1
00:00:00,111 --> 00:00:02,271
- [Colin] We have a saying 
in neuroscience sometimes:

2
00:00:02,271 --> 00:00:04,383
"Don't ask the person,
ask the brain,"

3
00:00:04,383 --> 00:00:06,484
because the brain activity
may be something

4
00:00:06,484 --> 00:00:09,256
that's less than fully conscious.

5
00:00:09,678 --> 00:00:11,795
♪ [music] ♪

6
00:00:18,815 --> 00:00:20,850
My research is about
behavioral economics

7
00:00:20,850 --> 00:00:22,105
and neuroeconomics.

8
00:00:22,105 --> 00:00:23,490
And behavioral economics

9
00:00:23,490 --> 00:00:26,828
is using ideas from psychology
and other social sciences

10
00:00:26,828 --> 00:00:28,799
to make economics
a little bit more lifelike

11
00:00:28,799 --> 00:00:31,310
and fit human behavior.

12
00:00:31,310 --> 00:00:33,761
The neuroeconomics part 
is that we actually try to see

13
00:00:33,761 --> 00:00:35,211
what's happening in the brain

14
00:00:35,211 --> 00:00:37,300
when people are making 
economic decisions.

15
00:00:37,300 --> 00:00:40,270
Hypothetical bias is a term 
for when you ask somebody

16
00:00:40,270 --> 00:00:41,748
whether they're going
to do something,

17
00:00:41,748 --> 00:00:43,147
but there's no actual consequences,

18
00:00:43,147 --> 00:00:45,042
like a lot of surveys:
"Are you going to vote?"

19
00:00:45,042 --> 00:00:47,061
"Will you buy 
this new product we have?"

20
00:00:47,621 --> 00:00:50,397
You tend to get a kind 
of upward "yes" bias.

21
00:00:50,397 --> 00:00:52,793
People are more likely to say,
"Yeah, I'd think I'd buy it"

22
00:00:52,793 --> 00:00:54,239
or "Oh, yeah,
I'm planning to vote."

23
00:00:54,239 --> 00:00:56,131
The hypothetical bias
can be pretty high,

24
00:00:56,131 --> 00:00:57,881
and it can be also retrospective.

25
00:00:57,881 --> 00:00:59,529
So if you ask people did they vote,

26
00:00:59,529 --> 00:01:02,862
70% say yes and really 
the answer was 45%.

27
00:01:03,206 --> 00:01:05,097
One application is 
in things like marketing.

28
00:01:05,097 --> 00:01:07,913
A lot of new products fail,
and one reason they fail

29
00:01:07,913 --> 00:01:09,631
is because when 
they test marketed,

30
00:01:09,631 --> 00:01:11,414
a lot of people said, 
"Yes, I'd buy it,"

31
00:01:11,414 --> 00:01:12,630
who weren't going to buy it.

32
00:01:12,630 --> 00:01:14,247
One thing that people 
have been chasing

33
00:01:14,247 --> 00:01:16,861
in different fields in economics
and psychology is

34
00:01:16,861 --> 00:01:20,303
how could we measure the size 
of this bias and adjust for it?

35
00:01:20,303 --> 00:01:23,102
So that if 70% of the people say
they're going to buy a new product,

36
00:01:23,102 --> 00:01:25,683
we know that 
the real number is 45%.

37
00:01:29,619 --> 00:01:32,742
We did a couple of studies
using brain imaging to say

38
00:01:32,742 --> 00:01:34,773
is there a kind
of signature in the brain

39
00:01:34,773 --> 00:01:37,026
of when somebody says, 
"Yes, I would buy it,"

40
00:01:37,026 --> 00:01:39,461
but when they really have 
to choose, they say no.

41
00:01:39,461 --> 00:01:41,442
So we showed them pictures
of different goods,

42
00:01:41,442 --> 00:01:43,601
and the first part 
of the experiment, we asked them,

43
00:01:43,601 --> 00:01:46,655
"Would you pay $27 
for this backpack, yes or no?" -

44
00:01:46,655 --> 00:01:47,948
that's the hypothetical part.

45
00:01:47,948 --> 00:01:50,671
And then we kind of surprised them
when they come out of the scanner,

46
00:01:50,671 --> 00:01:52,071
and we say to them,
"Oh, by the way,

47
00:01:52,071 --> 00:01:54,816
now we're going to actually
have you decide to spend money,

48
00:01:54,816 --> 00:01:56,746
so we're going to give you $50."

49
00:01:56,746 --> 00:01:59,118
If you want to buy
the backpack for $27,

50
00:01:59,118 --> 00:02:00,693
we're going to take it
out of your 50,

51
00:02:00,693 --> 00:02:02,560
so that now they have
to make real decisions.

52
00:02:02,560 --> 00:02:05,034
And then we study 
in the brain imaging,

53
00:02:05,034 --> 00:02:07,926
could we tell what areas 
were saying yes,

54
00:02:07,926 --> 00:02:09,966
but actually would later say no,

55
00:02:09,966 --> 00:02:12,911
compared to the areas that said,
"Yes, I think I'll buy it,"

56
00:02:12,911 --> 00:02:14,452
and, yes, they really did buy it.

57
00:02:14,452 --> 00:02:17,219
And we found both
more activity in certain regions

58
00:02:17,219 --> 00:02:18,927
associated with valuation

59
00:02:18,927 --> 00:02:20,843
and then activity
in different regions,

60
00:02:20,843 --> 00:02:22,126
which were somewhat predictive

61
00:02:22,126 --> 00:02:25,290
of when a yes was going to turn
into, "Well, not really."

62
00:02:28,821 --> 00:02:30,868
In some other studies 
on hypothetical bias,

63
00:02:30,868 --> 00:02:33,068
we used eye tracking, 
which is a computerized way

64
00:02:33,068 --> 00:02:35,467
of seeing what you're looking at
for how long.

65
00:02:35,467 --> 00:02:37,249
It also measures pupil dilation.

66
00:02:37,249 --> 00:02:39,679
When you're aroused 
by something that you like

67
00:02:39,679 --> 00:02:42,546
or possibly you're scared,
the pupil dilates a little bit.

68
00:02:42,546 --> 00:02:44,107
So we used that method

69
00:02:44,107 --> 00:02:46,217
and then another method
using mouse movements.

70
00:02:46,217 --> 00:02:48,005
And so we found 
that the mouse tracking

71
00:02:48,005 --> 00:02:50,337
and the eye tracking
actually could give us an idea

72
00:02:50,337 --> 00:02:52,974
of when people would say, "Yes, 
I'm going to buy this product,"

73
00:02:52,974 --> 00:02:55,282
but then they really didn't
when they had skin in the game

74
00:02:55,282 --> 00:02:56,498
and had to buy it.

75
00:02:56,498 --> 00:03:00,933
The quicker a person moves a mouse
to a box to click on something,

76
00:03:00,933 --> 00:03:02,284
the more they like it.

77
00:03:02,284 --> 00:03:05,190
It's like a fast trajectory,
"I really like this."

78
00:03:05,190 --> 00:03:08,500
A slow meandering trajectory is, 
"Well, I don't know. I'm not sure."

79
00:03:08,500 --> 00:03:12,457
The motor activity in the mouse is
actually an index in economic value

80
00:03:12,457 --> 00:03:15,542
as well as some other things,
like indecision.

81
00:03:19,701 --> 00:03:21,650
We live in a kind of golden age
of social science

82
00:03:21,650 --> 00:03:24,303
in which we can measure things
in lots and lots of different ways.

83
00:03:24,303 --> 00:03:25,819
So one thing we've just begun

84
00:03:25,819 --> 00:03:28,181
that I think is going 
to be really, really fun

85
00:03:28,181 --> 00:03:32,728
is in order to study habit
and a bunch of other things,

86
00:03:32,728 --> 00:03:34,371
we bought a smart vending machine,

87
00:03:34,371 --> 00:03:35,831
and a smart vending machine

88
00:03:35,831 --> 00:03:37,620
is basically a vending
machine in the back

89
00:03:37,620 --> 00:03:39,217
and a giant iPad in the front.

90
00:03:39,217 --> 00:03:42,285
So you could program the iPad
to show whatever you want.

91
00:03:42,285 --> 00:03:43,998
For example, if somebody
buys habitually,

92
00:03:43,998 --> 00:03:46,451
and you raise the price
by a few percent,

93
00:03:46,451 --> 00:03:47,524
do they just ignore that

94
00:03:47,524 --> 00:03:49,381
because they're not 
even looking at the price?

95
00:03:49,381 --> 00:03:51,539
Eventually, we'd like also
to be able to use

96
00:03:51,539 --> 00:03:53,508
webcams or cameras to record,

97
00:03:53,508 --> 00:03:55,516
so when I say somebody
is not looking at the price,

98
00:03:55,516 --> 00:03:57,537
I really mean they are
not looking at the price

99
00:03:57,537 --> 00:04:00,285
because the camera looked
at where their eyes were looking.

100
00:04:00,285 --> 00:04:02,418
- [Narrator] Want to see 
more economists in the wild?

101
00:04:02,418 --> 00:04:04,176
Check out our playlist.

102
00:04:04,176 --> 00:04:05,228
Are you a teacher?

103
00:04:05,228 --> 00:04:07,868
Here's some related material 
for your classroom.

104
00:04:07,868 --> 00:04:09,641
♪ [music] ♪