TEDGlobal 2011
Daniel Wolpert: The real reason for brains
Denijel Volpert: Zašto zaista imamo mozak?
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Neurobiolog, Denijel Volpert, polazi od iznenađujuće premise: razmišljanje ili osećanje nisu razlozi za evoluciju mozga, razlog je kontrolisanje kretanja. U ovom zabavnom govoru, bogatom informacijama, Volpert nam daje priliku da pogledamo kako ljudski mozak kreira elegantno i gipko kretanje.
Daniel Wolpert - Movement expert
A neuroscientist and engineer, Daniel Wolpert studies how the brain controls the body. Full bio
A neuroscientist and engineer, Daniel Wolpert studies how the brain controls the body. Full bio
Double-click the English transcript below to play the video.
00:15
I'm a neuroscientist.
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Ja sam neurobiolog.
00:17
And in neuroscience,
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U neurobiologiji,
00:19
we have to deal with many difficult questions about the brain.
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moramo da se bavimo mnogim teškim pitanjima o mozgu.
00:22
But I want to start with the easiest question
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Ali, hteo bih da počnem od najlakšeg pitanja,
00:24
and the question you really should have all asked yourselves at some point in your life,
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pitanja, koje bi svako trebalo sam sebi da postavi bar jednom u životu,
00:27
because it's a fundamental question
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jer je to fundamentalno pitanje
00:29
if we want to understand brain function.
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za razumevanje funkcije mozga.
00:31
And that is, why do we and other animals
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A to je, zbog čega, mi i druge životinje
00:33
have brains?
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imamo mozak?
00:35
Not all species on our planet have brains,
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Nemaju sve vrste na našoj planeti mozak,
00:38
so if we want to know what the brain is for,
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i ako želimo da znamo za šta služi mozak,
00:40
let's think about why we evolved one.
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hajde da pogledamo zbog čega mozak evoluira.
00:42
Now you may reason that we have one
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Mogli biste reći da imamo mozak
00:44
to perceive the world or to think,
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za opažanje ili za razmišljanje,
00:46
and that's completely wrong.
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a to je potpuno pogrešno.
00:48
If you think about this question for any length of time,
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Ako razmišljate o ovom pitanju nešto duže,
00:51
it's blindingly obvious why we have a brain.
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biće zaslepljujuće očigledno zbog čega imamo mozak.
00:53
We have a brain for one reason and one reason only,
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Imamo mozak iz samo jednog jedinog razloga,
00:56
and that's to produce adaptable and complex movements.
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a to je da bismo izvodili prilagodljivo i kompleksno kretanje.
00:59
There is no other reason to have a brain.
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Ne postoji drugi razlog zašto imamo mozak.
01:01
Think about it.
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Razmislite o tome.
01:03
Movement is the only way you have
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Kretanje je jedini način koji imamo
01:05
of affecting the world around you.
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da utičemo na svet oko sebe.
01:07
Now that's not quite true. There's one other way, and that's through sweating.
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To nije sasvim tačno. Postoji još jedan način, a to je znojenje.
01:10
But apart from that,
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Ali, pored toga,
01:12
everything else goes through contractions of muscles.
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sve drugo se dešava putem kontrakcija mišića.
01:14
So think about communication --
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Razmislite o komunikaciji
01:16
speech, gestures, writing, sign language --
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govor, gestovi, pisanje, jezik znakova
01:19
they're all mediated through contractions of your muscles.
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sve je to posredovano kroz kontrakcije mišića.
01:22
So it's really important to remember
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Zato je veoma važno zapamtiti
01:24
that sensory, memory and cognitive processes are all important,
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da su i senzorni i memorijski i kognitivni procesi važni,
01:28
but they're only important
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ali su važni jedino
01:30
to either drive or suppress future movements.
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kao pokretači ili supresori nekog budućeg kretanja.
01:32
There can be no evolutionary advantage
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Nema evolutivne prednosti
01:34
to laying down memories of childhood
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u formiranju sećanja iz detinjstva
01:36
or perceiving the color of a rose
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ili u primećivanju kakve boje je ruža
01:38
if it doesn't affect the way you're going to move later in life.
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ukoliko to ne utiče na način kretanja tokom života.
01:41
Now for those who don't believe this argument,
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Za one koji ne veruju u ovo tvrđenje,
01:43
we have trees and grass on our planet without the brain,
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imamo drveće i travu na našoj planeti, bez mozga,
01:45
but the clinching evidence is this animal here --
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ali ključni dokaz je ova životinja ovde,
01:47
the humble sea squirt.
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ovaj skromni morski plaštaš.
01:49
Rudimentary animal, has a nervous system,
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Primitivna životinja, ima nervni sistem,
01:52
swims around in the ocean in its juvenile life.
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pliva naokolo u okeanu dok je mlada.
01:54
And at some point of its life,
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U jednom stupnju svog života,
01:56
it implants on a rock.
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pričvrstiće se za kamen.
01:58
And the first thing it does in implanting on that rock, which it never leaves,
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I prvo što će uraditi na tom kamenu, koji više nikad neće napustiti,
02:01
is to digest its own brain and nervous system
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je da upotrebi svoj mozak i nervni sistem
02:04
for food.
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kao hranu.
02:06
So once you don't need to move,
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Znači, kad više ne morate da se krećete,
02:08
you don't need the luxury of that brain.
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više vam ne treba takav luksuz kao što je mozak.
02:11
And this animal is often taken
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Ova životinja se često uzima
02:13
as an analogy to what happens at universities
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kao analogija za ono što se dešava na univerzitetima
02:15
when professors get tenure,
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kada profesori dobiju stalnu poziciju,
02:17
but that's a different subject.
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ali to je već druga tema.
02:19
(Applause)
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(aplauz)
02:21
So I am a movement chauvinist.
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Znači, ja sam šovinista za kretanje.
02:24
I believe movement is the most important function of the brain --
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Verujem da je kretanje najvažnija funkcija mozga,
02:26
don't let anyone tell you that it's not true.
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i ne dozvolite da vam iko kaže da to nije tačno.
02:28
Now if movement is so important,
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E sad, ako je kretanje tako važno,
02:30
how well are we doing
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koliko dobro nam uspeva
02:32
understanding how the brain controls movement?
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da razumemo kako mozak kontroliše kretanje?
02:34
And the answer is we're doing extremely poorly; it's a very hard problem.
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Odgovor je da nam vrlo loše uspeva; to je vrlo težak problem.
02:36
But we can look at how well we're doing
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Možemo pogledati koliko nam uspeva
02:38
by thinking about how well we're doing building machines
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po tome koliko dobro nam ide pravljenje mašina
02:40
which can do what humans can do.
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koje mogu da izvršavaju ono što i ljudi.
02:42
Think about the game of chess.
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Uzmite šah za primer.
02:44
How well are we doing determining what piece to move where?
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Koliko dobro možemo da odredimo koju figuru da pomerimo i gde?
02:47
If you pit Garry Kasparov here, when he's not in jail,
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Ako stavite Garija Kasparova ovde, kad nije u zatvoru,
02:50
against IBM's Deep Blue,
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da igra protiv IBM-ovog "Deep Blue" kompjutera,
02:52
well the answer is IBM's Deep Blue will occasionally win.
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pa, odgovor je da će IBM-ov "Deep Blue" povremeno pobediti.
02:55
And I think if IBM's Deep Blue played anyone in this room, it would win every time.
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Mislim da, kad bi IBM-ov "Deep Blue" igrao protiv bilo koga u ovoj sali, da bi pobedio svaki put.
02:58
That problem is solved.
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Taj problem je rešen.
03:00
What about the problem
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A šta je sa problemom
03:02
of picking up a chess piece,
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podizanja šahovske figure,
03:04
dexterously manipulating it and putting it back down on the board?
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veštog pomeranja i vraćanja figure dole na tablu?
03:07
If you put a five year-old child's dexterity against the best robots of today,
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Ako uporedite veštinu 5-godišnjeg deteta i najboljih robota današnjice,
03:10
the answer is simple:
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odgovor je jednostavan:
03:12
the child wins easily.
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dete lako pobeđuje.
03:14
There's no competition at all.
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Tu uopšte i nema takmičenja.
03:16
Now why is that top problem so easy
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A zašto je taj gornji problem tako lagan,
03:18
and the bottom problem so hard?
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a donji problem tako težak?
03:20
One reason is a very smart five year-old
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Jedan razlog, vrlo pametan 5-godišnjak
03:22
could tell you the algorithm for that top problem --
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bi mogao da vam kaže algoritam za taj gornji problem
03:24
look at all possible moves to the end of the game
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a to je da ispitate sve moguće poteze do kraja partije
03:26
and choose the one that makes you win.
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i da izaberete onaj kojim pobeđujete.
03:28
So it's a very simple algorithm.
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To je vrlo prost algoritam.
03:30
Now of course there are other moves,
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Naravno da postoje drugi potezi,
03:32
but with vast computers we approximate
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ali sa ogromnim kompjuterima možemo uprostiti,
03:34
and come close to the optimal solution.
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i stići blizu optimalnom rešenju.
03:36
When it comes to being dexterous,
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Što se tiče manuelne veštine,
03:38
it's not even clear what the algorithm is you have to solve to be dexterous.
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čak nije jasno koji algoritam treba rešiti da bi se došlo do veštine.
03:40
And we'll see you have to both perceive and act on the world,
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Videćemo da je potrebno da se okolina detektuje
03:42
which has a lot of problems.
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i da se deluje na nju, što nosi mnogo problema.
03:44
But let me show you cutting-edge robotics.
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Ali, da vam pokažem vrhunsku robotiku.
03:46
Now a lot of robotics is very impressive,
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Mnogo robota je vrlo impresivno,
03:48
but manipulation robotics is really just in the dark ages.
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ali manipulativna robotika je ipak tek na početku.
03:51
So this is the end of a Ph.D. project
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Ovo je kraj jednog projekta za doktorat
03:53
from one of the best robotics institutes.
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iz jednog od najboljih instituta za robotiku.
03:55
And the student has trained this robot
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Ovaj student je istrenirao robota
03:57
to pour this water into a glass.
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da sipa vodu u čašu.
03:59
It's a hard problem because the water sloshes about, but it can do it.
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To je težak problem, jer se voda prosipa, ali robot uspeva.
04:02
But it doesn't do it with anything like the agility of a human.
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Ali ne uspeva da to uradi tako spretno kao ljudsko biće.
04:05
Now if you want this robot to do a different task,
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Ako želite da ovaj robot izvrši drugi zadatak,
04:08
that's another three-year Ph.D. program.
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to zahteva novi trogodišnji doktorat.
04:11
There is no generalization at all
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Uopšte ne postoji generalizacija
04:13
from one task to another in robotics.
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za izvršavanje zadataka u robotici.
04:15
Now we can compare this
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Možemo uporediti ovo
04:17
to cutting-edge human performance.
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sa vrhunskim ljudskim izvođenjem.
04:19
So what I'm going to show you is Emily Fox
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Sada ću vam pokazati Emili Foks,
04:21
winning the world record for cup stacking.
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kako postavlja svetski rekord u slaganju čaša.
04:24
Now the Americans in the audience will know all about cup stacking.
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Amerikanci u publici će svi znati šta je slaganje čaša.
04:26
It's a high school sport
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To je srednjoškolski sport
04:28
where you have 12 cups you have to stack and unstack
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u kom treba da prvo poslažete 12 čaša, a onda ih skupite
04:30
against the clock in a prescribed order.
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u propisanom redosledu i za određeno vreme.
04:32
And this is her getting the world record in real time.
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I evo nje kako postiže svetski rekord - snimak nije ubrzan.
04:39
(Laughter)
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(smeh)
04:47
(Applause)
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(aplauz)
04:52
And she's pretty happy.
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I prilično je srećna.
04:54
We have no idea what is going on inside her brain when she does that,
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Nemamo nikakvu ideju šta joj se dešava u mozgu dok to radi,
04:56
and that's what we'd like to know.
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a to je ono što bismo hteli da znamo.
04:58
So in my group, what we try to do
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U mojoj grupi pokušavamo da uradimo
05:00
is reverse engineer how humans control movement.
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obratno inženjerstvo o ljudskoj kontroli kretanja.
05:03
And it sounds like an easy problem.
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To zvuči kao jednostavan problem.
05:05
You send a command down, it causes muscles to contract.
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Pošaljete komandu dole, to dovede do kontrakcije mišića.
05:07
Your arm or body moves,
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Ruka ili telo se pokreću,
05:09
and you get sensory feedback from vision, from skin, from muscles and so on.
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i dobijate senzornu povratnu informaciju od čula vida, iz kože, mišića itd.
05:12
The trouble is
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Nevolja je u tome
05:14
these signals are not the beautiful signals you want them to be.
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što ti signali nisu tako divni signali kao što bismo mi hteli.
05:16
So one thing that makes controlling movement difficult
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Jedna stvar zbog koje je kontrolisanje kretanja teško
05:18
is, for example, sensory feedback is extremely noisy.
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je, npr. to što je senzorna povratna sprega vrlo bučna.
05:21
Now by noise, I do not mean sound.
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Pod "bučna", ne mislim na zvuk.
05:24
We use it in the engineering and neuroscience sense
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To kažemo u inženjerstvu i neurobiologiji u smislu
05:26
meaning a random noise corrupting a signal.
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da su to nasumične smetnje koje ometaju signal.
05:28
So the old days before digital radio when you were tuning in your radio
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U doba pre digitalnog radija, kad ste tražili stanicu na radiju
05:31
and you heard "crrcckkk" on the station you wanted to hear,
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i ako biste čuli "krrrrrr" tamo gde treba da bude stanica,
05:33
that was the noise.
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to su bile smetnje - buka.
05:35
But more generally, this noise is something that corrupts the signal.
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Ali, generalno, ta buka je nešto što ometa signal.
05:38
So for example, if you put your hand under a table
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Tako na primer, ako stavite šaku ispod stola
05:40
and try to localize it with your other hand,
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i pokušate da je locirate svojom drugom šakom,
05:42
you can be off by several centimeters
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promašićete čak i za nekoliko centimetara
05:44
due to the noise in sensory feedback.
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zbog smetnji u senzornoj povratnoj sprezi.
05:46
Similarly, when you put motor output on movement output,
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Slično tome, i u prenosu motornog signala u sam pokret,
05:48
it's extremely noisy.
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ima mnogo smetnji.
05:50
Forget about trying to hit the bull's eye in darts,
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Ne pokušavajte da pogodite centar mete u pikadu,
05:52
just aim for the same spot over and over again.
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pokušajte samo da pogodite istu tačku više puta za redom.
05:54
You have a huge spread due to movement variability.
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Pogodićete jako mnogo tačaka, zbog varijabilnosti pokreta ruke.
05:57
And more than that, the outside world, or task,
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I šta više, i spoljašnji svet, i sama radnja,
05:59
is both ambiguous and variable.
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su nejasni i varijabilni.
06:01
The teapot could be full, it could be empty.
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Čajnik je možda pun, a možda prazan.
06:03
It changes over time.
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Menja se tokom vremena.
06:05
So we work in a whole sensory movement task soup of noise.
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Mi radimo u pravoj čorbi raznih smetnji, nastalih zbog oseta, pokreta, radnje,
06:09
Now this noise is so great
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Te smetnje su tako velike
06:11
that society places a huge premium
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da društvo daje velike nagrade
06:13
on those of us who can reduce the consequences of noise.
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onima koji uspeju da smanje posledice tih smetnji.
06:16
So if you're lucky enough to be able to knock a small white ball
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Ako imate dovoljno sreće da ubacite belu lopticu
06:19
into a hole several hundred yards away using a long metal stick,
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u rupu udaljenu nekoliko stotina metara, koristeći dugački, metalni štap,
06:22
our society will be willing to reward you
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naše društvo će vas rado nagraditi
06:24
with hundreds of millions of dollars.
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stotinama miliona dolara.
06:27
Now what I want to convince you of
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Ono u šta želim da vas ubedim je
06:29
is the brain also goes through a lot of effort
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da se i mozak takođe jako trudi
06:31
to reduce the negative consequences
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da smanji negativne posledice
06:33
of this sort of noise and variability.
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ovakve vrste smetnji i varijabilnosti.
06:35
And to do that, I'm going to tell you about a framework
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I da bih vas ubedio, ispričaću vam o jednom konceptu
06:37
which is very popular in statistics and machine learning of the last 50 years
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koji je vrlo popularan u statistici i teoriji učenja kod mašina,
06:40
called Bayesian decision theory.
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u poslednjih 50 godina, i zove se Bajesova teorija odlučivanja.
06:42
And it's more recently a unifying way
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I od skora je to način razmišljanja koji
06:45
to think about how the brain deals with uncertainty.
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razmatra sve ono čime se mozak bori protiv neodređenosti.
06:48
And the fundamental idea is you want to make inferences and then take actions.
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Osnovna ideja je da želite nešto da zaključite i onda da preduzmete neku akciju.
06:51
So let's think about the inference.
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Hajde da razmislimo o zaključivanju.
06:53
You want to generate beliefs about the world.
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Želite da formulišete neke stavove o svetu oko vas.
06:55
So what are beliefs?
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Šta su ti stavovi?
06:57
Beliefs could be: where are my arms in space?
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To može biti npr. "gde mi se nalaze ruke u prostoru?"
06:59
Am I looking at a cat or a fox?
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"Da li sad gledam u mačku ili u lisicu?"
07:01
But we're going to represent beliefs with probabilities.
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Ali, mi ćemo predstaviti stavove sa verovatnoćama.
07:04
So we're going to represent a belief
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Zato ćemo predstaviti stav
07:06
with a number between zero and one --
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koristeći broj između nula i jedan
07:08
zero meaning I don't believe it at all, one means I'm absolutely certain.
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nula znači da uopšte ne verujem u stav, jedan znači da sam apsolutno siguran.
07:11
And numbers in between give you the gray levels of uncertainty.
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A brojevi između prave sivu zonu neodređenosti.
07:14
And the key idea to Bayesian inference
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I ključna ideja zaključivanja po Bajesovoj teoriji
07:16
is you have two sources of information
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je da imate dva izvora informacija
07:18
from which to make your inference.
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koje koristite da izvedete svoj zaključak.
07:20
You have data,
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Imate podatke,
07:22
and data in neuroscience is sensory input.
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"podaci" u neurobiologiji znače senzorni unos (input).
07:24
So I have sensory input, which I can take in to make beliefs.
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Znači, imam senzorni unos, koji mogu da koristim da formiram stav.
07:27
But there's another source of information, and that's effectively prior knowledge.
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Ali, postoji drugi izvor informacija, a to je prethodno znanje.
07:30
You accumulate knowledge throughout your life in memories.
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Tokom života čovek nagomilava znanje u vidu sećanja.
07:33
And the point about Bayesian decision theory
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Poenta Bajesove teorije odlučivanja je
07:35
is it gives you the mathematics
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u tome da pruža matematički način
07:37
of the optimal way to combine
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za optimalno kombinovanje
07:39
your prior knowledge with your sensory evidence
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prethodnog znanja sa senzornim unosima
07:41
to generate new beliefs.
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u cilju generisanja novih stavova.
07:43
And I've put the formula up there.
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Stavio sam formulu tu gore.
07:45
I'm not going to explain what that formula is, but it's very beautiful.
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Neću objašnjavati šta je ta formula, ali je to divna formula.
07:47
And it has real beauty and real explanatory power.
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Vrlo je lepa i može dobro da objasni.
07:50
And what it really says, and what you want to estimate,
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A šta zapravo govori, to je i ono što želite da procenite,
07:52
is the probability of different beliefs
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to je verovatnoća za različite stavove
07:54
given your sensory input.
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uzimajući u obzir senzorni unos o kom se radi.
07:56
So let me give you an intuitive example.
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Evo da vam dam jedan intuitivno jasan primer.
07:58
Imagine you're learning to play tennis
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Zamislite da učite da igrate tenis
08:01
and you want to decide where the ball is going to bounce
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i hoćete da odlučite gde će lopta odskočiti
08:03
as it comes over the net towards you.
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kada stigne preko mreže ka vama.
08:05
There are two sources of information
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Postoje dva izvora informacija
08:07
Bayes' rule tells you.
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po Bajesovom pravilu.
08:09
There's sensory evidence -- you can use visual information auditory information,
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Postoji senzorni podatak - to može biti vizuelna ili audio informacija,
08:12
and that might tell you it's going to land in that red spot.
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i to vam može ukazati gde da postavite tu crvenu tačku.
08:15
But you know that your senses are not perfect,
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Ali, znate da čula nisu savršena,
08:18
and therefore there's some variability of where it's going to land
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i zato postoji varijabilnost oko toga gde će loptica pasti
08:20
shown by that cloud of red,
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što se vidi kao oblak crvene boje,
08:22
representing numbers between 0.5 and maybe 0.1.
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koji predstavlja brojeve između 0.5 i možda 0.1
08:26
That information is available in the current shot,
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Tu informaciju imamo za trenutni udarac,
08:28
but there's another source of information
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ali postoji i drugi izvor informacija
08:30
not available on the current shot,
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koji ne postoji pri trenutnom udarcu,
08:32
but only available by repeated experience in the game of tennis,
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već se dobija iskustvom kroz ponovljeno igranje tenisa,
08:35
and that's that the ball doesn't bounce
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i ta informacija je da loptica ne odskače
08:37
with equal probability over the court during the match.
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sa istom verovatnoćom preko celog terena tokom meča.
08:39
If you're playing against a very good opponent,
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Ako igrate protiv jako dobrog igrača,
08:41
they may distribute it in that green area,
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takav protivnik može poslati lopticu u tu zelenu zonu
08:43
which is the prior distribution,
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što je prethodna distribucija,
08:45
making it hard for you to return.
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čime vama otežava da vratite lopticu.
08:47
Now both these sources of information carry important information.
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Oba ova izvora pružaju važne informacije.
08:49
And what Bayes' rule says
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A Bajesovo pravilo kaže
08:51
is that I should multiply the numbers on the red by the numbers on the green
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da treba pomnožiti crvene brojeve sa zelenim brojevima
08:54
to get the numbers of the yellow, which have the ellipses,
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da biste dobili žute brojeve, koji daju ove elipse,
08:57
and that's my belief.
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i to je moj stav.
08:59
So it's the optimal way of combining information.
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To je optimalni način kombinovanja informacija.
09:02
Now I wouldn't tell you all this if it wasn't that a few years ago,
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Ne bih vam pričao o ovome da, pre nekoliko godina,
09:04
we showed this is exactly what people do
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nismo pokazali da upravo na ovaj način
09:06
when they learn new movement skills.
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ljudi uče nove tehnike kretanja.
09:08
And what it means
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To znači
09:10
is we really are Bayesian inference machines.
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da smo mi, u stvari, bajesovske mašine za zaključivanje.
09:12
As we go around, we learn about statistics of the world and lay that down,
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Tokom života skupljamo statističke podatke o svetu i to pamtimo,
09:16
but we also learn
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ali takođe i učimo
09:18
about how noisy our own sensory apparatus is,
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da je naš sopstveni senzorni aparat pun smetnji,
09:20
and then combine those
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i zatim to sve kombinujemo
09:22
in a real Bayesian way.
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na pravi bajesovski način.
09:24
Now a key part to the Bayesian is this part of the formula.
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Ključni deo Bajesovskog zaključivanja je ovaj deo formule.
09:27
And what this part really says
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Taj deo zapravo govori
09:29
is I have to predict the probability
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da ja treba da predvidim verovatnoću
09:31
of different sensory feedbacks
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različitih senzornih povratnih reakcija
09:33
given my beliefs.
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u odnosu na moje stavove.
09:35
So that really means I have to make predictions of the future.
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To, u stvari, znači da treba da predvidim budućnost.
09:38
And I want to convince you the brain does make predictions
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Želim da vas ubedim da mozak zaista pravi ta predviđanja
09:40
of the sensory feedback it's going to get.
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o senzornim povratnim reakcijama koje će primiti.
09:42
And moreover, it profoundly changes your perceptions
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I šta više, tako mozak značajno menja percepciju
09:44
by what you do.
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zavisno od toga šta činite.
09:46
And to do that, I'll tell you
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Da bih vas ubedio, ispričaću vam
09:48
about how the brain deals with sensory input.
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kako mozak obrađuje senzorni unos.
09:50
So you send a command out,
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Znači, pošaljete komandu napolje,
09:53
you get sensory feedback back,
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dobijete povratnu senzornu reakciju,
09:55
and that transformation is governed
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a na tu transformaciju utiču
09:57
by the physics of your body and your sensory apparatus.
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vaše fizičke osobine i vaš senzorni aparat.
10:00
But you can imagine looking inside the brain.
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Ali, zamislite da gledate u unutrašnjost mozga.
10:02
And here's inside the brain.
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Evo unutrašnjosti mozga.
10:04
You might have a little predictor, a neural simulator,
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Možda imate mali pokazivač, nervni simulator,
10:06
of the physics of your body and your senses.
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fizičkih osobina vašeg tela i čula.
10:08
So as you send a movement command down,
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Pa, kad pošaljete komandu za pokret,
10:10
you tap a copy of that off
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pokrenete i kopiju te komande
10:12
and run it into your neural simulator
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i ubacite u svoj nervni simulator
10:14
to anticipate the sensory consequences of your actions.
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da biste predvideli senzorne posledice te akcije.
10:18
So as I shake this ketchup bottle,
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Kada protresem ovu flašu kečapa,
10:20
I get some true sensory feedback as the function of time in the bottom row.
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dobijam prave senzorne povratne reakcije u zavisnosti od vremena, u donjem redu.
10:23
And if I've got a good predictor, it predicts the same thing.
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Ako imam dobar pokazivač, predvideće istu tu stvar.
10:26
Well why would I bother doing that?
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A zašto da se bakćem da to sve radim?
10:28
I'm going to get the same feedback anyway.
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Primiću iste povratne reakcije u svakom slučaju.
10:30
Well there's good reasons.
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Pa, postoje dobri razlozi.
10:32
Imagine, as I shake the ketchup bottle,
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Zamisliite, dok ja mućkam flašu kečapa,
10:34
someone very kindly comes up to me and taps it on the back for me.
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neko mi se tiho prišunja i lupne dno flaše.
10:37
Now I get an extra source of sensory information
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Sad imam dodatni izvor senzornih informacija
10:39
due to that external act.
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zbog te spoljne akcije.
10:41
So I get two sources.
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Znači, imam dva izvora.
10:43
I get you tapping on it, and I get me shaking it,
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Imam to da je neko lupnuo flašu i to da ja tresem flašu,
10:46
but from my senses' point of view,
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ali sa gledišta mojih čula,
10:48
that is combined together into one source of information.
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to se zajedno kombinuje u jedan izvor informacija.
10:51
Now there's good reason to believe
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Postoje dobri razlozi za verovanje
10:53
that you would want to be able to distinguish external events from internal events.
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da želite da budete u stanju da razlikujete spoljne događaje od unutrašnjih.
10:56
Because external events are actually much more behaviorally relevant
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Zato što su spoljašnji događaji u stvari mnogo važniji za ponašanje
10:59
than feeling everything that's going on inside my body.
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nego to da se oseti sve što se dešava unutar tela.
11:02
So one way to reconstruct that
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Jedan način da se to rekonstruiše
11:04
is to compare the prediction --
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je da se uporedi to predviđanje
11:06
which is only based on your movement commands --
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koje se bazira samo na vašim komandama za pokret
11:08
with the reality.
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sa stvarnim stanjem.
11:10
Any discrepancy should hopefully be external.
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Svako neslaganje bi trebalo da bude zbog spoljnih uticaja.
11:13
So as I go around the world,
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I tako, dok se krećem po svetu,
11:15
I'm making predictions of what I should get, subtracting them off.
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pravim predviđanja o tome šta bi trebalo da dobijem i onda ih oduzimam.
11:18
Everything left over is external to me.
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I ono što preostane su spoljni uticaji.
11:20
What evidence is there for this?
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Kakvi dokazi postoje za ovo?
11:22
Well there's one very clear example
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Pa, postoji jedan vrlo jasan primer
11:24
where a sensation generated by myself feels very different
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u kom senzacija koju sam ja proizveo deluje sasvim drugačije
11:26
then if generated by another person.
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od one koju stvara neka druga osoba.
11:28
And so we decided the most obvious place to start
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Odlučili smo da je očigledno najbolje početi
11:30
was with tickling.
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sa golicanjem.
11:32
It's been known for a long time, you can't tickle yourself
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Dugo je već poznato da ne možete sami sebe golicati,
11:34
as well as other people can.
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a da drugi ljudi mogu da vas golicaju.
11:36
But it hasn't really been shown, it's because you have a neural simulator,
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Ali nije do sada pokazano da je to zato što imate nervni simulator,
11:39
simulating your own body
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kojim simulirate sopstveno telo
11:41
and subtracting off that sense.
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i oduzimate taj osećaj.
11:43
So we can bring the experiments of the 21st century
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U 21.veku možemo da eksperimentišemo
11:46
by applying robotic technologies to this problem.
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primenjujući robotičke tehnologije na ovaj problem.
11:49
And in effect, what we have is some sort of stick in one hand attached to a robot,
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Zapravo, imamo neku vrstu štapa u ruci pričvršćenoj za robota,
11:52
and they're going to move that back and forward.
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i roboti će to pomerati napred-nazad.
11:54
And then we're going to track that with a computer
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A mi ćemo to pratiti preko kompjutera
11:56
and use it to control another robot,
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i koristiti to za kontrolisanje drugog robota,
11:58
which is going to tickle their palm with another stick.
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koji će golicati po dlanu drugim štapom.
12:00
And then we're going to ask them to rate a bunch of things
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A onda ćemo ih pitati da ocene nekoliko stvari
12:02
including ticklishness.
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uključujući i golicljivost.
12:04
I'll show you just one part of our study.
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Pokazaću vam samo deo našeg istraživanja.
12:06
And here I've taken away the robots,
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I tu sam sklonio robote,
12:08
but basically people move with their right arm sinusoidally back and forward.
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i vidimo ljude koji sinusoidalno pomeraju desnu ruku napred-nazad.
12:11
And we replay that to the other hand with a time delay.
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To ponovo puštamo drugoj ruci, sa izvesnim kašnjenjem.
12:14
Either no time delay,
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U slučaju da nema kašnjenja,
12:16
in which case light would just tickle your palm,
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svetlosni snop će golicati vaš dlan,
12:18
or with a time delay of two-tenths of three-tenths of a second.
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ili će postojati kašnjenje od dve ili tri desetinke.
12:22
So the important point here
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Ono što je važno u ovome
12:24
is the right hand always does the same things -- sinusoidal movement.
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je da desna ruka uvek radi istu stvar -- sinusoidalni pokret.
12:27
The left hand always is the same and puts sinusoidal tickle.
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Leva ruka je uvek ista i pravi sinusoidalno golicanje.
12:30
All we're playing with is a tempo causality.
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Mi se samo igramo sa uzročnom vezom u odnosu na tempo pokreta.
12:32
And as we go from naught to 0.1 second,
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Kako napredujemo od nule ka 0.1 sekundi,
12:34
it becomes more ticklish.
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postaje sve golicljivije.
12:36
As you go from 0.1 to 0.2,
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Kako idemo od 0.1 ka 0.2,
12:38
it becomes more ticklish at the end.
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postaje još golicljivije na kraju.
12:40
And by 0.2 of a second,
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I na 0.2 sekunde
12:42
it's equivalently ticklish
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podjednako je golicljivo,
12:44
to the robot that just tickled you without you doing anything.
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kao robot koji vas je upravo zagolicao, a da vi niste ništa radili.
12:46
So whatever is responsible for this cancellation
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Što god da je odgovorno za ovo poništavanje
12:48
is extremely tightly coupled with tempo causality.
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je izuzetno blisko povezano sa uzročnom vezom tempa kretanja.
12:51
And based on this illustration, we really convinced ourselves in the field
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Na osnovu ove ilustracije, svi mi koji radimo u ovoj oblasti smo se uverili
12:54
that the brain's making precise predictions
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da mozak pravi precizna predviđanja
12:56
and subtracting them off from the sensations.
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koja oduzima od onoga što se oseti.
12:59
Now I have to admit, these are the worst studies my lab has ever run.
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Moram da priznam da su ovo najgora istraživanja ikad rađena
13:02
Because the tickle sensation on the palm comes and goes,
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u mojoj laboratoriji, jer osećaj golicanja po dlanu dođe i prođe,
13:04
you need large numbers of subjects
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treba vam veliki broj ispitanika
13:06
with these stars making them significant.
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sa ovim zvezdicama koje znače da je statistički značajno.
13:08
So we were looking for a much more objective way
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Zato smo tražili objektivniji način
13:10
to assess this phenomena.
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za ispitivanje ovih pojava.
13:12
And in the intervening years I had two daughters.
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A u međuvremenu sam ja dobio dve ćerke.
13:14
And one thing you notice about children in backseats of cars on long journeys,
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Primetićete da deca, na zadnjem sedištu auta, na dugom putovanju,
13:17
they get into fights --
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počinju da se tuku
13:19
which started with one of them doing something to the other, the other retaliating.
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što započne tako što jedno uradi nešto onom drugom, koje onda uzvrati.
13:22
It quickly escalates.
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To se brzo pojačava.
13:24
And children tend to get into fights which escalate in terms of force.
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Deca započnu tuču koja se pojača u smislu jačine udaraca.
13:27
Now when I screamed at my children to stop,
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Kad sam viknuo mojim ćerkama da prestanu,
13:29
sometimes they would both say to me
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nekad bi mi obe rekle
13:31
the other person hit them harder.
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da je ona druga udarala jače.
13:34
Now I happen to know my children don't lie,
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E sad, ja znam da moja deca ne lažu,
13:36
so I thought, as a neuroscientist,
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pa sam pomislio, kao neurobiolog,
13:38
it was important how I could explain
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da je važno da pokušam da objasnim
13:40
how they were telling inconsistent truths.
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kako to da govore suprotstavljene istinite stvari.
13:42
And we hypothesize based on the tickling study
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Postavili smo hipotezu, na osnovu ovog istraživanja golicljivosti,
13:44
that when one child hits another,
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da kad jedno dete udari drugo,
13:46
they generate the movement command.
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ono generiše komandu za pokret.
13:48
They predict the sensory consequences and subtract it off.
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Tako predviđa senzornu posledicu i to oduzima.
13:51
So they actually think they've hit the person less hard than they have --
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Zato dete zapravo misli da je udarilo drugu osobu
13:53
rather like the tickling.
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manjom jačinom nego što zaista jeste, slično kao za golicanje.
13:55
Whereas the passive recipient
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Dok pasivni primalac
13:57
doesn't make the prediction, feels the full blow.
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ne pravi to predviđanje i zato oseća punu jačinu udarca.
13:59
So if they retaliate with the same force,
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I ako hoće da uzvrati istom jačinom,
14:01
the first person will think it's been escalated.
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prva osoba će misliti da je to pojačano.
14:03
So we decided to test this in the lab.
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Odlučili smo da to testiramo u laboratoriji.
14:05
(Laughter)
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(smeh)
14:08
Now we don't work with children, we don't work with hitting,
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Mi ne radimo sa decom, ne koristimo udaranje,
14:10
but the concept is identical.
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ali koncept je isti.
14:12
We bring in two adults. We tell them they're going to play a game.
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Doveli smo dvoje odraslih. Rekli smo im da će igrati jednu igru.
14:15
And so here's player one and player two sitting opposite to each other.
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I evo prvog i drugog igrača kako sede jedan naspram drugog.
14:17
And the game is very simple.
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Igra je vrlo jednostavna.
14:19
We started with a motor
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Počeli smo sa motorom
14:21
with a little lever, a little force transfuser.
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sa polugicom, malim prenosnikom sile.
14:23
And we use this motor to apply force down to player one's fingers
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Koristimo taj motor da primenimo silu na prste prvog igrača
14:25
for three seconds and then it stops.
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tokom tri sekunde i onda prestaje.
14:28
And that player's been told, remember the experience of that force
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Tom igraču smo rekli da zapamti iskustvo o tom pritiskanju
14:31
and use your other finger
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i da koristi svoj drugi prst
14:33
to apply the same force
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da primeni istu takvu silu
14:35
down to the other subject's finger through a force transfuser -- and they do that.
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na prst drugog subjekta putem prenosnika sile -- i oni to rade.
14:38
And player two's been told, remember the experience of that force.
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I drugom igraču smo rekli da zapamti iskustvo o tom pritiskanju.
14:41
Use your other hand to apply the force back down.
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I da svojom drugom rukom uzvrati pritisak.
14:44
And so they take it in turns
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I tako oni naizmenično
14:46
to apply the force they've just experienced back and forward.
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pritiskaju istom silom koju su upravo iskusili.
14:48
But critically,
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Ali, jako je važno to da,
14:50
they're briefed about the rules of the game in separate rooms.
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smo im rekli o pravilima igre u odvojenim sobama.
14:53
So they don't know the rules the other person's playing by.
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Tako da ne znaju po kojim pravilima igra ona druga osoba.
14:55
And what we've measured
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I ono što smo izmerili
14:57
is the force as a function of terms.
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je sila u funkciji od uslova koji postoje.
14:59
And if we look at what we start with,
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Kada pogledamo na ono od čega smo počeli,
15:01
a quarter of a Newton there, a number of turns,
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četvrtina Njutna ovde, određen broj ponavljanja,
15:03
perfect would be that red line.
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idealno bi bilo ovo po crvenoj liniji.
15:05
And what we see in all pairs of subjects is this --
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A ono što vidimo kod svih parova subjekata je ovo
15:08
a 70 percent escalation in force
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70 odsto povećanja jačine pritiska
15:10
on each go.
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u svakom ciklusu.
15:12
So it really suggests, when you're doing this --
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Iz ovoga sledi da kada to radite,
15:14
based on this study and others we've done --
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i to je na osnovu ove studije i drugih koje smo sproveli,
15:16
that the brain is canceling the sensory consequences
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mozak poništava senzorne posledice
15:18
and underestimating the force it's producing.
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i potcenjuje silu koju stvara.
15:20
So it re-shows the brain makes predictions
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To ponovo pokazuje da mozak pravi predviđanja
15:22
and fundamentally changes the precepts.
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i fundamentalno menja naredbe.
15:25
So we've made inferences, we've done predictions,
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Do sada smo pravili zaključke i predviđanja,
15:28
now we have to generate actions.
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a sada treba da generišemo akcije.
15:30
And what Bayes' rule says is, given my beliefs,
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A Bajesovo pravilo kaže da, u odnosu na moje stavove,
15:32
the action should in some sense be optimal.
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akcija treba da bude, na neki način, optimalna.
15:34
But we've got a problem.
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Ali, imamo problem.
15:36
Tasks are symbolic -- I want to drink, I want to dance --
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Radnje su simbolične - hoću da pijem, hoću da igram -
15:39
but the movement system has to contract 600 muscles
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ali sistem za kretanje mora da kontrahuje
15:41
in a particular sequence.
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600 mišića u određenoj sekvenci.
15:43
And there's a big gap
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I tu postoji veliki jaz
15:45
between the task and the movement system.
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između određene radnje i sistema za kretanje.
15:47
So it could be bridged in infinitely many different ways.
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Taj jaz se može premostiti na bezgranično mnogo načina.
15:49
So think about just a point to point movement.
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Pomislite samo na kretanje od jedne do druge tačke.
15:51
I could choose these two paths
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Mogu da izaberem ove dve putanje
15:53
out of an infinite number of paths.
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od bezbrojno mnogo drugih putanja.
15:55
Having chosen a particular path,
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Pošto sam izabrao određenu putanju,
15:57
I can hold my hand on that path
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mogu da postavim ruku na toj putanji
15:59
as infinitely many different joint configurations.
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u beskonačno mnogo različitih položaja zgloba.
16:01
And I can hold my arm in a particular joint configuration
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I mogu da držim ruku u određenom položaju zgloba
16:03
either very stiff or very relaxed.
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vrlo ukočeno ili vrlo opušteno.
16:05
So I have a huge amount of choice to make.
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Znači da imam ogroman broj izbora koje mogu da napravim.
16:08
Now it turns out, we are extremely stereotypical.
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Ispostavlja se da smo izuzetno stereotipni.
16:11
We all move the same way pretty much.
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Svi se krećemo na skoro isti način.
16:14
And so it turns out we're so stereotypical,
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Ispostavlja se da smo toliko stereotipni,
16:16
our brains have got dedicated neural circuitry
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da naš mozak ima posebnu nervnu mrežu
16:18
to decode this stereotyping.
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za dekodiranje te stereotipnosti.
16:20
So if I take some dots
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Ako uzmem neke tačke
16:22
and set them in motion with biological motion,
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i pomeram ih tako da izgleda kao biološko kretanje,
16:25
your brain's circuitry would understand instantly what's going on.
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moždana mreža će odmah shvatiti o čemu se radi.
16:28
Now this is a bunch of dots moving.
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Ovo je grupa tačaka koje se kreću.
16:30
You will know what this person is doing,
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A vi znate šta ova osoba radi,
16:33
whether happy, sad, old, young -- a huge amount of information.
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da li je srećna, tužna, stara, mlada - ogromna količina informacija.
16:36
If these dots were cars going on a racing circuit,
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Da su ove tačke kola koja se kreću po trkačkoj stazi,
16:38
you would have absolutely no idea what's going on.
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ne biste uopšte znali o čemu se radi.
16:41
So why is it
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Pa, zašto se
16:43
that we move the particular ways we do?
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krećemo na tako određene načine?
16:45
Well let's think about what really happens.
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Hajde da razmislimo šta se zaista dešava.
16:47
Maybe we don't all quite move the same way.
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Možda se ne krećemo svi na sasvim isti način.
16:50
Maybe there's variation in the population.
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Možda postoji varijacija u populaciji.
16:52
And maybe those who move better than others
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I možda oni koji se bolje kreću od drugih
16:54
have got more chance of getting their children into the next generation.
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imaju veću šansu da dobiju potomstvo u sledećoj generaciji.
16:56
So in evolutionary scales, movements get better.
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Tako se, na evolutivnoj skali, načini kretanja poboljšavaju.
16:59
And perhaps in life, movements get better through learning.
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I možda tokom života, načini kretanja postaju bolji kroz učenje.
17:02
So what is it about a movement which is good or bad?
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A šta je to što čini neki način kretanja dobrim ili lošim?
17:04
Imagine I want to intercept this ball.
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Zamislite da hoću da presretnem ovu loptu
17:06
Here are two possible paths to that ball.
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Evo dve moguće putanje do te lopte.
17:09
Well if I choose the left-hand path,
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Pa, ako izaberem putanju sa leve strane,
17:11
I can work out the forces required
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mogu da izračunam sile koje su potrebne
17:13
in one of my muscles as a function of time.
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za jedan od mojih mišića u zavisnosti od vremena.
17:15
But there's noise added to this.
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Ali, postoje smetnje koje se dodaju na to.
17:17
So what I actually get, based on this lovely, smooth, desired force,
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Ono što zapravo dobijam, počevši od ove fine, uglađene, željene sile,
17:20
is a very noisy version.
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je verzija sa puno smetnji.
17:22
So if I pick the same command through many times,
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Ako izaberem istu komandu više puta,
17:25
I will get a different noisy version each time, because noise changes each time.
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dobiću svaki put verzije sa različitim smetnjama, jer se smetnje menjaju svaki put.
17:28
So what I can show you here
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Ono što mogu da vam ovde pokažem je
17:30
is how the variability of the movement will evolve
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kako varijabilnost pokreta evoluira
17:32
if I choose that way.
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ukoliko izaberem taj način.
17:34
If I choose a different way of moving -- on the right for example --
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Ako izaberem drugi način kretanja - npr. ovaj sa desne strane
17:37
then I'll have a different command, different noise,
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onda ću imati drugačiju komandu, drugačije smetnje,
17:39
playing through a noisy system, very complicated.
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vrlo je komplikovano snaći se u sistemu punom smetnji.
17:42
All we can be sure of is the variability will be different.
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Jedino u šta smo sigurni je da će varijabilnost biti različita.
17:45
If I move in this particular way,
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Ako se krećem na ovaj određeni način,
17:47
I end up with a smaller variability across many movements.
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imaću manju varijabilnost kroz mnoge pokrete.
17:50
So if I have to choose between those two,
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Pa, ako treba da biram između ta dva,
17:52
I would choose the right one because it's less variable.
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izabraću ovaj desni, jer ima manju varijabilnost.
17:54
And the fundamental idea
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Osnovna ideja
17:56
is you want to plan your movements
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je da želite da planirate svoje pokrete
17:58
so as to minimize the negative consequence of the noise.
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da biste što više smanjili negativne posledice smetnji.
18:01
And one intuition to get
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Može se primetiti
18:03
is actually the amount of noise or variability I show here
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da se, zapravo, količina smetnji ili varijabilnost koju pokazujem
18:05
gets bigger as the force gets bigger.
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povećava kako se sila pojačava.
18:07
So you want to avoid big forces as one principle.
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U principu, želite da izbegnete jake sile.
18:10
So we've shown that using this,
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Tako smo pokazali da, koristeći ovo,
18:12
we can explain a huge amount of data --
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možemo da objasnimo veliki broj podataka
18:14
that exactly people are going about their lives planning movements
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da, u stvari, ljudi stalno planiraju svoje kretanje
18:17
so as to minimize negative consequences of noise.
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sa ciljem smanjivanja negativnih posledica smetnji.
18:20
So I hope I've convinced you the brain is there
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Nadam se da sam vas ubedio da je razlog za postojanje
18:22
and evolved to control movement.
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i evoluciju mozga to da bi kontrolisao kretanje.
18:24
And it's an intellectual challenge to understand how we do that.
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Intelektualno je zahtevno da se razume kako mi to radimo.
18:27
But it's also relevant
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I vrlo je važno
18:29
for disease and rehabilitation.
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zbog bolesti i rehabilitacije.
18:31
There are many diseases which effect movement.
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Ima mnogo bolesti koje utiču na kretanje.
18:34
And hopefully if we understand how we control movement,
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I nadamo se, ako razumemo kako kontrolišemo kretanje,
18:36
we can apply that to robotic technology.
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da ćemo to moći da primenimo na robotičku tehnologiju.
18:38
And finally, I want to remind you,
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I konačno, želim da vas podsetim,
18:40
when you see animals do what look like very simple tasks,
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kada vidite životinje da izvode ono što izgleda kao jednostavna radnja,
18:42
the actual complexity of what is going on inside their brain
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kompleksnost onoga što se dešava u njihovom mozgu
18:44
is really quite dramatic.
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je, u stvari, veoma upečatljiva.
18:46
Thank you very much.
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Hvala vam mnogo.
18:48
(Applause)
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(aplauz)
18:56
Chris Anderson: Quick question for you, Dan.
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Kris Enderson: Jedno brzo pitanje za tebe, Den.
18:58
So you're a movement -- (DW: Chauvinist.) -- chauvinist.
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Ti si, znači -- (DV: šovinista) -- šovinista za kretanje.
19:02
Does that mean that you think that the other things we think our brains are about --
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Da li to znači da misliš da druge stvari za koje smatramo da nam mozak služi
19:05
the dreaming, the yearning, the falling in love and all these things --
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snovi, želje, zaljubljivanje i sve te stvari
19:08
are a kind of side show, an accident?
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su neka vrsta sporednih dešavanja, samo slučajnost?
19:11
DW: No, no, actually I think they're all important
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DV: Ne, ne, ja, u stvari, mislim da je sve to važno
19:13
to drive the right movement behavior to get reproduction in the end.
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da bi usmerilo ka pravom načinu kretanja koji, na kraju, vodi do reprodukcije.
19:16
So I think people who study sensation or memory
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Mislim da ljudi koji proučavaju senzacije ili pamćenje,
19:19
without realizing why you're laying down memories of childhood.
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ne shvataju zašto skupljamo ta sećanja iz detinjstva.
19:21
The fact that we forget most of our childhood, for example,
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Npr. činjenica da većinu stvari iz detinjstva zaboravimo,
19:24
is probably fine, because it doesn't effect our movements later in life.
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je verovatno prihvatljiva, jer to ne utiče na naše kretanje kasnije u životu.
19:27
You only need to store things which are really going to effect movement.
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Potrebno nam je da čuvamo samo one stvari koje će zaista uticati na kretanje.
19:30
CA: So you think that people thinking about the brain, and consciousness generally,
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KE: Znači smatraš da bi ljudi, kad razmišljaju o mozgu i generalno o svesti,
19:33
could get real insight
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mogli da donesu bolje zaključke
19:35
by saying, where does movement play in this game?
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ako bi se zapitali, "a, gde je kretanje u svemu tome?"
19:37
DW: So people have found out for example
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DV: Pa, ljudi su zaključili, na primer
19:39
that studying vision in the absence of realizing why you have vision
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da je proučavanje čula vida bez svesti o tome zbog čega postoji čulo vida
19:41
is a mistake.
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pogrešno.
19:43
You have to study vision with the realization
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Treba proučavati čulo vida uz shvatanje
19:45
of how the movement system is going to use vision.
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o tome kako će sistem za kretanje da iskoristi čulo vida.
19:47
And it uses it very differently once you think about it that way.
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I kad počnete da razmišljate na taj način, shvatite da je to drugačije.
19:49
CA: Well that was quite fascinating. Thank you very much indeed.
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KE: Ovo je bilo baš fascinantno. Stvarno, puno ti hvala.
19:52
(Applause)
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(aplauz)
ABOUT THE SPEAKER
Daniel Wolpert - Movement expertA neuroscientist and engineer, Daniel Wolpert studies how the brain controls the body.
Why you should listen
Consider your hand. You use it to lift things, to balance yourself, to give and take, to sense the world. It has a range of interacting degrees of freedom, and it interacts with many different objects under a variety of environmental conditions. And for most of us, it all just works. At his lab in the Engineering department at Cambridge, Daniel Wolpert and his team are studying why, looking to understand the computations underlying the brain's sensorimotor control of the body.
As he says, "I believe that to understand movement is to understand the whole brain. And therefore it’s important to remember when you are studying memory, cognition, sensory processing, they’re there for a reason, and that reason is action.” Movement is the only way we have of interacting with the world, whether foraging for food or attracting a waiter's attention. Indeed, all communication, including speech, sign language, gestures and writing, is mediated via the motor system. Taking this viewpoint, and using computational and robotic techniques as well as virtual reality systems, Wolpert and his team research the purpose of the human brain and the way it determines future actions.
Daniel Wolpert | Speaker | TED.com