1 00:00:05,040 --> 00:00:10,680 Speaker 1: Why can't you tickle yourself? Why don't hippopotamuses tell stories 2 00:00:10,840 --> 00:00:15,800 Speaker 1: around the campfire? What would I advise the president if 3 00:00:15,840 --> 00:00:20,120 Speaker 1: we find ourselves at war with extraterrestrials? And what does 4 00:00:20,160 --> 00:00:23,560 Speaker 1: any of this have to do with Wayne Gretzky or 5 00:00:23,800 --> 00:00:28,400 Speaker 1: the Greek Goddess of Memory and her children? Or poking 6 00:00:28,440 --> 00:00:31,280 Speaker 1: your finger into the side of your eyeball, or hitting 7 00:00:31,360 --> 00:00:34,440 Speaker 1: a bottle to get ketchup onto your French fries? And 8 00:00:34,560 --> 00:00:37,520 Speaker 1: why do we get so anxious about the world changing 9 00:00:37,680 --> 00:00:44,519 Speaker 1: around us? Welcome to Inner Cosmos with me David Eagleman. 10 00:00:45,000 --> 00:00:48,840 Speaker 1: I'm a neuroscientist and author at Stanford and in these 11 00:00:48,880 --> 00:00:52,680 Speaker 1: episodes we sail deeply into our three pound universe to 12 00:00:52,800 --> 00:00:57,080 Speaker 1: understand why and how our lives look the way they do. 13 00:01:05,280 --> 00:01:08,560 Speaker 1: Today's episode is about one of the most important things 14 00:01:08,640 --> 00:01:14,559 Speaker 1: that brains do, which is the simulation of possible futures. 15 00:01:15,880 --> 00:01:18,560 Speaker 1: The way we teach about brains in the classroom usually 16 00:01:18,600 --> 00:01:22,120 Speaker 1: has to do with the brain figuring out where it 17 00:01:22,200 --> 00:01:25,280 Speaker 1: is and what is happening around it, Like it detects 18 00:01:25,360 --> 00:01:28,480 Speaker 1: touch on its skin, and it detects photons from the 19 00:01:28,600 --> 00:01:31,160 Speaker 1: environment out there, and it picks up on sound waves 20 00:01:31,160 --> 00:01:34,360 Speaker 1: that are happening, and it stitches all of these together 21 00:01:34,920 --> 00:01:38,840 Speaker 1: in the massive hurricane of electrical spikes that race around 22 00:01:38,880 --> 00:01:42,920 Speaker 1: in the silence and darkness of your skull, and all 23 00:01:43,000 --> 00:01:46,720 Speaker 1: of this neural information allows you to put together a 24 00:01:46,760 --> 00:01:49,920 Speaker 1: picture of what is happening in the world out there, 25 00:01:50,280 --> 00:01:53,240 Speaker 1: and that is what allows you to operate in the 26 00:01:53,280 --> 00:01:56,120 Speaker 1: world and to catch that fish and put it in 27 00:01:56,120 --> 00:02:00,720 Speaker 1: your mouth, and to run from the predator, or more hosaically, 28 00:02:00,800 --> 00:02:04,640 Speaker 1: to find the right empty parking space, or tell the 29 00:02:04,680 --> 00:02:07,760 Speaker 1: cashier what you want from the fast food menu, or 30 00:02:07,920 --> 00:02:10,639 Speaker 1: apply the brakes on your bicycle when there's a pothole 31 00:02:10,680 --> 00:02:14,400 Speaker 1: in the road. So the brain gathers data from the 32 00:02:14,400 --> 00:02:17,760 Speaker 1: world around it so that it can operate inside of 33 00:02:17,800 --> 00:02:21,640 Speaker 1: that world. But what we talked about last week was 34 00:02:21,680 --> 00:02:25,679 Speaker 1: something surprising, which is that the brain doesn't spend all 35 00:02:25,720 --> 00:02:29,680 Speaker 1: of its time in the present. In fact, a lot 36 00:02:29,720 --> 00:02:33,480 Speaker 1: of its experiences are not in the here and now 37 00:02:33,639 --> 00:02:38,600 Speaker 1: at all, but instead in the past. Your brain holds 38 00:02:38,639 --> 00:02:42,280 Speaker 1: on to data about previous events in your life. In 39 00:02:42,320 --> 00:02:46,680 Speaker 1: other words, what happened and who was there, and what 40 00:02:46,720 --> 00:02:50,000 Speaker 1: the spatial configuration of the furniture was in the room, 41 00:02:50,120 --> 00:02:53,560 Speaker 1: and the building, and you spend quite a lot of 42 00:02:53,600 --> 00:02:58,480 Speaker 1: your time recalling that past. This whole process is what 43 00:02:58,520 --> 00:03:02,919 Speaker 1: we summarize as memory. And what I emphasized last week 44 00:03:03,440 --> 00:03:06,799 Speaker 1: is that we spend a good deal of our existence 45 00:03:06,880 --> 00:03:09,720 Speaker 1: unhooked from the here and now, and instead we time 46 00:03:09,919 --> 00:03:13,919 Speaker 1: travel to that past. But why do we care about 47 00:03:13,960 --> 00:03:17,840 Speaker 1: the past? This is for one reason only. We do 48 00:03:17,960 --> 00:03:24,240 Speaker 1: it to better simulate possible futures, and that's what today's 49 00:03:24,320 --> 00:03:27,440 Speaker 1: episode is about. It turns out we spend an enormous 50 00:03:27,480 --> 00:03:31,200 Speaker 1: amount of our time traveling to the future. Our time 51 00:03:31,280 --> 00:03:34,679 Speaker 1: travel is not one way, it goes both directions. We 52 00:03:35,200 --> 00:03:40,440 Speaker 1: simulate possibilities. We think of what our actions could lead to. 53 00:03:41,720 --> 00:03:44,080 Speaker 1: If I say this, then my spouse might say this 54 00:03:44,160 --> 00:03:47,040 Speaker 1: other thing back to me. If I open this cabinet 55 00:03:47,080 --> 00:03:50,280 Speaker 1: over here, I will expect to find soup cans in there. 56 00:03:50,720 --> 00:03:54,280 Speaker 1: If I do XYZ, I'll impress my boss, and I'll 57 00:03:54,280 --> 00:03:59,600 Speaker 1: hope to get that promotion. We walk down long paths 58 00:03:59,680 --> 00:04:03,600 Speaker 1: of possible chess moves that we can play in our lives. So, 59 00:04:03,680 --> 00:04:05,760 Speaker 1: as I said, we spend the vast majority of our 60 00:04:05,800 --> 00:04:08,080 Speaker 1: lives not in the here and now, but in the there, 61 00:04:08,320 --> 00:04:11,760 Speaker 1: and then in either direction. We spend most of our 62 00:04:11,880 --> 00:04:18,440 Speaker 1: days in daydreams and stories and confabulations in life narratives 63 00:04:18,480 --> 00:04:23,920 Speaker 1: of reminiscence and future projection. When you tally all this up, 64 00:04:24,040 --> 00:04:27,719 Speaker 1: the years that we spend in the realm of fantasy 65 00:04:27,920 --> 00:04:31,600 Speaker 1: outstrip the time that we spend in the present. So 66 00:04:32,200 --> 00:04:37,919 Speaker 1: why do we simulate the future? Well, first, it's much 67 00:04:38,000 --> 00:04:42,320 Speaker 1: more energy efficient to do that than to try everything 68 00:04:42,400 --> 00:04:45,400 Speaker 1: out in the real world. If I have to haul 69 00:04:45,560 --> 00:04:50,440 Speaker 1: this rock over there, I can sit and simulate several 70 00:04:50,680 --> 00:04:55,080 Speaker 1: possible approaches. I can imagine myself picking it up and 71 00:04:55,200 --> 00:04:59,000 Speaker 1: carrying it over there. But then I realized, now I'll 72 00:04:59,000 --> 00:05:01,880 Speaker 1: never be able to get it o over that big gulch. Well, 73 00:05:02,160 --> 00:05:06,240 Speaker 1: thank goodness that I ran that simulation from the comfort 74 00:05:06,360 --> 00:05:08,560 Speaker 1: of sitting on the ground and thinking about it. That's 75 00:05:08,680 --> 00:05:12,920 Speaker 1: super energy efficient, and I can try out different methods 76 00:05:12,920 --> 00:05:15,599 Speaker 1: of hauling the rock. What if I use a rope 77 00:05:15,839 --> 00:05:19,960 Speaker 1: or a wheelbarrow or a catapult. I can try out 78 00:05:20,440 --> 00:05:25,119 Speaker 1: all these different things without breaking a sweat or burning 79 00:05:25,200 --> 00:05:31,000 Speaker 1: any calories besides the few calories required for simulation, which 80 00:05:31,040 --> 00:05:35,719 Speaker 1: is orders of magnitude less than employing my muscles to 81 00:05:36,040 --> 00:05:40,560 Speaker 1: move my multi trillion cell body. Around in the world, 82 00:05:41,960 --> 00:05:44,719 Speaker 1: and this kind of simulation, this is what we humans 83 00:05:44,800 --> 00:05:48,919 Speaker 1: do all the time. So imagine you are a fire 84 00:05:49,040 --> 00:05:51,400 Speaker 1: chief and you and your team roll up on a 85 00:05:51,440 --> 00:05:55,279 Speaker 1: new fire that's engulfing a building. Your job is to 86 00:05:55,360 --> 00:06:00,479 Speaker 1: quickly make predictions about how to best position your team. So, 87 00:06:00,600 --> 00:06:05,120 Speaker 1: given your past experience with the world, you mentally simulate 88 00:06:05,200 --> 00:06:09,360 Speaker 1: different layouts and you evaluate their effectiveness, and then once 89 00:06:09,440 --> 00:06:13,799 Speaker 1: you've simulated a great plan or the best of what's available, 90 00:06:14,240 --> 00:06:17,120 Speaker 1: then you set it into action. In the real world, 91 00:06:17,600 --> 00:06:21,480 Speaker 1: you don't have to try out every single thing in 92 00:06:21,520 --> 00:06:26,280 Speaker 1: the physical world, and it's not just about energy efficiency. 93 00:06:26,880 --> 00:06:31,720 Speaker 1: More generally, the reason you simulate possible futures is because 94 00:06:31,800 --> 00:06:36,240 Speaker 1: it's much less dangerous than trying everything out in the 95 00:06:36,279 --> 00:06:39,159 Speaker 1: real world. So let's say you're parked in your car 96 00:06:39,200 --> 00:06:41,000 Speaker 1: and you need to go to some door, but there 97 00:06:41,120 --> 00:06:44,440 Speaker 1: is a dog barking at you, So you run a movie. 98 00:06:44,480 --> 00:06:46,840 Speaker 1: You simulate what would it be like if I make 99 00:06:46,880 --> 00:06:50,800 Speaker 1: a run for that door, and your brain might run 100 00:06:50,839 --> 00:06:53,279 Speaker 1: that and decide, you know what, that's not worth the risk. 101 00:06:53,440 --> 00:06:56,240 Speaker 1: So your brain simulates other plans, like maybe you stay 102 00:06:56,279 --> 00:06:58,480 Speaker 1: in the car and you dial the owner, or maybe 103 00:06:58,520 --> 00:07:01,240 Speaker 1: you crack the window and you yell for the owner. 104 00:07:01,320 --> 00:07:04,480 Speaker 1: Things like that, your brain doesn't actually have to run 105 00:07:04,520 --> 00:07:08,880 Speaker 1: the risk of confronting the dog. Or maybe some big 106 00:07:08,920 --> 00:07:11,160 Speaker 1: guy cuts in front of you when you're waiting in line, 107 00:07:11,560 --> 00:07:14,520 Speaker 1: and you might simulate all kinds of things that you 108 00:07:14,560 --> 00:07:17,480 Speaker 1: want to do to him in including calling him names 109 00:07:17,560 --> 00:07:19,920 Speaker 1: or pushing him in the back or whatever. But you 110 00:07:19,960 --> 00:07:24,480 Speaker 1: are usually better running the simulations in your head and 111 00:07:24,600 --> 00:07:27,840 Speaker 1: not taking advantage of all the things that you could do. 112 00:07:28,600 --> 00:07:32,520 Speaker 1: As the philosopher Carl Popper put it, simulation of the 113 00:07:32,600 --> 00:07:40,400 Speaker 1: future allows our hypotheses to die in our stead, So 114 00:07:40,800 --> 00:07:45,880 Speaker 1: intelligent brains do not want to engage in the expensive 115 00:07:46,120 --> 00:07:51,320 Speaker 1: and potentially fatal game of physically testing every action to 116 00:07:51,400 --> 00:07:55,160 Speaker 1: figure out what the consequences are. Instead, it is more 117 00:07:55,200 --> 00:08:00,600 Speaker 1: efficient and safer when possible to envisage consequences of a 118 00:08:00,680 --> 00:08:06,160 Speaker 1: proposed plan before you actually execute it. So by learning 119 00:08:06,240 --> 00:08:10,440 Speaker 1: up the rules of the world and simulating possible outcomes 120 00:08:10,440 --> 00:08:14,280 Speaker 1: and evaluating each one of them, your brain can play 121 00:08:14,320 --> 00:08:19,880 Speaker 1: out scenarios without the risk and expense of attempting them physically. 122 00:08:20,280 --> 00:08:24,280 Speaker 1: For this reason, prediction of possible futures is one of 123 00:08:24,320 --> 00:08:30,440 Speaker 1: the highest priorities of biological systems. Now, fascinatingly, there hasn't 124 00:08:30,480 --> 00:08:34,240 Speaker 1: been that much direct study of how brains do this, 125 00:08:34,559 --> 00:08:39,360 Speaker 1: mostly because of the difficulty of observing it in action, 126 00:08:40,120 --> 00:08:44,680 Speaker 1: because the whole purpose of mental simulation is to make 127 00:08:44,840 --> 00:08:49,720 Speaker 1: action unnecessary, and the traditional way that we study the 128 00:08:49,760 --> 00:08:54,120 Speaker 1: brain is to correlate something in the brain with an 129 00:08:54,280 --> 00:08:58,320 Speaker 1: action and explicit behavior. So this is what makes it 130 00:08:58,720 --> 00:09:02,880 Speaker 1: challenging to study simulation of the future. Nonetheless, what I'm 131 00:09:02,920 --> 00:09:04,600 Speaker 1: gonna tell you about today is the way that we 132 00:09:04,640 --> 00:09:09,520 Speaker 1: can pull together scattered data to begin to understand how 133 00:09:09,640 --> 00:09:15,360 Speaker 1: brains build possible worlds. So let's get started. The important 134 00:09:15,400 --> 00:09:17,800 Speaker 1: place to start is with an idea that I've talked 135 00:09:17,800 --> 00:09:20,280 Speaker 1: about a lot on this podcast, the idea of the 136 00:09:20,480 --> 00:09:25,559 Speaker 1: internal model. The idea is that the brain's job isn't 137 00:09:25,640 --> 00:09:29,480 Speaker 1: just about detecting and reacting in real time, but it's 138 00:09:29,480 --> 00:09:34,600 Speaker 1: about constructing a model on the inside about what's happening 139 00:09:34,679 --> 00:09:38,480 Speaker 1: in the outside world. It's like you're running a simulation 140 00:09:38,720 --> 00:09:41,880 Speaker 1: there in the silence and the darkness of the brain. 141 00:09:42,679 --> 00:09:46,600 Speaker 1: And the key is that the internal model can emulate 142 00:09:46,960 --> 00:09:50,440 Speaker 1: possible scenarios. Now, one of the first places that we 143 00:09:50,559 --> 00:09:54,000 Speaker 1: see this kind of simulation get studied is with our 144 00:09:54,120 --> 00:09:58,240 Speaker 1: physical interactions with the world. For example, think about when 145 00:09:58,280 --> 00:10:01,560 Speaker 1: you hold a ketchup bar bottle in your left hand 146 00:10:01,840 --> 00:10:04,760 Speaker 1: and you pound it with your right hand to try 147 00:10:04,760 --> 00:10:08,440 Speaker 1: to get the ketchup to come out onto your French fries. Now, 148 00:10:08,480 --> 00:10:12,439 Speaker 1: when you do that, your left arm doesn't move very 149 00:10:12,559 --> 00:10:15,440 Speaker 1: much when you pound with your right hand because your 150 00:10:15,559 --> 00:10:18,720 Speaker 1: muscles tighten up. But just the right moment. Now, if 151 00:10:18,720 --> 00:10:21,080 Speaker 1: you want to try an interesting experiment, just hold the 152 00:10:21,120 --> 00:10:25,840 Speaker 1: ketchup bottle and have a friend pound the bottle, and 153 00:10:25,880 --> 00:10:28,839 Speaker 1: what you'll see is that your arm moves a lot. 154 00:10:28,960 --> 00:10:32,640 Speaker 1: You can't keep your arm still when somebody else is 155 00:10:32,720 --> 00:10:35,959 Speaker 1: hitting the bottle, But when you hit it, you are 156 00:10:36,000 --> 00:10:38,960 Speaker 1: the one making the action, and your brain knows how 157 00:10:38,960 --> 00:10:42,760 Speaker 1: to simulate what's about to happen in this case, that 158 00:10:42,800 --> 00:10:45,240 Speaker 1: there's about to be a lot of pressure on your arm, 159 00:10:45,800 --> 00:10:49,480 Speaker 1: and so it can deal in real time with counterbalancing. 160 00:10:49,559 --> 00:10:53,880 Speaker 1: That This is the brain predicting something simple that hasn't 161 00:10:53,920 --> 00:10:57,240 Speaker 1: actually happened yet. Your hand is about to hit the 162 00:10:57,280 --> 00:11:02,120 Speaker 1: bottle and your arm tenses up in expense. Now, why 163 00:11:02,160 --> 00:11:06,120 Speaker 1: does the brain care about prediction? Well, if your brain 164 00:11:06,200 --> 00:11:10,079 Speaker 1: can simulate things into the future that can speed up 165 00:11:10,400 --> 00:11:14,400 Speaker 1: your response time. And this is really useful for potentially 166 00:11:14,480 --> 00:11:17,679 Speaker 1: dangerous things, like if you need to dodge a rock 167 00:11:17,760 --> 00:11:20,160 Speaker 1: that's being thrown at you, or if you need to 168 00:11:20,640 --> 00:11:23,960 Speaker 1: catch some prey and you can figure out where it's heading. 169 00:11:24,559 --> 00:11:27,720 Speaker 1: This reminds us, of course, of the great hockey player 170 00:11:27,800 --> 00:11:31,880 Speaker 1: Wayne Gretzky, who said, I skate to where the puck 171 00:11:32,120 --> 00:11:37,559 Speaker 1: is going to be, not to where it has been. Now, 172 00:11:37,559 --> 00:11:39,760 Speaker 1: you don't have to be Wayne Gretzky for your brain 173 00:11:39,840 --> 00:11:43,520 Speaker 1: to be predicting the next step of everything around you. Why, 174 00:11:43,880 --> 00:11:50,800 Speaker 1: because your brain's architecture is built to anticipate everything in advance. 175 00:11:51,320 --> 00:11:55,400 Speaker 1: How does it do this Well, First, your nervous system 176 00:11:55,480 --> 00:11:59,640 Speaker 1: doesn't just send out motor commands like move your arm. 177 00:12:00,160 --> 00:12:05,160 Speaker 1: It also sends out copies of that motor command along 178 00:12:05,280 --> 00:12:08,920 Speaker 1: other telegraph wires to let other parts of the brain 179 00:12:09,080 --> 00:12:12,679 Speaker 1: know that the command was just sent out. So this 180 00:12:12,720 --> 00:12:16,200 Speaker 1: is what's called an efference copy, and that has all 181 00:12:16,320 --> 00:12:21,880 Speaker 1: kinds of effects on what happens next. For example, when 182 00:12:21,920 --> 00:12:25,120 Speaker 1: you move your eyes around, your eyes are jumping about 183 00:12:25,200 --> 00:12:29,679 Speaker 1: three times per second. The world seems to remain stable 184 00:12:29,880 --> 00:12:34,480 Speaker 1: visually and This is because of the efference copy that 185 00:12:34,600 --> 00:12:38,880 Speaker 1: tells the rest of your brain, okay, visual cortex, get ready. 186 00:12:38,920 --> 00:12:42,199 Speaker 1: The whole world is about to streak past to the left. 187 00:12:43,120 --> 00:12:46,720 Speaker 1: So your visual cortex, which is locked in darkness, isn't 188 00:12:46,800 --> 00:12:50,520 Speaker 1: surprised when the eyes suddenly make a jump and the 189 00:12:50,640 --> 00:12:55,120 Speaker 1: data is now all different. Now contrast that with what 190 00:12:55,280 --> 00:12:58,559 Speaker 1: happens if you get a friend to gently push your 191 00:12:58,600 --> 00:13:03,439 Speaker 1: eyeball from the outside. Now, the visual world appears to shift. 192 00:13:04,480 --> 00:13:07,920 Speaker 1: In the first case, when you are moving your eyes voluntarily, 193 00:13:08,440 --> 00:13:12,040 Speaker 1: the eference copy tells the brain, hey, a move is 194 00:13:12,080 --> 00:13:16,440 Speaker 1: coming up, and that suppresses visual motion detection. But in 195 00:13:16,480 --> 00:13:20,640 Speaker 1: the second case, when somebody pushes your eye, the absence 196 00:13:20,760 --> 00:13:24,760 Speaker 1: of an effherence copy means, hey, that movement isn't mine, 197 00:13:24,800 --> 00:13:28,960 Speaker 1: it's external, And so you perceive visual motion in the world. 198 00:13:29,679 --> 00:13:32,760 Speaker 1: And I'll give you another example. What happens every time 199 00:13:32,840 --> 00:13:36,920 Speaker 1: you blink your eyes. When you do that, for about 200 00:13:36,960 --> 00:13:40,400 Speaker 1: a tenth of a second, the world goes dark. But 201 00:13:40,520 --> 00:13:43,640 Speaker 1: you don't perceive that because you know it is coming. 202 00:13:43,720 --> 00:13:47,240 Speaker 1: Your brain systems that send out the command to blink 203 00:13:47,280 --> 00:13:51,600 Speaker 1: the eyelids also let the visual system know, hey, this 204 00:13:51,640 --> 00:13:55,160 Speaker 1: is what's about to happen. This way, your visual cortex 205 00:13:55,280 --> 00:14:00,400 Speaker 1: can anticipate that's about to happen, so the blink gets ignored. Now, 206 00:14:00,480 --> 00:14:02,640 Speaker 1: if you don't believe me, maybe you think the blink 207 00:14:02,720 --> 00:14:05,360 Speaker 1: is just too fast or something like that. Just sit 208 00:14:05,400 --> 00:14:08,160 Speaker 1: in a room and have your friend flick the light 209 00:14:08,240 --> 00:14:11,040 Speaker 1: switch really fast so that everything goes dark for a 210 00:14:11,080 --> 00:14:14,079 Speaker 1: tenth of a second. You can't miss that. It's really 211 00:14:14,160 --> 00:14:19,120 Speaker 1: obvious since it wasn't you that caused the darkness, you 212 00:14:19,680 --> 00:14:23,960 Speaker 1: notice it. So, as we see with the ketchup bottle 213 00:14:24,000 --> 00:14:28,040 Speaker 1: and eye movements and blinks, brains make predictions about the 214 00:14:28,120 --> 00:14:33,120 Speaker 1: consequences of your own actions. And a great example of 215 00:14:33,160 --> 00:14:37,520 Speaker 1: this is tickling. It turns out that if someone is 216 00:14:37,560 --> 00:14:41,359 Speaker 1: coming after you and sticking their fingers under your underarms, 217 00:14:41,560 --> 00:14:46,240 Speaker 1: it makes you laugh uncontrollably. It tickles. But I don't 218 00:14:46,240 --> 00:14:49,400 Speaker 1: know if you've ever tried this. It turns out you're 219 00:14:49,480 --> 00:14:54,880 Speaker 1: not able to tickle yourself. Why not, It's because of 220 00:14:54,920 --> 00:14:59,600 Speaker 1: the simple fact that your own actions are predictable by 221 00:14:59,640 --> 00:15:04,920 Speaker 1: your brain. You can't surprise your left underarm with your 222 00:15:05,000 --> 00:15:08,480 Speaker 1: right hand because your brain is the one driving the 223 00:15:08,560 --> 00:15:11,880 Speaker 1: fingers of the right hand, and it determines exactly when 224 00:15:11,920 --> 00:15:15,600 Speaker 1: to move the fingers, and so there is zero surprise 225 00:15:16,120 --> 00:15:23,760 Speaker 1: when the left underarm senses that for tickling, you require unpredictability. 226 00:15:23,840 --> 00:15:27,960 Speaker 1: That's the whole trick to a tickle. When people study 227 00:15:28,000 --> 00:15:32,040 Speaker 1: this with brain imaging fMRI, they find that when you 228 00:15:32,240 --> 00:15:36,760 Speaker 1: try to tickle yourself, you get activity in the primary 229 00:15:36,840 --> 00:15:40,840 Speaker 1: somatosensory cortex, meaning your brain is detecting that there's a 230 00:15:40,880 --> 00:15:44,320 Speaker 1: feeling there. But the activity doesn't go further. It doesn't 231 00:15:44,360 --> 00:15:49,120 Speaker 1: activate all these other downstream areas that come online when 232 00:15:49,200 --> 00:15:53,680 Speaker 1: someone else is tickling you, like the secondary somatosensory cortex 233 00:15:53,760 --> 00:15:59,000 Speaker 1: and the anterior singular cortex. Your brain sees the tickle 234 00:15:59,160 --> 00:16:05,120 Speaker 1: coming and discounts it. In fact, other neuroimaging studies find 235 00:16:05,520 --> 00:16:08,680 Speaker 1: that these brain areas that come online when you're getting tickled, 236 00:16:09,040 --> 00:16:13,320 Speaker 1: these same areas become active when you are simply anticipating 237 00:16:13,320 --> 00:16:17,600 Speaker 1: a tickle. When somebody's fingers are moving close, your somatis 238 00:16:17,600 --> 00:16:21,880 Speaker 1: sensory areas start to go to town. Now it turns 239 00:16:21,920 --> 00:16:26,120 Speaker 1: out there is one way that you can tickle yourself, 240 00:16:26,680 --> 00:16:29,400 Speaker 1: and that is if you build a little device that 241 00:16:29,560 --> 00:16:34,840 Speaker 1: inserts randomness so you can no longer predict the tickle. 242 00:16:35,240 --> 00:16:38,000 Speaker 1: So imagine you set up a little machine where you 243 00:16:38,080 --> 00:16:41,560 Speaker 1: are moving a lever around like a joystick, and that 244 00:16:41,600 --> 00:16:46,080 Speaker 1: controls a feather that tickles your left underarm. But in 245 00:16:46,160 --> 00:16:49,240 Speaker 1: between the movement of the lever and the movement of 246 00:16:49,280 --> 00:16:55,920 Speaker 1: the feather, the computer injects random time delays. That way, 247 00:16:56,360 --> 00:16:59,840 Speaker 1: your brain can't know when the movement in your under 248 00:17:00,400 --> 00:17:04,720 Speaker 1: is going to occur. And now you rescue the tickle. 249 00:17:05,080 --> 00:17:07,719 Speaker 1: With the help of a little bit of technology, you 250 00:17:07,760 --> 00:17:13,199 Speaker 1: can tickle yourself. Importantly, it also turns out there is 251 00:17:13,600 --> 00:17:17,520 Speaker 1: one group of people who are able to tickle themselves, 252 00:17:17,600 --> 00:17:22,080 Speaker 1: and that is people who are suffering from schizophrenia. So 253 00:17:22,119 --> 00:17:25,040 Speaker 1: in episode thirty three, I talked about my hypothesis that 254 00:17:25,080 --> 00:17:30,040 Speaker 1: schizophrenia might be in part or in whole a disorder 255 00:17:30,080 --> 00:17:34,240 Speaker 1: of time perception. So in this light, it's very instructive 256 00:17:34,280 --> 00:17:39,879 Speaker 1: that people with schizophrenia can tickle themselves. This suggests that 257 00:17:39,920 --> 00:17:44,600 Speaker 1: they're unable to predict their own actions and how those 258 00:17:44,640 --> 00:17:49,720 Speaker 1: actions will lead to the next sensations. This inability to 259 00:17:49,960 --> 00:17:53,560 Speaker 1: understand one's own actions, this is a general deficit that 260 00:17:53,600 --> 00:17:56,879 Speaker 1: we see in schizophrenia. People will have a hard time 261 00:17:57,640 --> 00:18:01,560 Speaker 1: distinguishing things they call from things they didn't cause. A 262 00:18:01,680 --> 00:18:05,639 Speaker 1: person with schizophrenia will say something like, my hand picks 263 00:18:05,720 --> 00:18:08,280 Speaker 1: up the paper clip, but I'm not the one controlling 264 00:18:08,280 --> 00:18:11,679 Speaker 1: my hand. What my hand does has nothing to do 265 00:18:11,760 --> 00:18:14,800 Speaker 1: with me. And of course you've heard of things like 266 00:18:15,160 --> 00:18:20,200 Speaker 1: auditory hallucinations in schizophrenia. In that other episode, I talked 267 00:18:20,200 --> 00:18:24,400 Speaker 1: about how we all have an internal dialogue. You generate 268 00:18:24,440 --> 00:18:27,320 Speaker 1: a voice and you listen to it, and in schizophrenia, 269 00:18:27,760 --> 00:18:30,920 Speaker 1: the timing seems to be slightly off such that the 270 00:18:30,960 --> 00:18:37,640 Speaker 1: internal voice gets attributed to somebody else. Interestingly, my colleague 271 00:18:37,720 --> 00:18:41,840 Speaker 1: Chris Frith and his collaborators ran a study with people 272 00:18:41,840 --> 00:18:44,959 Speaker 1: who had schizophrenia, and they found that if a person 273 00:18:45,600 --> 00:18:50,359 Speaker 1: has schizophrenia but does not have auditory hallucinations, then they 274 00:18:50,359 --> 00:18:54,000 Speaker 1: are more ticklish when other people tickle them. But if 275 00:18:54,000 --> 00:18:59,320 Speaker 1: a person with schizophrenia does have auditory hallucinations, then they 276 00:18:59,359 --> 00:19:03,359 Speaker 1: can tickle them themselves. They judge no difference between someone 277 00:19:03,440 --> 00:19:07,359 Speaker 1: else tickling them and them tickling themselves. They are no 278 00:19:07,480 --> 00:19:29,560 Speaker 1: longer making the appropriate predictions. Okay, so this gives us 279 00:19:29,560 --> 00:19:33,480 Speaker 1: a sense of how our brains, under normal circumstances work 280 00:19:33,600 --> 00:19:37,760 Speaker 1: to constantly make predictions. And it's not just about predicting 281 00:19:37,880 --> 00:19:41,840 Speaker 1: things about your own actions and sensations, but more generally 282 00:19:42,320 --> 00:19:45,600 Speaker 1: about anything to do with the outside world. And the 283 00:19:45,720 --> 00:19:49,359 Speaker 1: key is that our brains are not simply reactive, but 284 00:19:49,440 --> 00:19:54,440 Speaker 1: they have these internal loops that are constantly making predictions 285 00:19:54,840 --> 00:19:59,360 Speaker 1: about what comes next. And having this kind of architecture 286 00:20:00,000 --> 00:20:04,000 Speaker 1: it allows brains to do not just stimulus response, but 287 00:20:04,040 --> 00:20:09,160 Speaker 1: instead to make predictions ahead of actual sensory input. So 288 00:20:09,200 --> 00:20:12,159 Speaker 1: think about trying to catch a baseball that someone is 289 00:20:12,240 --> 00:20:16,600 Speaker 1: tossing to you. If your brain was just doing feed 290 00:20:16,760 --> 00:20:20,800 Speaker 1: forward analysis of these signals, you couldn't catch the ball. 291 00:20:20,840 --> 00:20:24,320 Speaker 1: There'd be a delay of hundreds of milliseconds from the 292 00:20:24,359 --> 00:20:27,919 Speaker 1: time that the light strikes your eye until you could 293 00:20:28,200 --> 00:20:31,840 Speaker 1: execute the motor command of putting your hand up, and 294 00:20:32,040 --> 00:20:34,440 Speaker 1: your hand would always be reaching for a place where 295 00:20:34,480 --> 00:20:37,480 Speaker 1: the ball used to be. We are able to catch 296 00:20:37,520 --> 00:20:43,119 Speaker 1: baseballs because we have deeply hardwired internal models of physics, 297 00:20:43,520 --> 00:20:47,520 Speaker 1: and these internal models generate expectations about when and where 298 00:20:47,960 --> 00:20:51,480 Speaker 1: the ball is going to hit. It's making predictions about 299 00:20:51,800 --> 00:20:56,160 Speaker 1: the future. Now, how does this sort of prediction play 300 00:20:56,200 --> 00:20:59,200 Speaker 1: out in your daily life? Because it's not just about 301 00:20:59,359 --> 00:21:04,240 Speaker 1: hitting catch a bottles and catching baseballs. But prediction applies 302 00:21:04,400 --> 00:21:07,960 Speaker 1: to every decision you make about what you're going to do. 303 00:21:08,720 --> 00:21:11,000 Speaker 1: Let's say you're trying to figure out what you need 304 00:21:11,040 --> 00:21:12,760 Speaker 1: to do in an hour from now. You have a 305 00:21:12,800 --> 00:21:15,720 Speaker 1: bunch of things on your to do list, but given 306 00:21:15,760 --> 00:21:18,520 Speaker 1: the constraints of space and time, you can't do everything 307 00:21:18,560 --> 00:21:23,520 Speaker 1: at once, and so life is a constant series of choices. 308 00:21:23,920 --> 00:21:26,199 Speaker 1: So let's say you're looking at these choices. One you 309 00:21:26,800 --> 00:21:29,399 Speaker 1: have to write a very long and specific email for 310 00:21:29,440 --> 00:21:33,400 Speaker 1: your boss. Two you're thinking of going downtown to get 311 00:21:33,400 --> 00:21:37,720 Speaker 1: a boba tee, or three you're considering going to the gym, 312 00:21:37,800 --> 00:21:40,000 Speaker 1: which you've been promising yourself that you're going to do 313 00:21:40,040 --> 00:21:43,000 Speaker 1: for some days now. So how does the choice actually 314 00:21:43,000 --> 00:21:46,520 Speaker 1: get made in the brain. As far as our conscious 315 00:21:46,640 --> 00:21:49,440 Speaker 1: minds go, we get very little access to the details. 316 00:21:49,440 --> 00:21:52,040 Speaker 1: It just seems like, oh, okay, I've decided to do 317 00:21:52,480 --> 00:21:55,080 Speaker 1: this instead of that, but you don't necessarily know why. 318 00:21:55,920 --> 00:21:59,080 Speaker 1: But the last several decades of neuroscience have surfaced how 319 00:21:59,160 --> 00:22:05,800 Speaker 1: this actually happenins We run the simulations and we feel them. 320 00:22:06,160 --> 00:22:09,520 Speaker 1: So when you think about writing that email, your brain 321 00:22:09,600 --> 00:22:12,919 Speaker 1: actually runs the little movie of you doing that, and 322 00:22:13,000 --> 00:22:16,080 Speaker 1: possibly the ache that you might feel in your shoulder 323 00:22:16,160 --> 00:22:20,040 Speaker 1: from typing too much, and also the satisfaction at finishing 324 00:22:20,080 --> 00:22:24,160 Speaker 1: that task. Then your brain runs the simulation of going 325 00:22:24,240 --> 00:22:28,240 Speaker 1: and getting the boba t how delicious that will be 326 00:22:28,440 --> 00:22:32,280 Speaker 1: and how satisfying it'll be. And you also run the 327 00:22:32,320 --> 00:22:35,680 Speaker 1: simulation of going to the gym. It's gonna be a 328 00:22:35,760 --> 00:22:38,439 Speaker 1: little costly for you in terms of time, and it 329 00:22:38,520 --> 00:22:41,080 Speaker 1: might make your muscles hurt, but boy, are you gonna 330 00:22:41,080 --> 00:22:45,360 Speaker 1: feel great when you're done. You'll feel so satisfied. Now, 331 00:22:45,400 --> 00:22:49,080 Speaker 1: what happens is your brain runs all these simulations and 332 00:22:49,160 --> 00:22:53,320 Speaker 1: you feel the emotions with each one. Now again, this 333 00:22:53,400 --> 00:22:57,080 Speaker 1: happens essentially entirely under the hood. For most of the 334 00:22:57,680 --> 00:23:01,240 Speaker 1: decisions you make in life. You have no conscious access 335 00:23:01,240 --> 00:23:04,840 Speaker 1: to how you made the final call, But your brain 336 00:23:05,000 --> 00:23:08,840 Speaker 1: is simulating the possibilities, and you experience each one with 337 00:23:08,920 --> 00:23:13,439 Speaker 1: your emotions and often with physical sensations. And this is 338 00:23:13,600 --> 00:23:18,280 Speaker 1: how we weigh choices against one another. This is how 339 00:23:18,600 --> 00:23:22,320 Speaker 1: we determine our paths in life. You feel the pain 340 00:23:22,440 --> 00:23:25,919 Speaker 1: or the pleasure from your predicted futures. You think of 341 00:23:26,000 --> 00:23:30,200 Speaker 1: yourself in future times, and you get to live out 342 00:23:30,240 --> 00:23:35,120 Speaker 1: those little movies. Now like everything in our brains. We 343 00:23:35,359 --> 00:23:38,960 Speaker 1: think this just automatically works, but in fact we have 344 00:23:39,560 --> 00:23:43,239 Speaker 1: very particular networks that need to be in place and 345 00:23:43,320 --> 00:23:46,520 Speaker 1: working well for this to function. And the reason we 346 00:23:46,640 --> 00:23:50,359 Speaker 1: know this is because some people get damage to a 347 00:23:50,560 --> 00:23:52,879 Speaker 1: part in the front of their brain, the venture medial 348 00:23:52,920 --> 00:23:57,399 Speaker 1: prefrontal cortex, and they end up displaying a very strange 349 00:23:57,400 --> 00:24:00,240 Speaker 1: and unexpected symptom, which is, if you give them some 350 00:24:00,359 --> 00:24:03,040 Speaker 1: choice to make, like which restaurant do you want to 351 00:24:03,080 --> 00:24:07,280 Speaker 1: go to tonight, they can articulate everything about the decision, 352 00:24:07,880 --> 00:24:12,000 Speaker 1: but they can't decide. They can't land on a decision. 353 00:24:12,720 --> 00:24:15,840 Speaker 1: So what's going on here? Well, patients like this have 354 00:24:15,920 --> 00:24:20,440 Speaker 1: been studied by neurologists like Antonio Demacio and his colleagues, 355 00:24:21,040 --> 00:24:24,720 Speaker 1: and what's happening is that their brain can run a 356 00:24:24,960 --> 00:24:31,120 Speaker 1: quick future simulation, but it has become disconnected from the emotions. 357 00:24:31,280 --> 00:24:35,680 Speaker 1: So the different future simulations all feel the same way. 358 00:24:35,680 --> 00:24:38,719 Speaker 1: They all feel neutral, and therefore there's no way to 359 00:24:38,920 --> 00:24:43,720 Speaker 1: distinguish any choice from any other. In other words, you 360 00:24:43,840 --> 00:24:47,760 Speaker 1: need to feel the outcome of a simulation in order 361 00:24:47,840 --> 00:24:52,480 Speaker 1: to do decision making. So it turns out that under 362 00:24:52,520 --> 00:24:57,679 Speaker 1: normal circumstances, there's a core network of brain areas that 363 00:24:57,720 --> 00:25:02,960 Speaker 1: are involved in prospect, which just means seeing ahead. So 364 00:25:03,000 --> 00:25:04,800 Speaker 1: I want to give you a very quick sense of this. 365 00:25:05,840 --> 00:25:08,119 Speaker 1: So you've got this area of your brain, the venture 366 00:25:08,200 --> 00:25:12,720 Speaker 1: medial prefrontal cortex, which connects to areas involved in emotion 367 00:25:13,560 --> 00:25:16,760 Speaker 1: like the anterior insula and the amignla, and it also 368 00:25:16,920 --> 00:25:19,960 Speaker 1: connects to lots of other areas in the brain. And 369 00:25:20,160 --> 00:25:24,000 Speaker 1: activity in this area essentially specifies the things that are 370 00:25:24,119 --> 00:25:27,600 Speaker 1: pertinent to your current needs and goals, and this is 371 00:25:27,640 --> 00:25:34,080 Speaker 1: what guides the construction of relevant scenarios. Now, there are 372 00:25:34,080 --> 00:25:36,199 Speaker 1: a number of other brain areas that show up in 373 00:25:36,240 --> 00:25:40,160 Speaker 1: this core network. One is called the precuneus, and this 374 00:25:40,400 --> 00:25:44,520 Speaker 1: maps the locations of things in space, so its involvement 375 00:25:45,000 --> 00:25:50,520 Speaker 1: contributes to a spatial context for imagine scenarios and the 376 00:25:50,960 --> 00:25:56,480 Speaker 1: features inside of that. There's another area called the temporopridal junction, 377 00:25:56,760 --> 00:26:00,320 Speaker 1: and you see that area become active during the detection 378 00:26:01,080 --> 00:26:04,720 Speaker 1: of targets or events around you that are relevant to 379 00:26:04,760 --> 00:26:08,879 Speaker 1: what you're trying to do at this moment. This area 380 00:26:08,920 --> 00:26:11,679 Speaker 1: seems to run and do the same thing even in 381 00:26:11,800 --> 00:26:16,360 Speaker 1: your imagined scenarios. And then you've got an area called 382 00:26:16,400 --> 00:26:20,200 Speaker 1: the superior temporal sulcus, which is involved with lots of things, 383 00:26:20,240 --> 00:26:24,239 Speaker 1: but one of them is about interpreting social cues. So 384 00:26:24,320 --> 00:26:28,520 Speaker 1: one idea is that this area helps to specify other 385 00:26:28,880 --> 00:26:34,439 Speaker 1: individuals in their actions within imagined scenes. And then you 386 00:26:34,520 --> 00:26:38,159 Speaker 1: have the hippocampus. And one thing that's known is that 387 00:26:38,200 --> 00:26:41,199 Speaker 1: when you get damage to the hippocampus, that seems to 388 00:26:41,920 --> 00:26:46,840 Speaker 1: mess up all of the spatial coherence of a recalled 389 00:26:47,040 --> 00:26:52,440 Speaker 1: or imagined scene. So patients with hippocampal damage they can't 390 00:26:52,520 --> 00:26:58,479 Speaker 1: picture a specific place or detailed surrounding events. Here's one 391 00:26:58,520 --> 00:26:59,400 Speaker 1: way to think about this. 392 00:27:00,000 --> 00:27:04,560 Speaker 2: When you move in a virtual reality world, the goggles 393 00:27:04,680 --> 00:27:08,920 Speaker 2: keep track of where you are and all the objects 394 00:27:08,960 --> 00:27:11,560 Speaker 2: and how things change when you move. 395 00:27:12,080 --> 00:27:14,200 Speaker 1: And this is similar to what's going on in the brain. 396 00:27:14,280 --> 00:27:18,760 Speaker 1: You have special cells in the hippocampus called place cells, 397 00:27:19,280 --> 00:27:23,320 Speaker 1: which help to translate everything into a framework where you 398 00:27:23,359 --> 00:27:26,359 Speaker 1: are at the center of it. And the idea is 399 00:27:26,400 --> 00:27:31,640 Speaker 1: that these cells are critical to your imagination of scenes. 400 00:27:31,880 --> 00:27:37,240 Speaker 1: So as you virtually move through your imagined scene, the 401 00:27:37,320 --> 00:27:43,280 Speaker 1: hippocampal play cells keep the scene coherent and consistent, just 402 00:27:43,359 --> 00:27:47,199 Speaker 1: like the VR goggles would. So this all suggests that 403 00:27:47,240 --> 00:27:51,639 Speaker 1: the hippocampus is crucial for tying together the activity of 404 00:27:51,760 --> 00:27:58,480 Speaker 1: other brain areas to construct this rich and coherent imaginary experience. 405 00:28:00,040 --> 00:28:03,840 Speaker 1: You have this brainwide network of areas that are involved 406 00:28:03,960 --> 00:28:08,920 Speaker 1: when you are imagining future scenarios, and all of this 407 00:28:09,000 --> 00:28:12,480 Speaker 1: is what helps you to experience the movie and to 408 00:28:12,760 --> 00:28:17,760 Speaker 1: feel the emotions. If you are just a robot who 409 00:28:17,880 --> 00:28:21,080 Speaker 1: rolled into a room, you would just sit there because 410 00:28:21,400 --> 00:28:26,280 Speaker 1: you wouldn't have any particular reason to prioritize writing the 411 00:28:26,320 --> 00:28:30,080 Speaker 1: email versus getting the Boba tee versus going to the gym. 412 00:28:30,480 --> 00:28:34,439 Speaker 1: You would have no way to weigh these against one another. 413 00:28:35,240 --> 00:28:39,400 Speaker 1: But we assign feeling to all of our future scenarios. 414 00:28:39,880 --> 00:28:43,160 Speaker 1: So the brain makes predictions. But how does it know 415 00:28:43,880 --> 00:28:49,360 Speaker 1: how to improve these through time. Well, imagine a fire 416 00:28:49,400 --> 00:28:52,160 Speaker 1: department in a city, and every time a fire occurs 417 00:28:52,200 --> 00:28:55,080 Speaker 1: in the city, they go wailing out of the station 418 00:28:55,240 --> 00:28:58,000 Speaker 1: to take care of it, and they suspect that a 419 00:28:58,040 --> 00:29:00,720 Speaker 1: lot of the fires are going to happen around the 420 00:29:00,760 --> 00:29:04,120 Speaker 1: warehouse district, so they put their trucks there so they 421 00:29:04,160 --> 00:29:08,120 Speaker 1: can take care of things quickly. But eventually they realize 422 00:29:08,440 --> 00:29:11,880 Speaker 1: that their prediction was wrong. It's actually another part of 423 00:29:11,920 --> 00:29:16,560 Speaker 1: the city that keeps catching fire. So the area at 424 00:29:16,600 --> 00:29:19,080 Speaker 1: the foot of the mountains where the trees are dense 425 00:29:19,720 --> 00:29:24,320 Speaker 1: and interwoven with power lines, So the fire department starts 426 00:29:24,320 --> 00:29:28,480 Speaker 1: putting their resources there where they need to be in advance, 427 00:29:28,920 --> 00:29:32,080 Speaker 1: and that reduces the energy they need to expend every 428 00:29:32,120 --> 00:29:35,000 Speaker 1: time there's a new fire because they're no longer being 429 00:29:35,440 --> 00:29:37,920 Speaker 1: reactive to fires at the foot of the mountain, but 430 00:29:37,960 --> 00:29:42,080 Speaker 1: now they're making good predictions about it. So cities do 431 00:29:42,160 --> 00:29:44,360 Speaker 1: this kind of thing, by the way, in terms of fires, 432 00:29:44,600 --> 00:29:47,120 Speaker 1: in terms of where they expect crime is going to occur, 433 00:29:47,520 --> 00:29:50,160 Speaker 1: in terms of where and when the power usage is 434 00:29:50,200 --> 00:29:54,600 Speaker 1: going to happen. Everything. So the key about this example 435 00:29:54,760 --> 00:29:58,160 Speaker 1: is that the fire department's first predictions weren't so great, 436 00:29:58,600 --> 00:30:01,400 Speaker 1: and the data tells them, oh, that could be a 437 00:30:01,440 --> 00:30:04,760 Speaker 1: lot better. It tells them that something could be adjusted. 438 00:30:05,520 --> 00:30:07,880 Speaker 1: And that is the same thing that happens in the 439 00:30:08,000 --> 00:30:12,400 Speaker 1: brain all the time. The brain tries to predict everything, 440 00:30:13,240 --> 00:30:17,560 Speaker 1: and it pays attention to what's called the prediction error, 441 00:30:18,080 --> 00:30:21,440 Speaker 1: which means the difference between what it thought would happen 442 00:30:21,600 --> 00:30:25,880 Speaker 1: and what actually happened. And you see various cells in 443 00:30:25,920 --> 00:30:29,160 Speaker 1: the brain, for example, in the dopamine system that are 444 00:30:29,240 --> 00:30:35,920 Speaker 1: responding not to the reward or punishment, but the prediction error. 445 00:30:36,120 --> 00:30:39,400 Speaker 1: In other words, how different the reward or punishment was 446 00:30:39,920 --> 00:30:45,120 Speaker 1: from what was expected. And these dopamine systems broadcast their 447 00:30:45,160 --> 00:30:49,120 Speaker 1: signals all across the territory of the brain to announce 448 00:30:49,480 --> 00:30:52,720 Speaker 1: that the prediction wasn't quite right, there was a prediction error, 449 00:30:53,120 --> 00:30:58,680 Speaker 1: and therefore something needs to be adjusted. So our brain 450 00:30:58,880 --> 00:31:03,280 Speaker 1: has the architecture to make predictions and adjust them all 451 00:31:03,320 --> 00:31:05,880 Speaker 1: the time. And what is all this architecture of the 452 00:31:05,880 --> 00:31:11,600 Speaker 1: brain tell us. It tells us that the brain craves predictability. 453 00:31:12,280 --> 00:31:15,520 Speaker 1: Now why does it care about predictability? Well, first of 454 00:31:15,560 --> 00:31:20,280 Speaker 1: all because of energy efficiency. Because if you can predict 455 00:31:20,400 --> 00:31:23,239 Speaker 1: that something is going to happen, then you don't have 456 00:31:23,320 --> 00:31:25,960 Speaker 1: to burn up all this neural energy on it. You 457 00:31:26,000 --> 00:31:29,280 Speaker 1: already know it's coming. But if something is a surprise, 458 00:31:29,960 --> 00:31:34,640 Speaker 1: the brain turns its vast attentional mechanisms to it and 459 00:31:34,760 --> 00:31:37,000 Speaker 1: has to burn a lot of calories on understanding what 460 00:31:37,080 --> 00:31:41,600 Speaker 1: the heck just happened and eventually reshaping the internal model 461 00:31:41,880 --> 00:31:44,959 Speaker 1: to account for that. In the future, all of this 462 00:31:45,000 --> 00:31:47,760 Speaker 1: would be fine. Maybe if we could plug ourselves into 463 00:31:47,800 --> 00:31:52,280 Speaker 1: a wall, but instead we are mobile creatures who run 464 00:31:52,360 --> 00:31:56,280 Speaker 1: on batteries. We have to constantly find food sources and 465 00:31:56,360 --> 00:31:58,479 Speaker 1: stick them in our mouth so that we can have 466 00:31:58,600 --> 00:32:01,720 Speaker 1: enough energy to get to the next food source. So 467 00:32:01,840 --> 00:32:06,440 Speaker 1: mother nature evolved us to be highly efficient creatures. And 468 00:32:06,480 --> 00:32:11,080 Speaker 1: what we do is we make ourselves massively efficient by 469 00:32:11,200 --> 00:32:16,360 Speaker 1: predicting away the future. And this is, by the way, 470 00:32:16,560 --> 00:32:20,680 Speaker 1: why the method of torture referred to as the Chinese 471 00:32:20,760 --> 00:32:24,760 Speaker 1: water torture is so aversive to us. The idea is 472 00:32:24,800 --> 00:32:27,720 Speaker 1: that a drop of cold water drips onto your head, 473 00:32:28,320 --> 00:32:32,080 Speaker 1: and then let's say five seconds later, the next draw hits, 474 00:32:32,560 --> 00:32:35,479 Speaker 1: and then the next one three seconds later, and then 475 00:32:35,520 --> 00:32:39,920 Speaker 1: the next drop eight seconds slater, and the next one 476 00:32:39,960 --> 00:32:43,680 Speaker 1: six seconds slater, and then four seconds and suddenly one second, 477 00:32:43,800 --> 00:32:47,440 Speaker 1: and you get the idea. It's unpredictable. Your brain is 478 00:32:47,640 --> 00:32:52,080 Speaker 1: constantly trying to say when an event is going to happen, 479 00:32:52,480 --> 00:32:55,480 Speaker 1: and it's constantly having to pay attention here because it 480 00:32:55,520 --> 00:32:59,400 Speaker 1: can't make a good prediction. And perhaps you've never experienced 481 00:32:59,440 --> 00:33:03,080 Speaker 1: that form of torture explicitly that most of us have 482 00:33:03,200 --> 00:33:06,280 Speaker 1: at some point in our lives, had a leaky faucet 483 00:33:06,400 --> 00:33:08,720 Speaker 1: at our house, and this can often be just as 484 00:33:08,800 --> 00:33:14,600 Speaker 1: bad if it never falls into a rhythm, it goes drip, drip, drip. 485 00:33:16,680 --> 00:33:20,360 Speaker 1: We love rhythm because we can predict it away, and 486 00:33:20,440 --> 00:33:28,160 Speaker 1: anything that is unpredictable continues to demand all our attention. Now, 487 00:33:28,200 --> 00:33:30,960 Speaker 1: I'll just mention that my colleagues and I have proposed 488 00:33:30,960 --> 00:33:35,920 Speaker 1: in various places that maybe the brain activity that we see, 489 00:33:35,960 --> 00:33:40,400 Speaker 1: the spikes and neurons, these represent just the part of 490 00:33:40,440 --> 00:33:46,360 Speaker 1: the world that is unpredicted. In other words, silence is golden, 491 00:33:46,560 --> 00:33:49,840 Speaker 1: and the brain spends most of its efforts trying to 492 00:33:50,480 --> 00:33:54,240 Speaker 1: make perfect predictions of the world and burn that down 493 00:33:54,320 --> 00:33:56,960 Speaker 1: into the circuitry of the brain so it doesn't have 494 00:33:57,040 --> 00:34:00,360 Speaker 1: to use any activity. Now, of course, of course, the 495 00:34:00,360 --> 00:34:03,840 Speaker 1: world is way too complicated to ever reach perfect prediction. 496 00:34:04,120 --> 00:34:08,799 Speaker 1: Everything changes all the time, and so the speculation is 497 00:34:08,840 --> 00:34:12,040 Speaker 1: that the activity that we can measure in the brain, 498 00:34:12,560 --> 00:34:16,400 Speaker 1: whether that's with electrodes or fMRI or whatever, really that 499 00:34:16,480 --> 00:34:20,480 Speaker 1: activity just represents the surprise, the thing that the brain 500 00:34:20,520 --> 00:34:25,120 Speaker 1: didn't see coming. In other words, if you show a 501 00:34:25,600 --> 00:34:28,520 Speaker 1: yellow ball to a monkey and you find cells in 502 00:34:28,600 --> 00:34:32,759 Speaker 1: his brain, say in the visual cortex that respond vigorously 503 00:34:32,920 --> 00:34:37,440 Speaker 1: to that visual thing, then we say those cells prefer 504 00:34:38,160 --> 00:34:41,200 Speaker 1: yellow balls, or more colloquially, we say it likes the 505 00:34:41,280 --> 00:34:44,879 Speaker 1: yellow ball. But could it just be that the appearance 506 00:34:44,920 --> 00:34:48,120 Speaker 1: of a yellow ball was unpredicted by the system and 507 00:34:48,200 --> 00:34:53,040 Speaker 1: the cells activity is merely a reflection of that. This 508 00:34:53,160 --> 00:34:55,960 Speaker 1: is consistent with the fact that if you hide the 509 00:34:56,000 --> 00:34:59,160 Speaker 1: ball and then reshow it to the monkey five seconds later, 510 00:34:59,640 --> 00:35:04,080 Speaker 1: and then you do that again and again, the response diminishes. 511 00:35:04,360 --> 00:35:08,120 Speaker 1: This is known as repetition suppression, and it's not merely 512 00:35:08,160 --> 00:35:11,879 Speaker 1: about fatigue of the cell. Instead, it's the fact that 513 00:35:11,920 --> 00:35:15,319 Speaker 1: the monkey's brain knows that you're about to show the 514 00:35:15,360 --> 00:35:18,719 Speaker 1: stupid ball again, and so it has a prediction of 515 00:35:18,760 --> 00:35:22,120 Speaker 1: what is coming. And when it knows what is coming, 516 00:35:22,120 --> 00:35:25,560 Speaker 1: it doesn't have to burn any energy. We'll come back 517 00:35:25,600 --> 00:35:28,640 Speaker 1: to this in terms of our deep desired to have 518 00:35:28,800 --> 00:35:33,200 Speaker 1: predictions about our lives in just a few moments. But 519 00:35:33,320 --> 00:35:36,800 Speaker 1: first I want to ask can species other than human 520 00:35:36,920 --> 00:35:43,520 Speaker 1: beings engage in prospection and imagination? This question is difficult 521 00:35:43,520 --> 00:35:48,200 Speaker 1: to answer because animals can't verbally report their experiences to us. 522 00:35:48,680 --> 00:35:52,640 Speaker 1: Some researchers think, well, maybe animals lack the capacity for 523 00:35:52,719 --> 00:35:55,840 Speaker 1: this sort of thing, but in fact they do share 524 00:35:56,000 --> 00:35:59,240 Speaker 1: much of the same circuitry that we've been talking about, 525 00:35:59,680 --> 00:36:03,799 Speaker 1: and careful observation their behavior suggests that they have some 526 00:36:04,120 --> 00:36:10,560 Speaker 1: features of episodic memory and prospection. For example, look at 527 00:36:10,600 --> 00:36:14,640 Speaker 1: the scrubjay, which is a bird that stores food away. 528 00:36:15,200 --> 00:36:18,720 Speaker 1: It can recall not just where it hit a particular item, 529 00:36:19,000 --> 00:36:23,799 Speaker 1: but also what that item was and when it was stored. Now, 530 00:36:23,880 --> 00:36:28,120 Speaker 1: skeptics say, okay, look, maybe this is just procedural memory. 531 00:36:28,160 --> 00:36:31,200 Speaker 1: It's like a basic algorithm that's running. It's not conscious, 532 00:36:31,480 --> 00:36:34,440 Speaker 1: and it's just driven by the needs of the moment. 533 00:36:35,280 --> 00:36:38,560 Speaker 1: But the scrub jays also appear to cash food in 534 00:36:38,600 --> 00:36:43,200 Speaker 1: a way that reflects anticipated future needs. It's not just 535 00:36:43,239 --> 00:36:47,720 Speaker 1: their current motivational state. And when you look at rats, 536 00:36:48,120 --> 00:36:53,040 Speaker 1: when people do direct recordings of the hippocampus, that suggests 537 00:36:53,160 --> 00:36:58,200 Speaker 1: that they too might engage in prospection and recollection. So 538 00:36:58,480 --> 00:37:03,279 Speaker 1: as a rat move through adjacent places, you have these 539 00:37:03,360 --> 00:37:07,759 Speaker 1: hippocampal play cells that fire off in sequences, and these 540 00:37:07,880 --> 00:37:13,200 Speaker 1: play cells sometimes replay the activity sequence when the rat 541 00:37:13,280 --> 00:37:16,920 Speaker 1: is not moving, sometimes even when the rat is sleeping 542 00:37:17,120 --> 00:37:20,719 Speaker 1: after the experiment is over, and these cells can also 543 00:37:21,480 --> 00:37:25,480 Speaker 1: pre play a sequence of activity before the rat has 544 00:37:25,480 --> 00:37:28,920 Speaker 1: started to move along the route. So, for example, if 545 00:37:28,960 --> 00:37:32,239 Speaker 1: the rat has to go down a hallway and then 546 00:37:32,280 --> 00:37:35,160 Speaker 1: turn right or left, this is called a te maze, 547 00:37:35,480 --> 00:37:38,920 Speaker 1: and it's trying to decide which path to take. You 548 00:37:38,960 --> 00:37:43,480 Speaker 1: can see these play cells pre play one route and 549 00:37:43,520 --> 00:37:49,640 Speaker 1: then the other, as if in consideration of both these scenarios. Now, 550 00:37:49,680 --> 00:37:53,640 Speaker 1: this research is still early, but these kinds of findings 551 00:37:53,719 --> 00:37:56,920 Speaker 1: might increasingly point us in the direction of at least 552 00:37:57,040 --> 00:38:02,440 Speaker 1: roughly gauging whether our animal cuts and have internal experiences 553 00:38:03,080 --> 00:38:23,239 Speaker 1: of time travel the way that we do. So what 554 00:38:23,280 --> 00:38:26,080 Speaker 1: I've told you so far is how the brain predicts. 555 00:38:26,719 --> 00:38:30,080 Speaker 1: But how does it know how to do this? How 556 00:38:30,120 --> 00:38:35,280 Speaker 1: does it make good predictions about the world. So suppose 557 00:38:35,320 --> 00:38:38,239 Speaker 1: you're hungry and you decide you're going to get something 558 00:38:38,239 --> 00:38:41,680 Speaker 1: to eat. Where should you go? You need to have 559 00:38:41,840 --> 00:38:44,760 Speaker 1: a map in your head of all the nearby choices. 560 00:38:44,840 --> 00:38:48,320 Speaker 1: Then you have to decide which one would most likely 561 00:38:48,600 --> 00:38:53,560 Speaker 1: satisfy your current craving. And so to plan your excursion 562 00:38:54,160 --> 00:38:57,680 Speaker 1: you need to go through your past experiences of meals 563 00:38:58,080 --> 00:39:00,600 Speaker 1: and the places on your map. So you've got that 564 00:39:00,920 --> 00:39:05,200 Speaker 1: inexpensive tie restaurant which is the closest, but the food 565 00:39:05,280 --> 00:39:08,680 Speaker 1: there you remember, was too spicy for your taste. And 566 00:39:08,760 --> 00:39:11,520 Speaker 1: the food truck over there they make great burritos, but 567 00:39:11,560 --> 00:39:14,920 Speaker 1: it always has a really long line in your past experience. 568 00:39:15,440 --> 00:39:19,120 Speaker 1: And the fast food joint over here has fries that 569 00:39:19,200 --> 00:39:22,200 Speaker 1: are a little greasy, but you'd rather take the grease 570 00:39:22,280 --> 00:39:26,320 Speaker 1: than the spice or the long lines. So, putting together 571 00:39:26,440 --> 00:39:32,160 Speaker 1: the experiences of your recalled past and your imagined future, 572 00:39:32,920 --> 00:39:36,839 Speaker 1: you decide on your option. But the past and the 573 00:39:36,880 --> 00:39:41,320 Speaker 1: future are intertwined in most of our decisions. In other words, 574 00:39:41,880 --> 00:39:45,640 Speaker 1: the thing that allows the brain to construct possible futures 575 00:39:46,080 --> 00:39:50,719 Speaker 1: is memory. Memory is what allows us to write down 576 00:39:50,760 --> 00:39:55,480 Speaker 1: information and then use that as building blocks to build 577 00:39:55,480 --> 00:39:59,520 Speaker 1: out our future scenarios. Now, interestingly, that's not even a 578 00:39:59,560 --> 00:40:04,000 Speaker 1: new idea. Aristotle suspected this, as did Galen and all 579 00:40:04,040 --> 00:40:09,680 Speaker 1: their medieval commentators. They all emphasized memory as the key 580 00:40:09,760 --> 00:40:14,560 Speaker 1: tool in making successful predictions for the future. And in fact, 581 00:40:14,640 --> 00:40:18,239 Speaker 1: something that I find very interesting, presumably coincidental but maybe not, 582 00:40:19,040 --> 00:40:23,920 Speaker 1: is that in Greek mythology, the goddess of memory, Nemazine, 583 00:40:24,760 --> 00:40:28,680 Speaker 1: is the mother of the nine muses, who are the 584 00:40:28,719 --> 00:40:33,480 Speaker 1: goddesses who spark the imagination. In other words, memory is 585 00:40:33,520 --> 00:40:38,440 Speaker 1: the mother of imagination. Now, my friend and colleague Jeff 586 00:40:38,480 --> 00:40:42,480 Speaker 1: Hawkins made the argument that what we call intelligence boils 587 00:40:42,520 --> 00:40:45,640 Speaker 1: down to the brain's ability to make good predictions about 588 00:40:45,680 --> 00:40:50,319 Speaker 1: the world based on stored memories. In his version of 589 00:40:50,360 --> 00:40:55,960 Speaker 1: the memory prediction paradigm, the cortex is a pattern recognition 590 00:40:56,120 --> 00:41:01,640 Speaker 1: machine that breaks complicated events into smaller bunks. It stores 591 00:41:01,719 --> 00:41:05,440 Speaker 1: experiences in a way that reflects the structure of the world, 592 00:41:05,440 --> 00:41:10,000 Speaker 1: and then it's springboards off these known experiences to make predictions. 593 00:41:10,000 --> 00:41:13,799 Speaker 1: As Hawkins puts it, intelligence is the capacity of the 594 00:41:13,840 --> 00:41:20,959 Speaker 1: brain to predict the future by analogy to the past. Now, fascinatingly, 595 00:41:21,000 --> 00:41:24,759 Speaker 1: in two thousand and seven, Demisesabis and his colleagues at 596 00:41:24,800 --> 00:41:29,560 Speaker 1: London's Institute of Neurology made this really striking observation that 597 00:41:29,880 --> 00:41:34,000 Speaker 1: patience who had damage to their hippocampus not only had 598 00:41:34,320 --> 00:41:41,280 Speaker 1: amnesia for past experiences, but also couldn't imagine new ones. 599 00:41:41,640 --> 00:41:44,960 Speaker 1: So if you ask a patient with this brain damage 600 00:41:45,239 --> 00:41:47,600 Speaker 1: to remember his past, he can't do that, and we 601 00:41:47,680 --> 00:41:51,160 Speaker 1: expected that. But now you ask him to imagine the future, 602 00:41:51,480 --> 00:41:53,200 Speaker 1: and he just can't do it. You ask him to 603 00:41:53,960 --> 00:41:57,839 Speaker 1: imagine standing in a museum full of exhibits and he'll 604 00:41:57,840 --> 00:42:01,640 Speaker 1: say something like there's not a lot coming, I'm not 605 00:42:02,040 --> 00:42:06,640 Speaker 1: picturing anything. Or you might say, hey, look, imagine going 606 00:42:06,680 --> 00:42:09,760 Speaker 1: on a vacation to the beach. Really picture yourself lying 607 00:42:09,800 --> 00:42:12,600 Speaker 1: there on the beach, and describe the scene to me. 608 00:42:13,840 --> 00:42:16,799 Speaker 1: And the person might be able to say, well, there's 609 00:42:16,840 --> 00:42:20,960 Speaker 1: a blue sky, or maybe they describe an isolated sound, 610 00:42:21,080 --> 00:42:25,400 Speaker 1: but that's it. Otherwise they just draw a blank. And 611 00:42:25,440 --> 00:42:28,120 Speaker 1: by the way, if you provide the person with pictures 612 00:42:28,160 --> 00:42:31,160 Speaker 1: and sounds and smells to help them along, that doesn't 613 00:42:31,200 --> 00:42:36,040 Speaker 1: help them to imagine the scene. So unlike a healthy control, 614 00:42:36,840 --> 00:42:41,880 Speaker 1: the patient with hippocampal damage just can't simulate any vivid details. 615 00:42:41,920 --> 00:42:45,239 Speaker 1: It's not like an episode to them the way that 616 00:42:45,280 --> 00:42:49,160 Speaker 1: your imagination is. Their descriptions, if they exist at all, 617 00:42:49,200 --> 00:42:54,200 Speaker 1: are very unspecific. So the healthy control can describe a 618 00:42:54,280 --> 00:42:58,320 Speaker 1: spatial layout and people being present, and descriptions of the 619 00:42:58,320 --> 00:43:01,680 Speaker 1: smells and sights and sounds, thoughts and emotions they might have, 620 00:43:01,840 --> 00:43:05,040 Speaker 1: and actions they might take. But the patient with the 621 00:43:05,120 --> 00:43:10,080 Speaker 1: hippocampal damage can't do any of that. At best. What 622 00:43:10,120 --> 00:43:13,000 Speaker 1: they're able to come up with has a lack of 623 00:43:13,320 --> 00:43:18,759 Speaker 1: spatial coherence. The imagined experience is just a collection of 624 00:43:18,800 --> 00:43:24,360 Speaker 1: fragmentary sensations instead of a unified episode. In a particular setting, 625 00:43:25,600 --> 00:43:27,520 Speaker 1: you can ask them what they'll see if they go 626 00:43:27,600 --> 00:43:30,000 Speaker 1: over to a shopping mall, or what they might want 627 00:43:30,040 --> 00:43:32,279 Speaker 1: to eat if they go to a restaurant, and they 628 00:43:32,520 --> 00:43:35,360 Speaker 1: just draw a blank. They can only put together a 629 00:43:35,440 --> 00:43:39,239 Speaker 1: few details that are not well connected. So the deficits 630 00:43:39,360 --> 00:43:44,040 Speaker 1: in their memories apply to their simulations of the future 631 00:43:44,200 --> 00:43:47,919 Speaker 1: as well. Now, how do we understand this in terms 632 00:43:47,960 --> 00:43:51,680 Speaker 1: of the circuitry. Well, the key came from brain imaging 633 00:43:51,719 --> 00:43:55,000 Speaker 1: studies in the past two decades, which have uncovered that 634 00:43:55,040 --> 00:43:59,560 Speaker 1: there's a network of regions involved both for remembering the 635 00:43:59,600 --> 00:44:03,480 Speaker 1: past and imagining new ones. It's the same areas, so 636 00:44:03,520 --> 00:44:06,640 Speaker 1: the hippocampus and its surrounding area. That's one part of this, 637 00:44:07,239 --> 00:44:11,240 Speaker 1: but we also find several other areas like the medial, 638 00:44:11,280 --> 00:44:15,000 Speaker 1: prial cortex and prefront areas, and the lateral, temporal and 639 00:44:15,080 --> 00:44:20,240 Speaker 1: lateral pride lobes. All these regions are important for elaborating 640 00:44:20,239 --> 00:44:26,000 Speaker 1: on the details of imagined and also for remembered episodes. So, 641 00:44:26,120 --> 00:44:30,160 Speaker 1: in other words, the brain's episodic memory systems, which we 642 00:44:30,239 --> 00:44:34,200 Speaker 1: discussed in the last episode, are just as important for 643 00:44:34,280 --> 00:44:41,319 Speaker 1: imagining future experiences. In other words, this core network underlies 644 00:44:41,560 --> 00:44:46,520 Speaker 1: mental time travel in either direction. So this leads to 645 00:44:46,560 --> 00:44:50,000 Speaker 1: a really interesting thought about something, which is that if 646 00:44:50,120 --> 00:44:53,800 Speaker 1: recall of the past and simulation of the future both 647 00:44:54,000 --> 00:44:58,520 Speaker 1: use the same network, then maybe what we mean by 648 00:44:58,600 --> 00:45:02,279 Speaker 1: memory is something more like simulation. So I want to 649 00:45:02,320 --> 00:45:07,279 Speaker 1: propose this hypothesis that memories are not the fundamental thing, 650 00:45:07,320 --> 00:45:11,439 Speaker 1: but instead simulation is the fundamental thing, and memories are 651 00:45:11,480 --> 00:45:16,360 Speaker 1: just a special type of simulation. A memory is merely 652 00:45:16,400 --> 00:45:20,319 Speaker 1: a simulation that's pinned down to always flow a particular way. 653 00:45:21,040 --> 00:45:22,839 Speaker 1: And if this is the right way to look at it, 654 00:45:23,280 --> 00:45:28,880 Speaker 1: maybe what we call remembrance we will someday call resimulation. 655 00:45:29,840 --> 00:45:33,360 Speaker 1: So instead of dividing the territory into memory and prediction, 656 00:45:33,800 --> 00:45:37,880 Speaker 1: they may in fact be one thing. Any context is 657 00:45:38,000 --> 00:45:43,960 Speaker 1: run through a simulation to predict the outcome. So brains 658 00:45:44,120 --> 00:45:48,560 Speaker 1: simulate possible futures, and we constantly function by making predictions 659 00:45:48,560 --> 00:45:51,439 Speaker 1: about everything in our lives and our communities and our 660 00:45:51,719 --> 00:45:54,359 Speaker 1: nation and the world. But I want to be clear 661 00:45:54,400 --> 00:45:57,880 Speaker 1: that even though we use the word prediction, there's no 662 00:45:58,000 --> 00:46:03,000 Speaker 1: guarantee of accuracy. We are actually pretty bad at capturing 663 00:46:03,040 --> 00:46:07,520 Speaker 1: the future. As the baseball catcher Yogi Bearra said, it's 664 00:46:07,640 --> 00:46:13,440 Speaker 1: tough to make predictions, especially about the future. Why is 665 00:46:13,520 --> 00:46:18,479 Speaker 1: it tough. It's because we only simulate based on our 666 00:46:18,680 --> 00:46:23,640 Speaker 1: experience in the world. So if you've never seen something before, 667 00:46:24,160 --> 00:46:27,399 Speaker 1: you're going to have a pretty bad prediction about it. 668 00:46:28,239 --> 00:46:31,440 Speaker 1: For example, futurists make all kinds of predictions about the 669 00:46:31,480 --> 00:46:34,279 Speaker 1: next decade or two, and most of them turn out 670 00:46:34,320 --> 00:46:38,319 Speaker 1: to be wrong. In one study of famous forecasters, it 671 00:46:38,400 --> 00:46:42,520 Speaker 1: was found that their predictions were between ten to fifty 672 00:46:42,560 --> 00:46:46,640 Speaker 1: percent accuracy, which certainly isn't that great. But it's not 673 00:46:46,719 --> 00:46:49,480 Speaker 1: just futurists. It's all of us, with most of our 674 00:46:49,480 --> 00:46:54,080 Speaker 1: predictions about our lives, and by the way, our inability 675 00:46:54,200 --> 00:46:56,920 Speaker 1: to see the future, well, this is why we have 676 00:46:57,000 --> 00:47:03,040 Speaker 1: the existence of magicians or mystery writers or scam artists. 677 00:47:03,600 --> 00:47:06,200 Speaker 1: These are people who take advantage of the fact that 678 00:47:06,320 --> 00:47:10,320 Speaker 1: our ability to predict the future is not very good. 679 00:47:10,840 --> 00:47:14,280 Speaker 1: The magician knows that we're going to predict the location 680 00:47:14,360 --> 00:47:18,279 Speaker 1: of the object incorrectly and then will be surprised. The 681 00:47:18,600 --> 00:47:22,000 Speaker 1: mystery novelist knows that he can lead us down a 682 00:47:22,040 --> 00:47:26,239 Speaker 1: garden path and that we will extrapolate incorrectly in the 683 00:47:26,239 --> 00:47:29,719 Speaker 1: direction that he wants us to, so we don't correctly 684 00:47:29,880 --> 00:47:33,960 Speaker 1: see what's going to happen. The scam artist does the 685 00:47:34,000 --> 00:47:37,319 Speaker 1: same thing, but in real life, getting our brains to 686 00:47:37,480 --> 00:47:41,319 Speaker 1: see a vision of success that doesn't actually match with 687 00:47:41,440 --> 00:47:44,920 Speaker 1: what's going to happen. Now, I'll just note something here, 688 00:47:44,960 --> 00:47:48,480 Speaker 1: which is that's sometimes our inability to make good predictions 689 00:47:48,920 --> 00:47:52,480 Speaker 1: that helps us. So take the origin of the Oxford 690 00:47:52,520 --> 00:47:57,719 Speaker 1: English Dictionary, where a professor who loved words said, you 691 00:47:57,760 --> 00:47:59,840 Speaker 1: know what, I'm going to write down a definition for 692 00:48:00,120 --> 00:48:03,720 Speaker 1: every word there is. This can't take very long, especially 693 00:48:03,760 --> 00:48:06,920 Speaker 1: if I recruit help from others, which he did. But 694 00:48:07,040 --> 00:48:11,120 Speaker 1: despite an insane amount of work, the Oxford English Dictionary 695 00:48:11,200 --> 00:48:16,799 Speaker 1: finally got finished eighty years after his death. He would 696 00:48:16,840 --> 00:48:20,360 Speaker 1: have never started it had he been a good predictor. 697 00:48:20,400 --> 00:48:25,120 Speaker 1: So there's something useful about our optimism bias in the 698 00:48:25,120 --> 00:48:29,080 Speaker 1: form of our bad predictions. Now, we've all experienced this 699 00:48:29,239 --> 00:48:32,640 Speaker 1: kind of bad prediction on smaller levels, where we assume 700 00:48:33,040 --> 00:48:35,840 Speaker 1: that some task is going to take us less time 701 00:48:36,160 --> 00:48:39,520 Speaker 1: than it actually does. We also experience this on the 702 00:48:39,600 --> 00:48:43,120 Speaker 1: level of most of our life trajectories, where we think, 703 00:48:43,400 --> 00:48:45,760 Speaker 1: all right, I generally know where my life is going, 704 00:48:46,520 --> 00:48:49,040 Speaker 1: but if you look back at any decade of your life, 705 00:48:49,040 --> 00:48:53,200 Speaker 1: you'll realize that your predictions generally weren't so good. Why 706 00:48:53,719 --> 00:48:57,080 Speaker 1: it's because the only way we can make predictions is 707 00:48:57,120 --> 00:49:01,120 Speaker 1: by leveraging our memories what has all ready happen to us. 708 00:49:01,560 --> 00:49:06,000 Speaker 1: The memories serve as building blocks, and all we're able 709 00:49:06,040 --> 00:49:09,000 Speaker 1: to do is use those building blocks to make versions 710 00:49:09,040 --> 00:49:13,160 Speaker 1: of the future, which is really just an edifice constructed 711 00:49:13,200 --> 00:49:16,719 Speaker 1: of the bricks that we've seen before. And that's why 712 00:49:16,760 --> 00:49:22,400 Speaker 1: we are so inherently limited in seeing what's coming. And 713 00:49:22,440 --> 00:49:25,560 Speaker 1: there's a very specific way that we're terrible at predicting 714 00:49:25,600 --> 00:49:29,560 Speaker 1: the future. We generally assume the future will just be 715 00:49:29,640 --> 00:49:34,120 Speaker 1: a straightforward extension of the present in our lives. We 716 00:49:34,200 --> 00:49:37,480 Speaker 1: assume that we have changed up to this point, but 717 00:49:37,840 --> 00:49:41,279 Speaker 1: we're going to remain about like this from here on out. 718 00:49:41,760 --> 00:49:45,279 Speaker 1: For example, when people think back to their childhoods, they 719 00:49:45,280 --> 00:49:49,040 Speaker 1: see lots of change in their own bodies and personalities 720 00:49:49,080 --> 00:49:52,239 Speaker 1: and beliefs, and also in the technology that surrounds them. 721 00:49:52,920 --> 00:49:55,960 Speaker 1: But when people are asked to think about the future, 722 00:49:56,600 --> 00:49:59,880 Speaker 1: they generally assume everything is going to be roughly this 723 00:50:00,080 --> 00:50:02,400 Speaker 1: same as it is now. Maybe you'll have a little 724 00:50:02,440 --> 00:50:05,719 Speaker 1: more gray hair, maybe your electric car will have a 725 00:50:05,800 --> 00:50:09,279 Speaker 1: longer range, but that's about it. We feel like we've 726 00:50:09,440 --> 00:50:13,279 Speaker 1: arrived after a steep path and now the world will 727 00:50:13,360 --> 00:50:17,160 Speaker 1: mostly stay fixed as it is. So we think things 728 00:50:17,160 --> 00:50:19,560 Speaker 1: are going to stay as they are. And nowhere is 729 00:50:19,600 --> 00:50:24,840 Speaker 1: this more true than with our predictions about technology. The 730 00:50:24,920 --> 00:50:28,080 Speaker 1: fact is, the world is changing faster than ever as 731 00:50:28,200 --> 00:50:32,120 Speaker 1: a result of the law of accelerating returns, which simply 732 00:50:32,160 --> 00:50:37,000 Speaker 1: says that the more technology advances, the faster the next 733 00:50:37,040 --> 00:50:40,640 Speaker 1: generation of technology is going to advance. And we find 734 00:50:40,680 --> 00:50:45,120 Speaker 1: ourselves now at the cusp of such fundamental revolutions, not 735 00:50:45,160 --> 00:50:50,520 Speaker 1: only artificial intelligence, but also nanotech and biotech and quantum 736 00:50:50,520 --> 00:50:56,320 Speaker 1: computing and room temperature superconductivity and energy storage and genetic 737 00:50:56,400 --> 00:51:00,239 Speaker 1: engineering and on and on. All of these things going 738 00:51:00,320 --> 00:51:04,800 Speaker 1: to weave together in ways that we can't currently imagine. 739 00:51:04,880 --> 00:51:08,160 Speaker 1: And we are standing on an exponential curve that's about 740 00:51:08,239 --> 00:51:11,560 Speaker 1: to rise at a steeper slope than we've ever seen, 741 00:51:12,480 --> 00:51:16,960 Speaker 1: but we can't see it clearly coming. Why again, it's 742 00:51:17,000 --> 00:51:20,760 Speaker 1: because we rely on our memories to paint our vision 743 00:51:20,800 --> 00:51:25,960 Speaker 1: of the future, so our predictions are limited to remixes 744 00:51:26,000 --> 00:51:29,800 Speaker 1: of our past, which makes it really difficult to anticipate 745 00:51:30,520 --> 00:51:35,879 Speaker 1: the significant disruptions heading our way. And there's one other 746 00:51:35,960 --> 00:51:39,200 Speaker 1: issue here for our lives, because we're always trying to 747 00:51:39,239 --> 00:51:43,240 Speaker 1: make good predictions and therefore save brain energy. We really 748 00:51:43,280 --> 00:51:47,360 Speaker 1: hate change. I mentioned before how we don't like the 749 00:51:47,480 --> 00:51:51,680 Speaker 1: dripping faucet because it's unpredictable, But this hatred of the 750 00:51:51,800 --> 00:51:56,919 Speaker 1: unpredictable applies to everything, including being told that things will 751 00:51:57,000 --> 00:52:01,200 Speaker 1: change in the future, like climate change. Climate change makes 752 00:52:01,200 --> 00:52:04,000 Speaker 1: people very anxious because you look at a map of 753 00:52:04,040 --> 00:52:06,359 Speaker 1: the world and you see that over the next x 754 00:52:06,440 --> 00:52:11,320 Speaker 1: number of decades people will be shifting around as temperatures increase. 755 00:52:11,800 --> 00:52:15,880 Speaker 1: And we hate that because fundamentally we feel most comfortable 756 00:52:16,360 --> 00:52:20,200 Speaker 1: if things stay exactly the way they are. Like you 757 00:52:20,360 --> 00:52:23,759 Speaker 1: want to imagine that your house, which is exactly two 758 00:52:23,800 --> 00:52:26,360 Speaker 1: hundred and fifty seven feet from the shore of the ocean, 759 00:52:26,840 --> 00:52:31,880 Speaker 1: will remain precisely where it is centuries from now, But 760 00:52:32,000 --> 00:52:35,080 Speaker 1: of course it won't, even if you put aside everything 761 00:52:35,120 --> 00:52:39,719 Speaker 1: about man made pollution. The shorelines always change. Where I 762 00:52:39,800 --> 00:52:44,439 Speaker 1: live in California, the beach used to extend miles farther out. 763 00:52:44,520 --> 00:52:47,480 Speaker 1: I'm talking about fourteen thousand years ago, and when the 764 00:52:47,640 --> 00:52:51,080 Speaker 1: ice Age ended and the glaciers melted, the sea level 765 00:52:51,360 --> 00:52:54,600 Speaker 1: rose and ate up all of that beach. And that's 766 00:52:54,640 --> 00:52:58,560 Speaker 1: why we don't find coastal settlements from the first people 767 00:52:58,600 --> 00:53:01,960 Speaker 1: from Asia who came across cross the bearing Land Bridge 768 00:53:02,000 --> 00:53:05,960 Speaker 1: and settled here fourteen thousand years ago. Things were totally 769 00:53:05,960 --> 00:53:08,759 Speaker 1: different at that time. For example, there was no San 770 00:53:08,800 --> 00:53:12,000 Speaker 1: Francisco Bay. There was no water there that was all 771 00:53:12,040 --> 00:53:15,200 Speaker 1: locked up in glaciers. When any of us who live 772 00:53:15,280 --> 00:53:19,040 Speaker 1: here look at the San Francisco Bay, we imagine it's permanent, 773 00:53:19,080 --> 00:53:21,360 Speaker 1: but of course it's not. If you were one of 774 00:53:21,400 --> 00:53:24,719 Speaker 1: the first settlers in North America, the place would have 775 00:53:24,719 --> 00:53:28,000 Speaker 1: looked very different to you. You could have walked across 776 00:53:28,360 --> 00:53:31,239 Speaker 1: from what is now the city of San Francisco over 777 00:53:31,280 --> 00:53:34,839 Speaker 1: to Alcatraz without getting a single drop of water on 778 00:53:34,880 --> 00:53:40,479 Speaker 1: your feet. It's easy to study geography retrospectively and say, wow, 779 00:53:40,560 --> 00:53:43,520 Speaker 1: that's interesting. But when we look in the forward direction, 780 00:53:44,040 --> 00:53:47,160 Speaker 1: we get very anxious at the thought that populations of 781 00:53:47,160 --> 00:53:50,839 Speaker 1: people will move around and borders will change. Now that's 782 00:53:50,840 --> 00:53:54,040 Speaker 1: not to minimize what's happening with climate change, but it 783 00:53:54,120 --> 00:53:58,520 Speaker 1: is to say that change has always happened. I mean, 784 00:53:58,560 --> 00:54:02,360 Speaker 1: the last little ice Age just ended in eighteen fifty, 785 00:54:02,719 --> 00:54:05,920 Speaker 1: where for five hundred years it was two degrees colder 786 00:54:06,360 --> 00:54:10,239 Speaker 1: in Europe and mountain glaciers expanded and people had to 787 00:54:10,280 --> 00:54:14,000 Speaker 1: move around. The only issue I'm pointing to here is 788 00:54:14,000 --> 00:54:18,080 Speaker 1: that even though the world has always changed, we want 789 00:54:18,120 --> 00:54:22,600 Speaker 1: it to stay stable now. We fundamentally want to imagine 790 00:54:22,600 --> 00:54:26,120 Speaker 1: the future of the world looking exactly as we know 791 00:54:26,239 --> 00:54:30,399 Speaker 1: it now. And you can see this as easily at 792 00:54:30,480 --> 00:54:34,040 Speaker 1: small scales as you do on the large scales. For example, 793 00:54:34,040 --> 00:54:36,760 Speaker 1: in this past month, there have been a new round 794 00:54:36,800 --> 00:54:40,240 Speaker 1: of company layoffs in Silicon Valley, and this makes people 795 00:54:40,320 --> 00:54:44,320 Speaker 1: so nervous and anxious. Now, most people who have lost 796 00:54:44,320 --> 00:54:48,240 Speaker 1: a job end up saying later that they're happy because 797 00:54:48,280 --> 00:54:52,200 Speaker 1: it opens up new opportunities for them and exposes them 798 00:54:52,440 --> 00:54:54,840 Speaker 1: to things they didn't even know. They didn't know and 799 00:54:54,920 --> 00:54:58,399 Speaker 1: they realized there was more out there to be experienced 800 00:54:58,440 --> 00:55:02,680 Speaker 1: in the world. And yet the change itself proves very 801 00:55:02,719 --> 00:55:05,399 Speaker 1: hard for people in the moment. It's as though their 802 00:55:05,440 --> 00:55:08,560 Speaker 1: brains are screaming out for everything to stay exactly the 803 00:55:08,600 --> 00:55:12,080 Speaker 1: same as it was. Why, Because we are creatures who 804 00:55:12,160 --> 00:55:15,520 Speaker 1: try to predict Our brains are designed to do that 805 00:55:15,640 --> 00:55:19,560 Speaker 1: to save energy, and the most anxiety producing thing for 806 00:55:19,640 --> 00:55:22,880 Speaker 1: the accuracy of our predictions is when the world itself 807 00:55:23,000 --> 00:55:26,399 Speaker 1: changes out from under you. As an example, I've been 808 00:55:26,400 --> 00:55:30,600 Speaker 1: on the boards of many organizations. When somebody resigns and 809 00:55:30,760 --> 00:55:34,239 Speaker 1: so much trauma for the board, there's a long discussion 810 00:55:34,280 --> 00:55:38,719 Speaker 1: about how to keep the organization together. Everybody's feelings are bubbling, 811 00:55:39,120 --> 00:55:42,920 Speaker 1: and then the conversation eventually turns to how this presents 812 00:55:43,000 --> 00:55:46,080 Speaker 1: a real opportunity for us to mix things up, to 813 00:55:46,160 --> 00:55:48,919 Speaker 1: inject new blood, to do things in a way that's 814 00:55:48,960 --> 00:55:52,840 Speaker 1: no longer stale. It's fascinating to watch the conversation always 815 00:55:52,920 --> 00:55:56,480 Speaker 1: follow the same trajectory, as though everyone is reading from 816 00:55:56,480 --> 00:56:00,759 Speaker 1: a script. It reminds me of a notion from The 817 00:56:00,800 --> 00:56:06,320 Speaker 1: Simpsons where Homer is anxious about something and Lisa, his daughter, says, 818 00:56:06,880 --> 00:56:09,600 Speaker 1: look on the bright side, Dad, Did you know that 819 00:56:09,640 --> 00:56:12,879 Speaker 1: the Chinese use the same word for crisis as they 820 00:56:12,880 --> 00:56:18,200 Speaker 1: do for opportunity, and Homer says, yes, chrisis as tunity. 821 00:56:18,239 --> 00:56:22,359 Speaker 1: The point is that change of any sort presents a 822 00:56:22,560 --> 00:56:26,000 Speaker 1: crisis to the predictive systems of the brain, but it 823 00:56:26,040 --> 00:56:30,719 Speaker 1: is eventually seen as an opportunity. The bottom line is 824 00:56:30,760 --> 00:56:33,200 Speaker 1: that we get used to the world and we don't 825 00:56:33,239 --> 00:56:38,240 Speaker 1: want things to change. So, given that our predictive ability 826 00:56:38,280 --> 00:56:40,319 Speaker 1: is not so great and that we don't want things 827 00:56:40,320 --> 00:56:43,640 Speaker 1: to change, how did we ever become so successful as 828 00:56:43,680 --> 00:56:47,719 Speaker 1: a species. Well, the first answer is science. When it 829 00:56:47,719 --> 00:56:50,839 Speaker 1: comes to predicting big issues in the real world, our 830 00:56:50,880 --> 00:56:54,440 Speaker 1: intuitions just aren't up for the task. Our brains always 831 00:56:54,520 --> 00:56:58,080 Speaker 1: make predictions, but human brains are small and they're not 832 00:56:58,160 --> 00:57:02,440 Speaker 1: nearly as good as groups of brains working together, and 833 00:57:02,480 --> 00:57:06,080 Speaker 1: the scientific method simply gives us away a set of 834 00:57:06,160 --> 00:57:11,680 Speaker 1: rules for working together to find the most accurate predictions. Fundamentally, 835 00:57:11,960 --> 00:57:15,719 Speaker 1: that's all science is figuring out the rules so we 836 00:57:15,800 --> 00:57:20,440 Speaker 1: can best predict the future. But I want to highlight 837 00:57:20,480 --> 00:57:24,080 Speaker 1: what I propose is a second reason that humans got 838 00:57:24,120 --> 00:57:26,720 Speaker 1: really good at predicting the future, and this is perhaps 839 00:57:26,760 --> 00:57:31,200 Speaker 1: a more surprising reason why our species has become a 840 00:57:31,320 --> 00:57:40,400 Speaker 1: runaway species, and that reason is storytelling. So literature like stories, novels, 841 00:57:40,400 --> 00:57:44,160 Speaker 1: and movies. This is critically important for the success of 842 00:57:44,200 --> 00:57:48,120 Speaker 1: our species because we can take one person's imagined stories 843 00:57:48,200 --> 00:57:50,520 Speaker 1: something they've worked out all the pieces and parts of 844 00:57:50,560 --> 00:57:54,040 Speaker 1: over a long time, and that author can make that 845 00:57:54,200 --> 00:57:59,160 Speaker 1: scenario real for us, He can reify it. So stories 846 00:57:59,280 --> 00:58:05,280 Speaker 1: allow us to experience possible futures. Just think, for example, 847 00:58:05,280 --> 00:58:08,960 Speaker 1: of the nineteen eighties movie The Day After. It was 848 00:58:09,040 --> 00:58:11,760 Speaker 1: about nuclear war and what it is to wake up 849 00:58:11,880 --> 00:58:15,200 Speaker 1: the day after America has been turned to rubble by 850 00:58:15,280 --> 00:58:19,640 Speaker 1: nuclear bombs. It took something that required an unusually rich 851 00:58:19,680 --> 00:58:24,520 Speaker 1: imagination and it allowed us to see it, to experience 852 00:58:24,600 --> 00:58:29,920 Speaker 1: a situation that otherwise would have remained purely conceptual. And 853 00:58:29,960 --> 00:58:32,440 Speaker 1: this is why stories are so important. They allow us 854 00:58:32,520 --> 00:58:37,040 Speaker 1: to live in worlds that we otherwise would not, and 855 00:58:37,080 --> 00:58:40,440 Speaker 1: then that gives us new memories that we can use 856 00:58:40,760 --> 00:58:44,600 Speaker 1: as new building blocks to see a little farther out 857 00:58:44,640 --> 00:58:48,480 Speaker 1: than we would have otherwise. In this sense, literature allows 858 00:58:48,560 --> 00:58:51,520 Speaker 1: us to get out of our heads and share the 859 00:58:51,640 --> 00:58:54,880 Speaker 1: creative headspace of someone else who has thought down a 860 00:58:54,920 --> 00:58:59,000 Speaker 1: particular path, probably in great detail, and then we get 861 00:58:59,000 --> 00:59:03,200 Speaker 1: to enjoy that person's guidance. And the key, as far 862 00:59:03,240 --> 00:59:06,560 Speaker 1: as we can tell, is that other species, for example, 863 00:59:06,680 --> 00:59:11,680 Speaker 1: hippopotamuses don't tell stories around the campfire. And it's not 864 00:59:11,720 --> 00:59:15,320 Speaker 1: just hippopotamuses, but every other one of the millions of 865 00:59:15,440 --> 00:59:18,880 Speaker 1: species of animals on this earth. We have no evidence 866 00:59:19,280 --> 00:59:23,120 Speaker 1: that any of them tells stories. So the way I 867 00:59:23,160 --> 00:59:25,600 Speaker 1: think about this is that they just have a lot 868 00:59:25,760 --> 00:59:31,680 Speaker 1: less practice expanding beyond their own limited experience of the world. 869 00:59:31,920 --> 00:59:35,400 Speaker 1: But we spend a ton of our time imagining what's 870 00:59:35,480 --> 00:59:39,080 Speaker 1: not there. We are mental time travelers, and we use 871 00:59:39,160 --> 00:59:42,520 Speaker 1: other people's stories and books to get there. The class 872 00:59:42,520 --> 00:59:46,000 Speaker 1: that I'm teaching at Stanford this quarter is Literature and 873 00:59:46,080 --> 00:59:49,560 Speaker 1: the Brain. What I find so extraordinary about the active 874 00:59:49,640 --> 00:59:53,240 Speaker 1: reading is that we use a string of symbols to 875 00:59:53,480 --> 00:59:57,680 Speaker 1: fire up this whole imagination engine and to have us 876 00:59:57,880 --> 01:00:01,640 Speaker 1: live through scenarios. And I propose that we have come 877 01:00:01,720 --> 01:00:05,920 Speaker 1: to beat out every animal species on the planet, including 878 01:00:05,960 --> 01:00:08,640 Speaker 1: lions and tigers and bears, all of whom could tear 879 01:00:08,720 --> 01:00:12,000 Speaker 1: us to shreds easily. We have beat them out because 880 01:00:12,000 --> 01:00:16,280 Speaker 1: of stories. We have these fierce animals in our zoos 881 01:00:16,640 --> 01:00:20,480 Speaker 1: in every city, and they have no humans in their zoos. 882 01:00:21,040 --> 01:00:24,320 Speaker 1: It's not just about guns and spears, it's about planning. 883 01:00:25,080 --> 01:00:30,000 Speaker 1: We can capture them because we can outthink them. So 884 01:00:30,160 --> 01:00:32,760 Speaker 1: I posed at the beginning of the episode a question, 885 01:00:32,920 --> 01:00:36,920 Speaker 1: what would I advise the president if we found ourselves 886 01:00:37,040 --> 01:00:41,360 Speaker 1: at war with extraterrestrials. Well, here's what If we land 887 01:00:41,440 --> 01:00:45,880 Speaker 1: on a planet with fierce bug like creatures, we shouldn't 888 01:00:45,880 --> 01:00:49,680 Speaker 1: worry too much about their capacity to be anything but reactive. 889 01:00:49,880 --> 01:00:53,880 Speaker 1: We will probably be able to trick them, to outflank them, 890 01:00:53,920 --> 01:00:58,120 Speaker 1: to outthink them. But if we discover that these creatures 891 01:00:58,360 --> 01:01:03,440 Speaker 1: also have life libraries, we should quietly turn around and 892 01:01:03,600 --> 01:01:07,800 Speaker 1: sneak away, because it means they have exposed themselves to 893 01:01:08,080 --> 01:01:13,320 Speaker 1: thousands of other worlds beyond what they could otherwise experience, 894 01:01:13,880 --> 01:01:20,000 Speaker 1: and that cognitive practice makes them potentially a very wily opponent. 895 01:01:21,160 --> 01:01:25,560 Speaker 1: The degree to which an alien species has literature will 896 01:01:25,600 --> 01:01:29,560 Speaker 1: tell us how good they are at predicting possible futures 897 01:01:29,600 --> 01:01:34,200 Speaker 1: and developing rich scenarios of what ifs. It allows them 898 01:01:34,480 --> 01:01:40,720 Speaker 1: to expand their experiences far beyond a single head. So 899 01:01:40,840 --> 01:01:44,360 Speaker 1: let's wrap up what we saw today is that brains 900 01:01:44,640 --> 01:01:48,880 Speaker 1: simulate possible futures, and brains do this by relying on 901 01:01:49,040 --> 01:01:53,720 Speaker 1: the lessons of the past. This makes your memory the 902 01:01:53,760 --> 01:01:57,880 Speaker 1: mother of your imagination, just like the goddess of memory 903 01:01:58,240 --> 01:02:02,400 Speaker 1: gave birth to the muses. Now, in the last two episodes, 904 01:02:02,440 --> 01:02:06,200 Speaker 1: we've talked about running simulations in the backward direction, which 905 01:02:06,200 --> 01:02:08,960 Speaker 1: we call memory, and running them in the forward direction, 906 01:02:09,040 --> 01:02:13,320 Speaker 1: which is how we envision possible futures. But everything I've 907 01:02:13,320 --> 01:02:16,440 Speaker 1: told you so far is just a setup, because that 908 01:02:17,200 --> 01:02:20,360 Speaker 1: is just the beginning. Come back next week to see 909 01:02:20,360 --> 01:02:23,680 Speaker 1: how now we can leverage this concept of time travel 910 01:02:24,320 --> 01:02:28,960 Speaker 1: to deeply understand why our mental lives are as nuanced 911 01:02:29,240 --> 01:02:34,920 Speaker 1: and colorful and complex as they are simulating. Next week, 912 01:02:35,280 --> 01:02:43,480 Speaker 1: I'm David Eagleman, and this is Inner Cosmos. In the meantime, 913 01:02:43,560 --> 01:02:46,560 Speaker 1: go to eagleman dot com slash podcast for more information 914 01:02:46,680 --> 01:02:50,640 Speaker 1: and to find further reading. Send me an email at 915 01:02:50,760 --> 01:02:54,200 Speaker 1: podcasts at eagleman dot com with questions or discussion, and 916 01:02:54,240 --> 01:02:58,120 Speaker 1: I'll be making episodes in which I address those until 917 01:02:58,160 --> 01:03:01,120 Speaker 1: next time. Thank you for joining me in the outermost 918 01:03:01,120 --> 01:03:03,680 Speaker 1: reaches of the Inner Cosmos