there is. It would make sense to me.

31:03

Why, that's interesting, why is that? Because

31:05

maybe you don't feel this fear, I

31:07

don't know. Like normally

31:09

people react in a certain way to other

31:12

people because of just these natural social signals.

31:14

An actor, I imagine, I don't know, you

31:16

have to overcome those innate responses and maybe

31:18

it helps to not have

31:21

that recognition. Maybe I ought

31:23

to take advantage of that intuition of

31:25

yours because kind of the opposite

31:27

happens with me. And I

31:29

think it was happening long

31:32

before I realized there was such a thing

31:34

as face blindness. I

31:36

would be very anxious in a

31:39

social situation because I didn't

31:41

know if the person I was talking to was

31:43

somebody I shouldn't really recognize. Or

31:47

if they just looked vaguely familiar, had a type

31:49

of face that was similar to somebody I knew,

31:51

but I wouldn't even go that far. That'd just

31:53

be a blank to me. And

31:56

so I'd get nervous, anxious,

31:58

just talking with somebody. I thought

32:00

I might be meeting for the first time or maybe

32:02

not. But maybe I'll tell, maybe I can

32:04

take advantage of what you said before about actors

32:08

in kind of a protective mode.

32:11

Yeah, no, I just read a story

32:13

about someone who was completely passive-pagnosic and,

32:16

you know, he described how he got a

32:18

job working at the Oscars because he just

32:20

wasn't fazed when he saw these famous people.

32:23

He wasn't fazed, that's funny. Maybe

32:27

that's my next career. Is

32:33

it that the brain has

32:35

specialized areas for recognizing

32:37

faces, but not

32:39

for other objects? Or do

32:42

classes of objects have

32:44

their own circuits the same

32:47

way faces do? Yeah,

32:49

it turns out that faces are not that

32:51

special in this respect and there are multiple

32:53

other networks for representing other

32:56

regions of object space. It

32:58

turns out that there's a

33:00

region for representing stubby

33:02

things like a box or a USB stick

33:04

or a radio. So it's not a single

33:06

category, but it's a single shape. And

33:09

then there's another region that's representing spiky

33:11

things like chairs and helicopters and spiders.

33:14

And so those other networks

33:16

are not specialized for a specific meaningful category

33:18

the way the face area is, but

33:20

they are specialized for representing a

33:23

specific class of shape. And

33:25

they follow exactly the same principles as

33:28

the space patch network. So you have

33:30

increasing invariance as you go anterior and

33:33

they use the same type of code. And

33:36

so actually it was, you know,

33:39

when I first started working on the space

33:41

patch system, people would say, you know, that's

33:43

a total unicorn. You're not going to learn

33:45

anything general about object representation from setting face

33:47

areas. And I

33:50

think we've actually shown that that's not

33:52

true at all. And the face patch system

33:54

has actually been like a Rosetta stone for

33:56

figuring out how all types of objects are

33:58

represented. And it turns out it's. it

34:00

generalizes extraordinarily,

34:03

including the rest of object space. So

34:05

as the person develops from

34:08

an infant on up, they're learning, obviously they're

34:10

learning a lot of words, but they're also

34:12

learning a lot of objects, it sounds like

34:15

you're saying? Yeah, exactly.

34:17

You gain so much experience

34:19

with different objects. I

34:22

should say this insight about

34:25

the brain organizing objects

34:28

into this space, this object

34:30

space, really comes from recent

34:32

advances in AI. So people have these AI

34:35

systems that also know nothing about objects, and

34:37

just show them lots of objects. You

34:40

ask the network to either categorize the

34:42

objects, or you ask the network to

34:46

organize the objects such that objects that

34:48

are similar or close together. These

34:51

networks develop these representations and you can look in

34:53

them under the hood because you built this network

34:55

so you can look at every single unit with

34:57

selected form and turns out that

34:59

they're representing the objects in this orderly

35:01

space and that's how we got this

35:03

hint that the primate brain might

35:06

be doing the same thing and that's

35:08

how we found it. So yes, just through

35:10

exposure to different objects, you can learn to

35:13

build these maps. It almost seems like

35:15

an inevitable consequence of

35:19

doing object recognition. You

35:22

make me think of my ability to picture

35:24

a fire plug, and I

35:26

can picture it in different ways with

35:28

connectors to fire hoses in different parts

35:30

of the fire plug. I

35:32

have these parts in my brain that I can

35:34

put together, but whenever I see a fire plug,

35:36

it doesn't have to be like the last one

35:39

I saw. There's a generic

35:41

fire plug shape made of

35:43

constituent parts that let me identify it

35:46

as what it really is. But it

35:49

must be tantalizing to you

35:51

to figure out, try to figure out how

35:54

those parts come together. It

35:56

seems to me, here's my real question. It

35:59

seems to me, that you can't have pictures

36:01

of objects stored in your brain,

36:03

because that would mean if you

36:05

saw an object that was slightly

36:07

different, you wouldn't recognize it. You

36:10

have to be able to draw on

36:12

parts the same way you do with

36:14

faces. Wow. That is so profound. Exactly.

36:17

That is what we're after. We want

36:19

to understand how these parts are represented.

36:21

And that's one of the reasons we're

36:24

mucking around now a totally new part of the

36:26

brain, far away from the face patches, because

36:28

I think there are earlier processes that

36:30

are actually building 3D models

36:33

of the world around us. And they

36:35

work more like Legos. They're really building

36:38

these different objects from parts.

36:40

And you can build any object that you

36:42

want. And the way you represent

36:44

it as an object is based on a totally

36:46

different set of principles, based on topology and how

36:48

it's connected. And this is actually... I

36:51

wrote a paper about this with my father recently. Tell

36:54

me about that paper, because that brings us full

36:56

circle. We started off with

36:58

you studying your father's paper that got you

37:00

really motivated a lot into this field. And

37:03

now, what's the paper you're working on that

37:06

has to do with this topology? Yeah,

37:08

the title of paper is a

37:10

topological solution to object segmentation and

37:12

tracking. And it's

37:15

directly addressing the question that you just

37:17

raised. How do you put together

37:20

parts into holes? And

37:23

how do you do this flexibly so you can

37:25

create any part? And so, our

37:29

paper is addressing how the visual system

37:31

can take all these pixels from our

37:33

retina where they're completely not organized into

37:35

any structure, and how the

37:37

visual system can then organize it into

37:39

discrete objects. And

37:43

the answer that we come to is

37:45

that we have to take

37:47

advantage of the fact that we're living in a 3D world,

37:49

and so we can see things from different perspectives. And

37:52

based on how those perspectives are

37:54

related, we show mathematically

37:56

how to solve this problem of

37:58

dividing the world into... different

38:00

objects. So that's

38:02

what the paper is about. It's

38:04

a mathematical theory of how you solve

38:06

this problem of dividing pixels

38:09

into objects and also how you track

38:11

those objects as they move around. It

38:15

was my pandemic project. Well,

38:19

you used the pandemic well. My

38:22

dad and I were discussing these ideas for over

38:24

a decade, but we never somehow got to the

38:26

stage of being able to write it up. We

38:30

need to actually show that it works. So

38:32

I actually programmed it. I showed that the

38:35

system worked and then we

38:37

wrote it, we dealt with reviewers, we

38:39

got it published. That's

38:42

wonderful. What a wonderful story. What

38:44

a terrific conversation this has been

38:46

too. I thank you so

38:48

much. I know you're very busy and I really appreciate

38:50

you taking the time to talk with me. Thank

38:53

you. I really enjoyed this. This

39:03

program is sponsored by the

39:05

Kavli Prize, which honors scientists

39:07

for breakthroughs in astrophysics, nanoscience,

39:09

and neuroscience. The

39:11

Kavli Prize is a partnership among

39:13

the Norwegian Academy of Science and

39:15

Letters, the Norwegian Ministry of Education

39:17

and Research, and the

39:19

US-based Kavli Foundation in Los

39:22

Angeles, California. David

39:25

Charbonneau is professor of astrophysics in

39:27

the Department of Astronomy at Harvard

39:29

University. He recently

39:32

co-chaired the National Academy's Exoplanet

39:34

Science Strategy. He

39:36

shares the 2024 Kavli Prize for astrophysics

39:40

with Sarah Seger. Doris

39:43

Sow is professor of neurobiology at

39:45

the University of California, Berkeley. She

39:48

shares this year's Kavli Prize in

39:51

neuroscience with Nancy Kanwisher and

39:53

Vinrich Freiburg. This

39:56

episode was edited and produced by

39:58

our executive producer Graham. shed

40:01

with help from our associate producer

40:03

Jean Chumet. Our publicist

40:05

is Sarah Hill. Our

40:07

researcher is Elizabeth Ohini, and

40:09

the sound engineer is Erica Huang. The

40:12

music is courtesy of the Stefan-Kernig

40:15

Trio. Next

40:24

week, my guests will be two more 2024

40:27

Cauvery Prize Laureates, both

40:29

for nanoscience. Chad

40:31

Merkin pioneered the use of microscopic carriers

40:34

that are able to deliver new types

40:36

of medicines. I think of

40:38

the big opportunity is being able to build drugs

40:40

that are much bigger than the normal

40:42

drugs that the pharmaceutical industry works with, that

40:45

have a type of multifunctionality that allow

40:47

you to solve major challenges in terms

40:49

of treating disease. And that's why I'm

40:51

very bullish on the idea that not

40:53

just our work, but much of the

40:55

work in nanomedicine is going to lead

40:57

to treatments for disease where we need

40:59

really powerful treatments and ways of giving

41:01

a lot of people hope that don't

41:03

have much hope. Robert

41:06

Langer also made breakthroughs in drug

41:08

delivery by devising ways for

41:10

drugs to be released in the body

41:12

slowly and over time. He

41:14

also pioneered the development of lab-grown

41:17

human tissues and organs. Now

41:19

you can make skin for

41:21

burn victims and patients that have diabetic

41:23

skin ulcers. One of my

41:26

students has a company that's making new blood

41:28

vessels that probably will get approved by

41:30

the FDA, at least I hope so very soon.

41:33

But it's already being used to help people

41:35

in Ukraine who've been badly wounded. They're

41:38

making new blood vessels for them. And then

41:40

there's others at earlier stages. In fact, we've

41:42

worked with Steve Zytels, who's

41:45

a surgeon for Julie Andrews, and

41:47

making new vocal chords. Both

41:50

Merkin and Langer have played major

41:52

roles in developing the biotechnology industry.

41:56

Between them, they've filed thousands of patents

41:58

and helped found multiple companies. They

42:01

share the 2024 Nano Science

42:03

Cauvery Prize with Paul Alivisatos.

42:07

For more details about Clear and Vivid and

42:09

to sign up for my newsletter, please

42:11

visit alanalda.com. And

42:14

you can also find us on Facebook and

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Instagram at Clear and Vivid. Thanks

42:19

for listening. Bye-bye. Is

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Realtors do. Because that's who we

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are. Realtors are members of the

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National Association of Realtors. Thank

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you.