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 --> 00:00:22,105 My research is about behavioral economics and neuroeconomics. 7 00:00:22,105 --> 00:00:23,490 And behavioral economics 8 00:00:23,490 --> 00:00:26,828 is using ideas from psychology and other social sciences 9 00:00:26,828 --> 00:00:28,969 to make economics a little bit more lifelike 10 00:00:28,969 --> 00:00:31,310 and fit human behavior. 11 00:00:31,310 --> 00:00:33,761 The neuroeconomics part is that we actually try to see 12 00:00:33,761 --> 00:00:35,211 what's happening in the brain 13 00:00:35,211 --> 00:00:37,300 when people are making economic decisions. 14 00:00:37,300 --> 00:00:40,310 Hypothetical bias is a term for when you ask somebody 15 00:00:40,310 --> 00:00:43,127 whether they're going to something, but there's no actual consequences, 16 00:00:43,127 --> 00:00:45,042 like a lot of surveys: "Are you going to vote?" 17 00:00:45,042 --> 00:00:47,061 "Will you buy this new product we have?" 18 00:00:47,621 --> 00:00:50,397 You tend to get a kind of upward "yes" bias. 19 00:00:50,397 --> 00:00:52,793 People are more likely to say, "Yeah, I'd think I'd buy it" 20 00:00:52,793 --> 00:00:54,239 or "Oh yeah, I'm planning to vote." 21 00:00:54,239 --> 00:00:56,126 The hypothetical bias can be pretty high, 22 00:00:56,131 --> 00:00:57,881 and it can be also retrospective. 23 00:00:57,881 --> 00:00:59,529 So if you ask people did they vote, 24 00:00:59,529 --> 00:01:02,862 70% say yes and really the answer was 45%. 25 00:01:03,206 --> 00:01:05,097 One application is in things like marketing. 26 00:01:05,097 --> 00:01:07,913 A lot of new products fail, and one reason they fail 27 00:01:07,913 --> 00:01:09,631 is because when they test market it, 28 00:01:09,631 --> 00:01:11,414 a lot of people said, "Yes, I'd buy it," 29 00:01:11,414 --> 00:01:12,630 who weren't going to buy it. 30 00:01:12,630 --> 00:01:14,247 One thing that people have been chasing 31 00:01:14,247 --> 00:01:16,861 in different fields in economics and psychology is 32 00:01:16,861 --> 00:01:20,044 how could we measure the size of this bias and adjust for it. 33 00:01:20,044 --> 00:01:23,102 So that if 70% of the people say they're going to buy a new product, 34 00:01:23,102 --> 00:01:25,683 we know that the real number is 45%. 35 00:01:29,619 --> 00:01:32,742 We did a couple of studies using brain imaging to say 36 00:01:32,742 --> 00:01:34,773 is there a kind of signature in the brain 37 00:01:34,773 --> 00:01:37,026 of when somebody says, "Yes, I would buy it," 38 00:01:37,026 --> 00:01:39,461 but when they really have to choose, they say no. 39 00:01:39,461 --> 00:01:41,442 So we showed them pictures of different goods, 40 00:01:41,442 --> 00:01:43,601 and the first part of the experiment, we asked them, 41 00:01:43,601 --> 00:01:46,715 "Would you pay $27 for this backpack, yes or no?" 42 00:01:46,715 --> 00:01:49,261 That's the hypothetical part, and then we kind of surprised them 43 00:01:49,261 --> 00:01:51,631 when they come out of the scanner, and we say to them, 44 00:01:51,631 --> 00:01:54,042 "Oh, by the way, now we're going to actually have you decide 45 00:01:54,042 --> 00:01:56,746 to spend money so we're going to give you $50." 46 00:01:56,746 --> 00:01:59,118 If you want to buy the backpack for $27, 47 00:01:59,118 --> 00:02:00,693 we're going to take it out of your 50, 48 00:02:00,693 --> 00:02:02,560 so that now they have to make real decisions, 49 00:02:02,560 --> 00:02:05,034 and then we study in the brain imaging, 50 00:02:05,034 --> 00:02:07,926 could we tell what areas were saying yes, 51 00:02:07,926 --> 00:02:09,966 but actually would later say no 52 00:02:09,966 --> 00:02:12,911 compared to the areas that said, "Yes, I think I'll buy it," 53 00:02:12,911 --> 00:02:14,452 and, yes, they really did buy it. 54 00:02:14,452 --> 00:02:17,219 And we found both more activity in certain regions 55 00:02:17,219 --> 00:02:18,927 associated with valuation 56 00:02:18,927 --> 00:02:20,843 and then activity in different regions, 57 00:02:20,843 --> 00:02:22,126 which were somewhat predictive 58 00:02:22,126 --> 00:02:25,290 of when a yes was going to turn into, "Well, not really." 59 00:02:28,667 --> 00:02:30,868 In some other studies on hypothetical bias, 60 00:02:30,868 --> 00:02:33,068 we used eye tracking, which is a computerized way 61 00:02:33,068 --> 00:02:35,467 of seeing what you're looking at for how long. 62 00:02:35,467 --> 00:02:37,249 It also measures pupil dilation. 63 00:02:37,249 --> 00:02:39,679 When you're aroused by something that you like 64 00:02:39,679 --> 00:02:42,546 or possibly you're scared, the pupil dilates a little bit. 65 00:02:42,546 --> 00:02:44,871 So we used that method and then another method 66 00:02:44,871 --> 00:02:46,337 using mouse movements. 67 00:02:46,337 --> 00:02:48,005 And so we found that the mouse tracking 68 00:02:48,005 --> 00:02:50,337 and the eye tracking actually could give us an idea 69 00:02:50,337 --> 00:02:52,974 of when people would say, "Yes, I'm going to buy this product," 70 00:02:52,974 --> 00:02:55,282 but then they really didn't when they had skin in the game 71 00:02:55,282 --> 00:02:56,498 and had to buy it. 72 00:02:56,498 --> 00:03:00,933 The quicker a person moves a mouse to a box to click on something, 73 00:03:00,933 --> 00:03:02,284 the more they like it. 74 00:03:02,284 --> 00:03:05,190 It's like a fast trajectory, "I really like this." 75 00:03:05,190 --> 00:03:08,500 A slow meandering trajectory is, "Well, I don't know. I'm not sure." 76 00:03:08,500 --> 00:03:12,457 The motor activity in the mouse is actually an index in economic value 77 00:03:12,457 --> 00:03:15,542 as well as some other things, like indecision. 78 00:03:19,701 --> 00:03:21,650 We live in a kind of golden age of social science 79 00:03:21,650 --> 00:03:24,303 in which we can measure things in lots and lots of different ways. 80 00:03:24,303 --> 00:03:25,819 So one thing we've just begun 81 00:03:25,819 --> 00:03:28,181 that I think it's going to be really, really fun 82 00:03:28,181 --> 00:03:32,448 is in order to study habit and a bunch of other things, 83 00:03:32,448 --> 00:03:34,371 we bought a smart vending machine, 84 00:03:34,371 --> 00:03:35,831 and a smart vending machine 85 00:03:35,831 --> 00:03:37,620 is basically a vending machine in the back 86 00:03:37,620 --> 00:03:39,217 and a giant iPad in the front. 87 00:03:39,217 --> 00:03:42,285 So you could program the iPad to show whatever you want. 88 00:03:42,285 --> 00:03:44,850 For example, if somebody buys habitually and you raise the price 89 00:03:44,850 --> 00:03:47,434 by a few percent, do they just ignore that 90 00:03:47,434 --> 00:03:49,381 because they're not even looking at the price? 91 00:03:49,381 --> 00:03:52,798 Eventually, we'd like also to be able to use webcam or cameras 92 00:03:52,798 --> 00:03:55,226 to record so when I say somebody is not looking at the price, 93 00:03:55,226 --> 00:03:57,537 I really mean they are not looking at the price 94 00:03:57,537 --> 00:04:00,285 because the camera looked at where their eyes were looking. 95 00:04:00,285 --> 00:04:02,418 - [Narrator] Want to see more economists in the wild? 96 00:04:02,418 --> 00:04:04,176 Check out our playlist. 97 00:04:04,176 --> 00:04:05,228 Are you a teacher? 98 00:04:05,228 --> 00:04:07,868 Here's some related material for your classroom. 99 00:04:07,868 --> 00:04:09,641 ♪ [music] ♪