TEDGlobal 2012
Read Montague: What we're learning from 5,000 brains
Read Montague: Što učimo od 5.000 mozgova
Filmed:
Readability: 4
763,896 views
Miševi, bube i hrčci više nisu jedini način na koji možemo proučavati mozak. Funkcionalna MR (fMRI) dopušta znanstvenicima da mapiraju moždanu aktivnost ljudskih bića koja žive, dišu i donose odluke. Read Montague daje nam osvrt kako ova tehnologija pomaže da razumijemo komplicirane puteve kojima komuniciramo jedni s drugima.
Read Montague - Behavioral Neuroscientist
What does "normal behavior" look like? To find out, Read Montague is imaging thousands of brains at work. Full bio
What does "normal behavior" look like? To find out, Read Montague is imaging thousands of brains at work. Full bio
Double-click the English transcript below to play the video.
00:16
Other people. Everyone is interested in other people.
0
474
2809
Drugi ljudi. Svi su zainteresirani za druge ljude.
00:19
Everyone has relationships with other people,
1
3283
2123
Svi imaju odnose s drugim ljudima
00:21
and they're interested in these relationships
2
5406
1592
i zainteresirani su za te odnose
00:22
for a variety of reasons.
3
6998
1855
iz brojnih razloga.
00:24
Good relationships, bad relationships,
4
8853
2012
Dobri odnosi, loši odnosi,
00:26
annoying relationships, agnostic relationships,
5
10865
3146
dosadni odnosi, agnostični odnosi,
00:29
and what I'm going to do is focus on the central piece
6
14011
3424
a ono na što ću se ja fokusirati je središnji komad
00:33
of an interaction that goes on in a relationship.
7
17435
3303
interakcije koji se odvija u odnosu.
00:36
So I'm going to take as inspiration the fact that we're all
8
20738
2336
Kao inspiraciju ću uzeti činjenicu da smo svi
00:38
interested in interacting with other people,
9
23074
2425
zainteresirani za interakciju s drugim ljudima.
00:41
I'm going to completely strip it of all its complicating features,
10
25499
3832
Potpuno ću ogoliti sva komplicirana svojstva
00:45
and I'm going to turn that object, that simplified object,
11
29331
3894
i formirati taj predmet, taj pojednostavljen predmet
00:49
into a scientific probe, and provide the early stages,
12
33225
4150
u znanstveno istraživanje i omogućiti rane faze,
00:53
embryonic stages of new insights into what happens
13
37375
2449
embrionalne faze novih spoznaja o tome što se događa
00:55
in two brains while they simultaneously interact.
14
39824
3650
u dva mozga za vrijeme njihove simultane interakcije.
00:59
But before I do that, let me tell you a couple of things
15
43474
2293
No prije no što to učinim, dopustite mi da vam ispričam nekoliko stvari
01:01
that made this possible.
16
45767
1699
koje su ovo učinile mogućim.
01:03
The first is we can now eavesdrop safely
17
47466
2781
Prva je da sada možemo sa sigurnošću osluškivati
01:06
on healthy brain activity.
18
50247
2711
aktivnost zdravog mozga.
01:08
Without needles and radioactivity,
19
52958
2577
Bez igala i radioaktivnosti,
01:11
without any kind of clinical reason, we can go down the street
20
55535
2863
bez ikakvog kliničkog razloga
01:14
and record from your friends' and neighbors' brains
21
58398
3127
možemo ići niz ulicu i snimiti mozgove vaših prijatelja
01:17
while they do a variety of cognitive tasks, and we use
22
61525
2538
i susjeda dok obavljaju niz kognitivnih zadataka i koristimo
01:19
a method called functional magnetic resonance imaging.
23
64063
3734
metodu pod nazivom funkcionalna magnetska rezonanca.
01:23
You've probably all read about it or heard about in some
24
67797
2325
Svi ste vjerojatno čitali o tome ili čuli u nekom
01:26
incarnation. Let me give you a two-sentence version of it.
25
70122
4378
obliku. Dat ću vam verziju toga u dvije rečenice.
01:30
So we've all heard of MRIs. MRIs use magnetic fields
26
74500
3484
Dakle, svi smo čuli za MR. MR koristi magnetska polja
01:33
and radio waves and they take snapshots of your brain
27
77984
2029
i radio valove kojima se uzimaju snimci vašeg mozga,
01:35
or your knee or your stomach,
28
80013
2361
koljena ili želuca,
01:38
grayscale images that are frozen in time.
29
82374
2045
sive slike koje su zamrznute u vremenu.
01:40
In the 1990s, it was discovered you could use
30
84419
2321
U devedesetima je otkriveno da možete koristiti
01:42
the same machines in a different mode,
31
86740
2659
iste aparate na drugačiji način
01:45
and in that mode, you could make microscopic blood flow
32
89399
2346
i na taj način možete napraviti filmove mikroskopskog protoka krvi
01:47
movies from hundreds of thousands of sites independently in the brain.
33
91745
3300
iz stotinu i tisuću samostalnih dijelova u mozgu.
01:50
Okay, so what? In fact, the so what is, in the brain,
34
95045
3200
U redu, pa što onda? Zapravo, stvar je u tome da se u mozgu
01:54
changes in neural activity, the things that make your brain work,
35
98245
3832
mijenja neurološka aktivnost, stvari koje tjeraju vaš mozak na rad,
01:57
the things that make your software work in your brain,
36
102077
2010
stvari koje tjeraju vaš software na rad u vašem mozgu
01:59
are tightly correlated with changes in blood flow.
37
104087
2489
usko su povezane s promjenama u krvotoku.
02:02
You make a blood flow movie, you have an independent
38
106576
1973
Snimite film o protoku krvi i imate nezavisnog
02:04
proxy of brain activity.
39
108549
2339
zastupnika aktivnosti mozga.
02:06
This has literally revolutionized cognitive science.
40
110888
3034
Ovo je doslovno revolucionaliziralo kognitivnu znanost.
02:09
Take any cognitive domain you want, memory,
41
113922
1991
Uzmite bilo koje kognitivno područje koje želite, pamćenje,
02:11
motor planning, thinking about your mother-in-law,
42
115913
2141
motoričko planiranje, razmišljanje o vašoj punici,
02:13
getting angry at people, emotional response, it goes on and on,
43
118054
3715
ljutnja na ljude, emocionalni odgovor, to jednostavno traje
02:17
put people into functional MRI devices, and
44
121769
3089
i stavite ljude u uređaje za funkcionalni MR i
02:20
image how these kinds of variables map onto brain activity.
45
124858
3383
vidjet ćete kako se ove vrste varijabli mapiraju u moždanu aktivnost.
02:24
It's in its early stages, and it's crude by some measures,
46
128241
2849
To je u svojim ranim stadijima i veoma je primitivno u nekim mjerama,
02:26
but in fact, 20 years ago, we were at nothing.
47
131090
2568
ali zapravo prije 20 godina nismo bili nigdje.
02:29
You couldn't do people like this. You couldn't do healthy people.
48
133658
2359
Niste mogli ovako obraditi ljude. Niste mogli obraditi zdrave ljude.
02:31
That's caused a literal revolution, and it's opened us up
49
136017
2488
To je uzrokovalo doslovnu revoluciju i otvorilo nas
02:34
to a new experimental preparation. Neurobiologists,
50
138505
2818
novim eksperimentalnim pripravcima. Neurobiolozi,
02:37
as you well know, have lots of experimental preps,
51
141323
3760
kao što dobro znate, imaju mnogo eksperimentalnih pripravaka,
02:40
worms and rodents and fruit flies and things like this.
52
145083
3141
crva, glodavaca, vinskih mušica i stvari poput toga.
02:44
And now, we have a new experimental prep: human beings.
53
148224
3397
A sada mi imamo nove eksperimentalne pripravke: ljudska bića.
02:47
We can now use human beings to study and model
54
151621
3761
Sada možemo upotrijebiti ljudska bića kako bismo proučili i modelirali
02:51
the software in human beings, and we have a few
55
155382
2950
software u ljudskim bićima i imamo nekoliko
02:54
burgeoning biological measures.
56
158332
2835
rastućih bioloških mjera.
02:57
Okay, let me give you one example of the kinds of experiments that people do,
57
161167
3887
U redu, dopustite mi da vam dam jedan primjer načina eksperimenata koji ljudi rade
03:00
and it's in the area of what you'd call valuation.
58
165054
2677
i on je u području koje biste nazvali procjenom.
03:03
Valuation is just what you think it is, you know?
59
167731
2135
Procjena je upravo ono što mislite da je, znate?
03:05
If you went and you were valuing two companies against
60
169866
2804
Ako ste procjenjivali dvije tvrtke jednu naspram druge,
03:08
one another, you'd want to know which was more valuable.
61
172670
2736
voljeli biste znati koja je cjenjenija.
03:11
Cultures discovered the key feature of valuation thousands of years ago.
62
175406
3879
Kulture su otkrile ključno svojstvo procjene prije mnogo tisuća godina.
03:15
If you want to compare oranges to windshields, what do you do?
63
179285
2690
Ukoliko želite usporediti naranče s vjetrobranima, što ćete učiniti?
03:17
Well, you can't compare oranges to windshields.
64
181975
2356
Pa, ne možete usporediti naranče s vjetrobranima.
03:20
They're immiscible. They don't mix with one another.
65
184331
2255
Neusporedivi su. Ne miješaju se jedni s drugima.
03:22
So instead, you convert them to a common currency scale,
66
186586
2351
Umjesto toga, pretvorite ih u zajedničku novčanu ljestvicu,
03:24
put them on that scale, and value them accordingly.
67
188937
2706
stavite ih na tu ljestvicu i procjenjujete ih na odgovarajući način.
03:27
Well, your brain has to do something just like that as well,
68
191643
3436
Vaš mozak mora učiniti nešto poput toga
03:30
and we're now beginning to understand and identify
69
195079
2488
i sada počinjemo razumijevati i identificirati
03:33
brain systems involved in valuation,
70
197567
2137
sustave u mozgu uključene u procjenu,
03:35
and one of them includes a neurotransmitter system
71
199704
2632
a jedan od njih uključuje sustav neurotransmitera
03:38
whose cells are located in your brainstem
72
202336
2632
čije su stanice smještene u vašem moždanom deblu
03:40
and deliver the chemical dopamine to the rest of your brain.
73
204968
3175
i dostavljaju kemikaliju dopamin u ostatak vašeg mozga.
03:44
I won't go through the details of it, but that's an important
74
208143
2442
Neću ići u detalje što se toga tiče, no to je važno
03:46
discovery, and we know a good bit about that now,
75
210585
2157
otkriće i sada dosta znamo o tome,
03:48
and it's just a small piece of it, but it's important because
76
212742
2230
no to je samo mali dio, ali je važan zato
03:50
those are the neurons that you would lose if you had Parkinson's disease,
77
214972
3275
što su to neuroni koje biste izgubili da imate Parkinsonovu bolest
03:54
and they're also the neurons that are hijacked by literally
78
218247
2016
i to su također neuroni koji su doslovce ukradeni
03:56
every drug of abuse, and that makes sense.
79
220263
2232
prilikom svake zlouporabe droge i to ima smisla.
03:58
Drugs of abuse would come in, and they would change
80
222495
2336
Oni mijenjaju način
04:00
the way you value the world. They change the way
81
224831
1789
na koji vrednujete simbole
04:02
you value the symbols associated with your drug of choice,
82
226620
3199
udružene s drogom koju ste izabrali
04:05
and they make you value that over everything else.
83
229819
2514
i oni vas više od ičega tjeraju da to vrednujete.
04:08
Here's the key feature though. These neurons are also
84
232333
3021
Međutim, ovdje je ključno svojstvo. Ovi neuroni su također
04:11
involved in the way you can assign value to literally abstract ideas,
85
235354
3501
uključeni u način na koji možete raspodijeliti procjenu na doslovno apstraktne ideje
04:14
and I put some symbols up here that we assign value to
86
238855
2041
i staviti neke simbole ovdje gore gdje raspodjeljujemo procjenu
04:16
for various reasons.
87
240896
2720
iz raznih razloga.
04:19
We have a behavioral superpower in our brain,
88
243616
2689
Imamo bihevioralnu supermoć u svom mozgu
04:22
and it at least in part involves dopamine.
89
246305
1753
i ona barem dijelom uključuje dopamin.
04:23
We can deny every instinct we have for survival for an idea,
90
248058
4189
Možemo demantirati svaki instinkt koji imamo za preživljavanjem za idejom,
04:28
for a mere idea. No other species can do that.
91
252247
4005
za pukom idejom. Nijedna vrsta to ne može učiniti.
04:32
In 1997, the cult Heaven's Gate committed mass suicide
92
256252
3606
1997. kult Heaven's Gate (Rajska vrata) počinio je masovno samoubojstvo
04:35
predicated on the idea that there was a spaceship
93
259858
2215
izričeno idejom da postoji svemirski brod
04:37
hiding in the tail of the then-visible comet Hale-Bopp
94
262073
3785
koji se skriva na repu tada vidljivog kometa Hale-Bopp
04:41
waiting to take them to the next level. It was an incredibly tragic event.
95
265858
4272
koji tamo čeka ne bi li ih odveo na sljedeću razinu. To je bio nevjerojatno tragičan događaj.
04:46
More than two thirds of them had college degrees.
96
270130
3485
Više od dvije trećine tih ljudi bilo je visoko obrazovano.
04:49
But the point here is they were able to deny their instincts for survival
97
273615
3723
No poanta je u tome da su bili u mogućnosti negirati svoje instinkte za preživljavanjem
04:53
using exactly the same systems that were put there
98
277338
2866
koristeći upravo jednake sustave koji su tamo postavljeni
04:56
to make them survive. That's a lot of control, okay?
99
280204
4042
da im omoguće preživljavanje. To je mnogo kontrole, u redu?
05:00
One thing that I've left out of this narrative
100
284246
2089
Jedna stvar koju sam izostavio za vrijeme ovog pričanja
05:02
is the obvious thing, which is the focus of the rest of my
101
286335
2234
je očigledna stvar koja je centar ostatka
05:04
little talk, and that is other people.
102
288569
2159
mog malog govora, a to su drugi ljudi.
05:06
These same valuation systems are redeployed
103
290728
2996
Isti su procjenjivački sustavi pregrupirani
05:09
when we're valuing interactions with other people.
104
293724
2492
kad procjenjujemo interakcije s drugim ljudima.
05:12
So this same dopamine system that gets addicted to drugs,
105
296216
3271
Isti ovaj sustav dopamina koji nas čini ovisnima o drogama,
05:15
that makes you freeze when you get Parkinson's disease,
106
299487
2524
koji vas zamrzne kad obolite od Parkinsonove bolesti,
05:17
that contributes to various forms of psychosis,
107
302011
3077
koji pridonosi brojnim oblicima psihoza
05:20
is also redeployed to value interactions with other people
108
305088
3920
također je pregrupiran u vrijednost interakcija s drugim ljudima
05:24
and to assign value to gestures that you do
109
309008
2896
na dodijeljenu vrijednost pokreta koju činite
05:27
when you're interacting with somebody else.
110
311904
2574
kad komunicirate s nekim drugim.
05:30
Let me give you an example of this.
111
314478
2577
Da vam dam primjer ovoga.
05:32
You bring to the table such enormous processing power
112
317055
2967
Vi donosite nezamislivo korisnu ogromnu obrađenu moć
05:35
in this domain that you hardly even notice it.
113
320022
2624
u ovo područje da to teško uopće zamjećujete.
05:38
Let me just give you a few examples. So here's a baby.
114
322646
1467
Dat ću vam nekoliko primjera. Ovo je dijete.
05:40
She's three months old. She still poops in her diapers and she can't do calculus.
115
324113
3730
Ima tri mjeseca. Još uvijek kaka u svoje pelene i ne može računati.
05:43
She's related to me. Somebody will be very glad that she's up here on the screen.
116
327843
3353
Povezana je sa mnom. Netko će biti veoma sretan što je ona ovdje na ekranu.
05:47
You can cover up one of her eyes, and you can still read
117
331196
2376
Možete pokriti jedno njeno oko i još uvijek iščitati
05:49
something in the other eye, and I see sort of curiosity
118
333572
2755
nešto u drugom oku, a ja vidim neku vrstu znatiželje
05:52
in one eye, I see maybe a little bit of surprise in the other.
119
336327
3597
u jednom oku, možda vidim malo iznenađenja u drugom.
05:55
Here's a couple. They're sharing a moment together,
120
339924
3179
Ovdje je jedan par. Dijele zajednički trenutak
05:59
and we've even done an experiment where you can cut out
121
343103
1318
i čak smo napravili eksperiment gdje možete izvaditi
06:00
different pieces of this frame and you can still see
122
344421
3007
različite komadiće ovog okvira i još uvijek možete vidjeti
06:03
that they're sharing it. They're sharing it sort of in parallel.
123
347428
2504
da ga dijele. Dijele ga u nekoj vrsti paralele.
06:05
Now, the elements of the scene also communicate this
124
349932
2463
Elementi scene također komuniciraju s nama,
06:08
to us, but you can read it straight off their faces,
125
352395
2235
ali možete ih pročitati direktno s njihovih lica
06:10
and if you compare their faces to normal faces, it would be a very subtle cue.
126
354630
3503
i ako usporedite njihova lica s normalnim licima to bi bio veoma suptilan trag.
06:14
Here's another couple. He's projecting out at us,
127
358133
3347
Evo još jednog para. On se izbacuje prema nama,
06:17
and she's clearly projecting, you know,
128
361480
2888
a ona očito izbacuje, znate,
06:20
love and admiration at him.
129
364368
2263
ljubav i divljenje prema njemu.
06:22
Here's another couple. (Laughter)
130
366631
3635
Evo još jednog para. (Smijeh)
06:26
And I'm thinking I'm not seeing love and admiration on the left. (Laughter)
131
370266
5150
Mislim kako ne vidim ljubav i divljenje s lijeve strane. (Smijeh)
06:31
In fact, I know this is his sister, and you can just see
132
375416
2560
Zapravo, znam da mu je ovo sestra i možete vidjeti
06:33
him saying, "Okay, we're doing this for the camera,
133
377976
2513
kako on govori, “U redu, radimo ovo zbog slikanja,
06:36
and then afterwards you steal my candy and you punch me in the face." (Laughter)
134
380489
5702
a nakon toga ćeš mi ukrasti slatkiš i udariti me u lice.” (Smijeh)
06:42
He'll kill me for showing that.
135
386191
2106
Ubit će me zato što vam ovo pokazujem.
06:44
All right, so what does this mean?
136
388297
2797
U redu, što ovo znači?
06:46
It means we bring an enormous amount of processing power to the problem.
137
391094
3350
Znači da problemu dajemo ogromnu količinu obrađene moći.
06:50
It engages deep systems in our brain, in dopaminergic
138
394444
3648
To uključuje duboke sustave u našem mozgu, u našim dopaminskim
06:53
systems that are there to make you chase sex, food and salt.
139
398092
2818
sustavima koji su ovdje kako bi vas natjerali da želite seks, hranu i sol.
06:56
They keep you alive. It gives them the pie, it gives
140
400910
2894
Oni vas drže živima. Daju vam pitu, daju vam
06:59
that kind of a behavioral punch which we've called a superpower.
141
403804
2904
tu vrstu bihevioralnog udarca koji smo mi nazvali supermoći.
07:02
So how can we take that and arrange a kind of staged
142
406708
3654
Dakle, kako to možemo uzeti i dogovoriti neku vrstu predstavljene
07:06
social interaction and turn that into a scientific probe?
143
410362
2698
socijalne interakcije i pretvoriti to u znanstvenu sondu?
07:08
And the short answer is games.
144
413060
2691
Kratak odgovor su igrice.
07:11
Economic games. So what we do is we go into two areas.
145
415751
4404
Ekonomske igrice. Ono što radimo jest da idemo u dva područja.
07:16
One area is called experimental economics. The other area is called behavioral economics.
146
420155
3336
Jedno područje se naziva eksperimentalna ekonomija. Drugo područje se zove bihevioralna ekonomija.
07:19
And we steal their games. And we contrive them to our own purposes.
147
423491
4078
Mi krademo njihove igrice. Mi smo ih izmislili za vlastite namjene.
07:23
So this shows you one particular game called an ultimatum game.
148
427569
2967
Ovo vam pokazuje jednu određenu igricu pod nazivom igra ultimatuma.
07:26
Red person is given a hundred dollars and can offer
149
430536
1845
Crvenoj osobi ponuđeno je sto dolara i ona to može podijeliti
07:28
a split to blue. Let's say red wants to keep 70,
150
432381
3723
s plavom osobom na dva dijela. Recimo da crvena želi zadržati 70,
07:32
and offers blue 30. So he offers a 70-30 split with blue.
151
436104
4086
a plavoj nudi 30. Dakle, ona nudi dijeljenje na 70-30 s plavom.
07:36
Control passes to blue, and blue says, "I accept it,"
152
440190
2851
Kontrola prelazi na plavu i plava kaže, “Prihvaćam.”
07:38
in which case he'd get the money, or blue says,
153
443041
1956
u čijem slučaju bi onda dobila novac ili plava kaže “Odbijam.”
07:40
"I reject it," in which case no one gets anything. Okay?
154
444997
4307
u čijem slučaju nitko ne dobiva ništa. U redu?
07:45
So a rational choice economist would say, well,
155
449304
3392
Racionalan izbor, rekli bi ekonomisti,
07:48
you should take all non-zero offers.
156
452696
2056
bi bio da biste trebali prihvatiti bilo kakve ponude koje ne uključuju nulu.
07:50
What do people do? People are indifferent at an 80-20 split.
157
454752
3762
Što ljudi rade? Ljudi su nezainteresirani za dijeljenja na 80-20.
07:54
At 80-20, it's a coin flip whether you accept that or not.
158
458514
3524
Na 80-20 imate bacanje novčića bez obzira prihvatili vi to ili ne.
07:57
Why is that? You know, because you're pissed off.
159
462038
2891
Zašto je tome tako? Znate, zato što ste bijesni. Ljuti ste.
08:00
You're mad. That's an unfair offer, and you know what an unfair offer is.
160
464929
3609
To nije poštena ponuda, a vi znate što je nepoštena ponuda.
08:04
This is the kind of game done by my lab and many around the world.
161
468538
2704
To je vrsta igre koju je napravio moj laboratorij i mnogi širom svijeta.
08:07
That just gives you an example of the kind of thing that
162
471242
2544
To vam samo daje primjer na koji način ove igrice ispituju stvar.
08:09
these games probe. The interesting thing is, these games
163
473786
3738
Zanimljivo je da
08:13
require that you have a lot of cognitive apparatus on line.
164
477524
3707
te igrice trebaju mnogo simultanih kognitivnih aparata.
08:17
You have to be able to come to the table with a proper model of another person.
165
481231
2928
Morate biti u stanju doći do stola s određenim modelom druge osobe.
08:20
You have to be able to remember what you've done.
166
484159
3213
Morate biti u stanju zapamtiti što ste učinili.
08:23
You have to stand up in the moment to do that.
167
487372
1420
Morate se suprotstaviti kad je vrijeme za to.
08:24
Then you have to update your model based on the signals coming back,
168
488792
3350
Zatim morate dopuniti svoj model baziran na signalima koji se vraćaju
08:28
and you have to do something that is interesting,
169
492142
2972
i morate učiniti nešto što je zanimljivo,
08:31
which is you have to do a kind of depth of thought assay.
170
495114
2597
a to je da morate učiniti neku vrstu analize dubine vaših misli.
08:33
That is, you have to decide what that other person expects of you.
171
497711
3333
Zapravo, morate odrediti što ta druga osoba očekuje od vas.
08:36
You have to send signals to manage your image in their mind.
172
501044
2954
Morate poslati signale kako biste upravljali svojom slikom u njihovim umovima.
08:39
Like a job interview. You sit across the desk from somebody,
173
503998
2853
Poput razgovora za posao. Sjedite za stolom preko puta nekog,
08:42
they have some prior image of you,
174
506851
1369
oni imaju neku prvu sliku o vama,
08:44
you send signals across the desk to move their image
175
508220
2751
a vi šaljete signale preko stola kako biste pomaknuli njihovu sliku
08:46
of you from one place to a place where you want it to be.
176
510971
3920
vas s jednog mjesta na mjesto gdje želite da bude.
08:50
We're so good at this we don't really even notice it.
177
514891
3385
Toliko smo dobri u tome da čak ni ne primjećujemo da je tako.
08:54
These kinds of probes exploit it. Okay?
178
518276
3767
Ovakve vrste ispitivanja to iskorištavaju. U redu?
08:57
In doing this, what we've discovered is that humans
179
522043
1807
Radeći ovo, otkrili smo da su ljudi
08:59
are literal canaries in social exchanges.
180
523850
2331
doslovce kanarinci u socijalnim izmjenama.
09:02
Canaries used to be used as kind of biosensors in mines.
181
526181
3397
Kanarinci su bili poznati kao vrsta biosenzora u rudnicima.
09:05
When methane built up, or carbon dioxide built up,
182
529578
3560
Kada bi se metan nakupio ili ugljik dioksid
09:09
or oxygen was diminished, the birds would swoon
183
533138
4186
ili je ponestalo kisika, ptice bi se onesvijestile prije ljudi
09:13
before people would -- so it acted as an early warning system:
184
537324
2326
-- to je bio rani znak sustava:
09:15
Hey, get out of the mine. Things aren't going so well.
185
539650
2980
Hej, bježite iz rudnika. Stvari ne idu baš dobro.
09:18
People come to the table, and even these very blunt,
186
542630
2954
Ljudi dolaze do stola i čak i te veoma tupe,
09:21
staged social interactions, and they, and there's just
187
545584
2990
dogovorene socijalne interakcije, i oni, i tu su samo
09:24
numbers going back and forth between the people,
188
548574
3016
brojevi koji idu naprijed i natrag među ljudima,
09:27
and they bring enormous sensitivities to it.
189
551590
2199
a oni daju ogromnu osjetljivost tome.
09:29
So we realized we could exploit this, and in fact,
190
553789
2689
Shvatili smo da to možemo iskorištavati i zapravo,
09:32
as we've done that, and we've done this now in
191
556478
2556
kako smo to učinili, a učinili smo to sada mnogim
09:34
many thousands of people, I think on the order of
192
559034
2694
tisućama ljudi, mislim da govorimo
09:37
five or six thousand. We actually, to make this
193
561728
2165
o pet ili šest tisuća. Mi zapravo, kako bismo napravili
09:39
a biological probe, need bigger numbers than that,
194
563893
2224
ovo biološko ispitivanje trebamo veći broj od ovog,
09:42
remarkably so. But anyway,
195
566117
3674
baš neobično. Kako god,
09:45
patterns have emerged, and we've been able to take
196
569791
2004
modeli su izronili i bili smo u mogućnosti
09:47
those patterns, convert them into mathematical models,
197
571795
3836
uzeti sve modele
09:51
and use those mathematical models to gain new insights
198
575631
2689
kako bismo dobili nov uvid
09:54
into these exchanges. Okay, so what?
199
578320
2131
u ove razmjene. U redu, pa što onda?
09:56
Well, the so what is, that's a really nice behavioral measure,
200
580451
3313
Poanta je da je to veoma lijepo bihevioralno mjerenje,
09:59
the economic games bring to us notions of optimal play.
201
583764
3319
ekonomske igrice nam donose pojam optimalne igre.
10:02
We can compute that during the game.
202
587083
2484
Možemo to procijeniti tijekom igre.
10:05
And we can use that to sort of carve up the behavior.
203
589567
2953
To možemo upotrijebiti kako bismo na neki način isklesali ponašanje.
10:08
Here's the cool thing. Six or seven years ago,
204
592520
4330
Evo što je super stvar. Prije šest ili sedam godina
10:12
we developed a team. It was at the time in Houston, Texas.
205
596850
2550
stvorili smo ekipu. U to vrijeme nalazila se u Houstonu
10:15
It's now in Virginia and London. And we built software
206
599400
3394
u Teksasu. Sada je u Virginiji i Londonu. Napravili smo software
10:18
that'll link functional magnetic resonance imaging devices
207
602794
3207
koji će povezivati aparate za funkcionalnu magnetnu rezonancu
10:21
up over the Internet. I guess we've done up to six machines
208
606001
4035
širom interneta. Pretpostavljam da smo napravili nekih šest
10:25
at a time, but let's just focus on two.
209
610036
1981
uređaja u to vrijeme, ali usredotočimo se na samo dva.
10:27
So it synchronizes machines anywhere in the world.
210
612017
3058
Dakle, sinkronizira uređaje bilo gdje u svijetu.
10:30
We synchronize the machines, set them into these
211
615075
3169
Mi sinkroniziramo uređaje, postavljamo ih u te
10:34
staged social interactions, and we eavesdrop on both
212
618244
1983
predstavljene socijalne interakcije i prisluškujemo oba mozga
10:36
of the interacting brains. So for the first time,
213
620227
1666
koji vrše interakciju. Po prvi puta
10:37
we don't have to look at just averages over single individuals,
214
621893
3607
ne moramo gledati samo prosjek pojedinih individua
10:41
or have individuals playing computers, or try to make
215
625500
2897
ili imati individue koje igraju igrice na računalu ili pokušati
10:44
inferences that way. We can study individual dyads.
216
628397
2763
doći do zajedničkih zaključaka na taj način. Možemo proučavati individualne parove.
10:47
We can study the way that one person interacts with another person,
217
631160
2785
Možemo proučavati način na koji jedna osoba komunicira s drugom osobom,
10:49
turn the numbers up, and start to gain new insights
218
633945
2564
okrenuti brojeve i početi dobivati nove poglede
10:52
into the boundaries of normal cognition,
219
636509
2515
u granice normalne spoznaje,
10:54
but more importantly, we can put people with
220
639024
2732
no što je još važnije možemo uključiti ljude s
10:57
classically defined mental illnesses, or brain damage,
221
641756
3337
određenim mentalnim bolestima ili oštećenjima mozga
11:00
into these social interactions, and use these as probes of that.
222
645093
3551
u ove socijalne interakcije i upotrijebiti to kao ispitivanja navedenog.
11:04
So we've started this effort. We've made a few hits,
223
648644
2350
Stoga smo krenuli s ovim pokušajem. Dobili smo nekoliko pogodaka,
11:06
a few, I think, embryonic discoveries.
224
650994
2449
nekoliko, smatram ključnih otkrića.
11:09
We think there's a future to this. But it's our way
225
653443
2812
Smatramo kako to ima budućnost. No to je naš način
11:12
of going in and redefining, with a new lexicon,
226
656255
2560
ulaska unutar toga i redefiniranja s novim rječnikom,
11:14
a mathematical one actually, as opposed to the standard
227
658815
4022
zapravo s matematičkim rječnikom, nasuprot standardnim
11:18
ways that we think about mental illness,
228
662837
2578
načinima na koje razmišljamo o mentalnim bolestima,
11:21
characterizing these diseases, by using the people
229
665415
2067
karakterizirajući ih uz pomoć ljudi
11:23
as birds in the exchanges. That is, we exploit the fact
230
667482
3007
umjesto ptica, tj. izrabljujemo činjenicu
11:26
that the healthy partner, playing somebody with major depression,
231
670489
4244
da zdrav partner, igrajući se s nekim tko boluje od depresije
11:30
or playing somebody with autism spectrum disorder,
232
674733
2910
ili igrajući se s nekim tko je autističan
11:33
or playing somebody with attention deficit hyperactivity disorder,
233
677643
3850
ili igrajući se s nekim tko ima poremećaj pažnje,
11:37
we use that as a kind of biosensor, and then we use
234
681493
3219
koristimo kao neku vrstu biosenzora, a zatim
11:40
computer programs to model that person, and it gives us
235
684712
2644
koristimo računalne programe da bismo modelirali tu osobu i to nam daje
11:43
a kind of assay of this.
236
687356
2470
neku vrstu ovakve analize.
11:45
Early days, and we're just beginning, we're setting up sites
237
689826
2131
Ranije smo samo počinjali, postavljali smo web stranice
11:47
around the world. Here are a few of our collaborating sites.
238
691957
3410
širom svijeta. Ovdje je nekoliko naših suradničkih stranica.
11:51
The hub, ironically enough,
239
695367
2309
Središte je, dovoljno ironično,
11:53
is centered in little Roanoke, Virginia.
240
697676
2889
smješteno u malom gradu Roanoke u Virginiji.
11:56
There's another hub in London, now, and the rest
241
700565
2269
Postoji još jedno središte u Londonu, trenutno,
11:58
are getting set up. We hope to give the data away
242
702834
4009
a na ostalima se još radi. Nadamo se da ćemo u nekoj fazi moći dati podatke.
12:02
at some stage. That's a complicated issue
243
706843
3673
Komplicirana je stvar
12:06
about making it available to the rest of the world.
244
710516
2994
učiniti to dostupnim ostatku svijeta,
12:09
But we're also studying just a small part
245
713510
1847
no također proučavamo samo mali dio
12:11
of what makes us interesting as human beings, and so
246
715357
2267
onoga što nas čini zanimljivima kao ljudskim bićima
12:13
I would invite other people who are interested in this
247
717624
2041
pa bih želio pozvati druge ljude koji su zainteresirani
12:15
to ask us for the software, or even for guidance
248
719665
2569
za to da pitaju za software ili čak i za navođenje
12:18
on how to move forward with that.
249
722234
2219
u svezi toga kako krenuti dalje s time.
12:20
Let me leave you with one thought in closing.
250
724453
2341
Dopustite da vas za kraj ostavim s jednom mišlju.
12:22
The interesting thing about studying cognition
251
726794
1942
Zanimljiva stvar u vezi proučavanja spoznaje
12:24
has been that we've been limited, in a way.
252
728736
3732
je to da smo na neki način ograničeni.
12:28
We just haven't had the tools to look at interacting brains
253
732468
2943
Nismo imali oružje kako bismo simultano
12:31
simultaneously.
254
735411
1200
mogli gledati mozgove koji komuniciraju.
12:32
The fact is, though, that even when we're alone,
255
736611
2470
Činjenica je da čak i kada smo sami,
12:34
we're a profoundly social creature. We're not a solitary mind
256
739081
4111
potpuno smo društvena bića. Nismo usamljen um
12:39
built out of properties that kept it alive in the world
257
743192
4373
izgrađen od svojstava koji bi ga održali živim u svijetu
12:43
independent of other people. In fact, our minds
258
747565
3948
odvojenom od ostalih ljudi. Zapravo, naši umovi
12:47
depend on other people. They depend on other people,
259
751513
2870
ovise o drugim ljudima.
12:50
and they're expressed in other people,
260
754383
1541
Ovise o drugim ljudima i izraženi su kroz druge
12:51
so the notion of who you are, you often don't know
261
755924
3652
kao i pojam toga tko ste, često ne znate tko ste
12:55
who you are until you see yourself in interaction with people
262
759576
2688
dok se ne vidite kako komunicirate s bliskim ljudima,
12:58
that are close to you, people that are enemies of you,
263
762264
2406
s ljudima koji su vam neprijatelji
13:00
people that are agnostic to you.
264
764670
2545
i s ljudima koji su vam nepoznati.
13:03
So this is the first sort of step into using that insight
265
767215
3776
Ovo je prvi korak korištenja uvida
13:06
into what makes us human beings, turning it into a tool,
266
770991
3295
u to što nas čini ljudskim bićima, pretvarajući to u oruđe
13:10
and trying to gain new insights into mental illness.
267
774286
1978
i pokušavajući stvoriti nove uvide u mentalne bolesti.
13:12
Thanks for having me. (Applause)
268
776264
3121
Hvala što ste bili ovdje. (Pljesak)
13:15
(Applause)
269
779385
3089
(Pljesak)
ABOUT THE SPEAKER
Read Montague - Behavioral NeuroscientistWhat does "normal behavior" look like? To find out, Read Montague is imaging thousands of brains at work.
Why you should listen
Until recently, the world's curiosity about our brains seemed to focus on abnormal behavior. Which of course left a big question unanswered: Do we even know what "normal behavior" is? Through the landmark Roanoke Brain Study, Read Montague is hoping to find that out, exploring the everyday tasks of brains -- making decisions, understanding social context, and relating to others -- by neuroimaging some 5,000 people, ages 18-85, over a period of many years.
Montague's teams in Virginia and in London lead fascinating research in computational neuroscience (how the brain's "machinery" works), offering insight into the relationship between the social and cognitive functions. For instance, a recent study from his group found that in small social groups, some people will alter the expression of their IQ in reaction to social pressures -- revising, in almost all cases, downward.
Read Montague | Speaker | TED.com