1 00:00:05,160 --> 00:00:09,400 Speaker 1: Why do movies work so well? What does film reveal 2 00:00:09,440 --> 00:00:13,200 Speaker 1: about the way that the brain process is reality? And 3 00:00:13,240 --> 00:00:15,840 Speaker 1: what does any of this have to do with omniscience 4 00:00:16,200 --> 00:00:20,800 Speaker 1: or simulation or jumping around in time, or why dogs 5 00:00:20,880 --> 00:00:23,799 Speaker 1: don't do story or what you would think if you 6 00:00:23,920 --> 00:00:31,000 Speaker 1: saw aliens bluing all their attention to purple spheres. Welcome 7 00:00:31,000 --> 00:00:34,360 Speaker 1: to Entercosmos with me David Eagleman. I'm a neuroscientist and 8 00:00:34,400 --> 00:00:37,720 Speaker 1: an author at Stanford and in these episodes, we examine 9 00:00:38,200 --> 00:00:41,880 Speaker 1: brains to understand who we are and where we're going. 10 00:01:00,720 --> 00:01:04,000 Speaker 1: There's a peculiar kind of magic that happens when the 11 00:01:04,080 --> 00:01:07,760 Speaker 1: lights go down and the screen glows to life, and 12 00:01:07,800 --> 00:01:13,360 Speaker 1: we find ourselves pulled into another world. We find ourselves 13 00:01:13,440 --> 00:01:17,680 Speaker 1: in lots of new places. We become hobbits or hit men, 14 00:01:18,200 --> 00:01:22,440 Speaker 1: or lonely robots or star crossed lovers. We might find 15 00:01:22,440 --> 00:01:27,120 Speaker 1: ourselves in a courtroom showdown or an alien invasion. And 16 00:01:27,160 --> 00:01:30,680 Speaker 1: for the next two hours, we're not just watching something, 17 00:01:30,760 --> 00:01:34,640 Speaker 1: We're feeling it and living it. And I mean this physically. 18 00:01:34,680 --> 00:01:38,800 Speaker 1: Your heart beats faster when the protagonists on the screen 19 00:01:38,959 --> 00:01:43,480 Speaker 1: is in danger. You can physically weep at losses that 20 00:01:43,520 --> 00:01:47,480 Speaker 1: are someone else's. You root for people who don't exist, 21 00:01:48,080 --> 00:01:53,600 Speaker 1: and your brain genuinely enjoys triumph when that person succeeds 22 00:01:53,640 --> 00:01:56,320 Speaker 1: after all that they went through. So the bottom line 23 00:01:56,360 --> 00:01:58,200 Speaker 1: is that you can sit in front of a flat 24 00:01:58,240 --> 00:02:04,240 Speaker 1: screen and you're perfectly still, but your brain experiences movement 25 00:02:04,520 --> 00:02:10,040 Speaker 1: and depth and emotion and social connection and victories. So 26 00:02:10,080 --> 00:02:14,520 Speaker 1: to appreciate how weird this is, imagine that we landed 27 00:02:14,560 --> 00:02:17,360 Speaker 1: on some alien planet and we saw that at any 28 00:02:17,440 --> 00:02:21,920 Speaker 1: given moment, about half these funny looking aliens are carrying 29 00:02:21,960 --> 00:02:26,560 Speaker 1: around purple spheres and they're staring at these things like catnip, 30 00:02:27,000 --> 00:02:31,400 Speaker 1: and we would wonder what these purple spheres are. And 31 00:02:31,440 --> 00:02:36,119 Speaker 1: if you were a zeno neuroscientist who studied alien brains, 32 00:02:36,560 --> 00:02:41,959 Speaker 1: you'd suspect that something is going on with these spheres. Neurally, 33 00:02:42,080 --> 00:02:45,720 Speaker 1: whatever it is, the purple sphere is doing something exciting 34 00:02:46,160 --> 00:02:50,360 Speaker 1: to the alien's normal brain function. And whatever the sphere 35 00:02:50,440 --> 00:02:53,960 Speaker 1: was doing, these aliens liked it, or they couldn't stop 36 00:02:54,040 --> 00:02:57,120 Speaker 1: or whatever. So flip the script and imagine what it 37 00:02:57,160 --> 00:03:00,800 Speaker 1: would be like if some alien species came here and 38 00:03:00,800 --> 00:03:03,960 Speaker 1: they were hovering around over our planet, and what they 39 00:03:04,000 --> 00:03:09,160 Speaker 1: would see is billions of humans seating themselves in front 40 00:03:09,160 --> 00:03:13,280 Speaker 1: of flat screens. Most of these humans have these screens 41 00:03:13,320 --> 00:03:16,160 Speaker 1: installed on their walls where they live, and there are 42 00:03:16,240 --> 00:03:19,560 Speaker 1: big spaces where bunches of humans gather around to look 43 00:03:19,600 --> 00:03:22,919 Speaker 1: at a single screen, and increasingly the humans have figured 44 00:03:22,919 --> 00:03:26,000 Speaker 1: out how to shrink these down to carry these screens 45 00:03:26,000 --> 00:03:28,600 Speaker 1: around in their pocket. So the question the alien would 46 00:03:28,680 --> 00:03:32,920 Speaker 1: ask is how is it that something made of pixels 47 00:03:32,960 --> 00:03:36,800 Speaker 1: of light and little bits of sound can reach so 48 00:03:37,240 --> 00:03:41,920 Speaker 1: deep inside of the humans. And that's not a trivial question. 49 00:03:42,160 --> 00:03:45,680 Speaker 1: In neuroscience, we're still working to figure out many of 50 00:03:45,720 --> 00:03:49,160 Speaker 1: the mysteries of that question. It goes straight to the 51 00:03:49,200 --> 00:03:52,560 Speaker 1: heart of what it means to have a brain that 52 00:03:52,720 --> 00:03:57,760 Speaker 1: constructs reality, not just from sensory input, but from stories. 53 00:03:58,360 --> 00:04:03,520 Speaker 1: Our brains are narrative machines. They carve time into chunks, 54 00:04:03,520 --> 00:04:06,720 Speaker 1: they make predictions, they fill in the blanks, they form 55 00:04:07,160 --> 00:04:11,160 Speaker 1: internal models of what might happen next. And this is 56 00:04:11,280 --> 00:04:15,520 Speaker 1: the neural technology that allows us to navigate the real world. 57 00:04:15,600 --> 00:04:19,560 Speaker 1: But it also means that we are astonishingly susceptible to 58 00:04:19,680 --> 00:04:24,039 Speaker 1: being pulled into the unreal world, at least for now. 59 00:04:24,120 --> 00:04:27,960 Speaker 1: Movies are one of the most sophisticated forms of unreality 60 00:04:28,000 --> 00:04:32,880 Speaker 1: that we've invented. They manipulate time and space. They guide 61 00:04:32,880 --> 00:04:37,960 Speaker 1: our attention. They compress months into moments, They stretch seconds 62 00:04:38,000 --> 00:04:42,039 Speaker 1: into minutes. Often, what movies do is they bypass the 63 00:04:42,600 --> 00:04:46,920 Speaker 1: slow machinery of conscious thought and cut straight to the unconscious. 64 00:04:46,960 --> 00:04:51,200 Speaker 1: They use music and editing and facial expressions and body 65 00:04:51,279 --> 00:04:54,920 Speaker 1: language to cue us about what to feel and when 66 00:04:54,960 --> 00:04:57,479 Speaker 1: to feel it. And the most fascinating part is that 67 00:04:57,920 --> 00:05:01,080 Speaker 1: we go along with it. We forg get that we're 68 00:05:01,120 --> 00:05:04,240 Speaker 1: sitting in a theater or in our living room. Our 69 00:05:04,240 --> 00:05:08,880 Speaker 1: brains suspend disbelief, not by turning off, but by doing 70 00:05:09,000 --> 00:05:12,440 Speaker 1: exactly what they're wired to do, to make sense of 71 00:05:12,520 --> 00:05:17,480 Speaker 1: the world through sensation and story, even when that world 72 00:05:17,640 --> 00:05:21,160 Speaker 1: is completely invented. And this gives us some clues about 73 00:05:21,240 --> 00:05:25,200 Speaker 1: what is running in our brains under the hood, Because 74 00:05:25,360 --> 00:05:28,520 Speaker 1: when we watch a movie, our brains don't just observe. 75 00:05:28,640 --> 00:05:35,239 Speaker 1: They simulate, They mirror, they empathize. Brain regions involving vision 76 00:05:35,400 --> 00:05:39,080 Speaker 1: and movement and memory and prediction and pain. These all 77 00:05:39,080 --> 00:05:43,000 Speaker 1: get involved. When a character throws a punch towards the camera. 78 00:05:43,320 --> 00:05:48,200 Speaker 1: Our visual systems cause our motor systems to flinch when 79 00:05:48,240 --> 00:05:53,560 Speaker 1: two people kiss on screen. Our oxytocin system stirs when 80 00:05:53,560 --> 00:05:58,320 Speaker 1: a plot twist defies our expectations, our rewards circuits light 81 00:05:58,400 --> 00:06:01,600 Speaker 1: up like we just solved a puzz And the line 82 00:06:01,640 --> 00:06:06,279 Speaker 1: between film and memory can even blur. So sometimes people 83 00:06:06,760 --> 00:06:09,839 Speaker 1: will recall a moment from a movie as though it 84 00:06:09,880 --> 00:06:14,000 Speaker 1: were from their own memory. Some movie scenes feel more 85 00:06:14,080 --> 00:06:18,240 Speaker 1: vivid and emotionally charged than things that actually happen to us. 86 00:06:18,520 --> 00:06:21,880 Speaker 1: So all of this leads to a set of questions 87 00:06:22,200 --> 00:06:25,920 Speaker 1: that reach beyond the movie theater. What does film reveal 88 00:06:26,120 --> 00:06:30,600 Speaker 1: about the way that the brain processes reality. What tricks 89 00:06:30,640 --> 00:06:36,799 Speaker 1: of editing or framing or timing speak to our perceptual systems. 90 00:06:37,120 --> 00:06:42,279 Speaker 1: How does storytelling, and especially visual storytelling, exploit the quirks 91 00:06:42,320 --> 00:06:44,840 Speaker 1: of our cognition. So I'm very happy to be joined 92 00:06:44,839 --> 00:06:48,600 Speaker 1: today by doctor Jeff Zachs. He's a professor of psychological 93 00:06:48,600 --> 00:06:52,279 Speaker 1: and brain sciences at Washington University in Saint Louis, and 94 00:06:52,320 --> 00:06:57,400 Speaker 1: he's the author of Flicker Your Brain on Movies. His 95 00:06:57,440 --> 00:07:02,520 Speaker 1: research bridges cognitive neuroscience, psychology, and media studies, and his 96 00:07:02,560 --> 00:07:05,440 Speaker 1: book is about what is going on inside your skull 97 00:07:05,520 --> 00:07:07,719 Speaker 1: when you're caught up in a story on the screen. 98 00:07:07,960 --> 00:07:10,280 Speaker 1: So grab your popcorn and settle in. We're going to 99 00:07:10,600 --> 00:07:19,480 Speaker 1: find out what happens when your brain goes to the movies. So, Jeff, 100 00:07:19,520 --> 00:07:22,920 Speaker 1: from a neuroscience perspective, what is going on in the 101 00:07:22,960 --> 00:07:26,240 Speaker 1: brain when we sit in a dark theater and we 102 00:07:26,440 --> 00:07:27,679 Speaker 1: surrender it to story. 103 00:07:28,280 --> 00:07:31,080 Speaker 2: So the first thing is the same stuff that's going 104 00:07:31,120 --> 00:07:34,000 Speaker 2: on in the rest of your life. Our brains evolved 105 00:07:34,120 --> 00:07:39,520 Speaker 2: to deal with a complex, dynamic environment and being in 106 00:07:39,560 --> 00:07:43,800 Speaker 2: front of a film is one of those, and being 107 00:07:44,120 --> 00:07:46,480 Speaker 2: out in the world is one of those. And to 108 00:07:46,520 --> 00:07:48,440 Speaker 2: be honest, in lots of ways, it doesn't care that much. 109 00:07:48,440 --> 00:07:50,440 Speaker 2: Which is which and why is that? 110 00:07:50,520 --> 00:07:53,640 Speaker 1: Why when you're sitting in front of a flickering screen 111 00:07:53,840 --> 00:07:55,520 Speaker 1: does your brain not care so much? 112 00:07:56,040 --> 00:08:00,480 Speaker 2: One big reason in evolutionary terms is that movies are young, right, 113 00:08:00,600 --> 00:08:04,000 Speaker 2: moves have been around for a little less than one 114 00:08:04,120 --> 00:08:09,000 Speaker 2: hundred and thirty years, and we haven't experienced significant brain 115 00:08:09,000 --> 00:08:12,160 Speaker 2: evolution in that time. And another important piece is even 116 00:08:12,200 --> 00:08:16,480 Speaker 2: those of us who are real media addicts that diet 117 00:08:16,720 --> 00:08:19,200 Speaker 2: is being interspersed with a lot of experience being in 118 00:08:19,200 --> 00:08:22,880 Speaker 2: a three D world where we're locomoting and acting and 119 00:08:23,440 --> 00:08:26,120 Speaker 2: all of that is integrated and training each other up 120 00:08:26,120 --> 00:08:29,320 Speaker 2: all the time, and so it's not like the experiences 121 00:08:29,360 --> 00:08:31,600 Speaker 2: of being in a screen are reshaping our brains in 122 00:08:31,640 --> 00:08:32,280 Speaker 2: a major way. 123 00:08:32,520 --> 00:08:34,280 Speaker 1: Now, the way you set this up in the book 124 00:08:34,360 --> 00:08:38,200 Speaker 1: is you talked about the mirror rule and the success 125 00:08:38,360 --> 00:08:40,760 Speaker 1: rule as a way of introducing some of these ideas. 126 00:08:40,880 --> 00:08:42,440 Speaker 1: Can can you tell us what those are? 127 00:08:42,720 --> 00:08:46,800 Speaker 2: Yeah? Sure. The mirror rule says, other things being equal, 128 00:08:47,280 --> 00:08:52,560 Speaker 2: If you see other people doing stuff, you might want 129 00:08:52,600 --> 00:08:55,440 Speaker 2: to do things that are congruent with us. So if 130 00:08:55,480 --> 00:08:57,720 Speaker 2: somebody smiles, you might want to smile back. If someone 131 00:08:57,760 --> 00:08:59,959 Speaker 2: reaches out their hand to shake yours, you might want 132 00:09:00,080 --> 00:09:05,080 Speaker 2: on a reciprocate. The success rule is just learning. It says, 133 00:09:05,640 --> 00:09:07,920 Speaker 2: over time, the stuff that you have learned how to 134 00:09:07,960 --> 00:09:10,520 Speaker 2: do that's worked in the past, do that this time. 135 00:09:11,040 --> 00:09:14,040 Speaker 1: Other things being equal, And how does that apply to 136 00:09:14,960 --> 00:09:16,760 Speaker 1: watching a movie? These two rules. 137 00:09:17,480 --> 00:09:21,200 Speaker 2: So one important way that this has big effects on 138 00:09:21,240 --> 00:09:27,360 Speaker 2: our perceptions when we're watching film is that we evoke 139 00:09:27,880 --> 00:09:30,760 Speaker 2: actions that we might perform, and we evoke emotional states 140 00:09:30,760 --> 00:09:33,800 Speaker 2: that we might experience based on what we're seeing other 141 00:09:33,880 --> 00:09:40,520 Speaker 2: people do. So, if you're seeing someone who's face is 142 00:09:40,559 --> 00:09:43,320 Speaker 2: eight feet high on a big screen, crying in front 143 00:09:43,360 --> 00:09:46,360 Speaker 2: of you, You're going to tend to adopt an emotional 144 00:09:46,400 --> 00:09:49,600 Speaker 2: tone that's congruent with that. If you see someone who 145 00:09:50,200 --> 00:09:52,920 Speaker 2: is lunging towards you out of the screen, you're going 146 00:09:52,960 --> 00:09:54,640 Speaker 2: to tend to flinch a little bit. So that would 147 00:09:54,640 --> 00:09:56,360 Speaker 2: be an example of the mirror rule in the first 148 00:09:56,400 --> 00:09:58,439 Speaker 2: case and the success rule in the second case. 149 00:09:58,760 --> 00:10:01,160 Speaker 1: Okay, so you're in the theater, you're watching a movie. 150 00:10:01,720 --> 00:10:05,280 Speaker 1: You're mirroring what you're seeing on the screen. You're maybe 151 00:10:05,360 --> 00:10:08,040 Speaker 1: ducking a little bit moving when things are coming at you. 152 00:10:08,520 --> 00:10:12,680 Speaker 1: But we get deeply into the story. And one way 153 00:10:12,679 --> 00:10:14,440 Speaker 1: that you described this in your book is that we 154 00:10:14,559 --> 00:10:18,080 Speaker 1: have an event model of what is happening on the screen. 155 00:10:18,200 --> 00:10:19,560 Speaker 1: So let's dive into that. 156 00:10:20,000 --> 00:10:24,560 Speaker 2: Yeah. Sure, So as we're wandering around our complex, dynamic world, 157 00:10:25,160 --> 00:10:30,760 Speaker 2: there's islands of stability where stuff may be moving, but 158 00:10:30,840 --> 00:10:32,840 Speaker 2: it's moving in a consistent way. Like you're on a 159 00:10:32,880 --> 00:10:34,880 Speaker 2: swing and the swing is going back and forth, and 160 00:10:34,880 --> 00:10:37,400 Speaker 2: then you hop off the swing, and that island of 161 00:10:37,480 --> 00:10:40,400 Speaker 2: dynamic stability changes and we kind of march from one 162 00:10:40,840 --> 00:10:43,640 Speaker 2: stable regime to another and another. That's just the structure 163 00:10:43,720 --> 00:10:47,960 Speaker 2: of human experience. While you're in one of these stable regimes, 164 00:10:48,240 --> 00:10:50,520 Speaker 2: it's helpful to have a model in your head of 165 00:10:50,559 --> 00:10:52,760 Speaker 2: like what's going on If I'm on the swing, and 166 00:10:52,800 --> 00:10:56,920 Speaker 2: then my brain's processing this visual input and this motor input. 167 00:10:57,240 --> 00:10:59,240 Speaker 2: It can predict what's going to happen in your future 168 00:10:59,240 --> 00:11:01,320 Speaker 2: based on that. As soon as I hop off, it'd 169 00:11:01,320 --> 00:11:04,600 Speaker 2: be a good idea to update and change the parameters 170 00:11:04,600 --> 00:11:08,840 Speaker 2: of that situation. So we call those mental representations of 171 00:11:08,920 --> 00:11:13,960 Speaker 2: the current situation event models. And there's good reason to 172 00:11:13,960 --> 00:11:17,240 Speaker 2: think that a decent chunk of what the brain's doing 173 00:11:17,360 --> 00:11:20,760 Speaker 2: in normal, everyday living your life is building up a 174 00:11:20,880 --> 00:11:23,680 Speaker 2: model of what's happening now and then updating it from 175 00:11:23,679 --> 00:11:25,920 Speaker 2: time to time when the situation changes. 176 00:11:26,240 --> 00:11:29,360 Speaker 1: And so when that comes to story, when we're watching 177 00:11:29,480 --> 00:11:32,280 Speaker 1: Harry Potter or a Game of Thrones or something like that, 178 00:11:32,320 --> 00:11:35,439 Speaker 1: what does an event model look like in that context? 179 00:11:36,200 --> 00:11:39,560 Speaker 2: Yeah, So take Game of Thrones. You know, we're in 180 00:11:39,920 --> 00:11:42,640 Speaker 2: a room where there's a bunch of characters talking about 181 00:11:43,720 --> 00:11:47,959 Speaker 2: the next battle coming up, and then they walk out 182 00:11:48,000 --> 00:11:50,120 Speaker 2: of the room and a couple of them pull off 183 00:11:50,120 --> 00:11:53,840 Speaker 2: to the side to scheme and plot. And while they're 184 00:11:53,880 --> 00:11:56,840 Speaker 2: in that room, our brain is going to keep track 185 00:11:57,160 --> 00:11:59,959 Speaker 2: of who are the characters that are present, where they locate, 186 00:12:00,360 --> 00:12:03,400 Speaker 2: so that as you go from over the shoulder shots 187 00:12:03,480 --> 00:12:06,120 Speaker 2: and close ups, you're only seeing a couple people at 188 00:12:06,120 --> 00:12:08,640 Speaker 2: a time, which is totally like how our normal vision works. Right, 189 00:12:08,640 --> 00:12:11,000 Speaker 2: we're looking around the room, but we got a representation 190 00:12:11,080 --> 00:12:14,600 Speaker 2: of that whole scene. Then you walk outside and you've 191 00:12:14,640 --> 00:12:16,960 Speaker 2: got to update that so that you have a representation 192 00:12:17,280 --> 00:12:19,480 Speaker 2: of the subset of characters and the folks that are 193 00:12:19,520 --> 00:12:21,960 Speaker 2: not there, you're not expecting them to pop up. And 194 00:12:22,000 --> 00:12:24,720 Speaker 2: you're also representing that you're in a different location and 195 00:12:24,720 --> 00:12:27,520 Speaker 2: that the geometry of the stuff in that location is different. 196 00:12:27,840 --> 00:12:30,840 Speaker 1: So this is an example. You know, in most of 197 00:12:30,840 --> 00:12:33,559 Speaker 1: my episodes, I end up talking about the internal model, 198 00:12:33,640 --> 00:12:35,959 Speaker 1: how your brain is locked in silence and darkness. You're 199 00:12:35,960 --> 00:12:39,240 Speaker 1: putting together model. One way that we do that, for example, 200 00:12:39,360 --> 00:12:42,240 Speaker 1: is with you know, your spouse or your friend or 201 00:12:42,280 --> 00:12:44,200 Speaker 1: anything like that. You're running a model of what that 202 00:12:44,240 --> 00:12:46,439 Speaker 1: person is like. But in the context of a movie, 203 00:12:46,960 --> 00:12:50,160 Speaker 1: you're running a large model of all the things that 204 00:12:50,200 --> 00:12:53,240 Speaker 1: have happened and might happen and what's going on there. 205 00:12:53,240 --> 00:12:56,280 Speaker 1: And that's a big part of why we live story 206 00:12:56,360 --> 00:12:59,199 Speaker 1: and experience story with the kind of depth that we do. 207 00:12:59,320 --> 00:13:04,000 Speaker 2: Is that right, Yeah, totally. And you know that representation 208 00:13:04,520 --> 00:13:07,600 Speaker 2: of your spouse or your friend that you're hanging out with, 209 00:13:07,840 --> 00:13:11,280 Speaker 2: that's a huge part of your event model in real 210 00:13:11,320 --> 00:13:14,280 Speaker 2: life and also in film. You know, if you've got 211 00:13:14,000 --> 00:13:18,480 Speaker 2: the two characters who are sneaking off to plot their revenge, 212 00:13:19,080 --> 00:13:23,839 Speaker 2: their topic of conversation, their motives, they're affected, the way 213 00:13:23,880 --> 00:13:26,640 Speaker 2: that they're moving and where they're headed through the space. 214 00:13:26,760 --> 00:13:29,199 Speaker 2: All of that is a super important part of comprehending 215 00:13:29,240 --> 00:13:32,880 Speaker 2: what's going to happen. We're intensely social creatures, and so 216 00:13:33,040 --> 00:13:36,160 Speaker 2: a big part of ev models most of the time 217 00:13:37,000 --> 00:13:40,240 Speaker 2: in our models of the other people in our environment. 218 00:13:40,480 --> 00:13:43,160 Speaker 1: That's right. Now. This is related to an issue, which 219 00:13:43,200 --> 00:13:47,319 Speaker 1: is when I was younger, I was really surprised that 220 00:13:47,600 --> 00:13:51,520 Speaker 1: movies worked because you're watching some scene, let's say, some 221 00:13:51,559 --> 00:13:56,319 Speaker 1: people plotting something inside the castle, and then you cut 222 00:13:56,400 --> 00:13:59,840 Speaker 1: to a scene where you have, you know, bearded horsemen 223 00:14:00,400 --> 00:14:03,839 Speaker 1: across the planes, and you know, you're suddenly in a 224 00:14:03,880 --> 00:14:08,920 Speaker 1: completely different location and different part of the plot, and 225 00:14:09,320 --> 00:14:12,520 Speaker 1: people don't scream or get surprised, but they're totally fine 226 00:14:12,600 --> 00:14:15,559 Speaker 1: to see the things switch from one to the next 227 00:14:15,640 --> 00:14:18,080 Speaker 1: in the space of a frame, So why is that? 228 00:14:18,640 --> 00:14:21,760 Speaker 2: Yeah, so that part is really kind of bonkers that 229 00:14:21,840 --> 00:14:25,760 Speaker 2: it works, because in our real life are these continuity constraints. Right, 230 00:14:25,800 --> 00:14:29,080 Speaker 2: time doesn't jump around. Space changes like you walk through 231 00:14:29,120 --> 00:14:32,680 Speaker 2: a doorway, and that's a huge deal because the affordances 232 00:14:32,720 --> 00:14:34,920 Speaker 2: of the space totally change in a way that they 233 00:14:34,920 --> 00:14:37,960 Speaker 2: don't as you're walking within a room. But you know, 234 00:14:38,000 --> 00:14:40,560 Speaker 2: you don't walk through the doorway and wind up in France. 235 00:14:41,440 --> 00:14:44,360 Speaker 2: But in a movie that's totally totally normal. And my 236 00:14:44,480 --> 00:14:48,880 Speaker 2: understanding for reading the history is that early filmmakers figured 237 00:14:48,920 --> 00:14:51,480 Speaker 2: out that this was doable kind of by accident, right. 238 00:14:51,520 --> 00:14:53,600 Speaker 2: The original idea was that what we're going to do 239 00:14:53,600 --> 00:14:57,640 Speaker 2: with these cameras is we're going to film stage sets 240 00:14:57,960 --> 00:14:59,720 Speaker 2: and we're going to do things kind of like we 241 00:14:59,760 --> 00:15:03,120 Speaker 2: do theater. And just as people were putting stuff on 242 00:15:03,200 --> 00:15:06,240 Speaker 2: film and then looking back at the recordings and seeing 243 00:15:06,240 --> 00:15:08,640 Speaker 2: what happened when you went from one take to the other, 244 00:15:08,680 --> 00:15:11,880 Speaker 2: they realized, oh, you can do this, and the brain 245 00:15:12,000 --> 00:15:15,120 Speaker 2: is going to use the mechanisms that it uses to 246 00:15:16,000 --> 00:15:18,880 Speaker 2: update things when you walk through that doorway and apply 247 00:15:19,000 --> 00:15:22,320 Speaker 2: them to the situation where you're walking through a doorway 248 00:15:22,320 --> 00:15:24,640 Speaker 2: from one continent to another that might be hundreds of 249 00:15:24,760 --> 00:15:29,200 Speaker 2: years apart, and surprisingly it works. 250 00:15:31,400 --> 00:15:34,800 Speaker 1: It strikes me, actually that we do have some discontinuities 251 00:15:35,000 --> 00:15:37,600 Speaker 1: in our lives anyway, in the sense that you know, 252 00:15:37,640 --> 00:15:39,280 Speaker 1: you go to sleep and then you wake up eight 253 00:15:39,320 --> 00:15:43,280 Speaker 1: hours later. That's a discontinuity in time and in space 254 00:15:43,360 --> 00:15:46,320 Speaker 1: and time. We often have these things where we notice 255 00:15:46,480 --> 00:15:50,280 Speaker 1: something we hadn't seen before. Suddenly a scene changes because 256 00:15:50,280 --> 00:15:53,400 Speaker 1: we realize that our friend is standing over there, or 257 00:15:53,440 --> 00:15:56,240 Speaker 1: there's a danger over here, and so in a sense 258 00:15:56,400 --> 00:15:59,960 Speaker 1: that our model of what's happened is suddenly flipped into 259 00:16:00,120 --> 00:16:03,560 Speaker 1: something else. And so we do have a bit of 260 00:16:04,440 --> 00:16:08,560 Speaker 1: discontinuity in our lives. But this is a really interesting 261 00:16:08,640 --> 00:16:12,960 Speaker 1: question about what do films tell us about brains? Because 262 00:16:13,040 --> 00:16:16,040 Speaker 1: as people figured out how to make movies and made 263 00:16:16,080 --> 00:16:19,320 Speaker 1: discoveries along the way, it gives us some insight to 264 00:16:19,400 --> 00:16:20,720 Speaker 1: what's happening under the hood. 265 00:16:21,160 --> 00:16:24,760 Speaker 2: One of the pieces relates to the top you've covered 266 00:16:24,800 --> 00:16:29,040 Speaker 2: multiple times on the pod, which is the way that 267 00:16:29,120 --> 00:16:34,440 Speaker 2: this system for representing internal models can multiplex. So the 268 00:16:34,480 --> 00:16:38,840 Speaker 2: same machinery that we're using to simulate a model of 269 00:16:38,840 --> 00:16:42,400 Speaker 2: what's happening right now. We can drive in that case 270 00:16:42,840 --> 00:16:45,440 Speaker 2: the models being driven mostly by what's in front of 271 00:16:45,480 --> 00:16:49,400 Speaker 2: our eyeballs. But we can drive that same machinery by 272 00:16:49,480 --> 00:16:53,920 Speaker 2: retrieving stuff from episodic memory, by thinking about what might 273 00:16:53,960 --> 00:16:58,440 Speaker 2: happen in the future, by thinking about a story that 274 00:16:58,480 --> 00:17:02,320 Speaker 2: somebody's telling us. And so if you're wandering through your 275 00:17:02,400 --> 00:17:06,560 Speaker 2: environment and you see an old friend and that reminds 276 00:17:06,680 --> 00:17:10,639 Speaker 2: you of the last time you saw them, that's another 277 00:17:10,720 --> 00:17:14,240 Speaker 2: kind of discontinuity that I think is part of why 278 00:17:14,640 --> 00:17:17,919 Speaker 2: the ability to cut across time and space works is 279 00:17:17,920 --> 00:17:20,720 Speaker 2: because your brain is used to shifting these models rapidly 280 00:17:20,760 --> 00:17:24,760 Speaker 2: and across big shifts in order to think about stuff 281 00:17:24,760 --> 00:17:28,280 Speaker 2: that is offline and but related to the current situation. 282 00:17:28,840 --> 00:17:31,440 Speaker 1: Yeah. I think one of the things that's so striking 283 00:17:31,440 --> 00:17:33,320 Speaker 1: to me about movies is that we have all these 284 00:17:33,520 --> 00:17:37,880 Speaker 1: edits and cuts and changes in camera angles, but we're 285 00:17:37,920 --> 00:17:41,119 Speaker 1: not aware of the trickery of that at all. We 286 00:17:41,720 --> 00:17:44,480 Speaker 1: just don't even notice it. Even when there's a conversation 287 00:17:44,560 --> 00:17:47,960 Speaker 1: between two people and the camera keeps switching angles, we 288 00:17:48,040 --> 00:17:52,760 Speaker 1: don't suddenly react in fear that we've changed position. It's 289 00:17:52,880 --> 00:17:56,199 Speaker 1: like we're a floating ghost in the scene, and it 290 00:17:56,240 --> 00:17:58,880 Speaker 1: doesn't bother us that we're moving positions all over. 291 00:17:59,200 --> 00:18:03,440 Speaker 2: So part of the picture is that, you know, even 292 00:18:03,480 --> 00:18:08,120 Speaker 2: though the world out there is stable and doesn't move around, 293 00:18:09,560 --> 00:18:13,960 Speaker 2: our visual systems are anything but right. So we're moving 294 00:18:13,960 --> 00:18:17,600 Speaker 2: our bodies, we're moving our heads, and we're moving our 295 00:18:17,640 --> 00:18:23,200 Speaker 2: eyes a ton, and then we're blinking, and all of 296 00:18:23,240 --> 00:18:28,800 Speaker 2: that is maximizing our brain's estimate of the information content 297 00:18:28,840 --> 00:18:31,000 Speaker 2: that it's going to get out of the scene. Right. 298 00:18:31,000 --> 00:18:35,320 Speaker 2: It's very actively directing the eyeball to the points in 299 00:18:35,359 --> 00:18:39,360 Speaker 2: the scene that the brain at that moment thinks are 300 00:18:39,480 --> 00:18:41,360 Speaker 2: likely to be important. And it's got to do that 301 00:18:41,560 --> 00:18:46,080 Speaker 2: because we have a phobiated eyes you've discussed, and you know, 302 00:18:46,280 --> 00:18:49,480 Speaker 2: we've all got this tiny little bit of high resolution 303 00:18:50,160 --> 00:18:52,200 Speaker 2: sensor that's got to be pointed at the right stuff. 304 00:18:52,600 --> 00:18:55,600 Speaker 2: But I'll tell you one of my buddies in film 305 00:18:55,600 --> 00:18:58,840 Speaker 2: stays asked me a question a few weeks ago that 306 00:18:59,000 --> 00:19:03,040 Speaker 2: has totally baked noodle on this, which is that explains 307 00:19:03,040 --> 00:19:07,560 Speaker 2: the kind of camera motion where the point of view 308 00:19:07,680 --> 00:19:12,600 Speaker 2: of the camera is stable, but the camera is rotating 309 00:19:12,680 --> 00:19:15,440 Speaker 2: or even panning. Right. But if you think about a 310 00:19:15,480 --> 00:19:19,080 Speaker 2: simple life over the shoulder of the shot. It's totally 311 00:19:19,160 --> 00:19:22,840 Speaker 2: normal to have the camera's position jump around, So it's 312 00:19:22,840 --> 00:19:25,040 Speaker 2: not just that the camera is pointing in different directions, 313 00:19:25,320 --> 00:19:29,960 Speaker 2: but the camera's moving around the room discontinuously. And as 314 00:19:30,000 --> 00:19:33,919 Speaker 2: far as I know, there's no evidence that brains like 315 00:19:34,040 --> 00:19:40,280 Speaker 2: that any less though, that's a very ecologically implausible kind 316 00:19:40,280 --> 00:19:42,960 Speaker 2: of camera movement than camera movement like cuts where you're 317 00:19:43,000 --> 00:19:46,560 Speaker 2: just rotating the camera. And it seems to me that 318 00:19:46,560 --> 00:19:50,439 Speaker 2: that is surprising, Right, There's a lot. 319 00:19:50,320 --> 00:19:52,639 Speaker 1: Of this that I find surprising, and I think this 320 00:19:52,720 --> 00:19:54,919 Speaker 1: must come back to the issue of event models. So 321 00:19:55,160 --> 00:19:58,399 Speaker 1: a scene from Batman, Let's say your you know, he's 322 00:19:58,480 --> 00:20:02,800 Speaker 1: coming off the roof, and you are behind him, the 323 00:20:02,800 --> 00:20:04,600 Speaker 1: cameras behind him as he goes off through, and then 324 00:20:04,600 --> 00:20:08,440 Speaker 1: you're below him as he's coming down, and then you 325 00:20:09,119 --> 00:20:13,960 Speaker 1: suddenly find yourself in a taxi cab sitting below and 326 00:20:14,000 --> 00:20:17,800 Speaker 1: a taxiab driver is eating and then the roof suddenly 327 00:20:17,880 --> 00:20:20,440 Speaker 1: dentse in as Batman lands on top of the roof. 328 00:20:20,800 --> 00:20:23,440 Speaker 1: But it doesn't bother us that we've completely switched points 329 00:20:23,480 --> 00:20:27,040 Speaker 1: of view where now with the taxi driver. I guess 330 00:20:27,040 --> 00:20:31,000 Speaker 1: it's because we have this event model running about Okay, 331 00:20:31,040 --> 00:20:35,760 Speaker 1: batman is falling, and we're happy to switch perspectives on 332 00:20:35,760 --> 00:20:36,400 Speaker 1: it all the time. 333 00:20:36,840 --> 00:20:40,840 Speaker 2: I think that's exactly right. I really am mystified that 334 00:20:40,920 --> 00:20:42,960 Speaker 2: this works. But I think that the answer is going 335 00:20:43,040 --> 00:20:44,840 Speaker 2: to have something to do with the fact that these 336 00:20:44,920 --> 00:20:50,119 Speaker 2: representations are not primarily egocentric. They're what we call alicentric, right, 337 00:20:50,160 --> 00:20:54,280 Speaker 2: So they're divorced from our personal perspective, And so what 338 00:20:54,400 --> 00:20:57,040 Speaker 2: the brain is really caring about in that situation is 339 00:20:57,080 --> 00:21:00,320 Speaker 2: what's the thing I want to gather about information about. 340 00:21:01,320 --> 00:21:02,960 Speaker 2: So I want to get a good look at the 341 00:21:03,000 --> 00:21:05,160 Speaker 2: cab driver. If I were really there, I would kind 342 00:21:05,160 --> 00:21:07,720 Speaker 2: of crane my head and focus my eyes on them. 343 00:21:08,480 --> 00:21:11,879 Speaker 2: If the camera can just jump to in front of 344 00:21:11,880 --> 00:21:14,080 Speaker 2: the cab driver and give me that angle, as long 345 00:21:14,119 --> 00:21:16,920 Speaker 2: as it's telling me about the stuff that I'm trying 346 00:21:16,920 --> 00:21:19,800 Speaker 2: to learn about, my brain might be okay because it's 347 00:21:19,840 --> 00:21:23,160 Speaker 2: not so worried about the personal viewpoint on the scene. 348 00:21:23,160 --> 00:21:27,080 Speaker 2: It's building this somewhat perspectiveless representation of the scene. 349 00:21:27,240 --> 00:21:29,879 Speaker 1: That's right, And a loose analogy here is with this 350 00:21:30,000 --> 00:21:32,640 Speaker 1: concept of mental ease, which is to say, if you're 351 00:21:32,680 --> 00:21:35,240 Speaker 1: bilingual and I tell you a joke in one language, 352 00:21:35,240 --> 00:21:37,960 Speaker 1: you might turn right around and tell it in a 353 00:21:38,040 --> 00:21:41,440 Speaker 1: second language to someone else. And so what that means 354 00:21:41,520 --> 00:21:45,000 Speaker 1: is you haven't stored my joke in a series of phonemes. 355 00:21:45,040 --> 00:21:48,520 Speaker 1: But instead it's the concept of the joke, the concept 356 00:21:48,560 --> 00:21:51,120 Speaker 1: of the let's call it the event model of this 357 00:21:51,160 --> 00:21:53,639 Speaker 1: person said that to this person, and so on, and 358 00:21:54,040 --> 00:21:56,720 Speaker 1: it's somehow the same thing. So when Batman is falling, 359 00:21:57,200 --> 00:22:00,760 Speaker 1: it's this thing happening in the world, and we appreciate 360 00:22:00,800 --> 00:22:04,080 Speaker 1: that we can see that from different perspectives, including the 361 00:22:04,160 --> 00:22:07,920 Speaker 1: taxi cab driver's perspective. And what really matters is the 362 00:22:07,920 --> 00:22:12,679 Speaker 1: thing itself that's happening, that's not about the egocentric perspective. 363 00:22:30,119 --> 00:22:33,160 Speaker 1: Does slow motion challenge the brain in any way that's 364 00:22:33,160 --> 00:22:35,000 Speaker 1: different from normal scenes? 365 00:22:35,400 --> 00:22:38,879 Speaker 2: So I don't, I mean, I think you know a 366 00:22:38,960 --> 00:22:42,119 Speaker 2: ton more about that than I do. I don't know 367 00:22:42,240 --> 00:22:47,000 Speaker 2: that I have any genius ideas about slow mo. But no, 368 00:22:47,119 --> 00:22:49,400 Speaker 2: there's there's one there's one thing that I think is important, 369 00:22:49,440 --> 00:22:54,919 Speaker 2: which is that we can think about events on a 370 00:22:55,040 --> 00:23:00,200 Speaker 2: huge range of time scales, from like the a K 371 00:23:01,160 --> 00:23:04,679 Speaker 2: of a quantum particle to the heat depth of the 372 00:23:04,760 --> 00:23:10,200 Speaker 2: universe and I think that we do that by recycling 373 00:23:10,560 --> 00:23:15,919 Speaker 2: the machinery that we use to build event models on 374 00:23:15,960 --> 00:23:20,480 Speaker 2: a human scale, just like we can think about spatial 375 00:23:20,520 --> 00:23:26,040 Speaker 2: scales from against subatonic atomic particles or atoms up to 376 00:23:27,040 --> 00:23:31,840 Speaker 2: planets and galaxies, and we do that by using microscopes 377 00:23:31,920 --> 00:23:36,840 Speaker 2: or telescopes or drawings that take those very disparate spatial 378 00:23:36,880 --> 00:23:39,040 Speaker 2: scales and map them on to like the scale of 379 00:23:39,160 --> 00:23:42,160 Speaker 2: chairs and coffee cups and stuff. So the brain has 380 00:23:42,320 --> 00:23:46,080 Speaker 2: some way of taking these very different time scales and 381 00:23:46,400 --> 00:23:51,320 Speaker 2: mapping them onto a human scale, doing radical slowmos or 382 00:23:51,680 --> 00:23:52,320 Speaker 2: fast motion. 383 00:23:52,880 --> 00:23:55,639 Speaker 1: That is totally right. It's very surprising to me always 384 00:23:55,640 --> 00:23:58,040 Speaker 1: how the brain can extrapolate the sort of things that 385 00:23:58,080 --> 00:24:01,840 Speaker 1: it knows and do different scales of space or time. 386 00:24:02,160 --> 00:24:04,560 Speaker 1: Let me ask you something else, how does the brain 387 00:24:04,640 --> 00:24:09,600 Speaker 1: react to suspense? So what do filmmakers do to generate tension? 388 00:24:09,640 --> 00:24:11,280 Speaker 1: And how do our brains respond to that? 389 00:24:11,880 --> 00:24:15,760 Speaker 2: The brain is in to a significant degree of prediction. 390 00:24:15,880 --> 00:24:16,520 Speaker 1: Machine. 391 00:24:17,080 --> 00:24:19,280 Speaker 2: Your brain's always trying to figure out what's coming down 392 00:24:19,280 --> 00:24:21,840 Speaker 2: the pike, and just being able to do that on 393 00:24:21,880 --> 00:24:24,040 Speaker 2: the timescale of a fraction of a second or a 394 00:24:24,040 --> 00:24:28,480 Speaker 2: few seconds is hugely helpful. Right, So if there's an 395 00:24:28,480 --> 00:24:32,199 Speaker 2: object moving quickly in your general direction, being able to 396 00:24:32,240 --> 00:24:34,920 Speaker 2: predict the exact trajectory it's going to tell you whether 397 00:24:34,960 --> 00:24:38,239 Speaker 2: you got a duck or not. If you're starting to 398 00:24:38,480 --> 00:24:41,359 Speaker 2: talk about your friend's surprise party and you see them 399 00:24:41,400 --> 00:24:44,119 Speaker 2: getting close enough that they might hear, being able to 400 00:24:44,200 --> 00:24:45,879 Speaker 2: predict what they're going to be able to hear in 401 00:24:45,920 --> 00:24:49,480 Speaker 2: a couple seconds is important for avoiding that social folk. 402 00:24:49,520 --> 00:24:55,200 Speaker 2: Pa and Hitchcock wrote awesome things about surprise and suspense 403 00:24:55,280 --> 00:24:59,240 Speaker 2: and how they work in film. You know, suspense is 404 00:24:59,359 --> 00:25:05,760 Speaker 2: often a situation where there's a couple potential outcomes in 405 00:25:05,840 --> 00:25:08,240 Speaker 2: play and you don't know how it's going to unfold. 406 00:25:08,760 --> 00:25:13,439 Speaker 2: But there's also situations where you have a piece of 407 00:25:13,480 --> 00:25:19,520 Speaker 2: information that the characters don't have, and so the ambiguity 408 00:25:19,600 --> 00:25:23,560 Speaker 2: is not like about whether there's a bomb in the 409 00:25:23,600 --> 00:25:26,919 Speaker 2: trunk that's going to go off, but about how the 410 00:25:27,080 --> 00:25:29,879 Speaker 2: characters are going to figure out that there's a bomb 411 00:25:29,920 --> 00:25:33,600 Speaker 2: in the trunk and whether they're going to avoid getting 412 00:25:33,640 --> 00:25:37,840 Speaker 2: blown up when it goes off. And so that's all 413 00:25:38,600 --> 00:25:43,840 Speaker 2: stimulating that prediction system, confronting it with situations where the 414 00:25:44,240 --> 00:25:47,160 Speaker 2: entropy is maximal, where you're like super uncertain about what's 415 00:25:47,200 --> 00:25:50,800 Speaker 2: going on on dimensions that your brain is invested in. 416 00:25:51,240 --> 00:25:53,959 Speaker 1: And this comes back to the same issue about theory 417 00:25:54,040 --> 00:25:56,439 Speaker 1: of mind that we talked about with you know, the 418 00:25:56,480 --> 00:25:59,520 Speaker 1: cab driver who doesn't know that something's falling and suddenly 419 00:25:59,560 --> 00:26:04,400 Speaker 1: his roof caves in. It's the same thing here. We 420 00:26:04,520 --> 00:26:06,360 Speaker 1: know that there's a bomb in the trunk, and we 421 00:26:06,480 --> 00:26:10,159 Speaker 1: know that the characters don't know, and so there's the 422 00:26:10,280 --> 00:26:13,880 Speaker 1: suspense of hoping that they get there. But again, we're 423 00:26:13,920 --> 00:26:18,280 Speaker 1: able to take this omniscient view in a movie that 424 00:26:18,640 --> 00:26:22,720 Speaker 1: is sort of surprising. It's a it's an issue of 425 00:26:22,800 --> 00:26:25,199 Speaker 1: theory of mind in this case. So you studied how 426 00:26:25,280 --> 00:26:29,680 Speaker 1: sometimes people misremember events and films as though they were 427 00:26:29,720 --> 00:26:33,159 Speaker 1: real life memories. So what does that tell us about memory? 428 00:26:33,200 --> 00:26:37,840 Speaker 2: More generally, our brains are encoding information about what about 429 00:26:37,840 --> 00:26:43,640 Speaker 2: what's in the scene, what happened, and also about meta 430 00:26:43,760 --> 00:26:46,720 Speaker 2: information metadata about like how did I learn about that? 431 00:26:47,040 --> 00:26:50,800 Speaker 2: Did I learn that somebody got a broken arm because 432 00:26:50,840 --> 00:26:54,240 Speaker 2: I was there and I saw it, or because a 433 00:26:54,320 --> 00:26:56,960 Speaker 2: mutual friend told me about it, or because I saw 434 00:26:57,000 --> 00:26:59,920 Speaker 2: it on the news. If I'm going to reason in 435 00:27:00,080 --> 00:27:03,920 Speaker 2: the future about what to do next time, I see 436 00:27:03,920 --> 00:27:08,600 Speaker 2: that person, or what to do next time I'm on 437 00:27:08,680 --> 00:27:11,119 Speaker 2: the slippery sidewalk where they fell and broke their arm. 438 00:27:11,640 --> 00:27:15,280 Speaker 2: It actually doesn't matter so much how I learn the 439 00:27:15,320 --> 00:27:19,240 Speaker 2: information what the metadata is. And so it turns out 440 00:27:19,240 --> 00:27:23,920 Speaker 2: that we're much better at tracking the what than the metadata, 441 00:27:24,680 --> 00:27:28,480 Speaker 2: and so that leads to these confusions, which can be 442 00:27:28,600 --> 00:27:33,960 Speaker 2: important for memory errors, especially now that we live in 443 00:27:34,000 --> 00:27:39,440 Speaker 2: a social ecology of storytelling, let alone misinformations. Misinformation is 444 00:27:39,440 --> 00:27:43,199 Speaker 2: a whole other ball wax, but just you know, just 445 00:27:43,359 --> 00:27:48,520 Speaker 2: normal telling each other stories for entertainment leads to the 446 00:27:48,560 --> 00:27:52,439 Speaker 2: potential to confuse stuff that I learned in the context 447 00:27:52,560 --> 00:27:56,320 Speaker 2: of entertainment with stuff that I learned in the context 448 00:27:56,359 --> 00:27:59,280 Speaker 2: that I was actually there, or it was a source 449 00:27:59,320 --> 00:28:01,919 Speaker 2: purporting to be ritical, like a news source. 450 00:28:02,240 --> 00:28:05,399 Speaker 1: And this is the sense in which watching movies can 451 00:28:05,520 --> 00:28:09,560 Speaker 1: actually reshape our cognitive habits or the things we know, 452 00:28:10,280 --> 00:28:14,280 Speaker 1: because we essentially don't have good source memory. What that 453 00:28:14,440 --> 00:28:17,280 Speaker 1: means just for the listeners is you know, you know 454 00:28:17,320 --> 00:28:20,320 Speaker 1: what a pomegranate is, or you know that Paris is 455 00:28:20,359 --> 00:28:23,960 Speaker 1: the capital of France, but you probably can't remember when 456 00:28:24,040 --> 00:28:26,680 Speaker 1: and where you learned those items. You just know those 457 00:28:26,720 --> 00:28:28,879 Speaker 1: items and it may be the same thing with getting 458 00:28:29,240 --> 00:28:34,879 Speaker 1: lots of practice with some idea or heroism or doing 459 00:28:34,920 --> 00:28:39,120 Speaker 1: the right thing and so on. Movies can contribute stories 460 00:28:39,160 --> 00:28:41,680 Speaker 1: more generally can contribute to that even if you don't 461 00:28:41,680 --> 00:28:43,720 Speaker 1: remember the source, it as though it happened to. 462 00:28:43,680 --> 00:28:47,520 Speaker 2: You totally, and we learned both like specific about specific 463 00:28:47,600 --> 00:28:50,880 Speaker 2: events like you know, my buddy broke their arm, or 464 00:28:51,200 --> 00:28:53,720 Speaker 2: about general facts about the world, like you know, the 465 00:28:53,920 --> 00:28:57,600 Speaker 2: pomegranate has seeds that you can eat. And one of 466 00:28:57,640 --> 00:29:02,320 Speaker 2: the cool subtle of reasoning that our brains just do 467 00:29:02,480 --> 00:29:07,560 Speaker 2: effortless is if I'm reading a fiction story set in 468 00:29:07,680 --> 00:29:12,200 Speaker 2: a world that is maybe quite different than my world 469 00:29:12,360 --> 00:29:14,760 Speaker 2: or quite different than the real world, right say it's 470 00:29:14,840 --> 00:29:20,720 Speaker 2: fantasy or science fiction, and I read that a computer 471 00:29:20,840 --> 00:29:25,160 Speaker 2: works a particular way, or I read about I don't 472 00:29:25,200 --> 00:29:27,360 Speaker 2: know how a thresher on a farm works, and I 473 00:29:27,360 --> 00:29:30,400 Speaker 2: don't know anything about threshers. People are really pretty good 474 00:29:30,400 --> 00:29:32,120 Speaker 2: at figuring out like is that a fact that I 475 00:29:32,120 --> 00:29:34,360 Speaker 2: can import into my fact knowledge or does that have 476 00:29:34,400 --> 00:29:38,240 Speaker 2: to stay in the science fiction world. Which that's a 477 00:29:38,280 --> 00:29:39,720 Speaker 2: super subtle form of reasoning. 478 00:29:40,360 --> 00:29:43,959 Speaker 1: Yeah, oh that's a good one. So here's a question. 479 00:29:46,240 --> 00:29:48,959 Speaker 1: After you wrote your book. Do you feel that there 480 00:29:49,000 --> 00:29:53,680 Speaker 1: are filmmakers who can use findings from neuroscience to design 481 00:29:54,000 --> 00:29:57,160 Speaker 1: better or more emotionally impactful stories. 482 00:29:58,080 --> 00:30:02,400 Speaker 2: Yeah. And you know, one of the really awesome things 483 00:30:02,680 --> 00:30:06,120 Speaker 2: about the scientific life that I've been lucky to lead 484 00:30:06,280 --> 00:30:08,160 Speaker 2: is that I get to talk to some of my 485 00:30:08,200 --> 00:30:15,120 Speaker 2: heroes about their experiences as filmmakers. And filmmakers are amazing 486 00:30:15,160 --> 00:30:19,440 Speaker 2: psychologists and neuroscientists across the board. They don't have to 487 00:30:19,440 --> 00:30:22,800 Speaker 2: be like formal students of the field, and most aren't 488 00:30:22,880 --> 00:30:25,800 Speaker 2: like most people are busy, but film has been doing 489 00:30:25,880 --> 00:30:30,240 Speaker 2: these intensive experiments with many, many repetitions and large sample 490 00:30:30,320 --> 00:30:33,960 Speaker 2: sizes over more than one hundred years, with like real 491 00:30:33,960 --> 00:30:36,600 Speaker 2: money on the line. So they've learned a ton of things. 492 00:30:37,160 --> 00:30:40,240 Speaker 2: And so people even with no you know, who don't 493 00:30:40,280 --> 00:30:42,960 Speaker 2: read this stuff for pleasure, know a bunch of things. 494 00:30:43,000 --> 00:30:44,800 Speaker 2: And when I, you know, when I ask them questions, 495 00:30:44,840 --> 00:30:47,960 Speaker 2: they like they often know the right answers or no 496 00:30:48,520 --> 00:30:50,920 Speaker 2: answers that I didn't know were right until I ask them. 497 00:30:51,280 --> 00:30:53,600 Speaker 2: And then there are some who are like as you 498 00:30:53,640 --> 00:30:56,680 Speaker 2: may imagine, even more fun for me as a geek, 499 00:30:56,720 --> 00:30:59,640 Speaker 2: who like actually say, you know, I read this thing 500 00:31:00,080 --> 00:31:03,080 Speaker 2: in the nature briefing and like, you know, do you 501 00:31:03,120 --> 00:31:03,960 Speaker 2: think this would work? 502 00:31:04,520 --> 00:31:08,360 Speaker 1: And that's super cool too, Great a couple of rapid 503 00:31:08,400 --> 00:31:14,600 Speaker 1: fire questions. Is there a particular movie that you exemplifies 504 00:31:14,840 --> 00:31:16,840 Speaker 1: what the brain loves about cinema? 505 00:31:17,320 --> 00:31:19,920 Speaker 2: One of my favorite movies of all time is The Shining, 506 00:31:20,640 --> 00:31:26,200 Speaker 2: and The Shining is amazing for many reasons, but one 507 00:31:26,440 --> 00:31:32,360 Speaker 2: is that it plays with these model building the facilities 508 00:31:32,360 --> 00:31:35,920 Speaker 2: that we've talked about by taking just like one dimension, 509 00:31:36,040 --> 00:31:41,960 Speaker 2: like the saturation of a red or the geometry of 510 00:31:42,120 --> 00:31:48,040 Speaker 2: motion of a tricycle, and exaggerating it just enough so 511 00:31:48,080 --> 00:31:51,840 Speaker 2: that it crosses over from being normal to not being 512 00:31:52,480 --> 00:31:56,520 Speaker 2: totally absurd, but just being deeply disturbing. This is what 513 00:31:56,600 --> 00:31:59,680 Speaker 2: in robotics they call the uncann canny Valley. That movie 514 00:31:59,720 --> 00:32:02,120 Speaker 2: lives like half of its life in the Uncanny Valley, 515 00:32:02,120 --> 00:32:07,040 Speaker 2: and it's really it contributes a lot to the to 516 00:32:07,120 --> 00:32:08,560 Speaker 2: the creepiness of the film. 517 00:32:08,920 --> 00:32:12,720 Speaker 1: Great cool. What do you see is the line between 518 00:32:13,360 --> 00:32:16,600 Speaker 1: movies and games? How's that line going to blur as 519 00:32:16,640 --> 00:32:19,840 Speaker 1: we move into the future where we can change what 520 00:32:19,960 --> 00:32:21,200 Speaker 1: is happening on the screen. 521 00:32:21,600 --> 00:32:26,600 Speaker 2: Yeah. I think there's two important dimensions. One is point 522 00:32:26,600 --> 00:32:30,760 Speaker 2: of view and field of view. So you know, traditional film, 523 00:32:31,240 --> 00:32:33,479 Speaker 2: the width of the screen would be like twenty to 524 00:32:33,520 --> 00:32:35,719 Speaker 2: three d degrees of your visual angle. So if you've 525 00:32:35,720 --> 00:32:38,680 Speaker 2: got three hundred and sixty degree view, think about you know, 526 00:32:38,680 --> 00:32:40,800 Speaker 2: a little bit less than a tenth of that to 527 00:32:41,200 --> 00:32:44,760 Speaker 2: twentieth of that. Now we can have headmounted displays or 528 00:32:44,760 --> 00:32:48,280 Speaker 2: immersive displays that are the full three sixty. And then 529 00:32:48,320 --> 00:32:52,560 Speaker 2: the other thing is in a movie, obviously it's recorded. 530 00:32:52,760 --> 00:32:56,360 Speaker 2: You don't have any control over what happens, and in 531 00:32:56,440 --> 00:33:01,720 Speaker 2: gaming what you do of x the world. One of 532 00:33:01,720 --> 00:33:05,360 Speaker 2: the things that's really striking about movie perception is that 533 00:33:05,440 --> 00:33:08,480 Speaker 2: agree to which our brains don't turn off that control part, 534 00:33:08,520 --> 00:33:10,880 Speaker 2: Like you know, we do all these little twitchy behaviors 535 00:33:10,880 --> 00:33:13,840 Speaker 2: while we're watching that indicate that the brain is trying 536 00:33:13,880 --> 00:33:16,240 Speaker 2: to like, you know, get you to run away when 537 00:33:16,280 --> 00:33:19,480 Speaker 2: the thing is lunging, or trying to get you to 538 00:33:19,560 --> 00:33:23,440 Speaker 2: lean in when it's an emotionally engaging experience. But in games, 539 00:33:23,480 --> 00:33:25,440 Speaker 2: of course, like yeah, you got like you got to 540 00:33:25,560 --> 00:33:27,680 Speaker 2: duck out of the way, or you're the boulder is 541 00:33:27,680 --> 00:33:31,360 Speaker 2: going to land on you. So I think those are 542 00:33:31,400 --> 00:33:35,560 Speaker 2: the those are the dimensions that are super exciting, and 543 00:33:35,680 --> 00:33:37,920 Speaker 2: those aren't secrets, Like everybody who works in those fields 544 00:33:37,960 --> 00:33:40,000 Speaker 2: is working on that stuff. And the question of like, 545 00:33:40,640 --> 00:33:43,680 Speaker 2: how do you edit in an environment where you don't 546 00:33:43,720 --> 00:33:48,240 Speaker 2: know exactly like in a three sixty wild wide field 547 00:33:48,240 --> 00:33:50,480 Speaker 2: of view situation, if you don't know exactly what the 548 00:33:50,480 --> 00:33:54,760 Speaker 2: person's looking at, how do you edit? And if you 549 00:33:56,360 --> 00:33:58,240 Speaker 2: need them to get a piece of information out of 550 00:33:58,280 --> 00:34:00,520 Speaker 2: the scene, how do you make sure that they turn 551 00:34:00,600 --> 00:34:04,040 Speaker 2: around and look at the character, you know, look at 552 00:34:04,080 --> 00:34:07,200 Speaker 2: the villain creeping in through the window. If they are 553 00:34:07,200 --> 00:34:09,960 Speaker 2: looking in the other direction? Is your story still going 554 00:34:10,040 --> 00:34:10,440 Speaker 2: to work? 555 00:34:10,840 --> 00:34:12,680 Speaker 1: And I know you and I have both talked about 556 00:34:12,719 --> 00:34:16,680 Speaker 1: this issue of what this all is going to mean 557 00:34:16,760 --> 00:34:21,359 Speaker 1: in the future for having shared stories. So you know, 558 00:34:21,440 --> 00:34:23,800 Speaker 1: we all watch Game of Thrones, we watch the same 559 00:34:23,880 --> 00:34:26,240 Speaker 1: version of it, and we can talk about it later. 560 00:34:26,520 --> 00:34:29,799 Speaker 1: But if everyone is experiencing their own movie where they 561 00:34:29,800 --> 00:34:34,040 Speaker 1: get to do whatever and things happen that's responsive to them, 562 00:34:34,080 --> 00:34:37,279 Speaker 1: we no longer have shared literature. What's your take on that? 563 00:34:37,680 --> 00:34:43,279 Speaker 2: Yeah, I think it's a really interesting open question. You know, 564 00:34:43,320 --> 00:34:47,359 Speaker 2: I think there's some reason to think that. Well, first 565 00:34:47,360 --> 00:34:49,480 Speaker 2: of all, there's a continuum, right, there's like very open 566 00:34:49,520 --> 00:34:53,880 Speaker 2: world games where you know, my experience in the game 567 00:34:54,280 --> 00:34:59,040 Speaker 2: isn't going to be anything like yours, but still there's 568 00:34:59,040 --> 00:35:02,000 Speaker 2: some shared stuff, like how the physics of the game works, 569 00:35:02,000 --> 00:35:03,560 Speaker 2: and what kind of objects there are, and what kinds 570 00:35:03,600 --> 00:35:05,040 Speaker 2: of things you're trying to do, and what kind of 571 00:35:05,040 --> 00:35:07,960 Speaker 2: techniques you're learning. Like being a basketball player and meeting 572 00:35:07,960 --> 00:35:11,040 Speaker 2: another basketball player who you've never played any games against. 573 00:35:11,080 --> 00:35:13,680 Speaker 2: You don't have specific shared experiences, but you've got this 574 00:35:13,880 --> 00:35:18,759 Speaker 2: shared type of experience with the skills and the objects 575 00:35:18,800 --> 00:35:22,000 Speaker 2: and the situations that come up. And then there's other 576 00:35:22,120 --> 00:35:24,800 Speaker 2: kinds of gaming that are much more plotty, right where 577 00:35:25,440 --> 00:35:28,600 Speaker 2: you have choices that you can make, but they're all 578 00:35:28,640 --> 00:35:33,800 Speaker 2: going to lead to the same destination, and people like 579 00:35:33,880 --> 00:36:02,240 Speaker 2: both of those kinds of experiences. 580 00:35:50,280 --> 00:35:53,080 Speaker 1: Tell us what you mean by a temporal hierarchy and 581 00:35:53,160 --> 00:35:54,600 Speaker 1: what you're thinking about in terms of that. 582 00:35:55,320 --> 00:35:59,520 Speaker 2: So, as you're living your life, your brain's predicting what's 583 00:35:59,560 --> 00:36:01,520 Speaker 2: going to happen in the near future. You're building up 584 00:36:01,680 --> 00:36:04,000 Speaker 2: a model of what's happening now. But it turns out 585 00:36:04,840 --> 00:36:09,480 Speaker 2: that you build those models on multiple time scales, So 586 00:36:10,640 --> 00:36:15,000 Speaker 2: there are models that update every second or so, tens 587 00:36:15,000 --> 00:36:20,239 Speaker 2: of seconds, minutes, and people like Crisot bald Asano and 588 00:36:20,320 --> 00:36:25,000 Speaker 2: Linda Gerligs have studied how the patterns of activity change, 589 00:36:25,600 --> 00:36:28,640 Speaker 2: so different parts of your brain lurch from one stable 590 00:36:28,719 --> 00:36:34,080 Speaker 2: pattern to another on different time scales, and those larger 591 00:36:34,080 --> 00:36:37,560 Speaker 2: time scales are associated with integrating more modalities of information 592 00:36:37,640 --> 00:36:42,440 Speaker 2: and more complex stuff. And we over many years have 593 00:36:42,480 --> 00:36:46,000 Speaker 2: been interested in how at those boundaries you see big 594 00:36:46,040 --> 00:36:50,799 Speaker 2: phasic changes in brain activity across the brain, and those 595 00:36:50,840 --> 00:36:56,640 Speaker 2: are hierarchically nested, and those turn out to be related. 596 00:36:56,719 --> 00:36:59,400 Speaker 2: The magnitude of those turns out to be related to 597 00:36:59,440 --> 00:37:03,840 Speaker 2: how much you remember later. So one important thing turns 598 00:37:03,840 --> 00:37:06,280 Speaker 2: out to be that you're not just building a model. 599 00:37:06,920 --> 00:37:10,920 Speaker 2: You're building a model that has multiple time scales, and 600 00:37:11,880 --> 00:37:14,600 Speaker 2: you've got some stable patterns that the brain is actively 601 00:37:14,640 --> 00:37:17,200 Speaker 2: maintaining for like tens of minutes, and others that are 602 00:37:17,200 --> 00:37:20,040 Speaker 2: being updated very quickly. You know, as you're looking at 603 00:37:20,080 --> 00:37:23,359 Speaker 2: a film, right, a film is going to last maybe 604 00:37:23,440 --> 00:37:26,120 Speaker 2: forty five minutes for a really short narrative film to 605 00:37:26,560 --> 00:37:31,040 Speaker 2: you know, four hours if you're watching Reds, and those 606 00:37:31,080 --> 00:37:35,840 Speaker 2: longer films clearly exceed the capacity of our working memory 607 00:37:35,920 --> 00:37:41,240 Speaker 2: systems to be able to maintain information in an activated state. 608 00:37:41,480 --> 00:37:44,920 Speaker 2: But by building this nested hierarchy and then by reaching 609 00:37:44,960 --> 00:37:50,000 Speaker 2: back across long term memory, the brain can build representations 610 00:37:50,000 --> 00:37:54,120 Speaker 2: that allow it to track plot development over these long 611 00:37:54,239 --> 00:37:57,000 Speaker 2: narrative time scales. Now, there's a whole other deal that 612 00:37:57,080 --> 00:37:59,480 Speaker 2: happens when you think about like series fiction that's so 613 00:37:59,520 --> 00:38:01,400 Speaker 2: much of us and a lot of time on now, 614 00:38:01,760 --> 00:38:04,600 Speaker 2: And it's striking, like how much you can enjoy and 615 00:38:04,640 --> 00:38:08,279 Speaker 2: really understand a series even if you can't remember what 616 00:38:08,320 --> 00:38:10,160 Speaker 2: the heck was the name of that character from the 617 00:38:10,200 --> 00:38:14,040 Speaker 2: other episode, or exactly what happened. And part of that 618 00:38:14,120 --> 00:38:19,040 Speaker 2: illustrates like how powerful just the emotional trajectory of the 619 00:38:19,120 --> 00:38:21,560 Speaker 2: moment as sustained by what the characters are doing right now, 620 00:38:21,600 --> 00:38:24,359 Speaker 2: and importantly the music that's lying under it is and 621 00:38:24,400 --> 00:38:28,879 Speaker 2: like just sustaining that experience. If you look at people 622 00:38:28,960 --> 00:38:32,600 Speaker 2: like dementia patients, they can have rich, interactive relationships with 623 00:38:32,640 --> 00:38:36,040 Speaker 2: their loved ones even if they're not tracking the details 624 00:38:36,040 --> 00:38:39,400 Speaker 2: of the plots of their lives over those longer time scales. 625 00:38:39,640 --> 00:38:42,000 Speaker 1: You know, there's an interesting analogy here going on with 626 00:38:42,200 --> 00:38:46,160 Speaker 1: large language models, which is that they come to know you, 627 00:38:46,280 --> 00:38:49,200 Speaker 1: but of course they can't keep track of everything that 628 00:38:49,239 --> 00:38:52,640 Speaker 1: you've ever written into them, because that's just too big 629 00:38:52,680 --> 00:38:55,800 Speaker 1: a context window. So what they do is they summarize 630 00:38:55,880 --> 00:38:58,680 Speaker 1: along the way, and so what it knows about you 631 00:38:58,800 --> 00:39:01,879 Speaker 1: is the summary of the last five months of conversation. 632 00:39:02,440 --> 00:39:05,000 Speaker 1: And maybe there's a sense that that's what's happening. When 633 00:39:05,040 --> 00:39:07,680 Speaker 1: we watch a series, we just have sort of a 634 00:39:07,680 --> 00:39:10,719 Speaker 1: paragraph in our mind that tells us about the emotional 635 00:39:10,719 --> 00:39:12,520 Speaker 1: content of what's happening and where it is. 636 00:39:12,920 --> 00:39:18,120 Speaker 2: Yeah, totally. You know, there's the term semanticization of memory. 637 00:39:19,200 --> 00:39:21,320 Speaker 2: We think of as something that happens over like decades, 638 00:39:21,360 --> 00:39:24,120 Speaker 2: but it's also that stuff is happening also super fast 639 00:39:24,120 --> 00:39:25,680 Speaker 2: in the nervous system. 640 00:39:25,600 --> 00:39:28,520 Speaker 1: For the listenership. Can you define what the semanticization of 641 00:39:28,560 --> 00:39:29,120 Speaker 1: memory is about. 642 00:39:29,239 --> 00:39:32,480 Speaker 2: Yeah, so there's this term semanticization of memory, which means 643 00:39:32,560 --> 00:39:37,560 Speaker 2: that over time things become less like I'm really living this, 644 00:39:37,920 --> 00:39:40,839 Speaker 2: reliving this detailed experience, and more like I know this 645 00:39:41,000 --> 00:39:43,680 Speaker 2: stuff and I kind of generally know about what's going on. 646 00:39:44,239 --> 00:39:46,799 Speaker 2: And that's something that we think about is happening as 647 00:39:46,920 --> 00:39:51,600 Speaker 2: memories consolidate over decades, but it also happens super fast 648 00:39:51,680 --> 00:39:55,839 Speaker 2: within the experience of say watching a movie. 649 00:39:56,280 --> 00:39:57,960 Speaker 1: There's one last question I was going to ask you. 650 00:39:58,080 --> 00:40:03,480 Speaker 1: I was just curious why other animals don't enjoy movies. 651 00:40:03,680 --> 00:40:05,800 Speaker 1: As far as we can tell, obviously they're not making movies. 652 00:40:05,800 --> 00:40:09,200 Speaker 1: But even if I show to my dog a movie 653 00:40:09,239 --> 00:40:11,000 Speaker 1: on the screen and I try to get them interested 654 00:40:11,040 --> 00:40:13,360 Speaker 1: because maybe there's another dog, there's a barking dog in 655 00:40:13,400 --> 00:40:15,759 Speaker 1: the movie, he just doesn't care at all. It doesn't 656 00:40:15,800 --> 00:40:16,160 Speaker 1: hold his. 657 00:40:17,600 --> 00:40:19,759 Speaker 2: It's so funny that you asked that. I mean, maybe 658 00:40:19,800 --> 00:40:24,080 Speaker 2: your dog is weird, because I often open my talks 659 00:40:24,560 --> 00:40:29,200 Speaker 2: with pictures of like dogs and cats and monkeys pointing 660 00:40:29,239 --> 00:40:31,759 Speaker 2: with their heads and grossed looking at screens, and some 661 00:40:31,800 --> 00:40:35,000 Speaker 2: people report these like compelling. I mean, just go on 662 00:40:35,080 --> 00:40:39,320 Speaker 2: TikTok man. There's like all kinds of videos of pets 663 00:40:39,320 --> 00:40:44,319 Speaker 2: interacting with screens and reacting, and I think I think 664 00:40:44,320 --> 00:40:47,279 Speaker 2: there's some really actually interesting science, Like lots of pet 665 00:40:47,320 --> 00:40:50,760 Speaker 2: animals will react to the sounds in a TV show 666 00:40:51,160 --> 00:40:54,600 Speaker 2: as if they were really happening, right, And that'll happen regularly. 667 00:40:55,000 --> 00:40:57,239 Speaker 2: The dog starts barking on the screen, and my dog 668 00:40:57,320 --> 00:41:02,680 Speaker 2: starts barking, and then there sometimes at least some animals 669 00:41:02,680 --> 00:41:05,319 Speaker 2: seem to be doing that same kind of stuff with 670 00:41:05,480 --> 00:41:08,839 Speaker 2: the video channel, but like what exactly they're getting out 671 00:41:08,840 --> 00:41:10,799 Speaker 2: of it. I think there's some I think there's some 672 00:41:10,840 --> 00:41:12,000 Speaker 2: science to be done. 673 00:41:12,040 --> 00:41:14,080 Speaker 1: Right, And this is the question, is is there something 674 00:41:14,080 --> 00:41:18,319 Speaker 1: about the human brain that can appreciate story? Whereas if 675 00:41:18,360 --> 00:41:21,600 Speaker 1: I followed a dog sniffing around and something happens, and 676 00:41:21,600 --> 00:41:24,360 Speaker 1: then something else happens to this dog, I mean my 677 00:41:24,440 --> 00:41:27,279 Speaker 1: dog certainly even your dog presumably won't see it there 678 00:41:27,320 --> 00:41:28,600 Speaker 1: for half an hour and watch it. 679 00:41:28,920 --> 00:41:31,799 Speaker 2: Yeah, you know, Okay, that is totally interesting. So I 680 00:41:31,840 --> 00:41:36,320 Speaker 2: am one hundred percent convinced that our dog has event models, 681 00:41:36,360 --> 00:41:39,799 Speaker 2: and that like the capacity to build and update event 682 00:41:39,840 --> 00:41:43,960 Speaker 2: models is pretty widely conserved across the mammalian taxa. But 683 00:41:44,080 --> 00:41:49,000 Speaker 2: I'm not sure that my dog is tracking this story. Yeah, 684 00:41:49,040 --> 00:41:53,960 Speaker 2: and I think we don't know that much about what 685 00:41:54,400 --> 00:42:00,279 Speaker 2: aspects of the longer time scale of gold direct did 686 00:42:00,360 --> 00:42:05,080 Speaker 2: experience other species are tracking. It's got to be related 687 00:42:05,239 --> 00:42:08,960 Speaker 2: to these, you know, these questions about chain goals and 688 00:42:09,040 --> 00:42:12,920 Speaker 2: problem solving, which are surprisingly not conserved, right. You know, 689 00:42:13,360 --> 00:42:15,279 Speaker 2: on the one hand, you've got these crows and other 690 00:42:15,400 --> 00:42:19,600 Speaker 2: corvids who can do these amazing chain goal things, But 691 00:42:19,680 --> 00:42:22,080 Speaker 2: then you got lots of mammals who really don't seem 692 00:42:22,120 --> 00:42:24,640 Speaker 2: to be able to do very much of that stuff. 693 00:42:25,120 --> 00:42:28,680 Speaker 2: And I suspect that the same representations that are important 694 00:42:28,680 --> 00:42:34,200 Speaker 2: for action are important for comprehension, and so I think 695 00:42:34,239 --> 00:42:36,359 Speaker 2: it's possible that the dog's not tracking that. 696 00:42:36,360 --> 00:42:39,160 Speaker 1: Much of the story. Yeah, it seems like this would 697 00:42:39,160 --> 00:42:41,040 Speaker 1: be a really good clue for us to dig down 698 00:42:41,080 --> 00:42:44,920 Speaker 1: on into the future, because dogs do mirror, and they 699 00:42:45,000 --> 00:42:48,839 Speaker 1: do have this successful of reinforcement learning about doing what 700 00:42:48,920 --> 00:42:50,480 Speaker 1: worked for them in the past. 701 00:42:50,560 --> 00:42:53,560 Speaker 2: And they represent the locations of objects that are accluded 702 00:42:53,600 --> 00:42:57,680 Speaker 2: and stuff. You know, they're clearly building event models exactly, 703 00:42:57,800 --> 00:43:04,399 Speaker 2: but how they're integrating those into larger structures. It might 704 00:43:04,480 --> 00:43:07,279 Speaker 2: here's a possibility. It might be that dogs are kind 705 00:43:07,280 --> 00:43:10,600 Speaker 2: of like amnis are humans. Right, So I can build 706 00:43:10,600 --> 00:43:15,480 Speaker 2: a rich, detailed event model, and then as soon as 707 00:43:15,520 --> 00:43:18,040 Speaker 2: I walk out that doorway, I can't do anything with 708 00:43:18,080 --> 00:43:21,840 Speaker 2: that information from that list event model other than the 709 00:43:22,200 --> 00:43:25,680 Speaker 2: general stuff that I learned from it. Right, So I 710 00:43:25,719 --> 00:43:28,640 Speaker 2: can do learning, But the ability to pull that old 711 00:43:28,680 --> 00:43:32,759 Speaker 2: event model back and relive an experience based on that 712 00:43:32,760 --> 00:43:36,239 Speaker 2: that might be different across species. So that gets into 713 00:43:36,280 --> 00:43:42,200 Speaker 2: the debates about the degree to which other species share 714 00:43:42,320 --> 00:43:45,880 Speaker 2: episodic memory or episodic like memory. 715 00:43:47,719 --> 00:43:50,520 Speaker 1: That's fascinating. And it might also be that the dog 716 00:43:50,640 --> 00:43:53,960 Speaker 1: is incapable of taking on the omniscient point of view 717 00:43:54,360 --> 00:43:57,200 Speaker 1: and understanding what it's like to be outside the story 718 00:43:57,239 --> 00:44:00,000 Speaker 1: here and here and from these different angles. They're all 719 00:44:00,120 --> 00:44:04,399 Speaker 1: kinds of aspects to visual storytelling that we naturally fall 720 00:44:04,400 --> 00:44:08,040 Speaker 1: into and enjoy. But the interesting question for us for 721 00:44:08,120 --> 00:44:10,880 Speaker 1: the future is what are the elements that are missing 722 00:44:10,960 --> 00:44:14,040 Speaker 1: or just slightly worse in other animals such that at 723 00:44:14,120 --> 00:44:17,400 Speaker 1: least as far as we can tell, they don't do story. 724 00:44:17,800 --> 00:44:24,080 Speaker 2: Yeah, totally. Oh, that's an interesting one. 725 00:44:25,040 --> 00:44:28,320 Speaker 1: So that was Jeff Zach's, professor of psychology at WashU 726 00:44:28,360 --> 00:44:31,640 Speaker 1: in Saint Louis. Our conversation tried to raise the curtain 727 00:44:31,719 --> 00:44:34,399 Speaker 1: just a bit on one of the most captivating illusions 728 00:44:34,480 --> 00:44:39,239 Speaker 1: we engage with movies, and the conversation reminds us that 729 00:44:39,360 --> 00:44:43,400 Speaker 1: the way we experience movies is not separate from real life, 730 00:44:43,440 --> 00:44:47,319 Speaker 1: but it uses the same neural machinery that we use 731 00:44:47,400 --> 00:44:50,879 Speaker 1: to navigate the real world. When a film cuts from 732 00:44:50,880 --> 00:44:54,799 Speaker 1: one scene to another, your brain doesn't protest, It just 733 00:44:54,920 --> 00:44:59,520 Speaker 1: fills in the blanks. It assumes continuity and it moves forward, 734 00:44:59,560 --> 00:45:04,440 Speaker 1: because that's what it evolved to do. When a character cries, 735 00:45:05,200 --> 00:45:09,880 Speaker 1: your empathic systems don't ask if it's real. They simply resonate. 736 00:45:10,560 --> 00:45:13,920 Speaker 1: When a jump scare makes you flinch, it's not because 737 00:45:13,920 --> 00:45:16,399 Speaker 1: you believe you are in danger. It's because your brain 738 00:45:16,560 --> 00:45:19,960 Speaker 1: can't not respond as if you were. And all of 739 00:45:20,000 --> 00:45:23,040 Speaker 1: this tells us something important, something you hear me say 740 00:45:23,080 --> 00:45:28,160 Speaker 1: every week, which is that we live inside our internal models. 741 00:45:28,560 --> 00:45:34,000 Speaker 1: We don't catch the sensory details of everything happening around us. Instead, 742 00:45:34,120 --> 00:45:38,319 Speaker 1: we construct general models of what is happening out there, 743 00:45:38,600 --> 00:45:45,720 Speaker 1: and movies are particularly elegant manipulations of those models. Filmmakers, 744 00:45:45,760 --> 00:45:49,400 Speaker 1: whether they know it or not, they are cognitive engineers. 745 00:45:49,480 --> 00:45:55,160 Speaker 1: They shape attention, they manipulate time, they activate emotion, and 746 00:45:55,239 --> 00:45:58,960 Speaker 1: we as the viewers, we're not just passive recipients. We 747 00:45:59,080 --> 00:46:04,080 Speaker 1: are co constructing the experience. When you sit in a movie, 748 00:46:04,600 --> 00:46:08,880 Speaker 1: your brain is on fire. It's stitching together fragments of 749 00:46:09,000 --> 00:46:14,319 Speaker 1: light and sound into a narrative that feels whole and meaningful. 750 00:46:14,840 --> 00:46:17,680 Speaker 1: Moment to moment, seen by scene, you work to make 751 00:46:17,840 --> 00:46:21,120 Speaker 1: sense of what is happening. You're guessing what is coming next. 752 00:46:21,560 --> 00:46:25,880 Speaker 1: You're feeling your way through the unfolding story. And this 753 00:46:26,040 --> 00:46:30,000 Speaker 1: is part of why movies, or more generally, stories matters 754 00:46:30,080 --> 00:46:33,839 Speaker 1: so much. It's not just that they entertain or distract us. 755 00:46:34,400 --> 00:46:39,360 Speaker 1: It's that they let us practice being human. They sharpen 756 00:46:39,480 --> 00:46:44,360 Speaker 1: our empathy, They give us rehearsal space for emotion. They 757 00:46:44,480 --> 00:46:48,520 Speaker 1: let us explore lives that we're never gonna live. They 758 00:46:48,600 --> 00:46:51,720 Speaker 1: let us practice choices that we are never going to face. 759 00:46:52,360 --> 00:46:56,080 Speaker 1: They let us explore futures that we are never going 760 00:46:56,160 --> 00:46:59,840 Speaker 1: to see. And all of this can happen inside the 761 00:46:59,880 --> 00:47:05,040 Speaker 1: safe contained simulation of a story. So the next time 762 00:47:05,080 --> 00:47:08,959 Speaker 1: you find yourself moved by a film where your heart 763 00:47:09,040 --> 00:47:12,279 Speaker 1: rate elevates, or your chest titans, or you feel a 764 00:47:12,400 --> 00:47:15,800 Speaker 1: catch in your throat, remember it's not just the story 765 00:47:15,880 --> 00:47:21,000 Speaker 1: on the screen that's affecting you. It's the extraordinary adaptive 766 00:47:21,160 --> 00:47:28,600 Speaker 1: prediction making meaning seeking system in your head, responding exactly 767 00:47:28,800 --> 00:47:34,600 Speaker 1: as it was built to do. Go to Eagleman dot 768 00:47:34,600 --> 00:47:38,280 Speaker 1: com slash podcast for more information and to find further reading. 769 00:47:38,520 --> 00:47:40,919 Speaker 1: Check out my newsletter on substack and be a part 770 00:47:40,960 --> 00:47:43,560 Speaker 1: of the online chats there, where you can send me 771 00:47:43,640 --> 00:47:46,840 Speaker 1: an email at podcasts at eagleman dot com with questions 772 00:47:46,920 --> 00:47:50,520 Speaker 1: or discussion. Finally, watch the videos of Inner Cosmos on YouTube, 773 00:47:50,520 --> 00:47:53,719 Speaker 1: where you can leave comments until next time. I'm David 774 00:47:53,760 --> 00:48:03,200 Speaker 1: Eagleman and this is Inner Cosmos has not a far