What we're learning from 5,000 brains
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0:00 - 0:03Other people. Everyone is interested in other people.
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0:03 - 0:05Everyone has relationships with other people,
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0:05 - 0:07and they're interested in these relationships
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0:07 - 0:09for a variety of reasons.
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0:09 - 0:11Good relationships, bad relationships,
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0:11 - 0:14annoying relationships, agnostic relationships,
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0:14 - 0:17and what I'm going to do is focus on the central piece
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0:17 - 0:21of an interaction that goes on in a relationship.
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0:21 - 0:23So I'm going to take as inspiration the fact that we're all
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0:23 - 0:25interested in interacting with other people,
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0:25 - 0:29I'm going to completely strip it of all its complicating features,
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0:29 - 0:33and I'm going to turn that object, that simplified object,
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0:33 - 0:37into a scientific probe, and provide the early stages,
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0:37 - 0:40embryonic stages of new insights into what happens
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0:40 - 0:43in two brains while they simultaneously interact.
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0:43 - 0:46But before I do that, let me tell you a couple of things
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0:46 - 0:47that made this possible.
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0:47 - 0:50The first is we can now eavesdrop safely
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0:50 - 0:53on healthy brain activity.
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0:53 - 0:56Without needles and radioactivity,
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0:56 - 0:58without any kind of clinical reason, we can go down the street
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0:58 - 1:02and record from your friends' and neighbors' brains
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1:02 - 1:04while they do a variety of cognitive tasks, and we use
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1:04 - 1:08a method called functional magnetic resonance imaging.
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1:08 - 1:10You've probably all read about it or heard about in some
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1:10 - 1:14incarnation. Let me give you a two-sentence version of it.
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1:14 - 1:18So we've all heard of MRIs. MRIs use magnetic fields
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1:18 - 1:20and radio waves and they take snapshots of your brain
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1:20 - 1:22or your knee or your stomach,
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1:22 - 1:24grayscale images that are frozen in time.
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1:24 - 1:27In the 1990s, it was discovered you could use
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1:27 - 1:29the same machines in a different mode,
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1:29 - 1:32and in that mode, you could make microscopic blood flow
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1:32 - 1:35movies from hundreds of thousands of sites independently in the brain.
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1:35 - 1:38Okay, so what? In fact, the so what is, in the brain,
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1:38 - 1:42changes in neural activity, the things that make your brain work,
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1:42 - 1:44the things that make your software work in your brain,
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1:44 - 1:47are tightly correlated with changes in blood flow.
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1:47 - 1:49You make a blood flow movie, you have an independent
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1:49 - 1:51proxy of brain activity.
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1:51 - 1:54This has literally revolutionized cognitive science.
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1:54 - 1:56Take any cognitive domain you want, memory,
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1:56 - 1:58motor planning, thinking about your mother-in-law,
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1:58 - 2:02getting angry at people, emotional response, it goes on and on,
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2:02 - 2:05put people into functional MRI devices, and
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2:05 - 2:08image how these kinds of variables map onto brain activity.
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2:08 - 2:11It's in its early stages, and it's crude by some measures,
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2:11 - 2:14but in fact, 20 years ago, we were at nothing.
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2:14 - 2:16You couldn't do people like this. You couldn't do healthy people.
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2:16 - 2:19That's caused a literal revolution, and it's opened us up
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2:19 - 2:21to a new experimental preparation. Neurobiologists,
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2:21 - 2:25as you well know, have lots of experimental preps,
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2:25 - 2:28worms and rodents and fruit flies and things like this.
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2:28 - 2:32And now, we have a new experimental prep: human beings.
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2:32 - 2:35We can now use human beings to study and model
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2:35 - 2:38the software in human beings, and we have a few
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2:38 - 2:41burgeoning biological measures.
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2:41 - 2:45Okay, let me give you one example of the kinds of experiments that people do,
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2:45 - 2:48and it's in the area of what you'd call valuation.
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2:48 - 2:50Valuation is just what you think it is, you know?
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2:50 - 2:53If you went and you were valuing two companies against
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2:53 - 2:55one another, you'd want to know which was more valuable.
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2:55 - 2:59Cultures discovered the key feature of valuation thousands of years ago.
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2:59 - 3:02If you want to compare oranges to windshields, what do you do?
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3:02 - 3:04Well, you can't compare oranges to windshields.
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3:04 - 3:07They're immiscible. They don't mix with one another.
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3:07 - 3:09So instead, you convert them to a common currency scale,
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3:09 - 3:12put them on that scale, and value them accordingly.
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3:12 - 3:15Well, your brain has to do something just like that as well,
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3:15 - 3:18and we're now beginning to understand and identify
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3:18 - 3:20brain systems involved in valuation,
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3:20 - 3:22and one of them includes a neurotransmitter system
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3:22 - 3:25whose cells are located in your brainstem
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3:25 - 3:28and deliver the chemical dopamine to the rest of your brain.
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3:28 - 3:31I won't go through the details of it, but that's an important
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3:31 - 3:33discovery, and we know a good bit about that now,
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3:33 - 3:35and it's just a small piece of it, but it's important because
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3:35 - 3:38those are the neurons that you would lose if you had Parkinson's disease,
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3:38 - 3:40and they're also the neurons that are hijacked by literally
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3:40 - 3:42every drug of abuse, and that makes sense.
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3:42 - 3:45Drugs of abuse would come in, and they would change
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3:45 - 3:47the way you value the world. They change the way
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3:47 - 3:50you value the symbols associated with your drug of choice,
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3:50 - 3:52and they make you value that over everything else.
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3:52 - 3:55Here's the key feature though. These neurons are also
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3:55 - 3:59involved in the way you can assign value to literally abstract ideas,
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3:59 - 4:01and I put some symbols up here that we assign value to
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4:01 - 4:04for various reasons.
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4:04 - 4:06We have a behavioral superpower in our brain,
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4:06 - 4:08and it at least in part involves dopamine.
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4:08 - 4:12We can deny every instinct we have for survival for an idea,
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4:12 - 4:16for a mere idea. No other species can do that.
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4:16 - 4:20In 1997, the cult Heaven's Gate committed mass suicide
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4:20 - 4:22predicated on the idea that there was a spaceship
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4:22 - 4:26hiding in the tail of the then-visible comet Hale-Bopp
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4:26 - 4:30waiting to take them to the next level. It was an incredibly tragic event.
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4:30 - 4:34More than two thirds of them had college degrees.
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4:34 - 4:37But the point here is they were able to deny their instincts for survival
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4:37 - 4:40using exactly the same systems that were put there
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4:40 - 4:44to make them survive. That's a lot of control, okay?
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4:44 - 4:46One thing that I've left out of this narrative
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4:46 - 4:49is the obvious thing, which is the focus of the rest of my
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4:49 - 4:51little talk, and that is other people.
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4:51 - 4:54These same valuation systems are redeployed
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4:54 - 4:56when we're valuing interactions with other people.
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4:56 - 4:59So this same dopamine system that gets addicted to drugs,
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4:59 - 5:02that makes you freeze when you get Parkinson's disease,
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5:02 - 5:05that contributes to various forms of psychosis,
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5:05 - 5:09is also redeployed to value interactions with other people
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5:09 - 5:12and to assign value to gestures that you do
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5:12 - 5:14when you're interacting with somebody else.
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5:14 - 5:17Let me give you an example of this.
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5:17 - 5:20You bring to the table such enormous processing power
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5:20 - 5:23in this domain that you hardly even notice it.
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5:23 - 5:24Let me just give you a few examples. So here's a baby.
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5:24 - 5:28She's three months old. She still poops in her diapers and she can't do calculus.
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5:28 - 5:31She's related to me. Somebody will be very glad that she's up here on the screen.
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5:31 - 5:34You can cover up one of her eyes, and you can still read
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5:34 - 5:36something in the other eye, and I see sort of curiosity
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5:36 - 5:40in one eye, I see maybe a little bit of surprise in the other.
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5:40 - 5:43Here's a couple. They're sharing a moment together,
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5:43 - 5:44and we've even done an experiment where you can cut out
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5:44 - 5:47different pieces of this frame and you can still see
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5:47 - 5:50that they're sharing it. They're sharing it sort of in parallel.
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5:50 - 5:52Now, the elements of the scene also communicate this
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5:52 - 5:55to us, but you can read it straight off their faces,
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5:55 - 5:58and if you compare their faces to normal faces, it would be a very subtle cue.
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5:58 - 6:01Here's another couple. He's projecting out at us,
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6:01 - 6:04and she's clearly projecting, you know,
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6:04 - 6:07love and admiration at him.
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6:07 - 6:10Here's another couple. (Laughter)
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6:10 - 6:15And I'm thinking I'm not seeing love and admiration on the left. (Laughter)
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6:15 - 6:18In fact, I know this is his sister, and you can just see
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6:18 - 6:20him saying, "Okay, we're doing this for the camera,
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6:20 - 6:26and then afterwards you steal my candy and you punch me in the face." (Laughter)
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6:26 - 6:28He'll kill me for showing that.
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6:28 - 6:31All right, so what does this mean?
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6:31 - 6:34It means we bring an enormous amount of processing power to the problem.
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6:34 - 6:38It engages deep systems in our brain, in dopaminergic
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6:38 - 6:41systems that are there to make you chase sex, food and salt.
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6:41 - 6:44They keep you alive. It gives them the pie, it gives
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6:44 - 6:47that kind of a behavioral punch which we've called a superpower.
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6:47 - 6:50So how can we take that and arrange a kind of staged
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6:50 - 6:53social interaction and turn that into a scientific probe?
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6:53 - 6:56And the short answer is games.
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6:56 - 7:00Economic games. So what we do is we go into two areas.
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7:00 - 7:03One area is called experimental economics. The other area is called behavioral economics.
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7:03 - 7:08And we steal their games. And we contrive them to our own purposes.
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7:08 - 7:11So this shows you one particular game called an ultimatum game.
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7:11 - 7:12Red person is given a hundred dollars and can offer
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7:12 - 7:16a split to blue. Let's say red wants to keep 70,
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7:16 - 7:20and offers blue 30. So he offers a 70-30 split with blue.
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7:20 - 7:23Control passes to blue, and blue says, "I accept it,"
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7:23 - 7:25in which case he'd get the money, or blue says,
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7:25 - 7:29"I reject it," in which case no one gets anything. Okay?
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7:29 - 7:33So a rational choice economist would say, well,
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7:33 - 7:35you should take all non-zero offers.
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7:35 - 7:39What do people do? People are indifferent at an 80-20 split.
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7:39 - 7:42At 80-20, it's a coin flip whether you accept that or not.
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7:42 - 7:45Why is that? You know, because you're pissed off.
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7:45 - 7:49You're mad. That's an unfair offer, and you know what an unfair offer is.
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7:49 - 7:51This is the kind of game done by my lab and many around the world.
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7:51 - 7:54That just gives you an example of the kind of thing that
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7:54 - 7:58these games probe. The interesting thing is, these games
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7:58 - 8:01require that you have a lot of cognitive apparatus on line.
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8:01 - 8:04You have to be able to come to the table with a proper model of another person.
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8:04 - 8:07You have to be able to remember what you've done.
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8:07 - 8:09You have to stand up in the moment to do that.
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8:09 - 8:12Then you have to update your model based on the signals coming back,
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8:12 - 8:15and you have to do something that is interesting,
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8:15 - 8:18which is you have to do a kind of depth of thought assay.
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8:18 - 8:21That is, you have to decide what that other person expects of you.
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8:21 - 8:24You have to send signals to manage your image in their mind.
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8:24 - 8:27Like a job interview. You sit across the desk from somebody,
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8:27 - 8:28they have some prior image of you,
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8:28 - 8:31you send signals across the desk to move their image
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8:31 - 8:35of you from one place to a place where you want it to be.
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8:35 - 8:38We're so good at this we don't really even notice it.
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8:38 - 8:42These kinds of probes exploit it. Okay?
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8:42 - 8:44In doing this, what we've discovered is that humans
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8:44 - 8:46are literal canaries in social exchanges.
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8:46 - 8:50Canaries used to be used as kind of biosensors in mines.
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8:50 - 8:53When methane built up, or carbon dioxide built up,
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8:53 - 8:57or oxygen was diminished, the birds would swoon
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8:57 - 9:00before people would -- so it acted as an early warning system:
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9:00 - 9:03Hey, get out of the mine. Things aren't going so well.
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9:03 - 9:06People come to the table, and even these very blunt,
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9:06 - 9:09staged social interactions, and they, and there's just
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9:09 - 9:12numbers going back and forth between the people,
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9:12 - 9:14and they bring enormous sensitivities to it.
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9:14 - 9:16So we realized we could exploit this, and in fact,
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9:16 - 9:19as we've done that, and we've done this now in
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9:19 - 9:22many thousands of people, I think on the order of
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9:22 - 9:24five or six thousand. We actually, to make this
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9:24 - 9:26a biological probe, need bigger numbers than that,
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9:26 - 9:30remarkably so. But anyway,
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9:30 - 9:32patterns have emerged, and we've been able to take
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9:32 - 9:36those patterns, convert them into mathematical models,
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9:36 - 9:38and use those mathematical models to gain new insights
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9:38 - 9:40into these exchanges. Okay, so what?
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9:40 - 9:44Well, the so what is, that's a really nice behavioral measure,
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9:44 - 9:47the economic games bring to us notions of optimal play.
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9:47 - 9:50We can compute that during the game.
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9:50 - 9:53And we can use that to sort of carve up the behavior.
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9:53 - 9:57Here's the cool thing. Six or seven years ago,
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9:57 - 9:59we developed a team. It was at the time in Houston, Texas.
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9:59 - 10:03It's now in Virginia and London. And we built software
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10:03 - 10:06that'll link functional magnetic resonance imaging devices
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10:06 - 10:10up over the Internet. I guess we've done up to six machines
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10:10 - 10:12at a time, but let's just focus on two.
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10:12 - 10:15So it synchronizes machines anywhere in the world.
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10:15 - 10:18We synchronize the machines, set them into these
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10:18 - 10:20staged social interactions, and we eavesdrop on both
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10:20 - 10:22of the interacting brains. So for the first time,
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10:22 - 10:26we don't have to look at just averages over single individuals,
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10:26 - 10:28or have individuals playing computers, or try to make
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10:28 - 10:31inferences that way. We can study individual dyads.
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10:31 - 10:34We can study the way that one person interacts with another person,
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10:34 - 10:37turn the numbers up, and start to gain new insights
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10:37 - 10:39into the boundaries of normal cognition,
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10:39 - 10:42but more importantly, we can put people with
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10:42 - 10:45classically defined mental illnesses, or brain damage,
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10:45 - 10:49into these social interactions, and use these as probes of that.
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10:49 - 10:51So we've started this effort. We've made a few hits,
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10:51 - 10:53a few, I think, embryonic discoveries.
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10:53 - 10:56We think there's a future to this. But it's our way
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10:56 - 10:59of going in and redefining, with a new lexicon,
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10:59 - 11:03a mathematical one actually, as opposed to the standard
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11:03 - 11:05ways that we think about mental illness,
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11:05 - 11:07characterizing these diseases, by using the people
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11:07 - 11:10as birds in the exchanges. That is, we exploit the fact
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11:10 - 11:15that the healthy partner, playing somebody with major depression,
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11:15 - 11:18or playing somebody with autism spectrum disorder,
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11:18 - 11:21or playing somebody with attention deficit hyperactivity disorder,
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11:21 - 11:25we use that as a kind of biosensor, and then we use
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11:25 - 11:27computer programs to model that person, and it gives us
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11:27 - 11:30a kind of assay of this.
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11:30 - 11:32Early days, and we're just beginning, we're setting up sites
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11:32 - 11:35around the world. Here are a few of our collaborating sites.
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11:35 - 11:38The hub, ironically enough,
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11:38 - 11:41is centered in little Roanoke, Virginia.
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11:41 - 11:43There's another hub in London, now, and the rest
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11:43 - 11:47are getting set up. We hope to give the data away
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11:47 - 11:51at some stage. That's a complicated issue
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11:51 - 11:54about making it available to the rest of the world.
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11:54 - 11:55But we're also studying just a small part
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11:55 - 11:58of what makes us interesting as human beings, and so
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11:58 - 12:00I would invite other people who are interested in this
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12:00 - 12:02to ask us for the software, or even for guidance
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12:02 - 12:04on how to move forward with that.
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12:04 - 12:07Let me leave you with one thought in closing.
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12:07 - 12:09The interesting thing about studying cognition
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12:09 - 12:12has been that we've been limited, in a way.
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12:12 - 12:15We just haven't had the tools to look at interacting brains
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12:15 - 12:17simultaneously.
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12:17 - 12:19The fact is, though, that even when we're alone,
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12:19 - 12:23we're a profoundly social creature. We're not a solitary mind
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12:23 - 12:28built out of properties that kept it alive in the world
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12:28 - 12:32independent of other people. In fact, our minds
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12:32 - 12:34depend on other people. They depend on other people,
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12:34 - 12:36and they're expressed in other people,
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12:36 - 12:40so the notion of who you are, you often don't know
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12:40 - 12:42who you are until you see yourself in interaction with people
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12:42 - 12:45that are close to you, people that are enemies of you,
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12:45 - 12:47people that are agnostic to you.
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12:47 - 12:51So this is the first sort of step into using that insight
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12:51 - 12:54into what makes us human beings, turning it into a tool,
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12:54 - 12:56and trying to gain new insights into mental illness.
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12:56 - 12:59Thanks for having me. (Applause)
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12:59 - 13:02(Applause)
- Title:
- What we're learning from 5,000 brains
- Speaker:
- Read Montague
- Description:
-
Mice, bugs and hamsters are no longer the only way to study the brain. Functional MRI (fMRI) allows scientists to map brain activity in living, breathing, decision-making human beings. Read Montague gives an overview of how this technology is helping us understand the complicated ways in which we interact with each other.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 13:23
Thu-Huong Ha edited English subtitles for What we're learning from 5,000 brains | ||
Rute Joaquim edited English subtitles for What we're learning from 5,000 brains | ||
Rute Joaquim edited English subtitles for What we're learning from 5,000 brains | ||
Rute Joaquim edited English subtitles for What we're learning from 5,000 brains | ||
Rute Joaquim edited English subtitles for What we're learning from 5,000 brains | ||
Rute Joaquim edited English subtitles for What we're learning from 5,000 brains | ||
Jerome Su edited English subtitles for What we're learning from 5,000 brains | ||
Thu-Huong Ha approved English subtitles for What we're learning from 5,000 brains |