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 neurobiolog. U neurobiologiji, moramo da se bavimo mnogim teškim pitanjima o mozgu. Ali, hteo bih da počnem od najlakšeg pitanja, pitanja, koje bi svako trebalo sam sebi da postavi bar jednom u životu, jer je to fundamentalno pitanje za razumevanje funkcije mozga. A to je, zbog čega, mi i druge životinje imamo mozak? Nemaju sve vrste na našoj planeti mozak, i ako želimo da znamo za šta služi mozak, hajde da pogledamo zbog čega mozak evoluira. Mogli biste reći da imamo mozak za opažanje ili za razmišljanje, a to je potpuno pogrešno. Ako razmišljate o ovom pitanju nešto duže, biće zaslepljujuće očigledno zbog čega imamo mozak. Imamo mozak iz samo jednog jedinog razloga, a to je da bismo izvodili prilagodljivo i kompleksno kretanje. Ne postoji drugi razlog zašto imamo mozak. Razmislite o tome. Kretanje je jedini način koji imamo da utičemo na svet oko sebe. To nije sasvim tačno. Postoji još jedan način, a to je znojenje. Ali, pored toga, sve drugo se dešava putem kontrakcija 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.
Razmislite o komunikaciji govor, gestovi, pisanje, jezik znakova sve je to posredovano kroz kontrakcije mišića. Zato je veoma važno zapamtiti da su i senzorni i memorijski i kognitivni procesi važni, ali su važni jedino kao pokretači ili supresori nekog budućeg kretanja. Nema evolutivne prednosti u formiranju sećanja iz detinjstva ili u primećivanju kakve boje je ruža ukoliko to ne utiče na način kretanja tokom života.
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 veruju u ovo tvrđenje, imamo drveće i travu na našoj planeti, bez mozga, ali ključni dokaz je ova životinja ovde, ovaj skromni morski plaštaš. Primitivna životinja, ima nervni sistem, pliva naokolo u okeanu dok je mlada. U jednom stupnju svog života, pričvrstiće se za kamen. I prvo što će uraditi na tom kamenu, koji više nikad neće napustiti, je da upotrebi svoj mozak i nervni sistem kao hranu. Znači, kad više ne morate da se krećete, više vam ne treba takav luksuz kao što je mozak. Ova životinja se često uzima kao analogija za ono što se dešava na univerzitetima kada profesori dobiju stalnu poziciju, ali to je već druga tema.
(Applause)
(aplauz)
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.
Znači, ja sam šovinista za kretanje. Verujem da je kretanje najvažnija funkcija mozga, i ne dozvolite da vam iko kaže da to nije tačno. E sad, ako je kretanje tako važno, koliko dobro nam uspeva da razumemo kako mozak kontroliše kretanje? Odgovor je da nam vrlo loše uspeva; to je vrlo težak problem. Možemo pogledati koliko nam uspeva po tome koliko dobro nam ide pravljenje mašina koje mogu da izvršavaju ono što i ljudi.
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.
Uzmite šah za primer. Koliko dobro možemo da odredimo koju figuru da pomerimo i gde? Ako stavite Garija Kasparova ovde, kad nije u zatvoru, da igra protiv IBM-ovog "Deep Blue" kompjutera, pa, odgovor je da će IBM-ov "Deep Blue" povremeno pobediti. Mislim da, kad bi IBM-ov "Deep Blue" igrao protiv bilo koga u ovoj sali, da bi pobedio svaki put. Taj problem je rešen. A šta je sa problemom podizanja šahovske figure, veštog pomeranja i vraćanja figure dole na tablu? Ako uporedite veštinu 5-godišnjeg deteta i najboljih robota današnjice, odgovor je jednostavan: dete lako pobeđuje. Tu uopšte i nema takmičenja.
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.
A zašto je taj gornji problem tako lagan, a donji problem tako težak? Jedan razlog, vrlo pametan 5-godišnjak bi mogao da vam kaže algoritam za taj gornji problem a to je da ispitate sve moguće poteze do kraja partije i da izaberete onaj kojim pobeđujete. To je vrlo prost algoritam. Naravno da postoje drugi potezi, ali sa ogromnim kompjuterima možemo uprostiti, i stići blizu optimalnom rešenju. Što se tiče manuelne veštine, čak nije jasno koji algoritam treba rešiti da bi se došlo do veštine. Videćemo da je potrebno da se okolina detektuje i da se deluje na nju, što nosi 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.
Ali, da vam pokažem vrhunsku robotiku. Mnogo robota je vrlo impresivno, ali manipulativna robotika je ipak tek na početku. Ovo je kraj jednog projekta za doktorat iz jednog od najboljih instituta za robotiku. Ovaj student je istrenirao robota da sipa vodu u čašu. To je težak problem, jer se voda prosipa, ali robot uspeva. Ali ne uspeva da to uradi tako spretno kao ljudsko biće. Ako želite da ovaj robot izvrši drugi zadatak, to zahteva novi trogodišnji doktorat. Uopšte ne postoji generalizacija za izvršavanje 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.
Možemo uporediti ovo sa vrhunskim ljudskim izvođenjem. Sada ću vam pokazati Emili Foks, kako postavlja svetski rekord u slaganju čaša. Amerikanci u publici će svi znati šta je slaganje čaša. To je srednjoškolski sport u kom treba da prvo poslažete 12 čaša, a onda ih skupite u propisanom redosledu i za određeno vreme. I evo nje kako postiže svetski rekord - snimak nije ubrzan. (smeh) (aplauz) I prilično je srećna. Nemamo nikakvu ideju šta joj se dešava u mozgu dok to radi, a to je ono što bismo hteli da znamo.
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 da uradimo obratno inženjerstvo o ljudskoj kontroli kretanja. To zvuči kao jednostavan problem. Pošaljete komandu dole, to dovede do kontrakcije mišića. Ruka ili telo se pokreću, i dobijate senzornu povratnu informaciju od čula vida, iz kože, mišića itd. Nevolja je u tome što ti signali nisu tako divni signali kao što bismo mi hteli. Jedna stvar zbog koje je kontrolisanje kretanja teško je, npr. to što je senzorna povratna sprega vrlo bučna. Pod "bučna", ne mislim na zvuk. To kažemo u inženjerstvu i neurobiologiji u smislu da su to nasumične smetnje koje ometaju signal. U doba pre digitalnog radija, kad ste tražili stanicu na radiju i ako biste čuli "krrrrrr" tamo gde treba da bude stanica, to su bile smetnje - buka. Ali, generalno, ta buka je nešto što ometa 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.
Tako na primer, ako stavite šaku ispod stola i pokušate da je locirate svojom drugom šakom, promašićete čak i za nekoliko centimetara zbog smetnji u senzornoj povratnoj sprezi. Slično tome, i u prenosu motornog signala u sam pokret, ima mnogo smetnji. Ne pokušavajte da pogodite centar mete u pikadu, pokušajte samo da pogodite istu tačku više puta za redom. Pogodićete jako mnogo tačaka, zbog varijabilnosti pokreta ruke. I šta više, i spoljašnji svet, i sama radnja, su nejasni i varijabilni. Čajnik je možda pun, a možda prazan. Menja se tokom vremena. Mi radimo u pravoj čorbi raznih smetnji, nastalih zbog oseta, pokreta, radnje,
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.
Te smetnje su tako velike da društvo daje velike nagrade onima koji uspeju da smanje posledice tih smetnji. Ako imate dovoljno sreće da ubacite belu lopticu u rupu udaljenu nekoliko stotina metara, koristeći dugački, metalni štap, naše društvo će vas rado nagraditi stotinama miliona 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.
Ono u šta želim da vas ubedim je da se i mozak takođe jako trudi da smanji negativne posledice ovakve vrste smetnji i varijabilnosti. I da bih vas ubedio, ispričaću vam o jednom konceptu koji je vrlo popularan u statistici i teoriji učenja kod mašina, u poslednjih 50 godina, i zove se Bajesova teorija odlučivanja. I od skora je to način razmišljanja koji razmatra sve ono čime se mozak bori protiv neodređenosti. Osnovna ideja je da želite nešto da zaključite i onda da preduzmete neku akciju.
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.
Hajde da razmislimo o zaključivanju. Želite da formulišete neke stavove o svetu oko vas. Šta su ti stavovi? To može biti npr. "gde mi se nalaze ruke u prostoru?" "Da li sad gledam u mačku ili u lisicu?" Ali, mi ćemo predstaviti stavove sa verovatnoćama. Zato ćemo predstaviti stav koristeći broj između nula i jedan nula znači da uopšte ne verujem u stav, jedan znači da sam apsolutno siguran. A brojevi između prave sivu zonu neodređenosti. I ključna ideja zaključivanja po Bajesovoj teoriji je da imate dva izvora informacija koje koristite da izvedete svoj zaključak. Imate podatke, "podaci" u neurobiologiji znače senzorni unos (input). Znači, imam senzorni unos, koji mogu da koristim da formiram stav. Ali, postoji drugi izvor informacija, a to je prethodno znanje. Tokom života čovek nagomilava znanje u vidu sećanja. Poenta Bajesove teorije odlučivanja je u tome da pruža matematički način za optimalno kombinovanje prethodnog znanja sa senzornim unosima u cilju generisanja novih stavova.
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 formulu tu gore. Neću objašnjavati šta je ta formula, ali je to divna formula. Vrlo je lepa i može dobro da objasni. A šta zapravo govori, to je i ono što želite da procenite, to je verovatnoća za različite stavove uzimajući u obzir senzorni unos o kom se radi. Evo da vam dam jedan intuitivno jasan primer. Zamislite da učite da igrate tenis i hoćete da odlučite gde će lopta odskočiti kada stigne preko mreže ka vama. Postoje dva izvora informacija po Bajesovom pravilu. Postoji senzorni podatak - to može biti vizuelna ili audio informacija, i to vam može ukazati gde da postavite tu crvenu tačku. Ali, znate da čula nisu savršena, i zato postoji varijabilnost oko toga gde će loptica pasti što se vidi kao oblak crvene boje, koji predstavlja brojeve između 0.5 i možda 0.1
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.
Tu informaciju imamo za trenutni udarac, ali postoji i drugi izvor informacija koji ne postoji pri trenutnom udarcu, već se dobija iskustvom kroz ponovljeno igranje tenisa, i ta informacija je da loptica ne odskače sa istom verovatnoćom preko celog terena tokom meča. Ako igrate protiv jako dobrog igrača, takav protivnik može poslati lopticu u tu zelenu zonu što je prethodna distribucija, čime vama otežava da vratite lopticu. Oba ova izvora pružaju važne informacije. A Bajesovo pravilo kaže da treba pomnožiti crvene brojeve sa zelenim brojevima da biste dobili žute brojeve, koji daju ove elipse, i to je moj stav. To je optimalni način kombinovanja 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 pričao o ovome da, pre nekoliko godina, nismo pokazali da upravo na ovaj način ljudi uče nove tehnike kretanja. To znači da smo mi, u stvari, bajesovske mašine za zaključivanje. Tokom života skupljamo statističke podatke o svetu i to pamtimo, ali takođe i učimo da je naš sopstveni senzorni aparat pun smetnji, i zatim to sve kombinujemo na pravi bajesovski 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 deo Bajesovskog zaključivanja je ovaj deo formule. Taj deo zapravo govori da ja treba da predvidim verovatnoću različitih senzornih povratnih reakcija u odnosu na moje stavove. To, u stvari, znači da treba da predvidim budućnost. Želim da vas ubedim da mozak zaista pravi ta predviđanja o senzornim povratnim reakcijama koje će primiti. I šta više, tako mozak značajno menja percepciju zavisno od toga šta činite. Da bih vas ubedio, ispričaću vam kako mozak obrađuje senzorni unos. Znači, pošaljete komandu napolje, dobijete povratnu senzornu reakciju, a na tu transformaciju utiču vaše fizičke osobine i vaš senzorni aparat.
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.
Ali, zamislite da gledate u unutrašnjost mozga. Evo unutrašnjosti mozga. Možda imate mali pokazivač, nervni simulator, fizičkih osobina vašeg tela i čula. Pa, kad pošaljete komandu za pokret, pokrenete i kopiju te komande i ubacite u svoj nervni simulator da biste predvideli senzorne posledice te akcije. Kada protresem ovu flašu kečapa, dobijam prave senzorne povratne reakcije u zavisnosti od vremena, u donjem redu. Ako imam dobar pokazivač, predvideće istu tu 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.
A zašto da se bakćem da to sve radim? Primiću iste povratne reakcije u svakom slučaju. Pa, postoje dobri razlozi. Zamisliite, dok ja mućkam flašu kečapa, neko mi se tiho prišunja i lupne dno flaše. Sad imam dodatni izvor senzornih informacija zbog te spoljne akcije. Znači, imam dva izvora. Imam to da je neko lupnuo flašu i to da ja tresem flašu, ali sa gledišta mojih čula, to se zajedno kombinuje u jedan izvor informacija.
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.
Postoje dobri razlozi za verovanje da želite da budete u stanju da razlikujete spoljne događaje od unutrašnjih. Zato što su spoljašnji događaji u stvari mnogo važniji za ponašanje nego to da se oseti sve što se dešava unutar tela. Jedan način da se to rekonstruiše je da se uporedi to predviđanje koje se bazira samo na vašim komandama za pokret sa stvarnim stanjem. Svako neslaganje bi trebalo da bude zbog spoljnih uticaja. I tako, dok se krećem po svetu, pravim predviđanja o tome šta bi trebalo da dobijem i onda ih oduzimam. I ono što preostane su spoljni uticaji.
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? Pa, postoji jedan vrlo jasan primer u kom senzacija koju sam ja proizveo deluje sasvim drugačije od one koju stvara neka druga osoba. Odlučili smo da je očigledno najbolje početi sa golicanjem. Dugo je već poznato da ne možete sami sebe golicati, a da drugi ljudi mogu da vas golicaju. Ali nije do sada pokazano da je to zato što imate nervni simulator, kojim simulirate sopstveno telo i oduzimate taj osećaj. U 21.veku možemo da eksperimentišemo primenjujući robotičke tehnologije na ovaj problem. Zapravo, imamo neku vrstu štapa u ruci pričvršćenoj za robota, i roboti će to pomerati napred-nazad. A mi ćemo to pratiti preko kompjutera i koristiti to za kontrolisanje drugog robota, koji će golicati po dlanu drugim štapom. A onda ćemo ih pitati da ocene nekoliko stvari uključujući i golicljivost.
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.
Pokazaću vam samo deo našeg istraživanja. I tu sam sklonio robote, i vidimo ljude koji sinusoidalno pomeraju desnu ruku napred-nazad. To ponovo puštamo drugoj ruci, sa izvesnim kašnjenjem. U slučaju da nema kašnjenja, svetlosni snop će golicati vaš dlan, ili će postojati kašnjenje od dve ili tri desetinke. Ono što je važno u ovome je da desna ruka uvek radi istu stvar -- sinusoidalni pokret. Leva ruka je uvek ista i pravi sinusoidalno golicanje. Mi se samo igramo sa uzročnom vezom u odnosu na tempo pokreta. Kako napredujemo od nule ka 0.1 sekundi, postaje sve golicljivije. Kako idemo od 0.1 ka 0.2, postaje još golicljivije na kraju. I na 0.2 sekunde podjednako je golicljivo, kao robot koji vas je upravo zagolicao, a da vi niste ništa radili. Što god da je odgovorno za ovo poništavanje je izuzetno blisko povezano sa uzročnom vezom tempa kretanja. Na osnovu ove ilustracije, svi mi koji radimo u ovoj oblasti smo se uverili da mozak pravi precizna predviđanja koja oduzima od onoga što se oseti.
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 da priznam da su ovo najgora istraživanja ikad rađena u mojoj laboratoriji, jer osećaj golicanja po dlanu dođe i prođe, treba vam veliki broj ispitanika sa ovim zvezdicama koje znače da je statistički značajno. Zato smo tražili objektivniji način za ispitivanje ovih pojava. A u međuvremenu sam ja dobio dve ćerke. Primetićete da deca, na zadnjem sedištu auta, na dugom putovanju, počinju da se tuku što započne tako što jedno uradi nešto onom drugom, koje onda uzvrati. To se brzo pojačava. Deca započnu tuču koja se pojača u smislu jačine udaraca. Kad sam viknuo mojim ćerkama da prestanu, nekad bi mi obe rekle da je ona druga udarala jače.
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.
E sad, ja znam da moja deca ne lažu, pa sam pomislio, kao neurobiolog, da je važno da pokušam da objasnim kako to da govore suprotstavljene istinite stvari. Postavili smo hipotezu, na osnovu ovog istraživanja golicljivosti, da kad jedno dete udari drugo, ono generiše komandu za pokret. Tako predviđa senzornu posledicu i to oduzima. Zato dete zapravo misli da je udarilo drugu osobu manjom jačinom nego što zaista jeste, slično kao za golicanje. Dok pasivni primalac ne pravi to predviđanje i zato oseća punu jačinu udarca. I ako hoće da uzvrati istom jačinom, prva osoba će misliti da je to pojačano.
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.
Odlučili smo da to testiramo u laboratoriji. (smeh) Mi ne radimo sa decom, ne koristimo udaranje, ali koncept je isti. Doveli smo dvoje odraslih. Rekli smo im da će igrati jednu igru. I evo prvog i drugog igrača kako sede jedan naspram drugog. Igra je vrlo jednostavna. Počeli smo sa motorom sa polugicom, malim prenosnikom sile. Koristimo taj motor da primenimo silu na prste prvog igrača tokom tri sekunde i onda prestaje. Tom igraču smo rekli da zapamti iskustvo o tom pritiskanju i da koristi svoj drugi prst da primeni istu takvu silu na prst drugog subjekta putem prenosnika sile -- i oni to rade. I drugom igraču smo rekli da zapamti iskustvo o tom pritiskanju. I da svojom drugom rukom uzvrati pritisak. I tako oni naizmenično pritiskaju istom silom koju su upravo iskusili.
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.
Ali, jako je važno to da, smo im rekli o pravilima igre u odvojenim sobama. Tako da ne znaju po kojim pravilima igra ona druga osoba. I ono što smo izmerili je sila u funkciji od uslova koji postoje. Kada pogledamo na ono od čega smo počeli, četvrtina Njutna ovde, određen broj ponavljanja, idealno bi bilo ovo po crvenoj liniji. A ono što vidimo kod svih parova subjekata je ovo 70 odsto povećanja jačine pritiska u svakom ciklusu. Iz ovoga sledi da kada to radite, i to je na osnovu ove studije i drugih koje smo sproveli, mozak poništava senzorne posledice i potcenjuje silu koju stvara. To ponovo pokazuje da mozak pravi predviđanja i fundamentalno menja naredbe. Do sada smo pravili zaključke i predviđanja, a sada treba da generišemo akcije. A Bajesovo pravilo kaže da, u odnosu na moje stavove, akcija treba da bude, na neki način, 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.
Ali, imamo problem. Radnje su simbolične - hoću da pijem, hoću da igram - ali sistem za kretanje mora da kontrahuje 600 mišića u određenoj sekvenci. I tu postoji veliki jaz između određene radnje i sistema za kretanje. Taj jaz se može premostiti na bezgranično mnogo načina. Pomislite samo na kretanje od jedne do druge tačke. Mogu da izaberem ove dve putanje od bezbrojno mnogo drugih putanja. Pošto sam izabrao određenu putanju, mogu da postavim ruku na toj putanji u beskonačno mnogo različitih položaja zgloba. I mogu da držim ruku u određenom položaju zgloba vrlo ukočeno ili vrlo opušteno. Znači da imam ogroman broj izbora koje mogu da napravim. Ispostavlja se da smo izuzetno stereotipni. Svi se krećemo na skoro 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.
Ispostavlja se da smo toliko stereotipni, da naš mozak ima posebnu nervnu mrežu za dekodiranje te stereotipnosti. Ako uzmem neke tačke i pomeram ih tako da izgleda kao biološko kretanje, moždana mreža će odmah shvatiti o čemu se radi. Ovo je grupa tačaka koje se kreću. A vi znate šta ova osoba radi, da li je srećna, tužna, stara, mlada - ogromna količina informacija. Da su ove tačke kola koja se kreću po trkačkoj stazi, ne biste uopšte znali 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.
Pa, zašto se krećemo na tako određene načine? Hajde da razmislimo šta se zaista dešava. Možda se ne krećemo svi na sasvim isti način. Možda postoji varijacija u populaciji. I možda oni koji se bolje kreću od drugih imaju veću šansu da dobiju potomstvo u sledećoj generaciji. Tako se, na evolutivnoj skali, načini kretanja poboljšavaju. I možda tokom života, načini kretanja 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.
A šta je to što čini neki način kretanja dobrim ili lošim? Zamislite da hoću da presretnem ovu loptu Evo dve moguće putanje do te lopte. Pa, ako izaberem putanju sa leve strane, mogu da izračunam sile koje su potrebne za jedan od mojih mišića u zavisnosti od vremena. Ali, postoje smetnje koje se dodaju na to. Ono što zapravo dobijam, počevši od ove fine, uglađene, željene sile, je verzija sa puno smetnji. Ako izaberem istu komandu više puta, dobiću svaki put verzije sa različitim smetnjama, jer se smetnje menjaju svaki put. Ono što mogu da vam ovde pokažem je kako varijabilnost pokreta evoluira ukoliko izaberem taj način. Ako izaberem drugi način kretanja - npr. ovaj sa desne strane onda ću imati drugačiju komandu, drugačije smetnje, vrlo je komplikovano snaći se u sistemu punom smetnji. Jedino u šta smo sigurni je da će varijabilnost biti različita. Ako se krećem na ovaj određeni način, imaću manju varijabilnost kroz mnoge pokrete. Pa, ako treba da biram između ta dva, izabraću ovaj desni, jer ima manju varijabilnost.
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.
Osnovna ideja je da želite da planirate svoje pokrete da biste što više smanjili negativne posledice smetnji. Može se primetiti da se, zapravo, količina smetnji ili varijabilnost koju pokazujem povećava kako se sila pojačava. U principu, želite da izbegnete jake sile. Tako smo pokazali da, koristeći ovo, možemo da objasnimo veliki broj podataka da, u stvari, ljudi stalno planiraju svoje kretanje sa ciljem smanjivanja negativnih posledica smetnji.
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 ubedio da je razlog za postojanje i evoluciju mozga to da bi kontrolisao kretanje. Intelektualno je zahtevno da se razume kako mi to radimo. I vrlo je važno zbog bolesti i rehabilitacije. Ima mnogo bolesti koje utiču na kretanje. I nadamo se, ako razumemo kako kontrolišemo kretanje, da ćemo to moći da primenimo na robotičku tehnologiju. I konačno, želim da vas podsetim, kada vidite životinje da izvode ono što izgleda kao jednostavna radnja, kompleksnost onoga što se dešava u njihovom mozgu je, u stvari, veoma upečatljiva.
Thank you very much.
Hvala vam mnogo.
(Applause)
(aplauz)
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?
Kris Enderson: Jedno brzo pitanje za tebe, Den. Ti si, znači -- (DV: šovinista) -- šovinista za kretanje. Da li to znači da misliš da druge stvari za koje smatramo da nam mozak služi snovi, želje, zaljubljivanje i sve te stvari su neka vrsta sporednih dešavanja, samo slučajnost?
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.
DV: Ne, ne, ja, u stvari, mislim da je sve to važno da bi usmerilo ka pravom načinu kretanja koji, na kraju, vodi do reprodukcije. Mislim da ljudi koji proučavaju senzacije ili pamćenje, ne shvataju zašto skupljamo ta sećanja iz detinjstva. Npr. činjenica da većinu stvari iz detinjstva zaboravimo, je verovatno prihvatljiva, jer to ne utiče na naše kretanje kasnije u životu. Potrebno nam je da čuvamo samo one stvari koje će zaista uticati 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?
KE: Znači smatraš da bi ljudi, kad razmišljaju o mozgu i generalno o svesti, mogli da donesu bolje zaključke ako bi se zapitali, "a, gde je kretanje u svemu tome?"
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.
DV: Pa, ljudi su zaključili, na primer da je proučavanje čula vida bez svesti o tome zbog čega postoji čulo vida pogrešno. Treba proučavati čulo vida uz shvatanje o tome kako će sistem za kretanje da iskoristi čulo vida. I kad počnete da razmišljate na taj način, shvatite da je to drugačije.
CA: Well that was quite fascinating. Thank you very much indeed.
KE: Ovo je bilo baš fascinantno. Stvarno, puno ti hvala.
(Applause)
(aplauz)