TEDGlobal 2011
Daniel Wolpert: The real reason for brains
Daniel Wolpert: Prava svrha mozga
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Neuroznanstvenik Daniel Wolpert polazi od zanimljive pretpostavke: mozak se nije razvio da bismo razmišljali ili osjećali, nego za upravljanje pokretima. U ovom zabavnom i vrlo informativnom govoru daje nam uvid kako mozak stvara sklad i spretnost ljudskih pokreta.
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 neuroznanstvenik.
00:17
And in neuroscience,
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A u neuroznanosti,
00:19
we have to deal with many difficult questions about the brain.
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moramo se baviti mnoštvom teških pitanja o mozgu.
00:22
But I want to start with the easiest question
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No, želim početi s najlakšim pitanjem,
00:24
and the question you really should have all asked yourselves at some point in your life,
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pitanjem koje ste trebali postaviti sami sebi u nekom trenutku svojeg života,
00:27
because it's a fundamental question
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jer to je temeljno pitanje
00:29
if we want to understand brain function.
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ako želimo razumjeti funkciju mozga.
00:31
And that is, why do we and other animals
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A to pitanje jest: zašto mi i neke druge vrste
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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pa ako želimo saznati za što nam mozak služi,
00:40
let's think about why we evolved one.
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razmislimo prvo zašto smo ga uopće razvili tijekom evolucije.
00:42
Now you may reason that we have one
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Možete tvrditi da nam je potreban
00:44
to perceive the world or to think,
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kako bismo mogli spoznati svijet ili razmišljati,
00:46
and that's completely wrong.
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no to je potpuno krivo.
00:48
If you think about this question for any length of time,
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Ako razmislite o tom pitanju malo duže,
00:51
it's blindingly obvious why we have a brain.
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nevjerojatno je očito zašto imamo mozak.
00:53
We have a brain for one reason and one reason only,
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Imamo mozak iz jednog jedinog razloga:
00:56
and that's to produce adaptable and complex movements.
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da bismo mogli izvoditi primjerene i složene pokrete.
00:59
There is no other reason to have a brain.
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Nema drugog razloga 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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Kretnje su jedini način na koji možete
01:05
of affecting the world around you.
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utjecati na svijet oko vas.
01:07
Now that's not quite true. There's one other way, and that's through sweating.
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Dobro, to nije u potpunosti točno. Postoji jedan drugi način, a to je znojenje.
01:10
But apart from that,
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No, osim toga,
01:12
everything else goes through contractions of muscles.
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sve ostalo ide preko mišića.
01:14
So think about communication --
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Dakle, razmislite o komunikaciji –
01:16
speech, gestures, writing, sign language --
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govor, geste, pisanje, znakovni jezik –
01:19
they're all mediated through contractions of your muscles.
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sve to omogućavaju pokreti vaših mišića.
01:22
So it's really important to remember
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Zato je vrlo važno imati na umu
01:24
that sensory, memory and cognitive processes are all important,
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da su osjetila, pamćenje i kognitivni procesi važni,
01:28
but they're only important
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ali važni su samo
01:30
to either drive or suppress future movements.
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kako bi u budćnosti potaknuli ili potisnuli kretnje.
01:32
There can be no evolutionary advantage
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Nema evolucijske prednosti
01:34
to laying down memories of childhood
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u pohranjivanju sjećanja iz djetinjstva
01:36
or perceiving the color of a rose
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ili percepciji boje ruže,
01:38
if it doesn't affect the way you're going to move later in life.
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ako to neće utjecati na naše kretanje kasnije u životu.
01:41
Now for those who don't believe this argument,
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Za one koji ne vjeruju ovim argumentima --
01:43
we have trees and grass on our planet without the brain,
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drveće i trava na našem planetu nemaju mozak,
01:45
but the clinching evidence is this animal here --
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no ključni je dokaz ova životinja ovdje --
01:47
the humble sea squirt.
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skromni morski plaštenjaci.
01:49
Rudimentary animal, has a nervous system,
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Rudimentarna životinja, koja ima živčani sustav,
01:52
swims around in the ocean in its juvenile life.
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pliva oceanom u prvom razdoblju svog života.
01:54
And at some point of its life,
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U jednom trenutku u životu,
01:56
it implants on a rock.
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usadi se u kamen.
01:58
And the first thing it does in implanting on that rock, which it never leaves,
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A prva stvar koju učini kad se usadi u stijenu, koju više nikad ne napušta,
02:01
is to digest its own brain and nervous system
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jest da probavi svoj mozak i živčani sustav
02:04
for food.
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kao hranu.
02:06
So once you don't need to move,
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Dakle, jednom kad se više ne trebate kretati,
02:08
you don't need the luxury of that brain.
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posjedovanje mozga nepotreban je luksuz.
02:11
And this animal is often taken
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Ova životinja često se uzima
02:13
as an analogy to what happens at universities
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za usporedbu s onim što se događa na sveučilištima
02:15
when professors get tenure,
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kad netko postane redoviti profesor,
02:17
but that's a different subject.
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no to je druga tema.
02:19
(Applause)
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(Pljesak)
02:21
So I am a movement chauvinist.
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Dakle, ja sam šovinist pokreta.
02:24
I believe movement is the most important function of the brain --
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Vjerujem da je kretanje najvažnija funkcija mozga –
02:26
don't let anyone tell you that it's not true.
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neka vas nitko ne uvjeri kako to nije istina.
02:28
Now if movement is so important,
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Ako je pokret toliko važan,
02:30
how well are we doing
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koliko dobro mi uopće
02:32
understanding how the brain controls movement?
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razumijemo kako mozak kontrolira pokrete?
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 ide vrlo loše, to je prilično velik problem.
02:36
But we can look at how well we're doing
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No, možemo vidjeti kako nam dobro ide
02:38
by thinking about how well we're doing building machines
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ako razmislimo koliko dobro razvijamo strojeve
02:40
which can do what humans can do.
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koji mogu raditi ono što i ljudi rade.
02:42
Think about the game of chess.
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Razmislite o igri šaha.
02:44
How well are we doing determining what piece to move where?
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Koliko dobro možemo odrediti kamo koju figuru trebamo pomaknuti?
02:47
If you pit Garry Kasparov here, when he's not in jail,
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Kada bi se suočili Gary Kasparov, ako nije u zatvoru,
02:50
against IBM's Deep Blue,
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i IBM-ov Deep Blue,
02:52
well the answer is IBM's Deep Blue will occasionally win.
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odgovor je da bi IBM-ov Deep Blue ponekad pobijedio.
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 bi IBM-ov Deep Blue, kad bi igrao šah bilo s kim od vas u ovoj prostoriji, pobijedio svaki put.
02:58
That problem is solved.
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Taj je problem riješen.
03:00
What about the problem
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Što je s problemom
03:02
of picking up a chess piece,
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podizanja šahovske figurice,
03:04
dexterously manipulating it and putting it back down on the board?
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spretne manipulacije i spuštanja natrag na ploču?
03:07
If you put a five year-old child's dexterity against the best robots of today,
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Kada biste spretnost petogodišnjaka usporedili sa spretnošću najboljeg robota,
03:10
the answer is simple:
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odgovor je jednostavan:
03:12
the child wins easily.
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dijete bi ispalo spretnije.
03:14
There's no competition at all.
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Djetetu robot uopće nije neka konkurencija.
03:16
Now why is that top problem so easy
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No, zašto je gornji problem toliko jednostavan,
03:18
and the bottom problem so hard?
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a donji toliko težak?
03:20
One reason is a very smart five year-old
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Jedan razlog je taj što bi vam vrlo pametan petogodišnjak
03:22
could tell you the algorithm for that top problem --
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mogao reći algoritam za prvi problem –
03:24
look at all possible moves to the end of the game
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razmotriti sve moguće pokrete do kraja igre
03:26
and choose the one that makes you win.
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i odabrati onaj kojim bi pobijedio.
03:28
So it's a very simple algorithm.
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Algoritam je vrlo jednostavan.
03:30
Now of course there are other moves,
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Naravno, postoje i druga dobra rješenja,
03:32
but with vast computers we approximate
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no s dobrim računalom možemo
03:34
and come close to the optimal solution.
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otprilike odrediti optimalno rješenje.
03:36
When it comes to being dexterous,
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Kad je u pitanju spretnost –
03:38
it's not even clear what the algorithm is you have to solve to be dexterous.
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nije jasno ni koji se algoritam treba riješiti da bismo bili spretni.
03:40
And we'll see you have to both perceive and act on the world,
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Vidjet ćemo da morate i percipirati i djelovati na svijet,
03:42
which has a lot of problems.
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u kojem ima mnogo problema.
03:44
But let me show you cutting-edge robotics.
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No, pokazat ću vam najsuvremeniju robotiku.
03:46
Now a lot of robotics is very impressive,
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Velik dio robotike vrlo je impresivan,
03:48
but manipulation robotics is really just in the dark ages.
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no manipulativna robotika kao da je u srednjem vijeku.
03:51
So this is the end of a Ph.D. project
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Ovo je proizašlo iz jednog projekta za doktorat
03:53
from one of the best robotics institutes.
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iz jednog od najboljih instituta robotike.
03:55
And the student has trained this robot
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Student je izvježbao ovog robota
03:57
to pour this water into a glass.
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za nalijevanje vode u čašu.
03:59
It's a hard problem because the water sloshes about, but it can do it.
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Ovo je težak problem jer se voda i prolijeva, ali ipak uspijeva.
04:02
But it doesn't do it with anything like the agility of a human.
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Ali ne radi to ni s približnom spretnošću koju ima čovjek.
04:05
Now if you want this robot to do a different task,
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Želimo li da ovaj robot obavlja neki drugi zadatak
04:08
that's another three-year Ph.D. program.
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to je još jedan doktorski program od 3 godine.
04:11
There is no generalization at all
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Nema nikakve generalizacije
04:13
from one task to another in robotics.
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između različitih zadataka u robotici.
04:15
Now we can compare this
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To možemo usporediti
04:17
to cutting-edge human performance.
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s najboljom ljudskom izvedbom.
04:19
So what I'm going to show you is Emily Fox
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Pokazat ću vam kako je Emily Fox
04:21
winning the world record for cup stacking.
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pobijedila na svjetskom prvenstvu u slaganju čaša.
04:24
Now the Americans in the audience will know all about cup stacking.
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Amerikanci u publici znat će o čemu je riječ.
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 kojem imate 12 čaša koje morate slagati
04:30
against the clock in a prescribed order.
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u određenom roku po propisanom redu.
04:32
And this is her getting the world record in real time.
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A ovo je snimka kako postiže svjetski rekord u realnom vremenu.
04:39
(Laughter)
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(Smijeh)
04:47
(Applause)
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(Pljesak)
04:52
And she's pretty happy.
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I prilično je sretna.
04:54
We have no idea what is going on inside her brain when she does that,
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Nemamo pojma što se događa u njezinom 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 željeli saznati.
04:58
So in my group, what we try to do
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U mojoj grupi pokušavamo raditi
05:00
is reverse engineer how humans control movement.
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obrnuti inženjering kontrole pokreta kod ljudi.
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 naredbu, ona prouzrokuje stezanje mišića.
05:07
Your arm or body moves,
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Vaša ruka ili tijelo pokreće se,
05:09
and you get sensory feedback from vision, from skin, from muscles and so on.
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a dobivate povratnu informaciju iz osjetila - preko vida, iz kože, mišića itd.
05:12
The trouble is
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Problem je što
05:14
these signals are not the beautiful signals you want them to be.
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ovi signali nisu onako lijepi kako biste vi to željeli.
05:16
So one thing that makes controlling movement difficult
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Primjerice, jedna stvar koja otežava kontrolu pokreta
05:18
is, for example, sensory feedback is extremely noisy.
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jest to što osjetilna povratna informacija ima mnogo šumova.
05:21
Now by noise, I do not mean sound.
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Kad kažem "šumovi", ne mislim na zvuk.
05:24
We use it in the engineering and neuroscience sense
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Koristimo tu riječ u inženjeringu i neuroznanosti
05:26
meaning a random noise corrupting a signal.
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u smislu nepravilnog šuma koji remeti signal.
05:28
So the old days before digital radio when you were tuning in your radio
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To je kao s radijima prije digitalnog – kad ste namještali stanicu
05:31
and you heard "crrcckkk" on the station you wanted to hear,
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i čuli onaj ''khrkrhhrkkk'' na stanici koju ste željeli čuti –
05:33
that was the noise.
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to je bio taj šum.
05:35
But more generally, this noise is something that corrupts the signal.
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No, općenito, taj je šum nešto što remeti signal.
05:38
So for example, if you put your hand under a table
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Primjerice, ako stavite ruku pod stol
05:40
and try to localize it with your other hand,
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i želite locirati tu ruku drugom rukom,
05:42
you can be off by several centimeters
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možete pogriješiti nekoliko centimetara
05:44
due to the noise in sensory feedback.
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zbog šuma u osjetilnoj povratnoj informaciji.
05:46
Similarly, when you put motor output on movement output,
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Slično tome, kad postavite motorički izlaz na izlaz za kretnje,
05:48
it's extremely noisy.
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signal je pun šumova.
05:50
Forget about trying to hit the bull's eye in darts,
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Prestanite pokušavati pogoditi metu u pikadu,
05:52
just aim for the same spot over and over again.
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samo neprestano ciljajte jednu te istu točku.
05:54
You have a huge spread due to movement variability.
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Imate ogromne pomake zbog varijabilnosti pokreta.
05:57
And more than that, the outside world, or task,
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Pored toga, vanjski svijet ili sam zadatak
05:59
is both ambiguous and variable.
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dvosmislen je i varijabilan.
06:01
The teapot could be full, it could be empty.
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Ovaj bi čajnik mogao biti i pun i prazan.
06:03
It changes over time.
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Mijenja se tijekom vremena.
06:05
So we work in a whole sensory movement task soup of noise.
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Dakle, radimo motoričke pokrete pod skupom šumova izvana.
06:09
Now this noise is so great
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Šumovi su toliko veliki
06:11
that society places a huge premium
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da društvo iznimno cijeni
06:13
on those of us who can reduce the consequences of noise.
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one koji mogu reducirati posljedice šumova.
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 možete ubaciti malu bijelu lopticu
06:19
into a hole several hundred yards away using a long metal stick,
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u rupu koja je udaljena nekoliko stotina metara koristeći dug metalni štap,
06:22
our society will be willing to reward you
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naše će društvo biti spremno nagraditi vas
06:24
with hundreds of millions of dollars.
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stotinama milijuna dolara.
06:27
Now what I want to convince you of
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Želim vas zapravo uvjeriti
06:29
is the brain also goes through a lot of effort
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da mozak također ulaže puno truda
06:31
to reduce the negative consequences
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kako bi se smanjile negativne posljedice
06:33
of this sort of noise and variability.
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ovakvih šumova i varijabilnosti pokreta.
06:35
And to do that, I'm going to tell you about a framework
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Kako bih to učinio, predstavit ću vam radni okvir
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 strojnom učenju u zadnjih 50 godina,
06:40
called Bayesian decision theory.
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a zove se Bayesova teorija odlučivanja.
06:42
And it's more recently a unifying way
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To je u novije vrijeme ujedinjenje načina
06:45
to think about how the brain deals with uncertainty.
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razmišljanja o tome kako se mozak bavi nesigurnošću.
06:48
And the fundamental idea is you want to make inferences and then take actions.
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Temeljna je ideja da pokušavamo donijeti zaključke i onda djelovati.
06:51
So let's think about the inference.
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Razmislimo malo o zaključivanju.
06:53
You want to generate beliefs about the world.
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Želite stvoriti uvjerenja o svijetu.
06:55
So what are beliefs?
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A što su to uvjerenja?
06:57
Beliefs could be: where are my arms in space?
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Uvjerenje bi moglo biti: gdje su moje ruke u prostoru?
06:59
Am I looking at a cat or a fox?
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Gledam li mačku ili lisicu?
07:01
But we're going to represent beliefs with probabilities.
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No, predstavit ćemo uvjerenje kao vjerojatnost.
07:04
So we're going to represent a belief
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Predstavit ćemo uvjerenje
07:06
with a number between zero and one --
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kao broj između 0 i 1 –
07:08
zero meaning I don't believe it at all, one means I'm absolutely certain.
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gdje 0 znači "ne vjerujem uopće", a 1 znači "apsolutno sam siguran".
07:11
And numbers in between give you the gray levels of uncertainty.
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Brojevi između označavaju zonu nesigurnosti.
07:14
And the key idea to Bayesian inference
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Glavna ideja Bayesovog zaključivanja
07:16
is you have two sources of information
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jest da postoje dva izvora informacija
07:18
from which to make your inference.
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iz kojih se mogu donijeti zaključci.
07:20
You have data,
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Imamo podatke –
07:22
and data in neuroscience is sensory input.
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a podaci u neuroznanosti jesu informacije iz osjetila.
07:24
So I have sensory input, which I can take in to make beliefs.
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Dakle, imamo informacije iz osjetila, pomoću kojih možemo doći do uvjerenja.
07:27
But there's another source of information, and that's effectively prior knowledge.
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No, postoji još jedan izvor informacija, a to je prethodno znanje.
07:30
You accumulate knowledge throughout your life in memories.
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Znanje skupljate kroz život u obliku sjećanja.
07:33
And the point about Bayesian decision theory
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A svrha Bayesove teorije odlučivanja
07:35
is it gives you the mathematics
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jest da pomoću nje izračunate
07:37
of the optimal way to combine
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optimalni način kombiniranja
07:39
your prior knowledge with your sensory evidence
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prijašnjeg znanja i osjetilnih podražaja
07:41
to generate new beliefs.
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i pomoću njih stvorite nova uvjerenja.
07:43
And I've put the formula up there.
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Stavio sam ovdje gore formulu.
07:45
I'm not going to explain what that formula is, but it's very beautiful.
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Neću objašnjavati tu formulu, ali baš je lijepa.
07:47
And it has real beauty and real explanatory power.
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Ima istinsku ljepotu i pravu moć objašnjavanja.
07:50
And what it really says, and what you want to estimate,
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A ono što zbilja govori i što želite procijeniti
07:52
is the probability of different beliefs
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jest vjerojatnost različitih uvjerenja
07:54
given your sensory input.
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s obzirom na vaše informacije iz osjetila.
07:56
So let me give you an intuitive example.
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Dat ću vam intuitivan primjer.
07:58
Imagine you're learning to play tennis
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Zamislite da učite igrati tenis
08:01
and you want to decide where the ball is going to bounce
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i želite procijeniti kamo će loptica odskočiti
08:03
as it comes over the net towards you.
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dok dolazi preko mreže prema 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 Bayesovom pravilu.
08:09
There's sensory evidence -- you can use visual information auditory information,
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Imamo osjetilni dokaz – možete koristiti vidne ili slušne informacije,
08:12
and that might tell you it's going to land in that red spot.
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i zaključiti da će pasti na crvenu točku.
08:15
But you know that your senses are not perfect,
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No, znate da vaša osjetila nisu savršena
08:18
and therefore there's some variability of where it's going to land
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i zato postoje varijacije mjesta kamo će loptica pasti -
08:20
shown by that cloud of red,
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to pokazuje ovaj crveni dio –
08:22
representing numbers between 0.5 and maybe 0.1.
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predstavlja brojeve između 0,5 i možda 1.
08:26
That information is available in the current shot,
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To su informacije dostupne tijekom trenutnog napucavanja lopte,
08:28
but there's another source of information
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no postoji još jedan izvor informacija
08:30
not available on the current shot,
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koji nije dostupan u trenutku kada lopta putuje prema vama,
08:32
but only available by repeated experience in the game of tennis,
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nego tek nakon ponovljenog iskustva igranja tenisa -
08:35
and that's that the ball doesn't bounce
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a to je da loptica neće odskočiti
08:37
with equal probability over the court during the match.
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s jednakom vjerojatnošću na cijelom igralištu tijekom meča.
08:39
If you're playing against a very good opponent,
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Ako igrate protiv vrlo dobrog protivnika,
08:41
they may distribute it in that green area,
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može ju usmjeriti na neki od ovih zelenih dijelova,
08:43
which is the prior distribution,
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koji će zbog prethodno odigranog poteza,
08:45
making it hard for you to return.
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biti vama teško dohvatljiv dio.
08:47
Now both these sources of information carry important information.
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Oba ova izvora informacija donose važne informacije.
08:49
And what Bayes' rule says
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Bayesovo pravilo kaže nam
08:51
is that I should multiply the numbers on the red by the numbers on the green
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da bismo trebali pomnožiti brojeve na crvenoj površini s brojevima na zelenoj površini
08:54
to get the numbers of the yellow, which have the ellipses,
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kako bismo dobili brojeve na žutoj boji – to su elipse –
08:57
and that's my belief.
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i to je moje uvjerenje.
08:59
So it's the optimal way of combining information.
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Tako da je to optimalan način kombiniranja 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 rekao sve ovo da nismo prije nekoliko godina
09:04
we showed this is exactly what people do
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dokazali da je upravo to način na koji ljudi
09:06
when they learn new movement skills.
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uče nove motoričke sposobnosti.
09:08
And what it means
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A to znači
09:10
is we really are Bayesian inference machines.
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da zbilja i jesmo strojevi koji rade po Bayesovom zaključivanju.
09:12
As we go around, we learn about statistics of the world and lay that down,
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Idemo kroz svijet učeći statistike o svijetu i pohranjujemo ih,
09:16
but we also learn
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ali isto tako učimo i
09:18
about how noisy our own sensory apparatus is,
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koliko šumova ima u našim osjetilnim putovima
09:20
and then combine those
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pa ih stoga kombiniramo
09:22
in a real Bayesian way.
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na pravi Bayesovski način.
09:24
Now a key part to the Bayesian is this part of the formula.
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Ključni dio Bayesovog zaključivanja jest ovaj dio formule.
09:27
And what this part really says
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Ono što ovaj dio zapravo govori
09:29
is I have to predict the probability
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jest da moram predvidjeti vjerojatnost
09:31
of different sensory feedbacks
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različitih osjetilnih povratnih informacija
09:33
given my beliefs.
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s obzirom na svoja uvjerenja.
09:35
So that really means I have to make predictions of the future.
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To zapravo znači da moram pretpostaviti budućnost.
09:38
And I want to convince you the brain does make predictions
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Želim vas uvjeriti da mozak zaista daje pretpostavke
09:40
of the sensory feedback it's going to get.
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osjetilnih povratnih informacija koje će dobiti.
09:42
And moreover, it profoundly changes your perceptions
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I osim toga, duboko se mijenja percepcija
09:44
by what you do.
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onoga što činite.
09:46
And to do that, I'll tell you
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Kako bih vas u to uvjerio, reći ću vam
09:48
about how the brain deals with sensory input.
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kako se mozak nosi s informacijama iz osjetila.
09:50
So you send a command out,
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Dakle, šaljete naredbu iz mozga,
09:53
you get sensory feedback back,
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dobivate povratne informacije iz osjetila,
09:55
and that transformation is governed
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i tom transformacijom upravlja
09:57
by the physics of your body and your sensory apparatus.
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fizika vašeg tijela i funkcioniranje osjetilnog aparata.
10:00
But you can imagine looking inside the brain.
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Možete zamisliti da gledate u unutrašnjost mozga.
10:02
And here's inside the brain.
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Ovo je unutrašnjost mozga.
10:04
You might have a little predictor, a neural simulator,
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Možda je tu mali predviđač, neuralni simulator
10:06
of the physics of your body and your senses.
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fizike vašeg tijela i osjetila.
10:08
So as you send a movement command down,
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Dok šaljete zapovijed za kretnju na periferiju,
10:10
you tap a copy of that off
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uzmete kopiju toga
10:12
and run it into your neural simulator
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i unesete je u svoj neuralni simulator
10:14
to anticipate the sensory consequences of your actions.
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kako biste predvidjeli koje će posljedice vaše radnje imati na osjetila.
10:18
So as I shake this ketchup bottle,
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Dakle, ako protresete bocu kečapa,
10:20
I get some true sensory feedback as the function of time in the bottom row.
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dobit ćete prave osjetilne informacije kao funkciju vremena u donjem redu.
10:23
And if I've got a good predictor, it predicts the same thing.
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Ako imamo dobrog predviđača, on će predvidjeti istu stvar.
10:26
Well why would I bother doing that?
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Zašto se uopće gnjavimo time?
10:28
I'm going to get the same feedback anyway.
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Ionako ćemo dobiti jednake povratne informacije.
10:30
Well there's good reasons.
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Pa, postoje dobri razlozi za to.
10:32
Imagine, as I shake the ketchup bottle,
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Zamislite da dok ja tresem bocu kečapa,
10:34
someone very kindly comes up to me and taps it on the back for me.
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netko ljubazno dođe do mene i malo ju udari.
10:37
Now I get an extra source of sensory information
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Sada imam dodatni izvor osjetilnih informacija,
10:39
due to that external act.
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koji je nastao zbog vanjskog djelovanja.
10:41
So I get two sources.
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Imam dva izvora.
10:43
I get you tapping on it, and I get me shaking it,
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Vi je lagano udarate, a ja je tresem,
10:46
but from my senses' point of view,
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ali moja osjetila to doživljavaju
10:48
that is combined together into one source of information.
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kao djelovanje koje se ujedinjuje u jedan izvor informacije.
10:51
Now there's good reason to believe
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Postoji dobar razlog zašto biste željeli
10:53
that you would want to be able to distinguish external events from internal events.
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razlučiti vanjska djelovanja od unutarnjih.
10:56
Because external events are actually much more behaviorally relevant
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Zato što su vanjska djelovanja mnogo relevantnija za ponašanje
10:59
than feeling everything that's going on inside my body.
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od osjećaja što se sve događa unutar mojeg tijela.
11:02
So one way to reconstruct that
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Jedan način da to rekonstruiramo
11:04
is to compare the prediction --
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jest da usporedimo predviđanje,
11:06
which is only based on your movement commands --
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koje je utemeljeno samo na našim motoričkim naredbama,
11:08
with the reality.
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sa stvarnošću.
11:10
Any discrepancy should hopefully be external.
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Svaka razlika trebala bi biti pod utjecajem vanjske sile.
11:13
So as I go around the world,
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Dakle, dok hodam uokolo,
11:15
I'm making predictions of what I should get, subtracting them off.
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izrađujem predviđanja o tome što bih trebao dobiti ulaganjem motoričkih naredbi.
11:18
Everything left over is external to me.
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Sve ostalo prepoznajem kao vanjsku silu.
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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Postoji jedan vrlo jasan primjer,
11:24
where a sensation generated by myself feels very different
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u kojem je osjećaj koji se stvara u meni vrlo različit
11:26
then if generated by another person.
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od osjećaja koji se stvara pod utjecajem druge osobe.
11:28
And so we decided the most obvious place to start
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I tako smo odlučili početi s očitim -
11:30
was with tickling.
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sa škakljanjem.
11:32
It's been known for a long time, you can't tickle yourself
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Već je dugo poznato da ne možete poškakljati sami sebe
11:34
as well as other people can.
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kao što vas mogu poškakljati drugi.
11:36
But it hasn't really been shown, it's because you have a neural simulator,
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No, to nije zaista dokazano, jer posjedujete neuralni stimulator
11:39
simulating your own body
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koji simulira vaše vlastito tijelo
11:41
and subtracting off that sense.
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i poništava taj osjet.
11:43
So we can bring the experiments of the 21st century
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Možemo eksperimente dovesti u 21. stoljeće
11:46
by applying robotic technologies to this problem.
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koristeći robotske tehnologije.
11:49
And in effect, what we have is some sort of stick in one hand attached to a robot,
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Imamo nekakav štap u jednoj ruci pričvršćenoj na robota,
11:52
and they're going to move that back and forward.
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i to će se micati naprijed-nazad.
11:54
And then we're going to track that with a computer
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Zatim ćemo to pratiti računalom
11:56
and use it to control another robot,
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i koristiti za upravljanje drugim robotom,
11:58
which is going to tickle their palm with another stick.
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koji će poškakljati dlanove osobe drugim štapom.
12:00
And then we're going to ask them to rate a bunch of things
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Onda ćemo ih zamoliti da ocijene razne stvari,
12:02
including ticklishness.
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uključujući i razinu škakljanja.
12:04
I'll show you just one part of our study.
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Pokazat ću vam samo jedan dio našeg istraživanja.
12:06
And here I've taken away the robots,
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Ovdje smo uklonili robote,
12:08
but basically people move with their right arm sinusoidally back and forward.
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i zapravo osoba miče desnu ruku sinusoidno naprijed-nazad.
12:11
And we replay that to the other hand with a time delay.
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Mi tu kretnju prenesemo na drugu ruku s vremenskim odmakom.
12:14
Either no time delay,
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Ili bez vremenskog odmaka,
12:16
in which case light would just tickle your palm,
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pri čemu bi osobi samo lagano zagolicao dlan,
12:18
or with a time delay of two-tenths of three-tenths of a second.
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ili s vremenskim odmakom od dvije ili tri desetinke sekunde.
12:22
So the important point here
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Dakle, važno je
12:24
is the right hand always does the same things -- sinusoidal movement.
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da desna ruka cijelo vrijeme čini istu kretnju – sinusoidni pokret.
12:27
The left hand always is the same and puts sinusoidal tickle.
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Lijeva je ruka uvijek u istom položaju i prima sinusoidno škakljanje.
12:30
All we're playing with is a tempo causality.
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Igramo se učincima promjene tempa.
12:32
And as we go from naught to 0.1 second,
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Kako mijenjamo od 0 do 0,1 sekunde,
12:34
it becomes more ticklish.
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počinje sve više škakljati.
12:36
As you go from 0.1 to 0.2,
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Povećavajući kašnjenje od 0,1 do 0,2 –
12:38
it becomes more ticklish at the end.
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dodatno se povećava škakljivost.
12:40
And by 0.2 of a second,
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I na kraju – od 0,2 s pa nadalje –
12:42
it's equivalently ticklish
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jednako će vas škakljati
12:44
to the robot that just tickled you without you doing anything.
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kao i robot koji vas je upravo poškakljao dok vi niste ništa radili.
12:46
So whatever is responsible for this cancellation
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Što god je odgovorno za izostanak osjećaja škakljanja
12:48
is extremely tightly coupled with tempo causality.
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vrlo je usko vezano s učincima promjene tempa.
12:51
And based on this illustration, we really convinced ourselves in the field
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Na temelju ovih ilustracija, uvjerili smo se
12:54
that the brain's making precise predictions
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da mozak čini precizna predviđanja
12:56
and subtracting them off from the sensations.
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i odvaja ih od osjeta.
12:59
Now I have to admit, these are the worst studies my lab has ever run.
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Moram priznati da su ovo najgora istraživanja provedena u mojem laboratoriju.
13:02
Because the tickle sensation on the palm comes and goes,
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Budući da osjećaj golicanja na dlanu dolazi i odlazi,
13:04
you need large numbers of subjects
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potreban vam je ogroman broj ispitanika
13:06
with these stars making them significant.
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kako bi istraživanje bilo značajno.
13:08
So we were looking for a much more objective way
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Dakle, tražili smo neki mnogo objektivniji način
13:10
to assess this phenomena.
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istraživanja ovog fenomena.
13:12
And in the intervening years I had two daughters.
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U međuvremenu sam dobio dvije kćeri.
13:14
And one thing you notice about children in backseats of cars on long journeys,
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Nešto što uočite kod djece na stražnjem sjedištu auta tijekom dužih vožnji –
13:17
they get into fights --
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započinju tučnjave --
13:19
which started with one of them doing something to the other, the other retaliating.
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što počne tako što jedna napravi nešto drugoj, pa ova vrati.
13:22
It quickly escalates.
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To brzo eskalira.
13:24
And children tend to get into fights which escalate in terms of force.
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Djeca su sklona tučnjavama u kojima se koristi sve više sile.
13:27
Now when I screamed at my children to stop,
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Kad bih viknuo na njih da prestanu,
13:29
sometimes they would both say to me
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ponekad bih od obje dobio odgovor
13:31
the other person hit them harder.
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da je ona druga jače udarila.
13:34
Now I happen to know my children don't lie,
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Slučajno znam da moja djeca ne lažu
13:36
so I thought, as a neuroscientist,
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pa mi je zato, kao neuroznanstveniku,
13:38
it was important how I could explain
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bilo važno dokazati
13:40
how they were telling inconsistent truths.
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kako su obje nedosljedno govorile istinu.
13:42
And we hypothesize based on the tickling study
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Napravili smo hipotezu na temelju studije o škakljanju,
13:44
that when one child hits another,
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da kad jedno dijete udari ono drugo,
13:46
they generate the movement command.
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stvaraju naredbu pokreta.
13:48
They predict the sensory consequences and subtract it off.
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Djeca predviđaju osjetilne posljedice i zanemaruju ih.
13:51
So they actually think they've hit the person less hard than they have --
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Tako da zapravo misle da su udarili osobu slabije nego što zaist jesu --
13:53
rather like the tickling.
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kao što je slučaj i sa škakljanjem.
13:55
Whereas the passive recipient
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A pasivni primatelj
13:57
doesn't make the prediction, feels the full blow.
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ne predviđa posljedice udarca, nego osjeća punu jačinu.
13:59
So if they retaliate with the same force,
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Dakle, ako se uzvrati istom mjerom,
14:01
the first person will think it's been escalated.
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druga će osoba to jače osjećati.
14:03
So we decided to test this in the lab.
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Tako da smo odlučili to testirati u laboratoriju.
14:05
(Laughter)
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(Smijeh)
14:08
Now we don't work with children, we don't work with hitting,
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Ne radimo s djecom, ne udaramo se,
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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Dvije odrasle osobe. Kažemo im da će igrati neku igru.
14:15
And so here's player one and player two sitting opposite to each other.
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Ovdje dva igrača sjede na suprotnim stranama.
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 s motorom
14:21
with a little lever, a little force transfuser.
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s malom polugom, mali pretvarač sile.
14:23
And we use this motor to apply force down to player one's fingers
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Koristimo taj motor kako bismo primjenili silu na prste prvog igrača
14:25
for three seconds and then it stops.
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na tri sekunde i zatim popustili.
14:28
And that player's been told, remember the experience of that force
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Tom je igraču rečeno da zapamti jačinu te sile
14:31
and use your other finger
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i da svojim drugim prstom
14:33
to apply the same force
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primijeni jednaku 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 igrača preko pretvarača sile - i onda bi to učinili.
14:38
And player two's been told, remember the experience of that force.
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Drugom je igraču rečeno da zapamti jačinu te sile
14:41
Use your other hand to apply the force back down.
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i da primijeni jednaku silu drugom rukom.
14:44
And so they take it in turns
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Tako su oni naizmjence pokušavali
14:46
to apply the force they've just experienced back and forward.
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odgovoriti jednakom silom na podražaj.
14:48
But critically,
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No, važno je naglasiti
14:50
they're briefed about the rules of the game in separate rooms.
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da su s pravilima igre upoznati u odvojenim prostorijama.
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 druga osoba.
14:55
And what we've measured
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Ono što smo mi mjerili
14:57
is the force as a function of terms.
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jest sila ovisna o uvjetima.
14:59
And if we look at what we start with,
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Kad uzmemo u obzir da smo počeli s
15:01
a quarter of a Newton there, a number of turns,
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četvrtinom Newtona, i nakon brojnih ponavljanja,
15:03
perfect would be that red line.
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savršena bi bila ova crvena crta.
15:05
And what we see in all pairs of subjects is this --
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Kod svih smo parova primijetili
15:08
a 70 percent escalation in force
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da dolazi do 70%-tne eskalacije sile
15:10
on each go.
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u svakoj rundi.
15:12
So it really suggests, when you're doing this --
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To zapravo znači, da kad to radite –
15:14
based on this study and others we've done --
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na temelju ovog i drugih istraživanja koja smo provodili --
15:16
that the brain is canceling the sensory consequences
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mozak zanemaruje osjetilne posljedice
15:18
and underestimating the force it's producing.
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i podcjenjuje silu koju primjenjujete.
15:20
So it re-shows the brain makes predictions
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Dakle, to ponovno pokazuje kako mozak radi pretpostavke
15:22
and fundamentally changes the precepts.
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i na taj način temeljito mijenja percepciju.
15:25
So we've made inferences, we've done predictions,
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Donijeli smo zaključke, napravili smo pretpostavke –
15:28
now we have to generate actions.
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a sada trebamo djelovati.
15:30
And what Bayes' rule says is, given my beliefs,
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Bayesovo pravilo kaže da bi s obzirom na moja uvjerenja
15:32
the action should in some sense be optimal.
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radnja na neki način trebala biti optimalna.
15:34
But we've got a problem.
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No, imamo problem.
15:36
Tasks are symbolic -- I want to drink, I want to dance --
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Zadaci su simbolični – želim piti, želim plesati –
15:39
but the movement system has to contract 600 muscles
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no za to moram pokrenuti 600 mišića
15:41
in a particular sequence.
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u određenom slijedu.
15:43
And there's a big gap
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A postoji velika razlika
15:45
between the task and the movement system.
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između zadatka i sustava za kretanje,
15:47
So it could be bridged in infinitely many different ways.
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a mogla bi se premostiti na beskrajno mnogo različitih načina.
15:49
So think about just a point to point movement.
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Razmislite o pokretu od točke do točke.
15:51
I could choose these two paths
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Mogao bih odabrati ova dva načina
15:53
out of an infinite number of paths.
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od beskonačnog broja načina.
15:55
Having chosen a particular path,
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Nakon što odaberem određeni način,
15:57
I can hold my hand on that path
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mogu na njemu držati ruku
15:59
as infinitely many different joint configurations.
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u beskonačnom broju položaja zglobova.
16:01
And I can hold my arm in a particular joint configuration
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A u određenom zglobnom položaju,
16:03
either very stiff or very relaxed.
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mogao bih mišiće ruke ili čvrsto stisnuti ili opustiti.
16:05
So I have a huge amount of choice to make.
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Dakle, moram donijeti ogromnu količinu odluka.
16:08
Now it turns out, we are extremely stereotypical.
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Ispada da smo vrlo podložni stereotipima.
16:11
We all move the same way pretty much.
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Većinom se svi krećemo na isti način.
16:14
And so it turns out we're so stereotypical,
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Ispada da smo toliko stereotipni,
16:16
our brains have got dedicated neural circuitry
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da naš mozak ima određene neuralne krugove
16:18
to decode this stereotyping.
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kojima dekodira taj obrazac.
16:20
So if I take some dots
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Ako uzmemo ove točkice
16:22
and set them in motion with biological motion,
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i pokrenemo ih u biološkom načinu kretanja –
16:25
your brain's circuitry would understand instantly what's going on.
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vaš mozak će odmah razumjeti o čemu se radi.
16:28
Now this is a bunch of dots moving.
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Ovo je hrpa točkica koje se kreću.
16:30
You will know what this person is doing,
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No, vi ćete prepoznati što ta osoba radi,
16:33
whether happy, sad, old, young -- a huge amount of information.
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je li sretna, tužna, stara, mlada – ogromna količina informacija.
16:36
If these dots were cars going on a racing circuit,
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Da ove točkice predstavljaju aute koji kruže u utrci,
16:38
you would have absolutely no idea what's going on.
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ne biste imali pojma o čemu se radi.
16:41
So why is it
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Zašto se, onda, krećemo
16:43
that we move the particular ways we do?
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baš na ovaj način?
16:45
Well let's think about what really happens.
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Razmislimo o tome što se zaista događa.
16:47
Maybe we don't all quite move the same way.
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Možda se ne krećemo baš svi na jednak način.
16:50
Maybe there's variation in the population.
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Možda postoje varijacije u populaciji.
16:52
And maybe those who move better than others
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Možda oni koji se kreću bolje
16:54
have got more chance of getting their children into the next generation.
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imaju veću vjerojatnost dobivanja potomstva.
16:56
So in evolutionary scales, movements get better.
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Tijekom evolucije pokreti postaju bolji.
16:59
And perhaps in life, movements get better through learning.
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A vjerojatno tijekom života isto tako pokreti postaju bolji kroz učenje.
17:02
So what is it about a movement which is good or bad?
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Što je to u pokretu dobro ili loše?
17:04
Imagine I want to intercept this ball.
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Zamislite da želim presresti ovu loptu.
17:06
Here are two possible paths to that ball.
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Postoje dva različita načina na koje to mogu učiniti.
17:09
Well if I choose the left-hand path,
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Ako odaberem put s lijeve strane,
17:11
I can work out the forces required
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mogu proizvesti snagu potrebnu
17:13
in one of my muscles as a function of time.
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u određenom mišiću, kao funkciju vremena.
17:15
But there's noise added to this.
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No, tome trebamo pridodati šum.
17:17
So what I actually get, based on this lovely, smooth, desired force,
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Ono što zapravo dobivam na temelju ove željene glatke sile,
17:20
is a very noisy version.
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zapravo je verzija puna šumova.
17:22
So if I pick the same command through many times,
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Dakle, ako istu zapovijed pošaljem mnogo puta,
17:25
I will get a different noisy version each time, because noise changes each time.
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svaki ću put dobiti drugačiju verziju punu šumova jer se oni svaki put mijenjaju.
17:28
So what I can show you here
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Ovo što vam mogu pokazati
17:30
is how the variability of the movement will evolve
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jest kako će se varijabilnost pokreta razviti
17:32
if I choose that way.
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ako izaberem ovaj način.
17:34
If I choose a different way of moving -- on the right for example --
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Ako odaberem drugi način kretanja, primjerice ovaj s desne strane,
17:37
then I'll have a different command, different noise,
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tada ću imati drugačiju naredbu i drugačije šumove,
17:39
playing through a noisy system, very complicated.
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koji dolaze kroz sustav pun šumova, vrlo komplicirano.
17:42
All we can be sure of is the variability will be different.
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Jedino u što možemo biti sigurni jest da će varijabilnost biti drugačija.
17:45
If I move in this particular way,
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Ako se krećem na taj određeni način,
17:47
I end up with a smaller variability across many movements.
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pokreti će mi postajati manje varijabilni.
17:50
So if I have to choose between those two,
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Kad bih morao birati između ta dva načina,
17:52
I would choose the right one because it's less variable.
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odabrao bih desni način jer bi mi pokreti bili manje varijabilni.
17:54
And the fundamental idea
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Temeljna ideja
17:56
is you want to plan your movements
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jest da želite planirati svoje pokrete
17:58
so as to minimize the negative consequence of the noise.
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tako da se maksimalno smanje negativne posljedice šumova.
18:01
And one intuition to get
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Intuicija koju trebate steći
18:03
is actually the amount of noise or variability I show here
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jest zapravo da se količina šuma ili varijabilnosti koju pokazujem
18:05
gets bigger as the force gets bigger.
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povećava kako se povećava i sila.
18:07
So you want to avoid big forces as one principle.
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Želite izbjeći uporabu velike sile.
18:10
So we've shown that using this,
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Ovime smo pokazali
18:12
we can explain a huge amount of data --
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kako možemo objasniti ogromne količine podataka --
18:14
that exactly people are going about their lives planning movements
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ljudi u svom životu planiraju pokrete
18:17
so as to minimize negative consequences of noise.
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kako bi smanjili negativne posljedice šumova.
18:20
So I hope I've convinced you the brain is there
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Nadam se da sam vas uvjerio kako mozak postoji
18:22
and evolved to control movement.
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i kako se razvio da bi mogao upravljati pokretima.
18:24
And it's an intellectual challenge to understand how we do that.
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Intelektualni je izazov shvatiti kako to činimo.
18:27
But it's also relevant
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No, to je bitno
18:29
for disease and rehabilitation.
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i za mnoge bolesti i rehabilitaciju.
18:31
There are many diseases which effect movement.
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Postoje mnoge bolesti koje utječu na kretanje.
18:34
And hopefully if we understand how we control movement,
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Nadamo se, ako shvatimo kako kontroliramo pokrete,
18:36
we can apply that to robotic technology.
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da ćemo to znanje moći primjeniti i na robote.
18:38
And finally, I want to remind you,
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I, za kraj, želim vas podsjetiti
18:40
when you see animals do what look like very simple tasks,
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da kad vidite životinje kako vrše naizgled vrlo jednostavne zadatke,
18:42
the actual complexity of what is going on inside their brain
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imajte na umu da je složenost onoga što se događa u njihovom mozgu
18:44
is really quite dramatic.
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zapravo vrlo dramatična.
18:46
Thank you very much.
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Hvala vam.
18:48
(Applause)
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(Pljesak)
18:56
Chris Anderson: Quick question for you, Dan.
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Chris Anderson: Imam kratko pitanje za vas, Dan.
18:58
So you're a movement -- (DW: Chauvinist.) -- chauvinist.
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Rekli ste da ste pokretni.... /Dan: Šovinist./ - šovinist.
19:02
Does that mean that you think that the other things we think our brains are about --
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Znači li to da mislite da druge stvari za koje mislimo da naš mozak služi --
19:05
the dreaming, the yearning, the falling in love and all these things --
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kao što su sanjanje, čežnje, zaljubljenost i te stvari –
19:08
are a kind of side show, an accident?
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sve zapravo popratni sadržaj, slučajnosti?
19:11
DW: No, no, actually I think they're all important
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DW: Ne, ne, zapravo mislim da je to sve važno
19:13
to drive the right movement behavior to get reproduction in the end.
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kao utjecaj na kretanje koje će nam na kraju osigurati potomstvo.
19:16
So I think people who study sensation or memory
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Smatram da ljudi koji proučavaju osjete ili pamćenje,
19:19
without realizing why you're laying down memories of childhood.
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zapravo ne razumiju zašto pohranjujemo sjećanja iz djetinjstva.
19:21
The fact that we forget most of our childhood, for example,
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Činjenica da smo zaboravili većinu toga iz djetinjstva, primjerice,
19:24
is probably fine, because it doesn't effect our movements later in life.
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vjerojatno je u redu, zato što to nema utjecaja na naše kasnije pokrete.
19:27
You only need to store things which are really going to effect movement.
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Pohraniti trebate samo ono što će vam kasnije utjecati na kretanje.
19:30
CA: So you think that people thinking about the brain, and consciousness generally,
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CA: Dakle, vi mislite da bi ljudi koji razmišljaju o mozgu i općenito o svijesti
19:33
could get real insight
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mogli dobiti pravi uvid
19:35
by saying, where does movement play in this game?
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kad bi odgovorili na pitanje kakvu ulogu kretanje ima u cijeloj priči?
19:37
DW: So people have found out for example
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DW: Ljudi su otkrili, primjerice,
19:39
that studying vision in the absence of realizing why you have vision
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da je proučavanje osjeta vida bez shvaćanja zašto uopće imamo vid
19:41
is a mistake.
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pogrešno.
19:43
You have to study vision with the realization
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Vid se mora proučavati zajedno sa spoznajom
19:45
of how the movement system is going to use vision.
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kako će sustav za kretanje iskoristiti taj vid.
19:47
And it uses it very differently once you think about it that way.
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A koristi ga vrlo različito, jednom kad o tome počnete tako razmišljati.
19:49
CA: Well that was quite fascinating. Thank you very much indeed.
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CA: To je zaista fascinantno. Hvala vam puno!
19:52
(Applause)
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(Pljesak)
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