When you're making a deal, what's going on in your brain? | Colin Camerer

186,625 views ใƒป 2013-03-28

TED


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ.

00:00
Transcriber: Joseph Geni Reviewer: Thu-Huong Ha
0
0
7000
ืžืชืจื’ื: Omer Lavian ืžื‘ืงืจ: Shlomo Adam
00:12
I'm going to talk about the strategizing brain.
1
12857
2373
ืื ื™ ืื“ื‘ืจ ืขืœ ื”ืžื•ื— ื”ืžืชื›ื ืŸ.
ืื ื• ื ืฉืชืžืฉ ื‘ืฉื™ืœื•ื‘ ื™ื•ืฆื-ื“ื•ืคืŸ ืฉืœ ื›ืœื™ื
00:15
We're going to use an unusual combination of tools
2
15254
2386
00:17
from game theory and neuroscience
3
17664
1590
ืžืชืื•ืจื™ื™ืช ื”ืžืฉื—ืงื™ื ื•ืžื“ืขื™ ื”ืžื•ื—
00:19
to understand how people interact socially when value is on the line.
4
19278
3270
ื›ื“ื™ ืœื”ื‘ื™ืŸ ืืช ื™ื—ืกื™ ื”ื’ื•ืžืœื™ืŸ ื‘ื™ืŸ ืื ืฉื™ื ื›ืฉืžื“ื•ื‘ืจ ืขืœ ืขืจืš ื›ืกืคื™.
00:22
So game theory is a branch of, originally, applied mathematics,
5
22572
3472
ืชืื•ืจื™ื™ืช ื”ืžืฉื—ืงื™ื ื”ื™ื ื‘ืžืงื•ืจื” ืขื ืฃ ืฉืœ ืžืชืžื˜ื™ืงื” ืฉื™ืžื•ืฉื™ืช
00:26
used mostly in economics and political science, a little bit in biology,
6
26068
3428
ืฉืžืฉืžืฉ ื‘ืขื™ืงืจ ื‘ื›ืœื›ืœื”, ื‘ืžื“ืขื™ ื”ืžื“ื™ื ื”, ืงืฆืช ื‘ื‘ื™ื•ืœื•ื’ื™ื”,
ื•ืฉืžืกืคืง ืœื ื• ืืจื’ื•ืŸ ืžืชืžื˜ื™ ืฉืœ ื”ื—ื™ื™ื ื”ื—ื‘ืจืชื™ื™ื
00:29
that gives us a mathematical taxonomy of social life,
7
29520
2843
00:32
and it predicts what people are likely to do
8
32387
2319
ื•ื”ื™ื ืžื ื‘ืืช ืžื” ืฆืคื•ื™ ืฉืื ืฉื™ื ื™ืขืฉื•
00:34
and believe others will do
9
34730
1316
ื•ืžื” ืœื“ืขืชื ืื—ืจื™ื ื™ืขืฉื•
ื‘ืžืฆื‘ื™ื ืฉื‘ื”ื ืžืขืฉื™ื• ืฉืœ ื›ืœ ืื—ื“ ืžืฉืคื™ืขื™ื ืขืœ ื›ืœ ื”ื™ืชืจ.
00:36
in cases where everyone's actions affect everyone else.
10
36070
2973
ืžื“ื•ื‘ืจ ื‘ื”ืจื‘ื” ื“ื‘ืจื™ื: ืชื—ืจื•ืช, ืฉื™ืชื•ืฃ ืคืขื•ืœื”, ื”ืชืžืงื—ื•ืช,
00:39
That's a lot of things: competition, cooperation, bargaining,
11
39067
3685
00:42
games like hide-and-seek and poker.
12
42776
2352
ืžืฉื—ืงื™ื ื›ืžื• ืžื—ื‘ื•ืื™ื ื•ืคื•ืงืจ.
00:45
Here's a simple game to get us started.
13
45954
1933
ื”ื ื” ืžืฉื—ืง ืคืฉื•ื˜ ื‘ืชื•ืจ ื”ืชื—ืœื”.
00:47
Everyone chooses a number from zero to 100.
14
47911
2464
ื›ืœ ืื—ื“ ื‘ื•ื—ืจ ืžืกืคืจ ื‘ื™ืŸ ืืคืก ืœืžืื”,
00:50
We're going to compute the average of those numbers,
15
50399
2460
ื ื—ืฉื‘ ืืช ื”ืžืžื•ืฆืข ืฉืœ ื”ืžืกืคืจื™ื ื”ืืœื”,
00:52
and whoever's closest to two-thirds of the average wins a fixed prize.
16
52883
4039
ื•ืžื™ ืฉื”ื›ื™ ืงืจื•ื‘ ืœ-2/3 ืžื”ืžืžื•ืฆืข ื–ื•ื›ื” ื‘ืคืจืก ืžื•ื’ื“ืจ ืžืจืืฉ.
00:56
So you want to be a little bit below the average number
17
56946
2746
ืื– ื”ืฉืื™ืคื” ืฉืœ ื›ืœ ืื—ื“ ื”ื™ื ืœื‘ื—ื•ืจ ืงืฆืช ืคื—ื•ืช ืžื”ืžืžื•ืฆืข,
00:59
but not too far below,
18
59716
1152
ืื‘ืœ ืœื ืคื—ื•ืช ืžื“ื™, ื•ื–ื• ื’ื ื”ืฉืื™ืคื”
01:00
and everyone else wants to be a little bit below the average number as well.
19
60892
3621
ืฉืœ ื›ืœ ื”ืฉืืจ.
ื—ื™ืฉื‘ื• ืžื”ื• ื”ืžืกืคืจ ืฉื‘ืจืฆื•ื ื›ื ืœื‘ื—ื•ืจ.
01:04
Think about what you might pick.
20
64537
1547
ื‘ื–ืžืŸ ืฉืืชื ื—ื•ืฉื‘ื™ื, ื–ื”ื• ื“ื’ื ืฆืขืฆื•ืข ืฉืœ ืžืฉื”ื• ื›ืžื•
01:06
As you're thinking,
21
66108
1161
01:07
this is a toy model of something like selling in the stock market
22
67293
3054
ืžื›ื™ืจืช ืžื ื™ื•ืช ื‘ื–ืžืŸ ืฉื”ืฉื•ืง ื‘ืขืœื™ื™ื”, ื ื›ื•ืŸ?
01:10
during a rising market:
23
70371
1618
ื”ืฉืื™ืคื” ื”ื™ื ืœื ืœืžื›ื•ืจ ืžื•ืงื“ื ืžื“ื™, ื›ื™ ืื– ืžืคืกื™ื“ื™ื ืจื•ื•ื—,
01:12
You don't want to sell too early, because you miss out on profits,
24
72013
3110
ืื‘ืœ ื’ื ืœื ืœื—ื›ื•ืช ื™ื•ืชืจ ืžื“ื™,
01:15
but you don't want to wait too late, to when everyone else sells,
25
75147
3048
ืื—ืจื™ ืฉื›ื•ืœื ืžื›ืจื•, ื•ืœื’ืจื•ื ืœืฉื•ืง ืœื™ืคื•ืœ.
01:18
triggering a crash.
26
78219
1225
ื”ืฉืื™ืคื” ื”ื™ื ืœื”ื™ื•ืช ืงืฆืช ืœืคื ื™ ืฉืืจ ื”ืžืชื—ืจื™ื, ืื‘ืœ ืœื ื™ื•ืชืจ ืžื“ื™.
01:19
You want to be a little bit ahead of the competition, but not too far ahead.
27
79468
3580
ื”ื ื” ืฉืชื™ ืชื™ืื•ืจื™ื•ืช, ืื™ืš ืื ืฉื™ื ืขืฉื•ื™ื™ื ืœื—ืฉื•ื‘ ืขืœ ื–ื”,
01:23
OK, here's two theories about how people might think about this,
28
83072
3085
ื•ืื– ื ืจืื” ืงืฆืช ื ืชื•ื ื™ื.
01:26
then we'll see some data.
29
86181
1200
ื—ืœืง ืžื–ื” ื™ื™ืฉืžืข ืžื•ื›ืจ ื›ื™ ืืชื ื•ื“ืื™
01:27
Some of these will sound familiar
30
87405
1596
ื—ื•ืฉื‘ื™ื ื›ื›ื”. ืื ื™ ืจื•ืื” ืืช ื–ื” ื‘ืขื–ืจืช ื”ืชืื•ืจื™ื” ืฉืœื™ ืขืœ ื”ืžื•ื—.
01:29
because you probably are thinking that way.
31
89025
2059
01:31
I'm using my brain theory to see.
32
91108
1652
01:32
A lot of people say, "I really don't know what people are going to pick,
33
92784
3414
ื”ืจื‘ื” ืื ืฉื™ื ืื•ืžืจื™ื, "ืื ื™ ืœื ื™ื•ื“ืข ืžื” ืื ืฉื™ื ื™ื‘ื—ืจื•,
ืื– ืœื“ืขืชื™ ื”ืžืžื•ืฆืข ื™ื”ื™ื” 50."
01:36
so I think the average will be 50" -- they're not being strategic at all --
34
96222
3527
- ื–ื” ื‘ื›ืœืœ ืœื ืชื›ื ื•ืŸ -
01:39
and "I'll pick two-thirds of 50, that's 33."
35
99773
2062
ืื• "ืื ื™ ืื‘ื—ืจ 2/3 ืž-50, ืฉื–ื” 33." ื–ื• ื›ื‘ืจ ื”ืชื—ืœื”.
01:41
That's a start.
36
101859
1154
ืื—ืจื™ื, ืงืฆืช ื™ื•ืชืจ ืžืชื•ื—ื›ืžื™ื,
01:43
Other people, who are a little more sophisticated,
37
103037
2389
ืฉืžืฉืชืžืฉื™ื ื‘ื™ื•ืชืจ ื–ื›ืจื•ืŸ ืขื‘ื•ื“ื”,
01:45
using more working memory,
38
105450
1241
01:46
say, "I think people will pick 33,
39
106715
1629
ืื•ืžืจื™ื, "ืœื“ืขืชื™ ืื ืฉื™ื ื™ื‘ื—ืจื• 33, ื›ื™ ื”ื ืจื•ืฆื™ื ืœื‘ื—ื•ืจ ืœืคื™ ื”-50,
01:48
because they're going to pick a response to 50,
40
108368
2211
ืื– ืื ื™ ืื‘ื—ืจ 22, ืฉื–ื” 2/3 ืž-33."
01:50
and so I'll pick 22, which is two-thirds of 33."
41
110603
2298
01:52
They're doing one extra step of thinking, two steps.
42
112925
2567
ื”ื ืขื•ืฉื™ื ืฉืœื‘ ื—ืฉื™ื‘ื” ื ื•ืกืฃ, ืฉื ื™ ืฉืœื‘ื™ื.
01:55
That's better.
43
115913
1204
ื–ื” ื™ื•ืชืจ ื˜ื•ื‘. ื•ื›ืžื•ื‘ืŸ ืฉืขืงืจื•ื ื™ืช,
01:57
Of course, in principle, you could do three, four or more,
44
117141
2727
ืืคืฉืจ ืœื‘ืฆืข 3, 4 ืฉืœื‘ื™ ื—ืฉื™ื‘ื” ืื• ื™ื•ืชืจ,
01:59
but it starts to get very difficult.
45
119892
1946
ืื‘ืœ ื–ื” ืžืชื—ื™ืœ ืœื”ื™ื•ืช ืžืื“ ืงืฉื”.
02:01
Just like in language and other domains,
46
121862
1931
ื›ืžื• ื‘ืฉืคื” ื•ื‘ืชื—ื•ืžื™ื ืื—ืจื™ื, ืื ื• ื™ื•ื“ืขื™ื ืฉืงืฉื” ืœืื ืฉื™ื ืœื ืชื—
02:03
we know that it's hard for people to parse very complex sentences
47
123817
3065
ืžืฉืคื˜ื™ื ืžื•ืจื›ื‘ื™ื ืžืื“ ืขื ืžื‘ื ื” ื—ื–ืจืชื™.
02:06
with a recursive structure.
48
126906
1291
ื–ื” ื ืงืจื "ืชื™ืื•ืจื™ื™ืช ื”ื”ื™ืจืจื›ื™ื” ืงื•ื’ื ื™ื˜ื™ื‘ื™ืช", ืื’ื‘.
02:08
This is called the cognitive hierarchy theory,
49
128221
2149
ืขื‘ื“ืชื™ ืขืœื™ื” ืขื ืขื•ื“ ื›ืžื” ืื ืฉื™ื,
02:10
something I've worked on and a few other people,
50
130394
2249
02:12
and it indicates a kind of hierarchy,
51
132667
1770
ื•ื”ื™ื ืžืฆื‘ื™ืขื” ืขืœ ื”ื™ืจืจื›ื™ื” ืžืกื•ื™ืžืช
02:14
along with some assumptions about how many people stop at different steps
52
134461
3439
ื•ืžื ื™ื—ื” ื›ืžื” ื”ื ื—ื•ืช ืœื’ื‘ื™ ืžืกืคืจ ื”ืื ืฉื™ื ืฉื™ืขืฆืจื• ื‘ืฉืœื‘ื™ื ื”ืฉื•ื ื™ื
ื•ืื™ืš ืฉืœื‘ื™ ื”ื—ืฉื™ื‘ื” ืžื•ืฉืคืขื™ื
02:17
and how the steps of thinking are affected
53
137924
2007
ืžื”ืจื‘ื” ืžืฉืชื ื™ื ืžืขื ื™ื™ื ื™ื ื•ืกื•ื’ื™ ืื ืฉื™ื, ื›ืžื• ืฉื ืจืื” ื‘ืขื•ื“ ื“ืงื”.
02:19
by lots of interesting variables and variant people,
54
139955
2444
02:22
as we'll see in a minute.
55
142423
1200
ืชื™ืื•ืจื™ื” ืžืื“ ืฉื•ื ื”, ื”ืจื‘ื” ื™ื•ืชืจ ืคื•ืคื•ืœืจื™ืช, ืžื™ื•ืฉื ืช ื™ื•ืชืจ,
02:23
A very different theory, a much more popular one and an older one,
56
143647
3138
ืฉื ื•ื“ืขื” ื‘ืขื™ืงืจ ื”ื•ื“ื•ืช ืœื’'ื•ืŸ ื ืืฉ ื•ืœืกืจื˜ "ื ืคืœืื•ืช ื”ืชื‘ื•ื ื”",
02:26
due largely to John Nash of "A Beautiful Mind" fame,
57
146809
2479
02:29
is what's called "equilibrium analysis."
58
149312
2085
ื ืงืจืืช "ื ื™ืชื•ื— ืฉื™ื•ื•ื™ ื”ืžืฉืงืœ".
02:31
So if you've ever taken a game theory course at any level,
59
151421
2813
ืื ืื™-ืคืขื ืœืงื—ืชื ืงื•ืจืก ื‘ืชืื•ืจื™ื™ืช ื”ืžืฉื—ืงื™ื, ื‘ืจืžื” ื›ืœืฉื”ื™,
ื•ื“ืื™ ืœืžื“ืชื ืขืœ ื–ื” ืžืฉื”ื•.
02:34
you'll have learned a bit about this.
60
154258
1777
ืฉื™ื•ื•ื™-ืžืฉืงืœ ื”ื•ื ืžืฆื‘ ืžืชืžื˜ื™ ืฉื‘ื• ื›ืœ ืื—ื“
02:36
An equilibrium is a mathematical state
61
156059
1828
02:37
in which everybody has figured out exactly what everyone else will do.
62
157911
3300
ื™ื•ื“ืข ื‘ื“ื™ื•ืง ืžื” ื™ืขืฉื” ื›ืœ ืื—ื“ ืื—ืจ.
ื–ื” ืจืขื™ื•ืŸ ืžืื•ื“ ืฉื™ืžื•ืฉื™, ืื‘ืœ ืžื‘ื—ื™ื ื” ื”ืชื ื”ื’ื•ืชื™ืช,
02:41
It is a very useful concept,
63
161235
1344
02:42
but behaviorally, it may not exactly explain
64
162603
2054
ื™ื™ืชื›ืŸ ืฉื–ื” ืœื ืžืกื‘ื™ืจ ื‘ืžื“ื•ื™ืง ืžื” ืื ืฉื™ื ืขื•ืฉื™ื
02:44
what people do the first time they play these types of economic games
65
164681
3274
ื›ืฉื”ื ืžืฉื—ืงื™ื ืœืจืืฉื•ื ื” ื‘ืžืฉื—ืงื™ื ื›ืœื›ืœื™ื™ื ื›ืืœื”,
02:47
or in situations in the outside world.
66
167979
1921
ืื• ื‘ื ืกื™ื‘ื•ืช ืื—ืจื•ืช ื‘ืขื•ืœื ื”ืจื’ื™ืœ.
02:49
In this case, the equilibrium makes a very bold prediction,
67
169924
2801
ื‘ืžืงืจื” ื”ื–ื”, ืฉื™ื•ื•ื™ ื”ืžืฉืงืœ ืžืฆื™ื’ ืชื—ื–ื™ืช ื ื•ืขื–ืช ืžืื“:
02:52
which is: everyone wants to be below everyone else,
68
172749
2709
ื›ืœ ืื—ื“ ืžืขื•ื ื™ื™ืŸ ืœื‘ื—ื•ืจ ืžืกืคืจ ื ืžื•ืš ื™ื•ืชืจ ืžื›ืœ ืื—ื“ ืื—ืจ,
02:55
therefore, they'll play zero.
69
175482
1701
ื•ืœื›ืŸ ื›ื•ืœื ื™ื‘ื—ืจื• ื‘ืืคืก.
02:57
Let's see what happens.
70
177723
1157
ื‘ื•ืื• ื ืจืื” ืžื” ืงื•ืจื”. ื”ื ื™ืกื•ื™ ื”ื–ื” ื ืขืฉื” ื›ื‘ืจ ื”ืจื‘ื” ืžืื“ ืคืขืžื™ื.
02:58
This experiment's been done many, many times.
71
178904
2107
ืืช ื›ืžื” ืžื”ื ื™ืกื•ื™ื™ื ื”ืจืืฉื•ื ื™ื ืขืฉื™ื ื• ื‘ืฉื ื•ืช ื”-90,
03:01
Some of the earliest ones were done in the '90s
72
181035
2202
ืื ื•ื›ื™, ืจื•ื–ืžืจื™ ื ื™ื™ื’ืœ ื•ืื—ืจื™ื.
03:03
by me and Rosemarie Nagel and others.
73
183261
1806
ื–ื” ืžืขืจืš ื ืชื•ื ื™ื ื™ืคื” ืฉืœ 9,000 ืื ืฉื™ื ืฉื›ืชื‘ื•
03:05
This is a beautiful data set of 9,000 people
74
185091
2520
03:07
who wrote in to three newspapers and magazines that had a contest.
75
187635
3221
ืœืฉืœื•ืฉื” ืขื™ืชื•ื ื™ื ื•ื›ืชื‘ื™-ืขืช ืฉืขืจื›ื• ืชื—ืจื•ืช.
03:10
The contest said, send in your numbers,
76
190880
2043
ื‘ืชื—ืจื•ืช ื ืืžืจ, "ืฉื™ืœื—ื• ืืœื™ื ื• ืืช ื”ืžืกืคืจื™ื ืฉืœื›ื
03:12
and whoever is close to two-thirds of the average will win a big prize.
77
192947
3334
ื•ืžื™ ืฉื”ื›ื™ ืงืจื•ื‘ ืœ-2/3 ืžื”ืžืžื•ืฆืข ื™ืงื‘ืœ ืคืจืก ื’ื“ื•ืœ."
ื•ืืชื ืจื•ืื™ื ืฉื”ื ืชื•ื ื™ื ื›ื” ืจื‘ื™ื, ืขื“ ืฉืจื•ืื™ื ื‘ื‘ื™ืจื•ืจ ืืช ื”ืฉื™ืื™ื.
03:16
As you can see, there's so much data here, you can see the spikes very visibly.
78
196305
3733
ื™ืฉ ืฉื™ื ื‘-33. ืืœื” ื”ืื ืฉื™ื ืฉืขื•ืฉื™ื ืจืง ืฉืœื‘ ืื—ื“.
03:20
There's a spike at 33 -- those are people doing one step.
79
200062
2714
03:22
There is another spike visible at 22.
80
202800
2219
ื™ืฉ ืขื•ื“ ืฉื™ื ื‘ืจื•ืจ ื‘-22.
ื•ืฉื™ืžื• ืœื‘, ืื’ื‘, ืฉืจื•ื‘ ื”ืื ืฉื™ื ื‘ื•ื—ืจื™ื ืžืกืคืจื™ื ื‘ืื–ื•ืจ ื”ื–ื”.
03:25
Notice, by the way, most people pick numbers right around there;
81
205043
3016
ื”ื ืœื ื‘ื”ื›ืจื— ื‘ื•ื—ืจื™ื ื‘ื“ื™ื•ืง ืืช 33 ื•-22.
03:28
they don't necessarily pick exactly 33 and 22.
82
208083
2158
ื™ืฉ ืงืฆืช ืจืขืฉ ื‘ืื–ื•ืจ ื”ื–ื”.
03:30
There's something a bit noisy around it.
83
210265
1916
ืื‘ืœ ืืชื ืจื•ืื™ื ืืช ื”ืฉื™ืื™ื ื”ืืœื”, ื•ื–ื” ื‘ืื–ื•ืจื™ื ื”ืืœื”.
03:32
But you can see those spikes on that end.
84
212205
1968
ื”ื ื” ืขื•ื“ ืงื‘ื•ืฆืช ืื ืฉื™ื, ืฉื›ื ืจืื”
03:34
There's another group of people
85
214197
1485
ื ืฆืžื“ื™ื ืžืื“ ืœื ื™ืชื•ื— ืฉื™ื•ื•ื™ ื”ืžืฉืงืœ,
03:35
who seem to have a firm grip on equilibrium analysis,
86
215706
2487
ื›ื™ ื”ื ื‘ื•ื—ืจื™ื ืืคืก ืื• ืื—ื“.
03:38
because they're picking zero or one.
87
218217
1736
03:39
But they lose, right?
88
219977
1647
ืื‘ืœ ื”ื ืžืคืกื™ื“ื™ื, ื ื›ื•ืŸ?
03:41
Because picking a number that low is actually a bad choice
89
221648
3384
ื›ื™ ืœื‘ื—ื•ืจ ืžืกืคืจ ื›ื” ื ืžื•ืš ื–ื” ื‘ืขืฆื ื‘ื—ื™ืจื” ื’ืจื•ืขื”
ืื ื’ื ื”ืื—ืจื™ื ืœื ืขื•ืฉื™ื ื ื™ืชื•ื— ืฉืœ ืฉื™ื•ื•ื™-ืžืฉืงืœ.
03:45
if other people aren't doing equilibrium analysis as well.
90
225056
2739
03:47
So they're smart, but poor.
91
227819
1675
ืื– ื”ื ื—ื›ืžื™ื, ืื‘ืœ ืขื ื™ื™ื.
03:49
(Laughter)
92
229518
2064
(ืฆื—ื•ืง)
03:51
Where are these things happening in the brain?
93
231606
2467
ืื™ืคื” ื‘ืžื•ื— ืงื•ืจื™ื ื”ื“ื‘ืจื™ื ื”ืืœื”?
ืžื—ืงืจ ืื—ื“ ืฉืœ ืงื•ืจื™ืฆ'ืœื™ ื•ื ื™ื™ื’ืœ ื ื•ืชืŸ ืชืฉื•ื‘ื” ืžืื•ื“ ื‘ืจื•ืจื” ื•ืžืขื ื™ื™ื ืช.
03:54
One study by Coricelli and Nagel gives a really sharp, interesting answer.
94
234097
3693
03:57
They had people play this game while they were being scanned in an fMRI,
95
237814
3812
ื ืชื ื• ืœืื ืฉื™ื ืœืฉื—ืง ื‘ืžืฉื—ืง ื”ื–ื”
ื›ืฉื”ื™ื• ืชื—ืช ืกืจื™ืงื” ื‘ืžื›ืฉื™ืจ ื“ื™ืžื•ืช ืชื”ื•ื“ื” ืžื’ื ื˜ื™ ืชืคืงื•ื“ื™,
04:01
and two conditions:
96
241650
1157
ื•ื‘ืฉื ื™ ืžืฆื‘ื™ื: ื‘ื—ืœืง ืžื”ื ื™ืกื•ื™ื™ื
04:02
in some trials, they're told,
97
242831
1386
ื ืืžืจ ืœื”ื: "ืืชื ืžืฉื—ืงื™ื ื ื’ื“ ืื“ื ืื—ืจ
04:04
"You're playing another person who's playing right now.
98
244241
2597
ืฉืžืฉื—ืง ื‘ืื•ืชื• ืจื’ืข, ื•ืื ื• ื ืฉื•ื•ื” ื‘ืกื•ืฃ
04:06
We'll match up your behavior at the end and pay you if you win."
99
246862
3003
ื‘ื™ืŸ ื”ื”ืชื ื”ื’ื•ื™ื•ืช ืฉืœื›ื ื•ื ืฉืœื ืœื›ื ืื ืชื ืฆื—ื•."
ื‘ื ื™ืกื•ื™ื™ื ื”ืื—ืจื™ื, ื ืืžืจ ืœื”ื: "ืืชื ืžืฉื—ืงื™ื ื ื’ื“ ืžื—ืฉื‘
04:09
In other trials, they're told, "You're playing a computer,
100
249889
2728
ืฉื‘ื•ื—ืจ ืžืกืคืจื™ื ื‘ืื•ืคืŸ ืืงืจืื™."
04:12
they're just choosing randomly."
101
252641
1524
ืื– ืžื” ืฉืืชื ืจื•ืื™ื ืคื” ื–ื• ื”ืคื—ืชื”
04:14
So what you see here is a subtraction of areas
102
254189
2162
ืฉืœ ื”ืื–ื•ืจื™ื ืฉื‘ื”ื ื™ืฉ ื™ื•ืชืจ ืคืขื™ืœื•ืช ื‘ืžื•ื—
04:16
in which there's more brain activity when you're playing people
103
256375
2959
ื›ืฉืžืฉื—ืงื™ื ื ื’ื“ ืื ืฉื™ื ืœืขื•ืžืช ื ื’ื“ ืžื—ืฉื‘.
04:19
compared to playing the computer.
104
259358
1578
04:20
And you see activity in some regions we've seen today,
105
260960
2536
ื•ืืชื ืจื•ืื™ื ืคืขื™ืœื•ืช ื‘ื›ืžื” ืื–ื•ืจื™ื ืฉืจืื™ื ื• ื”ื™ื•ื,
ื”ืงื•ืจื˜ืงืก ื”ืคืจื”-ืคืจื•ื ื˜ืœื™ ื”ืžื“ื™ืืœื™, ืื‘ืœ ื‘ื“ื•ืจืกื•-ืžื“ื™ืืœื™, ืคื”,
04:23
medial prefrontal cortex, dorsomedial, up here,
106
263520
2249
04:25
ventromedial prefrontal cortex, anterior cingulate,
107
265793
2392
ื‘ืงื•ืจื˜ืงืก ื”ื•ื•ื ื˜ืจื•-ืžื“ื™ืืœื™ ื”ืคืจื”-ืคืจื•ื ื˜ืœื™,
ื‘ืกื™ื ื’ื•ืœื•ื ื”ืงื“ืžื™, ืื–ื•ืจ ืฉืžืขื•ืจื‘
04:28
an area that's involved in lots of types of conflict resolution,
108
268209
3015
ื‘ืกื•ื’ื™ื ืจื‘ื™ื ืฉืœ ืคืชืจื•ืŸ ืกื›ืกื•ื›ื™ื, ื›ืžื• ื‘ืžืฉื—ืง "ื”ืจืฆืœ ืืžืจ,"
04:31
like if you're playing "Simon Says,"
109
271248
1736
ื•ื’ื ื‘ืฆื•ืžืช ื“ื•ืคืŸ ื”ืจืงื” ืžืฉืžืืœ ื•ืžื™ืžื™ืŸ.
04:33
and also the right and left temporoparietal junction.
110
273008
3173
04:36
And these are all areas which are fairly reliably known to be
111
276205
2875
ื›ืœ ื”ืื–ื•ืจื™ื ื”ืืœื” ืฉืžื•ื›ืจื™ื ื”ื™ื˜ื‘
ื›ื—ืœืง ืžืžื” ืฉื ืงืจื "ืชื™ืื•ืจื™ื™ืช ืžืขื’ืœื™ ื”ืžื•ื—",
04:39
part of what's called a "theory of mind" circuit
112
279104
2251
ืื• "ืžืขื’ืœ ื”ื—ืฉื™ื‘ื”."
04:41
or "mentalizing circuit."
113
281379
1526
04:42
That is, it's a circuit that's used to imagine what other people might do.
114
282929
3507
ื›ืœื•ืžืจ, ืžืขื’ืœ ืฉืžืฉืžืฉ ื›ื“ื™ ืœื“ืžื™ื™ืŸ ืžื” ืื—ืจื™ื ื™ืขืฉื•.
04:46
These were some of the first studies to see this tied in to game theory.
115
286460
3907
ืืœื” ื”ื™ื• ื›ืžื” ืžื”ืžื—ืงืจื™ื ื”ืจืืฉื•ื ื™ื ืฉื’ื™ืœื• ืฉื–ื”
ืงืฉื•ืจ ืœืชื™ืื•ืจื™ื™ืช ื”ืžืฉื—ืงื™ื.
04:50
What happens with these one- and two-step types?
116
290778
2246
ืžื” ืงื•ืจื” ืขื ืกื•ื’ื™ "ืฉืœื‘ ืื—ื“" ื•"ืฉื ื™ ืฉืœื‘ื™ื" ื”ืืœื”?
ืื ื• ืžืกื•ื•ื’ื™ื ืื ืฉื™ื ืœืคื™ ืžื” ืฉื‘ื—ืจื•,
04:53
So, we classify people by what they picked,
117
293048
2251
ื•ืื– ืื ื• ืžืกืชื›ืœื™ื ืขืœ ื”ื”ื‘ื“ืœ ืฉื‘ื™ืŸ
04:55
and then we look at the difference between playing humans versus computers,
118
295323
3530
ืœืฉื—ืง ื ื’ื“ ื‘ื ื™ ืื“ื ื•ืœืฉื—ืง ื ื’ื“ ืžื—ืฉื‘ื™ื,
04:58
which brain areas are differentially active.
119
298877
2065
ืื™ื–ื” ืื–ื•ืจื™ื ื‘ืžื•ื— ืคืขื™ืœื™ื ื‘ืื•ืคืŸ ืฉื•ื ื”.
05:00
On the top, you see the one-step players.
120
300966
1968
ืœืžืขืœื” ืจื•ืื™ื ืืช ืฉื—ืงื ื™ ื”ืฉืœื‘ ื”ื‘ื•ื“ื“.
ื›ืžืขื˜ ืื™ืŸ ื”ื‘ื“ืœ,
05:02
There's almost no difference.
121
302958
1385
ื›ื™ ื”ื ืžืชื™ื™ื—ืกื™ื ืœืื ืฉื™ื ืื—ืจื™ื ื›ืžื• ืœืžื—ืฉื‘, ื•ื›ืš ื’ื ื ืจืื” ื”ืžื•ื—.
05:04
The reason is, they're treating other people like a computer,
122
304367
2877
ื‘ืฉื—ืงื ื™ื ืฉืœืžื˜ื” ืจื•ืื™ื ืืช ื›ืœ ื”ืคืขื™ืœื•ืช ื‘ืงื•ืจื˜ืงืก ื”ืžื“ื™ืืœื™ ื”ืคืจื”-ืคืจื•ื ื˜ืœื™ ื”ืื—ื•ืจื™.
05:07
and the brain is too.
123
307268
1151
05:08
The bottom players, you see all the activity in dorsomedial PFC.
124
308443
3023
ืื– ืื ื• ื™ื•ื“ืขื™ื ืฉืฉื—ืงื ื™ ืฉื ื™ ื”ืฉืœื‘ื™ื ืขื•ืฉื™ื ืžืฉื”ื• ืฉื•ื ื”.
05:11
So we know the two-step players are doing something differently.
125
311490
3007
ืื ื”ื™ื™ืชื ืขื•ืฆืจื™ื ืจื’ืข ื•ืื•ืžืจื™ื, "ืžื” ืืคืฉืจ ืœืขืฉื•ืช ืขื ื”ืžื™ื“ืข ื”ื–ื”?"
05:14
Now, what can we do with this information?
126
314521
2001
ืืชื ื™ื›ื•ืœื™ื ืœื”ืกืชื›ืœ ืขืœ ืคืขื™ืœื•ืช ื”ืžื•ื— ื•ืœื”ื’ื™ื“,
05:16
You might be able to look at brain activity and say,
127
316546
2441
"ื”ืื“ื ื”ื–ื” ื™ื”ื™ื” ืฉื—ืงืŸ ืคื•ืงืจ ื˜ื•ื‘,"
05:19
"This person will be a good poker player," or "This person's socially naive."
128
319011
3643
ืื•, "ื”ืื“ื ื”ื–ื” ื ืื™ื‘ื™ ืžื‘ื—ื™ื ื” ื—ื‘ืจืชื™ืช,"
ื•ืื•ืœื™ ื’ื ื ื•ื›ืœ ืœื—ืงื•ืจ ื“ื‘ืจื™ื
05:22
We might also be able to study things like development of adolescent brains
129
322678
3535
ื›ืžื• ื”ื”ืชืคืชื—ื•ืช ืฉืœ ื”ืžื•ื— ื”ืžืชื‘ื’ืจ
ื›ืฉื™ื”ื™ื” ืœื ื• ืžื•ืฉื’ ืื™ืคื” ืงื™ื™ืžื™ื ื”ืžืขื’ืœื™ื ื”ืืœื”.
05:26
once we have an idea of where this circuitry exists.
130
326237
2437
ื‘ืกื“ืจ. ืชืชื›ื•ื ื ื•.
05:28
OK. Get ready.
131
328698
1152
05:29
I'm saving you some brain activity,
132
329874
2100
ืื ื™ ื—ื•ืกืš ืœื›ื ืงืฆืช ืคืขื™ืœื•ืช-ืžื•ื—,
05:31
because you don't need to use your hair detector cells.
133
331998
2737
ื›ื™ ืœื ืชืฆื˜ืจื›ื• ืœื”ืฉืชืžืฉ ื‘ืชืื™ ื–ื™ื”ื•ื™ ื”ืฉื™ืขืจ ืฉืœื›ื.
05:34
You should use those cells to think carefully about this game.
134
334759
3262
ื›ื“ืื™ ืœื›ื ืœื”ืฉืชืžืฉ ื‘ืชืื™ื ืืœื” ื›ื“ื™ ืœื—ืฉื•ื‘ ื”ื™ื˜ื‘ ืขืœ ื”ืžืฉื—ืง ื”ื–ื”.
ื–ื” ืžืฉื—ืง ืฉืœ ื”ืชืžืงื—ื•ืช.
05:38
This is a bargaining game.
135
338045
1513
05:39
Two players who are being scanned using EEG electrodes
136
339582
3017
ืฉื ื™ ืฉื—ืงื ื™ื ืฉื ืกืจืงื™ื ื‘ืืžืฆืขื•ืช ืืœืงื˜ืจื•ื“ื•ืช ืจืฉืžืช ืžื•ื— ื—ืฉืžืœื™ืช
05:42
are going to bargain over one to six dollars.
137
342623
2778
ืขื•ืžื“ื™ื ืœื”ืชืžืงื— ืขืœ ื‘ื™ืŸ 1-6 ื“ื•ืœืจื™ื.
05:45
If they can do it in 10 seconds, they'll earn that money.
138
345425
2683
ืื ื”ื ื™ืฆืœื™ื—ื• ืœืขืฉื•ืช ืืช ื–ื” ื‘-10 ืฉื ื™ื•ืช, ื”ื ื™ื–ื›ื• ื‘ื›ืกืฃ.
ืื ืœื ืกื’ืจื• ืขืกืงื” ืื—ืจื™ 10 ืฉื ื™ื•ืช , ื”ื ืœื ื–ื•ื›ื™ื ื‘ื›ืœื•ื.
05:48
If 10 seconds go by and they haven't made a deal, they get nothing.
139
348132
3149
ื–ื• ืžืขื™ืŸ ื˜ืขื•ืช ืžืฉื•ืชืคืช.
05:51
That's kind of a mistake together.
140
351305
1623
05:52
The twist is that one player, on the left,
141
352952
2616
ื”ืงื˜ืข ื”ื•ื ืฉืฉื—ืงืŸ ืื—ื“, ื–ื” ืฉื‘ืฆื“ ืฉืžืืœ,
05:55
is informed about how much on each trial there is.
142
355592
2349
ื™ื•ื“ืข ื›ืžื” ื›ืกืฃ ื™ืฉ ื‘ื›ืœ ื ื™ืกื•ื™.
05:57
They play lots of trials with different amounts each time.
143
357965
2718
ืขื•ืฉื™ื ื”ืจื‘ื” ื ื™ืกื•ื™ื™ื ืขื ืคืจืงื™ ื–ืžืŸ ืฉื•ื ื™ื.
06:00
In this case, they know there's four dollars.
144
360707
2097
ื‘ืžืงืจื” ื”ื–ื”, ื”ื ื™ื•ื“ืขื™ื ืฉื™ืฉ 4 ื“ื•ืœืจื™ื.
06:02
The uninformed player doesn't know, but they know the informed player knows.
145
362828
3600
ื”ืฉื—ืงืŸ ืฉืœื ื”ื•ื“ื™ืขื• ืœื• ืœื ื™ื•ื“ืข ืขืœื™ื”ื.
ืื‘ืœ ื”ื•ื ื™ื•ื“ืข ืฉื”ืฉื—ืงืŸ ื”ืฉื ื™ ื™ื•ื“ืข.
06:06
So the uninformed player's challenge is to say,
146
366452
2195
ืื– ื”ืืชื’ืจ ืฉืœ ื”ืฉื—ืงืŸ ืฉืื™ื ื• ื™ื•ื“ืข ื”ื•ื ืœื•ืžืจ,
06:08
"Is this guy being fair,
147
368671
1151
"ื”ืื ื”ืื“ื ื”ื–ื” ื”ื•ื’ืŸ ืื™ืชื™
06:09
or are they giving me a very low offer
148
369846
1929
ืื• ื ื•ืชืŸ ืœื™ ื”ืฆืขื” ื ืžื•ื›ื” ืžืื“
06:11
in order to get me to think there's only one or two dollars available to split?"
149
371799
3773
ื›ื“ื™ ืœื’ืจื•ื ืœื™ ืœื—ืฉื•ื‘ ืฉื™ืฉ ืจืง ื“ื•ืœืจ ืื• ืฉื ื™ื™ื ืœื—ืœื•ืง ื‘ื”ื?"
ื•ื‘ืžืงืจื” ื–ื” ื”ื ืœื ื™ื’ื™ืขื• ืœื”ืกื›ื.
06:15
in which case they might reject it and not come to a deal.
150
375596
2719
ื›ืš ืฉื™ืฉ ื›ืืŸ ืžืชื— ืžืกื•ื™ื ื‘ื™ืŸ ื”ื ืกื™ื•ืŸ ืœื”ืฉื™ื’ ื”ื›ื™ ื”ืจื‘ื” ื›ืกืฃ
06:18
So there's some tension here between trying to get the most money
151
378339
3053
ืœื‘ื™ืŸ ื”ื ืกื™ื•ืŸ ืœืฉื›ื ืข ืืช ื”ืฉื—ืงืŸ ื”ืฉื ื™ ืœืชืช ื™ื•ืชืจ.
06:21
but trying to goad the other player into giving you more.
152
381416
2675
ืฆื•ืจืช ื”ืžื™ืงื•ื— ื”ื™ื ืœื”ืฆื‘ื™ืข ืขืœ ืฉื•ืจืช ืžืกืคืจื™ื
06:24
And the way they bargain is to point on a number line
153
384115
2478
ืžืืคืก ื•ืขื“ ืฉื™ืฉื” ื“ื•ืœืจื™ื,
06:26
that goes from zero to six dollars.
154
386617
1679
ื•ื”ื ืžืชืžืงื—ื™ื ื›ืžื” ื›ืกืฃ ื™ืงื‘ืœ ื”ืฉื—ืงืŸ ืฉืื™ื ื• ื™ื•ื“ืข,
06:28
They're bargaining over how much the uninformed player gets,
155
388320
2834
ื•ืื– ื”ืฉื—ืงืŸ ืฉื›ืŸ ื™ื•ื“ืข ื™ืงื‘ืœ ืืช ื”ืฉืืจ.
06:31
and the informed player will get the rest.
156
391178
2001
ื–ื” ื›ืžื• ืžื•"ืž ื‘ื™ืŸ ื”ื”ื ื”ืœื” ื•ื”ืขื•ื‘ื“ื™ื
06:33
So this is like a management-labor negotiation
157
393203
2144
ืฉื‘ื• ื”ืขื•ื‘ื“ื™ื ืœื ื™ื•ื“ืขื™ื ื›ืžื” ืžืจื•ื•ื™ื—
06:35
in which the workers don't know
158
395371
1730
06:37
how much profits the privately held company has,
159
397125
3206
ื”ืขืกืง ื”ืคืจื˜ื™,
06:40
and they want to maybe hold out for more money,
160
400355
2435
ื•ื”ื ืžื ืกื™ื ืœื”ืฉื™ื’ ื™ื•ืชืจ ื›ืกืฃ,
06:42
but the company might want to create the impression
161
402814
2396
ืื‘ืœ ื™ื™ืชื›ืŸ ืฉื”ื—ื‘ืจื” ืจื•ืฆื” ืœื™ืฆื•ืจ ืืช ื”ืจื•ืฉื
ืฉืื™ืŸ ื”ืจื‘ื” ืœื—ืœื•ืง: "ืื ื™ ื ื•ืชื ืช ืœื›ื ื”ื›ื™ ื”ืจื‘ื” ืฉืื ื™ ื™ื›ื•ืœื”."
06:45
that there's very little to split: "I'm giving the most I can."
162
405234
2960
ืจืืฉื™ืช, ืžืขื˜ ืขืœ ื”ืชื ื”ื’ื•ืช. ื›ืžื” ืžื”ื–ื•ื’ื•ืช ื‘ื ื™ืกื•ื™ ืžืฉื—ืงื™ื ืคื ื™ื ืืœ ืคื ื™ื.
06:48
First, some behavior: a bunch of the subject pairs play face-to-face.
163
408218
3404
06:51
We have other data where they play across computers.
164
411646
2440
ื™ืฉ ืœื ื• ื ืชื•ื ื™ื ืื—ืจื™ื ืฉื‘ื”ื ื”ื ืžืฉื—ืงื™ื ื“ืจืš ืžื—ืฉื‘ื™ื.
ื–ื” ื”ื‘ื“ืœ ืžืขื ื™ื™ืŸ, ื›ืคื™ ืฉืืชื ื•ื“ืื™ ืžืชืืจื™ื ืœืขืฆืžื›ื.
06:54
That's an interesting difference, as you might imagine.
165
414110
2574
ืื‘ืœ ื›ืžื” ืžื”ื–ื•ื’ื•ืช ืฉื™ื•ืฉื‘ื™ื ืคื ื™ื ืืœ ืคื ื™ื
06:56
But a bunch of the face-to-face pairs
166
416708
1774
ืžืกื›ื™ืžื™ื ืœื—ืœืง ืืช ื”ื›ืกืฃ ืฉื•ื•ื” ื‘ืฉื•ื•ื” ื‘ื›ืœ ืคืขื.
06:58
agree to divide the money evenly every single time.
167
418506
2727
ืžืฉืขืžื. ื–ื” ืœื ืžืขื ื™ื™ืŸ ืžื‘ื—ื™ื ื” ืขืฆื‘ื™ืช.
07:01
Boring. It's just not interesting neurally.
168
421257
2662
ืžื‘ื—ื™ื ืชื ื–ื” ื˜ื•ื‘. ื”ื ืžืจื•ื•ื™ื—ื™ื ื”ืจื‘ื” ื›ืกืฃ.
07:04
It's good for them -- they make a lot of money.
169
424308
2224
07:06
But we're interested in:
170
426556
1540
ืื‘ืœ ืื•ืชื ื• ืžืขื ื™ื™ืŸ ืื ืืคืฉืจ ืœื”ื’ื™ื“ ืžืฉื”ื•
07:08
Can we say something about when disagreements occur versus don't occur?
171
428120
3753
ืขืœ ืžื” ืฉืงื•ืจื” ื›ืฉื™ืฉ ืื™-ื”ืกื›ืžื” ืื• ื›ืฉื™ืฉ ื”ืกื›ืžื”.
07:11
So this is the other group of subjects, who often disagree.
172
431897
2762
ื–ื• ื”ืงื‘ื•ืฆื” ื”ืฉื ื™ื”, ืฉืœ ืื ืฉื™ื ืฉืžืจื‘ื™ื ืœื ืœื”ืกื›ื™ื.
ื™ืฉ ืœื”ื ืกื™ื›ื•ื™-- ื”ื ืจื‘ื™ื ื•ืžืชื•ื•ื›ื—ื™ื
07:14
They bicker and disagree and end up with less money.
173
434683
3477
ื•ื‘ืกื•ืฃ ื™ื”ื™ื” ืœื”ื ืคื—ื•ืช ื›ืกืฃ.
07:18
They might be eligible to be on "Real Housewives," the TV show.
174
438184
2963
ื™ืฉ ืœื”ื ืกื™ื›ื•ื™ ืœื”ื•ืคื™ืข ื‘"ืขืงืจื•ืช ื”ื‘ื™ืช ื”ืืžื™ืชื™ื•ืช".
07:21
(Laughter)
175
441171
1088
ื”ื‘ื™ื˜ื• ื‘ืฆื“ ืฉืžืืœ:
07:22
You see on the left,
176
442283
1683
07:23
when the amount to divide is one, two or three dollars,
177
443990
2634
ื›ืฉื›ืžื•ืช ื”ื›ืกืฃ ืฉืืคืฉืจ ืœื—ืœื•ืง ื”ื™ื 1, 2 ืื• 3 ื“ื•ืœืจื™ื,
07:26
they disagree about half the time;
178
446648
1622
ื”ื ืœื ืžืกื›ื™ืžื™ื ื‘ืขืจืš ื—ืฆื™ ืžื”ื–ืžืŸ,
07:28
when it's four, five, six, they agree quite often.
179
448294
2351
ื•ืฉื”ื›ืžื•ืช ื”ื™ื 4, 5 ืื• 6, ื”ื ืžืกื›ื™ืžื™ื ืœืขืชื™ื ืงืจื•ื‘ื•ืช.
07:30
This turns out to be something that's predicted
180
450669
2199
ืžืกืชื‘ืจ ืฉื–ื” ืžืฉื”ื• ืฉื ื™ืชืŸ ืœื ื‘ื
07:32
by a very complicated type of game theory
181
452892
1961
ืœืคื™ ืกื•ื’ ืžื•ืจื›ื‘ ืžืื“ ืฉืœ ืชื™ืื•ืจื™ื™ืช ืžืฉื—ืงื™ื
07:34
you should come to graduate school at CalTech and learn about.
182
454877
3107
ืฉืฆืจื™ืš ืœืœืžื•ื“ ืื•ืชื• ื‘ืชื•ืืจ ืฉื ื™ ืคื” ื‘"ืงืืœ-ื˜ืง".
ื–ื” ืงืฆืช ืžื•ืจื›ื‘ ืžื›ื“ื™ ืœื”ืกื‘ื™ืจื• ืขื›ืฉื™ื•,
07:38
It's a little too complicated to explain right now,
183
458008
2388
ืื‘ืœ ืœืคื™ ื”ืชื™ืื•ืจื™ื”, ืฆืจื™ื›ื” ืœื”ื™ื•ื•ืฆืจ ืฆื•ืจื” ื›ื–ืืช.
07:40
but the theory tells you that this shape should occur.
184
460420
2643
ืื•ืœื™ ื–ื” ื›ืš ื’ื ืœืคื™ ื”ืื™ื ื˜ื•ืื™ืฆื™ื” ืฉืœื›ื.
07:43
Your intuition might tell you that, too.
185
463087
2062
07:45
Now I'm going to show you the results from the EEG recording.
186
465173
2867
ืขื›ืฉื™ื• ืืจืื” ืœื›ื ืืช ื”ืชื•ืฆืื•ืช ืฉืœ ืจืฉืžืช ื”ืžื•ื— ื”ื—ืฉืžืœื™ืช.
ืžื•ืจื›ื‘ ืžืื“. ืชืจืฉื™ื ื”ืžื•ื— ื‘ืฆื“ ื™ืžื™ืŸ
07:48
Very complicated.
187
468064
1151
07:49
The right brain schematic is the uninformed person,
188
469239
2392
ื”ื•ื ื”ืื“ื ืฉืื™ื ื• ื™ื•ื“ืข, ื•ื”ืฉืžืืœื™ ื”ื•ื ื–ื” ืฉื™ื•ื“ืข ืžื” ื›ืžื•ืช ื”ื›ืกืฃ.
07:51
and the left is the informed.
189
471655
1400
ื–ื™ื›ืจื• ืฉืกืจืงื ื• ืืช ืฉื ื™ ื”ืžื•ื—ื•ืช ื‘ืื•ืชื• ื–ืžืŸ,
07:53
Remember that we scanned both brains at the same time,
190
473079
2746
07:55
so we can ask about time-synced activity
191
475849
2269
ืื– ืื ื• ื™ื›ื•ืœื™ื ืœืฉืื•ืœ ืขืœ ืคืขื™ืœื•ืช ื‘ื•-ื–ืžื ื™ืช
ื‘ืื–ื•ืจื™ื ื“ื•ืžื™ื ืื• ืฉื•ื ื™ื,
07:58
in similar or different areas simultaneously,
192
478142
3016
ื‘ื“ื™ื•ืง ื›ืžื• ื›ืฉืจื•ืฆื™ื ืœื—ืงื•ืจ ืฉื™ื—ื”
08:01
just like if you wanted to study a conversation,
193
481182
2265
08:03
and you were scanning two people talking to each other.
194
483471
2578
ืชื•ืš ื›ื“ื™ ืกืจื™ืงื” ืฉืœ ืฉื ื™ ืื ืฉื™ื ืžืฉื•ื—ื—ื™ื
ื‘ืฆื™ืคื™ื™ื” ืœืคืขื™ืœื•ืช ืžืฉื•ืชืคืช ื‘ืื–ื•ืจื™ ื”ืฉืคื”
08:06
You'd expect common activity in language regions
195
486073
2258
ืฉื”ื ืžืงืฉื™ื‘ื™ื ื•ืžืชืงืฉืจื™ื ื‘ืคื•ืขืœ.
08:08
when they're listening and communicating.
196
488355
1961
ื”ื—ืฆื™ื ืžืงืฉืจื™ื ื‘ื™ืŸ ืื–ื•ืจื™ื ืฉืคืขื™ืœื™ื ื‘ืื•ืชื• ื–ืžืŸ,
08:10
So the arrows connect regions that are active at the same time.
197
490340
3831
ื•ื›ื™ื•ื•ืŸ ื”ื—ืฆื™ื ื”ื•ื
08:14
The direction of the arrows
198
494195
1322
08:15
flows from the region that's active first in time,
199
495541
2766
ืžื”ืื–ื•ืจ ืฉืคืขื™ืœ ืจืืฉื•ืŸ,
08:18
and the arrowhead goes to the region that's active later.
200
498331
3795
ื•ืจืืฉ ื”ื—ืฅ ืžืฆื‘ื™ืข ืœืื–ื•ืจ ืฉืคืขื™ืœ ื™ื•ืชืจ ืžืื•ื—ืจ.
ื‘ืžืงืจื” ื–ื”, ืื ืชืกืชื›ืœื• ื”ื™ื˜ื‘,
08:22
So in this case, if you look carefully,
201
502150
2047
08:24
most of the arrows flow from right to left.
202
504221
2023
ืจื•ื‘ ื”ื—ืฆื™ื ืžืฆื‘ื™ืขื™ื ืžื™ืžื™ืŸ ืœืฉืžืืœ.
ื›ืœื•ืžืจ, ื ืจืื” ืฉืคืขื™ืœื•ืช ื”ืžื•ื— ืฉืœ ื”ืื“ื ืฉืื™ื ื• ื™ื•ื“ืข
08:26
That is, it looks as if the uninformed brain activity
203
506268
3284
08:29
is happening first,
204
509576
1611
ืžืชืจื—ืฉืช ืจืืฉื•ื ื”,
08:31
and then it's followed by activity in the informed brain.
205
511211
3852
ื•ืื—ืจื™ื” ืคืขื™ืœื•ืช ื”ืžื•ื— ืฉืœ ื”ืื“ื ืฉื™ื•ื“ืข.
ืื’ื‘, ืืœื” ื”ื™ื• ื ื™ืกื•ื™ื™ื ืฉื‘ื”ื ื ืกื’ืจื• ืขืกืงืื•ืช.
08:35
And by the way, these are trials where their deals were made.
206
515087
3451
08:38
This is from the first two seconds.
207
518562
1757
ื–ื” ืžืฉืชื™ ื”ืฉื ื™ื•ืช ื”ืจืืฉื•ื ื•ืช.
08:40
We haven't finished analyzing this data, so we're still peeking in,
208
520343
3156
ืœื ืกื™ื™ืžื ื• ืœื ืชื— ืืช ื”ื ืชื•ื ื™ื ื”ืืœื”,
ืื– ืื ื—ื ื• ืขื“ื™ื™ืŸ ืžืฆื™ืฆื™ื ืคื ื™ืžื”, ืื‘ืœ ื”ืชืงื•ื•ื” ื”ื™ื
08:43
but the hope is that we can say something in the first couple of seconds
209
523523
3408
ืฉื ื•ื›ืœ ืœื”ื’ื™ื“ ืžืฉื”ื• ืขืœ ืฉืชื™ ื”ืฉื ื™ื•ืช ื”ืจืืฉื•ื ื•ืช
08:46
about whether they'll make a deal or not,
210
526955
1963
ืื ื ืกื’ืจื” ื‘ื”ืŸ ืขืกืงื” ืื• ืœื,
08:48
which could be very useful in thinking about avoiding litigation
211
528942
3005
ื•ื–ื” ื™ื›ื•ืœ ืœื”ื•ืขื™ืœ ื‘ื—ืฉื™ื‘ื” ืขืœ ื”ื™ืžื ืขื•ืช ืžื”ืชื“ื™ื™ื ื•ืช ืžืฉืคื˜ื™ืช,
ื’ื™ืจื•ืฉื™ื ืžื›ื•ืขืจื™ื ื•ื“ื‘ืจื™ื ื›ืืœื”.
08:51
and ugly divorces and things like that.
212
531971
1864
ื›ืœ ืืœื” ื”ื ืžืงืจื™ื ืฉื‘ื”ื ืื‘ื“ ื”ืจื‘ื” ืขืจืš ื›ืกืคื™
08:53
Those are all cases in which a lot of value is lost by delay and strikes.
213
533859
4077
ื‘ื’ืœืœ ื“ื—ื™ื•ืช ื•ืฉื‘ื™ืชื•ืช.
08:58
Here's the case where the disagreements occur.
214
538630
2164
ื”ื ื” ื”ืžืงืจื” ืฉื‘ื• ื™ืฉ ื—ื™ืœื•ืงื™ ื“ืขื•ืช.
09:00
You can see it looks different than the one before.
215
540818
2394
ืืคืฉืจ ืœื”ืกืชื›ืœ ืขืœ ื–ื” ืื—ืจืช ืžืืฉืจ ืขืœ ื”ืžืงืจื” ื”ืงื•ื“ื.
ื™ืฉ ื”ืจื‘ื” ื™ื•ืชืจ ื—ืฆื™ื.
09:03
There's a lot more arrows.
216
543236
1341
09:04
That means that the brains are synced up more closely
217
544601
2651
ื–ื” ืื•ืžืจ ืฉื”ืžื•ื—ื•ืช ื™ื•ืชืจ
ืงืจื•ื‘ื™ื ืœื—ืฉื™ื‘ื” ืกื™ืžื•ืœื˜ื ื™ืช,
09:07
in terms of simultaneous activity,
218
547276
1620
09:08
and the arrows flow clearly from left to right.
219
548920
2203
ื•ื”ื—ืฆื™ื ืžืฆื‘ื™ืขื™ื ื‘ืื•ืคืŸ ื‘ืจื•ืจ ืžืื“ ืžืฉืžืืœ ืœื™ืžื™ืŸ.
ื›ืœื•ืžืจ, ื ืจืื” ืฉื”ืžื•ื— ืฉืœ ื”ืื“ื ืฉื™ื•ื“ืข ืžื—ืœื™ื˜,
09:11
That is, the informed brain seems to be deciding,
220
551147
2288
"ื›ื ืจืื” ืฉืœื ื ืกื’ื•ืจ ืขืกืงื”."
09:13
"We're probably not going to make a deal here."
221
553459
2192
09:15
And then later, there's activity in the uninformed brain.
222
555675
2743
ื•ืื—"ื› ื™ืฉ ืคืขื™ืœื•ืช ื‘ืžื•ื— ืฉืื™ื ื• ื™ื•ื“ืข.
09:18
Next, I'm going to introduce you to some relatives.
223
558799
2404
ื›ืขืช ืื›ื™ืจ ืœื›ื ื›ืžื” ืงืจื•ื‘ื™ ืžืฉืคื—ื”.
ื”ื ืฉืขื™ืจื™ื, ืžืกืจื™ื—ื™ื, ืžื”ื™ืจื™ื, ื•ื—ื–ืงื™ื.
09:21
They're hairy, smelly, fast and strong.
224
561227
2161
09:23
You might be thinking back to your last Thanksgiving.
225
563412
2494
ืื•ืœื™ ืืชื ื ื–ื›ืจื™ื ื‘ื—ื’ ื”ื”ื•ื“ื™ื” ื”ืื—ืจื•ืŸ ืฉืœื›ื.
09:25
(Laughter)
226
565930
1016
09:26
Maybe, if you had a chimpanzee with you.
227
566970
2476
ืื•ืœื™ ืื ื”ื™ื” ืื™ืชื›ื ืฉื™ืžืคื ื–ื”.
09:29
Charles Darwin and I and you broke off from the family tree from chimpanzees
228
569470
4006
ืฆ'ืจืœืก ื“ืจื•ื•ื™ืŸ, ืื ื™ ื•ืืชื ื ืคืจื“ื ื• ืžืขืฅ ื”ืžืฉืคื—ื”
ืฉืœ ื”ืฉื™ืžืคื ื–ื™ื ืœืคื ื™ ื›-5 ืžื™ืœื™ื•ืŸ ืฉื ื™ื.
09:33
about five million years ago.
229
573500
1400
09:34
They're still our closest genetic kin.
230
574924
1811
ื”ื ืขื“ื™ื™ืŸ ื”ื›ื™ ืงืจื•ื‘ื™ื ืืœื™ื ื• ืžื‘ื—ื™ื ื” ื’ื ื˜ื™ืช.
09:36
We share 98.8 percent of the genes.
231
576759
1719
98.8% ืžื”ื’ื ื™ื ืฉืœื ื• ื–ื”ื™ื.
09:38
We share more genes with them than zebras do with horses.
232
578502
2961
ื™ืฉ ืœื ื• ื™ื•ืชืจ ื’ื ื™ื ืžืฉื•ืชืคื™ื ืžืืฉืจ ื‘ื™ืŸ ื–ื‘ืจื•ืช ื•ืกื•ืกื™ื.
09:41
And we're also their closest cousin.
233
581487
1910
ื•ืื ื• ื’ื ื‘ื ื™ ื”ื“ื•ื“ื™ื ื”ื›ื™ ืงืจื•ื‘ื™ื ืฉืœื”ื.
09:43
They have more genetic relation to us than to gorillas.
234
583421
2621
ื™ืฉ ืœื”ื ื™ื•ืชืจ ืงืฉืจ ื’ื ื˜ื™ ืื™ืชื ื• ืžืืฉืจ ืขื ื’ื•ืจื™ืœื•ืช.
09:46
So, how humans and chimpanzees behave differently
235
586066
2739
ืื– ื”ืชื ื”ื’ื•ืช ืฉื•ื ื” ื‘ื™ืŸ ื‘ื ื™ ืื“ื ื•ืฉื™ืžืคื ื–ื™ื
09:48
might tell us a lot about brain evolution.
236
588829
2094
ื™ื›ื•ืœื” ืœื”ื’ื™ื“ ืœื ื• ื”ืจื‘ื” ืขืœ ื”ืชืคืชื—ื•ืช ื”ืžื•ื—.
09:51
This is an amazing memory test
237
591326
2300
ื–ื”ื• ืžื‘ื—ืŸ ื–ื›ืจื•ืŸ ืžื“ื”ื™ื
09:53
from [Kyoto], Japan, the Primate Research Institute,
238
593650
2792
ืžืžื›ื•ืŸ ืžื—ืงืจ ื”ืคืจื™ืžื˜ื™ื ื‘ื ื’ื•ื™ื” ืฉื‘ื™ืคืŸ.
09:56
where they've done a lot of this research.
239
596466
2003
ื”ื ื—ืงืจื• ื”ืจื‘ื” ืืช ื”ื ื•ืฉื.
09:58
This goes back a ways. They're interested in working memory.
240
598493
2824
ื–ื” ื“ื™ ื™ืฉืŸ. ื”ื ืžืขื•ื ื™ื™ื ื™ื ื‘ื–ื›ืจื•ืŸ ื”ืขื‘ื•ื“ื”.
ื”ืฉื™ืžืคื ื–ื” ืขื•ืžื“ ืœืจืื•ืช, ื”ื‘ื™ื˜ื• ื”ื™ื˜ื‘,
10:01
The chimp will see, watch carefully,
241
601341
1716
ื”ื•ื ื™ืจืื” ื—ืฉื™ืคื” ืฉืœ 200 ืืœืคื™ื•ืช ืฉื ื™ื”
10:03
they'll see 200 milliseconds' exposure -- that's fast, eight movie frames --
242
603081
3584
--ื–ื” ืžื”ืจ, 8 ืคืจื™ื™ืžื™ื ืฉืœ ืกืจื˜--
10:06
of numbers one, two, three, four, five.
243
606689
1977
ืฉืœ ื”ืžืกืคืจื™ื 1, 2, 3, 4 ื•-5.
10:08
Then they disappear and are replaced by squares,
244
608690
2245
ื•ืื– ื”ื ื™ื™ืขืœืžื• ื•ื™ืชื—ืœืคื• ื‘ืจื™ื‘ื•ืขื™ื,
10:10
and they have to press the squares
245
610959
1627
ื•ื”ื ืฆืจื™ื›ื™ื ืœืœื—ื•ืฅ ืขืœ ื”ืจื™ื‘ื•ืขื™ื
10:12
that correspond to the numbers from low to high
246
612610
2200
ืžื”ืžืกืคืจ ื”ื ืžื•ืš ื•ืขื“ ื”ืžืกืคืจ ื”ื’ื‘ื•ื”
10:14
to get an apple reward.
247
614834
1303
ื›ื“ื™ ืœืงื‘ืœ ืคืจืก, ืชืคื•ื—.
ื‘ื•ืื• ื ืจืื” ืื™ืš ื”ื ื™ื›ื•ืœื™ื ืœืขืฉื•ืช ืืช ื–ื”.
10:16
Let's see how they can do it.
248
616161
1497
10:28
This is a young chimp.
249
628478
1162
ื–ื”ื• ืฉื™ืžืคื ื–ื” ืฆืขื™ืจ. ื”ืฆืขื™ืจื™ื
10:29
The young ones are better than the old ones, just like humans.
250
629664
2917
ื™ื•ืชืจ ื˜ื•ื‘ื™ื ืžื”ื–ืงื ื™ื, ื›ืžื• ืืฆืœ ื‘ื ื™ ืื“ื.
10:32
(Laughter)
251
632605
1002
ื•ื”ื ืžื ื•ืกื™ื ืžืื“. ื”ื ืขืฉื• ืืช ื–ื”
10:33
And they're highly experienced,
252
633631
1478
ืืœืคื™ ืคืขืžื™ื.
10:35
they've done this thousands of times.
253
635133
2323
ื‘ืจื•ืจ ืฉืœืื™ืžื•ืŸ ื™ืฉ ื”ืฉืคืขื” ื’ื“ื•ืœื”, ื›ืคื™ ืฉืืชื ืžืชืืจื™ื ืœืขืฆืžื›ื.
10:37
Obviously there's a big training effect, as you can imagine.
254
637480
2886
(ืฆื—ื•ืง)
10:40
(Laughter)
255
640390
1012
10:41
You can see they're very blasรฉ and effortless.
256
641426
2148
ืจื•ืื™ื ืฉื”ื ืžืฉื•ืขืžืžื™ื ื•ืขื•ืฉื™ื ืืช ื–ื” ืœืœื ืžืืžืฅ.
10:43
Not only can they do it very well, they do it in a sort of lazy way.
257
643598
3211
ืœื ืจืง ืฉื”ื ืขื•ืฉื™ื ืืช ื–ื” ืžืžืฉ ื˜ื•ื‘. ื”ื ืขื•ืฉื™ื ืืช ื–ื” ื‘ืขืฆืœืชื™ื™ื.
10:46
(Laughter)
258
646833
1004
10:47
Who thinks you could beat the chimps?
259
647861
1762
ื ื›ื•ืŸ? ืžื™ ื—ื•ืฉื‘ ืฉื™ื•ื›ืœ ืœื ืฆื— ืืช ื”ืฉื™ืžืคื ื–ื™ื?
10:49
(Laughter)
260
649647
1060
10:50
Wrong. (Laughter)
261
650731
1535
ืœื ื ื›ื•ืŸ. (ืฆื—ื•ืง)
10:52
We can try. We'll try. Maybe we'll try.
262
652290
2585
ืืคืฉืจ ืœื ืกื•ืช. ืื ื• ื ื ืกื”. ืื•ืœื™ ื ื ืกื”.
10:54
OK, so the next part of the study I'm going to go quickly through
263
654899
3994
ื‘ืกื“ืจ, ืื ื™ ื”ื•ืœืš ืœืขื‘ื•ืจ ื‘ืžื”ื™ืจื•ืช
ืขืœ ื”ื—ืœืง ื”ื‘ื ืฉืœ ื”ืžื—ืงืจ ื”ื–ื”.
10:58
is based on an idea of Tetsuro Matsuzawa.
264
658917
2976
ื”ื•ื ืžื‘ื•ืกืก ืขืœ ืจืขื™ื•ืŸ ืฉืœ ื˜ื˜ืกื•ืจื• ืžืื˜ืกื•ื–ืื•ื•ื”.
11:01
He had a bold idea he called the "cognitive trade-off hypothesis."
265
661917
3120
ื”ื™ื” ืœื• ืจืขื™ื•ืŸ ื ื•ืขื–-- "ื”ื™ืคื•ืชื™ื–ืช ื”ื—ืœื™ืคื™ืŸ ื”ืงื•ื’ื ื™ื˜ื™ื‘ื™ื™ื".
ืื ื• ื™ื•ื“ืขื™ื ืฉื”ืฉื™ืžืคื ื–ื™ื ืžื”ื™ืจื™ื ื•ื—ื–ืงื™ื ื™ื•ืชืจ,
11:05
We know chimps are faster and stronger; they're also obsessed with status.
266
665061
3482
ื•ื’ื ื—ืฉื•ื‘ ืœื”ื ืžืื“ ื”ืžืขืžื“ ื”ื—ื‘ืจืชื™.
ื”ื•ื ื—ืฉื‘ ืฉืื•ืœื™ ื”ื ื—ื•ืกื›ื™ื ื‘ืคืขื™ืœื•ื™ื•ืช ืžื•ื—
11:08
His thought was, maybe they've preserved brain activities
267
668567
2681
ื•ืžืชืจื’ืœื™ื ืื•ืชืŸ ื‘ืชื—ื•ืžื™ ื”ืชืคืชื—ื•ืช
11:11
and practice them in development
268
671272
1603
11:12
that are really, really important to them to negotiate status and to win,
269
672899
3815
ืฉืžืื“ ืžืื“ ื—ืฉื•ื‘ื™ื ืœื”ื
ื›ื“ื™ ืœื ื”ืœ ืžื•"ืž ืขืœ ืžืขืžื“ ื—ื‘ืจืชื™ ื•ืœื ืฆื—,
11:16
which is something like strategic thinking during competition.
270
676738
2992
ืฉื–ื” ื“ื•ืžื” ืœื—ืฉื™ื‘ื” ืืกื˜ืจื˜ื’ื™ืช ื‘ื–ืžืŸ ืชื—ืจื•ืช.
11:19
So we're going to check that out
271
679754
1536
ื ื‘ื“ื•ืง ืืช ื–ื”
11:21
by having the chimps actually play a game
272
681314
2627
ื‘ืžืฉื—ืง ืฉื”ืฉื™ืžืคื ื–ื™ื ืžืฉื—ืงื™ื ื‘ื•,
11:23
by touching two touch screens.
273
683965
2510
ื›ืฉื”ื ื ื•ื’ืขื™ื ื‘ืฉื ื™ ืžืกื›ื™ื.
11:26
The chimps are interacting with each other through the computers.
274
686499
3060
ื”ืฉื™ืžืคื ื–ื™ื ืžืžืฉ ืžืชืงืฉืจื™ื ื‘ื™ื ื™ื”ื ื‘ืืžืฆืขื•ืช ืžื—ืฉื‘ื™ื.
ื”ื ื™ืœื—ืฆื• ืขืœ ืฉืžืืœ ืื• ืขืœ ื™ืžื™ืŸ.
11:29
They'll press left or right.
275
689583
1349
11:30
One chimp is called a matcher; they win if they press left-left,
276
690956
3478
ืฉื™ืžืคื ื–ื” ืื—ื“ ื ืงืจื "ื”ืžืชืื™ื".
ื”ื•ื ื™ื ืฆื— ืื ื™ืœื—ืฅ ืฉืžืืœ, ืฉืžืืœ,
11:34
like a seeker finding someone in hide-and-seek, or right-right.
277
694458
3145
ื›ืžื• ื–ื” ืฉืžื—ืคืฉ ื‘ืžื—ื‘ื•ืื™ื, ืื• ื™ืžื™ืŸ, ื™ืžื™ืŸ.
11:37
The mismatcher wants to mismatch;
278
697627
1605
ื”ืฉื™ืžืคื ื–ื” ื”ืœื-ืžืชืื™ื ืจื•ืฆื” ืœื ืœื”ืชืื™ื.
ื”ื•ื ืจื•ืฆื” ืœืœื—ื•ืฅ ืขืœ ื”ืžืกืš ื”ืžื ื•ื’ื“.
11:39
they want to press the opposite screen of the chimp.
279
699256
2699
11:41
And the rewards are apple cube rewards.
280
701979
2472
ื•ื”ืคืจืกื™ื ื”ื ืงื•ื‘ื™ื•ืช ืฉืœ ืชืคื•ื—.
11:44
So here's how game theorists look at these data.
281
704475
2327
ื•ื›ืš ืžืกืชื›ืœื™ื ืขืœ ื–ื” ืชื™ืื•ืจื˜ื™ืงื ื™ื ืฉืœ ืžืฉื—ืงื™ื.
11:46
This is a graph of the percentage of times
282
706826
2022
ื–ื”ื• ื’ืจืฃ ืฉืœ ืื—ื•ื– ื”ืคืขืžื™ื
11:48
the matcher picked right on the x-axis
283
708872
2206
ืฉื”ืžืชืื™ื ื‘ื—ืจ ื ื›ื•ืŸ ืขืœ ืฆื™ืจ ื”"ืื™ืงืก",
11:51
and the percentage of times they picked right
284
711102
2154
ื•ืื—ื•ื– ื”ืคืขืžื™ื ืฉื ื™ื—ืฉ ื ื›ื•ืŸ
ื‘ื–ื›ื•ืช ื”ืฉื™ืžืคื ื–ื” ื”ืฉื ื™ ื‘ืฆื™ืจ ื”"ื•ื•ืื™".
11:53
by the mismatcher on the y-axis.
285
713280
2205
11:55
So a point here is the behavior by a pair of players,
286
715509
3329
ืื– ื ืงื•ื“ื” ื›ืืŸ ื”ื™ื ื”ื”ืชื ื”ื’ื•ืช ืฉืœ ืฉื ื™ ืฉื—ืงื ื™ื,
11:58
one trying to match, one trying to mismatch.
287
718862
2196
ื”ืื—ื“ ืžื ืกื” ืœื”ืชืื™ื, ื”ืฉื ื™ ืžื ืกื” ืฉืœื ืœื”ืชืื™ื.
12:01
The NE square in the middle -- actually, NE, CH and QRE --
288
721082
3317
ื”ืจื‘ื•ืข ื‘ืืžืฆืข ืขื NE-- ื‘ืขืฆื CH, NE, ื•-QRE--
12:04
those are three different theories of Nash equilibrium and others,
289
724423
3124
ืืœื” ืฉืœื•ืฉ ืชื™ืื•ืจื™ื•ืช ืฉื•ื ื•ืช ืฉืœ ืฉื™ื•ื•ื™ ื”ืžืฉืงืœ ืฉืœ ื ืืฉ ื•ืื—ืจื™ื,
ืฉืื•ืžืจ ืžื” ื”ืชื™ืื•ืจื™ื” ืžื ื‘ืืช,
12:07
tells you what the theory predicts,
290
727571
1683
12:09
which is that they should match 50-50,
291
729278
2125
ื•ื”ื•ื ืฉื”ื ืืžื•ืจื™ื ืœื”ืฉื™ื’ ื”ืชืืžื” ืฉืœ 50-50,
12:11
because if you play left too much, for example,
292
731427
2427
ื›ื™ ืื ืืชื” ืžืฉื—ืง ืฉืžืืœื” ื™ื•ืชืจ ืžื“ื™, ืœืžืฉืœ,
12:13
I can exploit that if I'm the mismatcher by then playing right.
293
733878
2966
ืื ื™ ื™ื›ื•ืœ ืœื ืฆืœ ืืช ื–ื” ื•ืœืฉื—ืง ื™ืžื™ื ื”.
12:16
And as you can see, the chimps -- each chimp is one triangle --
294
736868
2972
ื•ื›ืคื™ ืฉืืชื ืจื•ืื™ื, ื”ืฉื™ืžืคื ื–ื™ื, ื›ืœ ืื—ื“ ื”ื•ื ืžืฉื•ืœืฉ ืื—ื“,
12:19
are circled around, hovering around that prediction.
295
739864
2659
ื”ื ื‘ืขื™ื’ื•ืœ, ื•ื ืžืฆืื™ื ื‘ืื–ื•ืจ ื”ื ื™ื‘ื•ื™ ื”ื–ื”.
ื›ืขืช ื ื–ื™ื– ืืช ื”ืคืจืกื™ื.
12:23
Now we move the payoffs.
296
743205
1706
12:24
We're going to make the left-left payoff for the matcher a little higher.
297
744935
3487
ื ื’ืจื•ื ืœื–ื” ืฉื”ื•ืœืš ืฉืžืืœื” ืœืฉืœื ืœืžืชืื™ื ืงืฆืช ื™ื•ืชืจ.
12:28
Now they get three apple cubes.
298
748446
1495
ืขื›ืฉื™ื• ื”ื ื™ืงื‘ืœื• 3 ืงื•ื‘ื™ื•ืช ืชืคื•ื—.
12:29
Game theoretically, that should make the mismatcher's behavior shift:
299
749965
3275
ืœืคื™ ืชื™ืื•ืจื™ื™ืช ื”ืžืฉื—ืงื™ื, ื–ื” ืฆืจื™ืš ืœื’ืจื•ื ืœืฉื™ื ื•ื™ ื‘ื”ืชื ื”ื’ื•ืชื• ืฉืœ ื”ืœื-ืžืชืื™ื,
ื›ื™ ืžื” ืฉืงื•ืจื” ื”ื•ื ืฉื”ืœื ืžืชืื™ื ื™ื—ืฉื•ื‘,
12:33
the mismatcher will think, "Oh, this guy's going to go for the big reward,
300
753264
3507
"ืื”, ื”ื•ื ื”ื•ืœืš ืขืœ ื”ืคืจืก ื”ื’ื“ื•ืœ,
ืื– ืื ื™ ืืœืš ื™ืžื™ื ื” ื›ื“ื™ ืœื”ื‘ื˜ื™ื— ืฉื”ื•ื ืœื ื™ืฉื™ื’ ืื•ืชื•."
12:36
so I'll go to the right, make sure he doesn't get it."
301
756795
2528
ื•ื›ืคื™ ืฉืืชื ืจื•ืื™ื, ื”ื”ืชื ื”ื’ื•ืช ืฉืœื”ื ืขื•ืœื”
12:39
And as you can see, their behavior moves up
302
759347
2028
ืœื›ื™ื•ื•ืŸ ืฉืœ ื”ืฉื™ื ื•ื™ ื‘ืฉื™ื•ื•ื™ ื”ืžืฉืงืœ ืฉืœ ื ืืฉ.
12:41
in the direction of this change in the Nash equilibrium.
303
761399
2698
12:44
Finally, we changed the payoffs one more time.
304
764121
2246
ืœื‘ืกื•ืฃ ื”ื—ืœืคื ื• ืฉื•ื‘ ืืช ื”ืคืจืก.
12:46
Now it's four apple cubes,
305
766391
1248
ื•ืขื›ืฉื™ื• ื”ื•ื 4 ืงื•ื‘ื™ื•ืช ืฉืœ ืชืคื•ื—,
12:47
and their behavior again moves towards the Nash equilibrium.
306
767663
2833
ื•ื”ื”ืชื ื”ื’ื•ืช ืฉืœื”ื ืฉื•ื‘ ื ืขื” ืœืขื‘ืจ ืฉื™ื•ื•ื™ ื”ืžืฉืงืœ ืฉืœ ื ืืฉ.
ื–ื” ืžืชืคื–ืจ, ืื‘ืœ ืื ืžื—ืฉื‘ื™ื ืืช ื”ืžืžื•ืฆืข ืฉืœ ื”ืฉื™ืžืคื ื–ื™ื,
12:50
It's sprinkled around, but if you average the chimps out,
307
770520
2674
ื”ื ืžืžืฉ ืงืจื•ื‘ื™ื. ื‘ืชื—ื•ื ื”-0.01.
12:53
they're really close, within .01.
308
773218
1574
ืœืžืขืฉื” ื”ื ื™ื•ืชืจ ืงืจื•ื‘ื™ื ืžื›ืœ ืžื™ืŸ ืฉืฆืคื™ื ื• ื‘ื•.
12:54
They're actually closer than any species we've observed.
309
774816
2628
12:57
What about humans? You think you're smarter than a chimpanzee?
310
777468
3098
ืžื” ืขื ื‘ื ื™ ืื“ื? ืœื“ืขืชื›ื ืืชื ื—ื›ืžื™ื ืžื”ืฉื™ืžืคื ื–ื™ื?
ื”ื ื” ืฉืชื™ ืงื‘ื•ืฆื•ืช ืฉืœ ื‘ื ื™ ืื“ื ื‘ื™ืจื•ืง ื•ื›ื—ื•ืœ.
13:01
Here's two human groups in green and blue.
311
781350
3301
ื”ื ืงืจื•ื‘ื™ื ื™ื•ืชืจ ืœ-50-50. ื”ื ืœื ืžื’ื™ื‘ื™ื ื‘ืื•ืชื” ืžื™ื“ื” ืœืคืจืกื™ื,
13:04
They're closer to 50-50; they're not responding to payoffs as closely.
312
784675
3293
13:07
And also if you study their learning in the game,
313
787992
2296
ื•ืื ื‘ื•ื—ื ื™ื ืื™ืš ื”ื ืœื•ืžื“ื™ื ืืช ื”ืžืฉื—ืง,
ื”ื ืคื—ื•ืช ืจื’ื™ืฉื™ื ืœืคืจืกื™ื ืงื•ื“ืžื™ื.
13:10
they aren't as sensitive to previous rewards.
314
790312
2101
ื”ืฉื™ืžืคื ื–ื™ื ืžืฉื—ืงื™ื ื™ื•ืชืจ ื˜ื•ื‘ ืžื‘ื ื™ ื”ืื“ื.
13:12
The chimps play better than the humans, in terms of adhering to game theory.
315
792437
3585
ื”ื ื™ื•ืชืจ ื˜ื•ื‘ื™ื ืžื‘ื—ื™ื ืช ื”ื’ืฉืžืช ืชื™ืื•ืจื™ื™ืช ื”ืžืฉื—ืงื™ื.
ื•ืืœื” ื”ืŸ ืฉืชื™ ืงื‘ื•ืฆื•ืช ืฉื•ื ื•ืช ืฉืœ ื‘ื ื™ ืื“ื
13:16
And these are two different groups of humans, from Japan and Africa;
316
796046
3201
ืžื™ืคืŸ ื•ืืคืจื™ืงื”. ื”ื ืžืฉื—ื–ืจื™ื ื“ื™ ื™ืคื” ืืช ื”ื ื™ืกื•ื™.
13:19
they replicate quite nicely.
317
799271
1340
ืืฃ ืื—ื“ ืžื”ื ืœื ืžืชืงืจื‘ ืœืฉื™ืžืคื ื–ื™ื.
13:20
None of them are close to where the chimps are.
318
800635
2545
ืื– ื”ื ื” ื›ืžื” ื“ื‘ืจื™ื ืฉืœืžื“ื ื• ื”ื™ื•ื.
13:23
So, some things we learned:
319
803670
1294
13:24
people seem to do a limited amount of strategic thinking using theory of mind.
320
804988
3746
ื›ื ืจืื” ืฉืื ืฉื™ื ืœื ืขื•ืกืงื™ื ื”ืจื‘ื” ื‘ื—ืฉื™ื‘ื” ืืกื˜ืจื˜ื’ื™ืช
ืœืคื™ ืชื™ืื•ืจื™ื™ืช ื”ืžื•ื—.
13:28
We have preliminary evidence from bargaining
321
808758
2094
ื™ืฉ ืœื ื• ืงืฆืช ื”ื•ื›ื—ื•ืช ืจืืฉื•ื ื™ื•ืช ืžื”ืชืžืงื—ื•ืช
13:30
that early warning signs in the brain might be used to predict
322
810876
2915
ืœืคื™ื”ืŸ ืืคืฉืจ ืœื”ืฉืชืžืฉ ื‘ืกื™ืžื ื™ ืื–ื”ืจื” ืžื•ืงื“ืžื™ื ื‘ืžื•ื— ื›ื“ื™ ืœื ื‘ื
ืื ืชื”ื™ื” ืื™-ื”ืกื›ืžื” ืจืฆื™ื ื™ืช ืฉืชืขืœื” ื›ืกืฃ,
13:33
whether there'll be a bad disagreement that costs money,
323
813815
2631
ื•ืฉืฉื™ืžืคื ื–ื™ื ื”ื ืžืชื—ืจื™ื ื˜ื•ื‘ื™ื ื™ื•ืชืจ ืžื‘ื ื™ ืื“ื,
13:36
and chimps are "better" competitors than humans,
324
816470
2239
ื›ืคื™ ืฉืงื•ื‘ืขืช ืชื™ืื•ืจื™ื™ืช ื”ืžืฉื—ืงื™ื.
13:38
as judged by game theory.
325
818733
1242
13:39
Thank you.
326
819999
1151
ืชื•ื“ื” ืจื‘ื”.
13:41
(Applause)
327
821174
3119
(ืžื—ื™ืื•ืช ื›ืคื™ื™ื)
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

https://forms.gle/WvT1wiN1qDtmnspy7