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