1 00:00:04,680 --> 00:00:08,760 Speaker 1: What is memory in the brain? How is that different 2 00:00:08,800 --> 00:00:12,640 Speaker 1: from the way a computer stores information? How do you 3 00:00:12,680 --> 00:00:18,239 Speaker 1: put together billions of specialized cells and you store your 4 00:00:18,400 --> 00:00:21,560 Speaker 1: home address in their activity? And what does any of 5 00:00:21,600 --> 00:00:24,880 Speaker 1: this have to do with the happy Birthday song? Or 6 00:00:24,920 --> 00:00:29,880 Speaker 1: with squirrels hiding acorns, or with bards memorizing epic poems, 7 00:00:30,280 --> 00:00:34,200 Speaker 1: or with people who cannot forget any of the events 8 00:00:34,360 --> 00:00:41,320 Speaker 1: in their life. Welcome to Inner Cosmos with me David Eagelman. 9 00:00:41,720 --> 00:00:45,280 Speaker 1: I'm a neuroscientist and an author at Stanford and in 10 00:00:45,320 --> 00:00:49,320 Speaker 1: these episodes we sail into our three pound universe to 11 00:00:49,440 --> 00:00:53,240 Speaker 1: understand why and how our lives look the way they do. 12 00:01:01,880 --> 00:01:06,039 Speaker 1: Today's episode is about memory. What is memory? How does 13 00:01:06,080 --> 00:01:09,560 Speaker 1: it work? How do details get stored in your brains 14 00:01:09,640 --> 00:01:12,760 Speaker 1: such that if I say, what was the name of 15 00:01:12,800 --> 00:01:16,840 Speaker 1: your fifth grade teacher? You can retrieve that name even 16 00:01:16,880 --> 00:01:20,000 Speaker 1: if you haven't thought about it in decades? And how 17 00:01:20,080 --> 00:01:23,480 Speaker 1: is this totally different from the way that computers store memory? 18 00:01:24,160 --> 00:01:26,959 Speaker 1: So in this episode, I'll give you a foundation into 19 00:01:27,080 --> 00:01:32,479 Speaker 1: understanding the landscape of memory. Now, this is the first 20 00:01:32,520 --> 00:01:35,160 Speaker 1: of a three part series about the way that our 21 00:01:35,240 --> 00:01:40,840 Speaker 1: brains constantly unhook from the here and now, and they 22 00:01:40,840 --> 00:01:46,240 Speaker 1: go somewhere else. We are time travelers, and only because 23 00:01:46,280 --> 00:01:50,040 Speaker 1: we do this so constantly, we don't even notice how 24 00:01:50,080 --> 00:01:53,440 Speaker 1: amazing this is. I mean, everything you might study in 25 00:01:53,480 --> 00:01:56,480 Speaker 1: a textbook about the brain has to do with Okay, 26 00:01:56,480 --> 00:02:01,000 Speaker 1: here's how vision works. Here's how the visual quartet analyzes 27 00:02:01,440 --> 00:02:04,880 Speaker 1: photons captured at the retina and makes an assessment of 28 00:02:04,920 --> 00:02:08,960 Speaker 1: what's in front of you. And yet we're able to 29 00:02:09,160 --> 00:02:13,400 Speaker 1: travel back to previous times. You can put yourself back 30 00:02:13,440 --> 00:02:17,440 Speaker 1: in that fifth grade classroom, or the first house you 31 00:02:17,480 --> 00:02:21,720 Speaker 1: grew up in, or the moment of your first kiss. 32 00:02:22,720 --> 00:02:27,320 Speaker 1: You can simulate the sights and sounds and smells. You 33 00:02:27,400 --> 00:02:30,880 Speaker 1: can remember how you felt in certain moments. You can 34 00:02:31,400 --> 00:02:36,640 Speaker 1: recreate and reexperience that. You may be able to recollect 35 00:02:36,680 --> 00:02:40,600 Speaker 1: lots of the details that were around you. To do that, 36 00:02:41,120 --> 00:02:46,840 Speaker 1: you just time traveled across years or decades to place 37 00:02:46,880 --> 00:02:51,800 Speaker 1: yourself in another era. Your visual system is not simply 38 00:02:51,840 --> 00:02:54,799 Speaker 1: looking at the photons in front of you. Instead, it's 39 00:02:54,880 --> 00:02:59,600 Speaker 1: now involved in running a simulation of what had transpired 40 00:02:59,680 --> 00:03:03,960 Speaker 1: and an earlier time wherever you physically are right now 41 00:03:04,000 --> 00:03:07,919 Speaker 1: listening to this podcast, You just time traveled. When you 42 00:03:08,000 --> 00:03:13,040 Speaker 1: thought about your fifth grade experience. Now it turns out 43 00:03:13,040 --> 00:03:16,320 Speaker 1: that brains don't just time travel backwards, but they can 44 00:03:16,360 --> 00:03:19,880 Speaker 1: also move forward. So in the next episode, Part two, 45 00:03:20,320 --> 00:03:24,520 Speaker 1: we're going to talk about prediction, and then in part 46 00:03:24,639 --> 00:03:27,680 Speaker 1: three we're going to pull pieces of the puzzle together 47 00:03:28,320 --> 00:03:32,800 Speaker 1: to unlock some wild surprises about how we experience the 48 00:03:32,840 --> 00:03:39,440 Speaker 1: world emotionally given our time traveling talents. So today we'll 49 00:03:39,480 --> 00:03:42,240 Speaker 1: start with memory. And I just want to say this 50 00:03:42,280 --> 00:03:44,920 Speaker 1: is a huge topic that I could teach you over 51 00:03:44,960 --> 00:03:48,080 Speaker 1: the course of years. So for today's podcast, we're going 52 00:03:48,160 --> 00:03:51,560 Speaker 1: to take a high level ride to get a feel 53 00:03:51,800 --> 00:03:55,840 Speaker 1: for the landscape. So one way to approach any big 54 00:03:55,880 --> 00:03:59,680 Speaker 1: topic is to look at the extremes. So a lot 55 00:03:59,720 --> 00:04:03,440 Speaker 1: of memories come easily to us the day we moved 56 00:04:03,440 --> 00:04:05,640 Speaker 1: into our new home, or the day we got the 57 00:04:05,760 --> 00:04:08,640 Speaker 1: job offer, or the time that the phone rang with 58 00:04:08,800 --> 00:04:13,520 Speaker 1: news of somebody's death. But imagine having a clear and 59 00:04:13,680 --> 00:04:17,760 Speaker 1: immediate memory for all of the events of your day 60 00:04:17,800 --> 00:04:21,040 Speaker 1: to day life, such as the dinner that you had 61 00:04:21,160 --> 00:04:24,280 Speaker 1: on November thirtieth, twenty twenty three, or the friend who 62 00:04:24,400 --> 00:04:28,600 Speaker 1: visited you on May twentieth, twenty nineteen, or the swimming 63 00:04:28,640 --> 00:04:31,880 Speaker 1: pool party you went to on July eighth, twenty seventeen. 64 00:04:32,000 --> 00:04:36,560 Speaker 1: Imagine having a vivid, movie like recollection of what you 65 00:04:36,640 --> 00:04:41,520 Speaker 1: did on today's day ten years ago. Now imagine that 66 00:04:41,560 --> 00:04:46,760 Speaker 1: these recollections dominate your thoughts. They run in parallel with 67 00:04:46,880 --> 00:04:49,840 Speaker 1: the events of your waking life, as you commute to work, 68 00:04:49,880 --> 00:04:53,840 Speaker 1: as you talk with your friends, as you shop for groceries, 69 00:04:53,960 --> 00:04:56,880 Speaker 1: as you spend time with your family. You would be 70 00:04:56,960 --> 00:05:01,920 Speaker 1: right that other people would marvel and probably envy your 71 00:05:01,960 --> 00:05:06,400 Speaker 1: exceptional powers of memory, but you might find the process 72 00:05:06,760 --> 00:05:12,000 Speaker 1: uncontrollable and overwhelming. Some memories are painful, or traumatic, or 73 00:05:12,040 --> 00:05:14,640 Speaker 1: just annoying. Then you might wish that you could be 74 00:05:15,000 --> 00:05:18,840 Speaker 1: rid of them, and exhausted by their presence, you might 75 00:05:18,920 --> 00:05:22,520 Speaker 1: finally contact a neuroscientist, hoping that they could help you 76 00:05:22,640 --> 00:05:27,880 Speaker 1: understand what's happening. And that is the real life story 77 00:05:28,000 --> 00:05:31,840 Speaker 1: for a woman named Jill Price, who is a mneminist, 78 00:05:32,160 --> 00:05:35,520 Speaker 1: which means she has an extraordinary memory and she can't 79 00:05:35,760 --> 00:05:38,839 Speaker 1: forget things. So some years ago she went to the 80 00:05:39,279 --> 00:05:43,640 Speaker 1: University of California at Irmine and described her experiences to 81 00:05:43,680 --> 00:05:48,320 Speaker 1: two memory researchers there my colleagues Magaw and Cale, and 82 00:05:48,480 --> 00:05:52,599 Speaker 1: at first they were skeptical, but they investigated her memory 83 00:05:52,640 --> 00:05:55,640 Speaker 1: with a series of careful tests and interviews over a 84 00:05:55,680 --> 00:06:00,000 Speaker 1: period of five years, and the results showed that Price 85 00:06:00,320 --> 00:06:05,400 Speaker 1: indeed had a rare and astonishingly powerful memory, at least 86 00:06:05,400 --> 00:06:09,919 Speaker 1: for certain types of information. Given a particular date, she 87 00:06:10,000 --> 00:06:14,080 Speaker 1: could recall within seconds the day of the week that was, 88 00:06:14,440 --> 00:06:17,200 Speaker 1: and the details of what she did on that day, 89 00:06:17,520 --> 00:06:20,840 Speaker 1: and the news were the events that occurred on that day, 90 00:06:21,440 --> 00:06:25,839 Speaker 1: without any preparations. She easily recalled the dates of every 91 00:06:26,040 --> 00:06:29,040 Speaker 1: Easter from nineteen eighty to two thousand and three and 92 00:06:29,200 --> 00:06:33,799 Speaker 1: her activities on those days. She also gave correct dates 93 00:06:33,839 --> 00:06:38,600 Speaker 1: and personal anecdotes for randomly selected news events like the 94 00:06:38,640 --> 00:06:41,599 Speaker 1: start of the First Gulf War, or the bombing of 95 00:06:41,680 --> 00:06:47,159 Speaker 1: the Atlanta Olympic Games, or the death of Princess Diana. Now, 96 00:06:47,200 --> 00:06:51,160 Speaker 1: while her recollections were detailed and consistent, they were generally 97 00:06:51,600 --> 00:06:57,839 Speaker 1: specific to events of high personal relevance, like illnesses or relationships, 98 00:06:58,200 --> 00:07:04,359 Speaker 1: or big news stories. Interestingly, her general intelligence was average, 99 00:07:04,400 --> 00:07:08,160 Speaker 1: and her memory for other types of information was really 100 00:07:08,200 --> 00:07:11,960 Speaker 1: no better than anyone else's. She never excelled in school 101 00:07:11,960 --> 00:07:16,080 Speaker 1: and wasn't particularly good at memorizing dates and history books 102 00:07:16,800 --> 00:07:19,880 Speaker 1: and at one point her interviewers asked her to close 103 00:07:19,920 --> 00:07:24,280 Speaker 1: her eyes and describe their clothing, and she drew a blank. 104 00:07:25,120 --> 00:07:28,400 Speaker 1: So her memory abilities were so powerful, but they were 105 00:07:28,440 --> 00:07:33,800 Speaker 1: also strangely limited. So when these researchers published their first 106 00:07:33,880 --> 00:07:37,840 Speaker 1: report on Jill Price, she became a news item. Her 107 00:07:37,880 --> 00:07:40,920 Speaker 1: story drew headlines around the world, She was a guest 108 00:07:40,920 --> 00:07:44,760 Speaker 1: on Oprah, and she published her memoirs. But by the way, 109 00:07:44,960 --> 00:07:49,320 Speaker 1: Jill is not the only mneminist. The first official report 110 00:07:49,400 --> 00:07:52,520 Speaker 1: ofneminism that I know about was a book from the 111 00:07:52,600 --> 00:07:58,520 Speaker 1: Russian neuropsychologist named Luria about a newspaper reporter named Sharyshevski 112 00:07:59,120 --> 00:08:03,400 Speaker 1: who had the gift of an untaxable memory, essentially total 113 00:08:03,520 --> 00:08:08,120 Speaker 1: recall for all the moments of his life. Shiashevski first 114 00:08:08,200 --> 00:08:11,240 Speaker 1: came to him in the nineteen twenties because he got 115 00:08:11,280 --> 00:08:14,600 Speaker 1: in trouble at a meeting for not taking any notes 116 00:08:14,640 --> 00:08:18,160 Speaker 1: when there was a speaker, and Sharishevski responded to his 117 00:08:18,200 --> 00:08:22,520 Speaker 1: boss by recalling the speech word for word, and this 118 00:08:22,640 --> 00:08:26,320 Speaker 1: blew everyone away there, and it also blew Sharyshevski away 119 00:08:26,560 --> 00:08:29,280 Speaker 1: that the other people couldn't do that, and so this 120 00:08:29,400 --> 00:08:33,360 Speaker 1: started this thirty year relationship with Luria, who tested him 121 00:08:33,400 --> 00:08:37,160 Speaker 1: in all kinds of ways and had him memorize math 122 00:08:37,240 --> 00:08:41,679 Speaker 1: formulas or huge blocks of numbers, or even poems and 123 00:08:41,840 --> 00:08:46,120 Speaker 1: languages he didn't speak, and in all these cases, Sharyshevski 124 00:08:46,200 --> 00:08:50,199 Speaker 1: could memorize these in a matter of minutes with perfect accuracy. 125 00:08:50,760 --> 00:08:54,160 Speaker 1: Luria tested and observed him for three decades and then 126 00:08:54,240 --> 00:08:58,720 Speaker 1: wrote a book called The Mind of a Mneminist. So, 127 00:08:58,840 --> 00:09:03,439 Speaker 1: although they are rare, neminists exist, and as scientists continue 128 00:09:03,480 --> 00:09:06,480 Speaker 1: to search for the source of their remarkable abilities, it's 129 00:09:06,559 --> 00:09:11,679 Speaker 1: worth asking why their stories capture our imaginations so strongly. 130 00:09:12,400 --> 00:09:16,000 Speaker 1: Perhaps it's because we'd all love to have supercharged memories, 131 00:09:16,480 --> 00:09:20,800 Speaker 1: Or maybe we see how failing memory is so debilitating 132 00:09:20,840 --> 00:09:24,120 Speaker 1: in old age and we hope to escape the fate 133 00:09:24,200 --> 00:09:28,719 Speaker 1: of our grandparents. Or maybe we recognize how little we 134 00:09:28,800 --> 00:09:34,080 Speaker 1: actually retain from our vast, rich set of life experiences 135 00:09:34,720 --> 00:09:37,920 Speaker 1: that shape us. So in today's episode, we're going to 136 00:09:37,960 --> 00:09:41,079 Speaker 1: dive into where memory comes from and how it works, 137 00:09:41,520 --> 00:09:43,400 Speaker 1: and this is going to give us insight into both 138 00:09:43,480 --> 00:09:47,319 Speaker 1: remarkable memories and bad memories. We'll see what memory is, 139 00:09:47,400 --> 00:09:51,280 Speaker 1: how it functions, how it fails, and what purpose it serves. 140 00:09:52,360 --> 00:09:54,959 Speaker 1: So the first thing I want to tackle is the 141 00:09:55,000 --> 00:09:58,520 Speaker 1: mistake we make by calling the storage of zeros and 142 00:09:58,559 --> 00:10:04,800 Speaker 1: ones in computer memory, because computer memory is actually nothing 143 00:10:04,920 --> 00:10:10,640 Speaker 1: like human memory. Computers store in their memory exactly what 144 00:10:10,679 --> 00:10:13,400 Speaker 1: you give them. So if I write a document, I 145 00:10:13,480 --> 00:10:17,120 Speaker 1: expect my computer memory to function perfectly such that it 146 00:10:17,160 --> 00:10:20,880 Speaker 1: gives me exactly that document back. I don't want something 147 00:10:21,040 --> 00:10:25,880 Speaker 1: similar or a degraded version of it. But human memory 148 00:10:26,000 --> 00:10:30,439 Speaker 1: is a completely different ballgame because you're not storing things 149 00:10:30,640 --> 00:10:34,360 Speaker 1: in zeros and ones. Let's imagine that you speak two 150 00:10:34,480 --> 00:10:37,960 Speaker 1: languages and I tell you a joke in English and 151 00:10:38,000 --> 00:10:40,520 Speaker 1: you turn and tell it to someone else in your 152 00:10:40,600 --> 00:10:44,520 Speaker 1: other language. So the joke is obviously not stored as 153 00:10:44,640 --> 00:10:49,760 Speaker 1: zeros and ones. You're not memorizing my exact words and inflections. Instead, 154 00:10:50,400 --> 00:10:55,040 Speaker 1: the joke is stored in a conceptual space in your mind, 155 00:10:55,440 --> 00:10:58,920 Speaker 1: the way that the characters and events fit together, the 156 00:10:59,160 --> 00:11:03,320 Speaker 1: gist of the action. It doesn't matter the language with 157 00:11:03,480 --> 00:11:06,640 Speaker 1: which you output it. And note that this is the 158 00:11:06,679 --> 00:11:15,920 Speaker 1: same thing with a tune. When you remember a tune, 159 00:11:16,080 --> 00:11:21,120 Speaker 1: it's not about the individual notes. Instead, it's about the 160 00:11:21,160 --> 00:11:26,600 Speaker 1: relationship between the notes, and that's why you can transpose 161 00:11:26,720 --> 00:11:29,439 Speaker 1: the tune to any other key and you still have 162 00:11:29,559 --> 00:11:40,080 Speaker 1: no trouble recognizing the tune. It's not about the exact notes, 163 00:11:40,240 --> 00:11:44,400 Speaker 1: but about the relationship between them. And this is the 164 00:11:44,480 --> 00:11:49,600 Speaker 1: same thing when you're recognizing your friend's face. What has 165 00:11:49,640 --> 00:11:53,560 Speaker 1: been stored in your brain is the relationship between their 166 00:11:53,600 --> 00:11:57,040 Speaker 1: nose length and lip thickness, and distance between their eyes 167 00:11:57,080 --> 00:11:59,640 Speaker 1: and shape of their ears, and where their hairline is 168 00:12:00,160 --> 00:12:04,840 Speaker 1: and so on. It's not about the individual features, but 169 00:12:05,000 --> 00:12:09,320 Speaker 1: the relationships between them. And by the way, this is 170 00:12:09,360 --> 00:12:13,520 Speaker 1: the only way we could have good recognition memory because 171 00:12:13,960 --> 00:12:18,560 Speaker 1: other details change. For example, imagine that you learn a 172 00:12:19,040 --> 00:12:22,000 Speaker 1: picture of the face where the lighting is on the left, 173 00:12:22,280 --> 00:12:24,920 Speaker 1: and later you see the same face, but it's lit 174 00:12:24,960 --> 00:12:28,920 Speaker 1: from the other side. If you are memorizing pixel by pixel, 175 00:12:28,960 --> 00:12:31,480 Speaker 1: then the pattern is totally different and you would never 176 00:12:31,520 --> 00:12:34,640 Speaker 1: be able to recognize it or account for all the 177 00:12:34,720 --> 00:12:37,880 Speaker 1: lighting differences that might happen in the world, much less 178 00:12:38,120 --> 00:12:40,920 Speaker 1: the size differences if you see a big picture or 179 00:12:40,920 --> 00:12:45,760 Speaker 1: a small picture. So understanding the relationship between things is 180 00:12:45,840 --> 00:12:51,440 Speaker 1: the only way to have a successful recognition and literally, 181 00:12:51,480 --> 00:12:55,960 Speaker 1: for decades, computer scientists were chasing the wrong ideas by 182 00:12:56,000 --> 00:13:00,760 Speaker 1: trying to get pixel by pixel recognition until they realized 183 00:13:00,840 --> 00:13:04,959 Speaker 1: that it's all about the relationships. And this is not 184 00:13:05,040 --> 00:13:07,920 Speaker 1: just about vision, but this applies to everything we learn. 185 00:13:08,080 --> 00:13:14,040 Speaker 1: Like facts, your brain encodes new ideas with respect to 186 00:13:14,280 --> 00:13:18,160 Speaker 1: other things you have learned. Just imagine two people looking 187 00:13:18,200 --> 00:13:22,880 Speaker 1: at a list of important dates in Mongolian history. If 188 00:13:22,960 --> 00:13:27,640 Speaker 1: one of them already has a richly developed Mongolian history timeline, 189 00:13:28,200 --> 00:13:33,080 Speaker 1: then new facts are readily incorporated into that person's network 190 00:13:33,120 --> 00:13:37,720 Speaker 1: of knowledge. We don't simply memorize facts. We fit new 191 00:13:37,800 --> 00:13:43,440 Speaker 1: data into the relationship lattice of our internal model. Everything 192 00:13:43,440 --> 00:13:47,280 Speaker 1: we've learned before based on what was relevant to us. 193 00:13:47,840 --> 00:13:50,680 Speaker 1: And I talked about this in episode twenty seven. Everything 194 00:13:50,760 --> 00:13:53,960 Speaker 1: you learn is represented in terms of what you already know. 195 00:13:54,520 --> 00:13:57,880 Speaker 1: So when you listen to this podcast, the points made 196 00:13:58,240 --> 00:14:01,400 Speaker 1: would be meaningless unless you all already we're living a 197 00:14:01,520 --> 00:14:05,199 Speaker 1: human life and knew what a brain was and understood 198 00:14:05,520 --> 00:14:10,640 Speaker 1: the particular language I am speaking. We do not encode 199 00:14:10,679 --> 00:14:14,199 Speaker 1: things as little files of zeros and ones. What's happening 200 00:14:14,240 --> 00:14:18,160 Speaker 1: in the human brain is much different. So that's the 201 00:14:18,200 --> 00:14:22,080 Speaker 1: first thing. And there's another reason why human memory is 202 00:14:22,120 --> 00:14:24,880 Speaker 1: not at all like computer memory. And this is because 203 00:14:25,040 --> 00:14:27,880 Speaker 1: over the past several decades, we've come to realize that 204 00:14:28,080 --> 00:14:33,040 Speaker 1: human memory is not one thing, but instead it's made 205 00:14:33,120 --> 00:14:36,920 Speaker 1: up of lots and lots of different subsystems. There are 206 00:14:37,400 --> 00:14:41,520 Speaker 1: many things going on under this one umbrella term that 207 00:14:41,560 --> 00:14:45,400 Speaker 1: we call human memory. So for the next few minutes, 208 00:14:45,880 --> 00:14:49,240 Speaker 1: let's dive into the nuts and bolts of that landscape. 209 00:14:49,920 --> 00:14:52,840 Speaker 1: The first thing to know is that memory is divided 210 00:14:52,960 --> 00:14:58,480 Speaker 1: between short term and long term. So short term memory 211 00:14:58,960 --> 00:15:02,880 Speaker 1: is about in information that decays in a really short 212 00:15:02,920 --> 00:15:05,240 Speaker 1: time if you don't use it, like seconds to minutes. 213 00:15:05,320 --> 00:15:09,960 Speaker 1: This is also called working memory by neuroscientists. The idea 214 00:15:10,680 --> 00:15:15,320 Speaker 1: is that short term memory uses information to address immediate situations. 215 00:15:15,400 --> 00:15:19,120 Speaker 1: So let's say I say, oh, hey, your verification code 216 00:15:19,200 --> 00:15:22,760 Speaker 1: to log into LinkedIn is six one, nine, five, three 217 00:15:22,760 --> 00:15:26,680 Speaker 1: to two. You can remember that for some seconds or minutes, 218 00:15:26,720 --> 00:15:30,720 Speaker 1: but you're probably not going to remember that number next week, 219 00:15:30,960 --> 00:15:34,600 Speaker 1: or probably even in an hour from now. There's no 220 00:15:34,760 --> 00:15:38,080 Speaker 1: strict time limit here, but short term memory typically lasts 221 00:15:38,760 --> 00:15:42,480 Speaker 1: just for the duration of the task at hand. Now, 222 00:15:42,520 --> 00:15:45,400 Speaker 1: there are lots of ways that your brain helps itself 223 00:15:45,520 --> 00:15:48,480 Speaker 1: long with short term memories. Sometimes you do this by 224 00:15:49,000 --> 00:15:52,400 Speaker 1: talking to yourself like six one nine five three two 225 00:15:52,440 --> 00:15:55,240 Speaker 1: six one nine five three two, or sometimes you might 226 00:15:55,440 --> 00:15:59,120 Speaker 1: hold on to information by visualizing something in front of you. 227 00:15:59,480 --> 00:16:02,760 Speaker 1: But how whoever you do it, working memory has a 228 00:16:03,120 --> 00:16:08,320 Speaker 1: limited capacity for holding information. In nineteen fifty six, a 229 00:16:08,520 --> 00:16:14,040 Speaker 1: cognitive psychologist wrote a paper called the Magical Number seven 230 00:16:14,120 --> 00:16:18,160 Speaker 1: plus or minus two. Now, why that weird title is 231 00:16:18,200 --> 00:16:21,640 Speaker 1: because he found that most people can hold about seven 232 00:16:21,720 --> 00:16:25,040 Speaker 1: items of information in short term memory, sometimes a little 233 00:16:25,080 --> 00:16:27,840 Speaker 1: more so as a less It doesn't matter whether that's 234 00:16:28,200 --> 00:16:32,000 Speaker 1: shapes or locations or colors or numbers or whatever. You 235 00:16:32,040 --> 00:16:36,040 Speaker 1: can hold about seven items. And by the way, this 236 00:16:36,080 --> 00:16:39,520 Speaker 1: has been proposed to explain why telephone numbers in most 237 00:16:39,560 --> 00:16:43,040 Speaker 1: countries use seven digits, because back in the day people 238 00:16:43,080 --> 00:16:46,520 Speaker 1: often needed to memorize phone numbers and if they were 239 00:16:46,720 --> 00:16:49,720 Speaker 1: like sixteen digits, no one would be able to do 240 00:16:49,800 --> 00:16:54,200 Speaker 1: it anyway, So that's short term memory. I ask you 241 00:16:54,240 --> 00:16:56,680 Speaker 1: to memorize a combination lock or a log in code 242 00:16:56,760 --> 00:16:59,600 Speaker 1: or a phone number for some task and you say, cool, 243 00:16:59,680 --> 00:17:02,800 Speaker 1: got it. That lives in your short term memory. And 244 00:17:02,840 --> 00:17:06,840 Speaker 1: that memory is built of the activity of millions or 245 00:17:06,880 --> 00:17:10,840 Speaker 1: billions of neurons holding onto a loop of spikes that 246 00:17:10,920 --> 00:17:15,000 Speaker 1: run around and around across giant swaths of territory. The 247 00:17:15,040 --> 00:17:20,040 Speaker 1: activity is kept alive, and that represents the thing you 248 00:17:20,240 --> 00:17:24,280 Speaker 1: are remembering now. Often, something you hold in your short 249 00:17:24,359 --> 00:17:28,960 Speaker 1: term memory tends not to transfer to your long term memory. 250 00:17:29,000 --> 00:17:31,639 Speaker 1: But long term memory is the really interesting thing in 251 00:17:31,680 --> 00:17:33,520 Speaker 1: the brain. So that's what we're going to concentrate on 252 00:17:33,840 --> 00:17:36,920 Speaker 1: from here on out. Because with long term memory, it's 253 00:17:36,960 --> 00:17:42,440 Speaker 1: not about active neurons. Instead, there are changes to the 254 00:17:42,480 --> 00:17:46,440 Speaker 1: physical structure of your brain such that you can pull 255 00:17:46,520 --> 00:17:49,760 Speaker 1: up information that happened to you a long, long time ago. 256 00:17:49,880 --> 00:17:54,720 Speaker 1: You're actually changing the stuff of your brain. I mentioned 257 00:17:54,760 --> 00:17:58,240 Speaker 1: in a previous episode that if a rock hits your 258 00:17:58,280 --> 00:18:03,520 Speaker 1: windshield and leaves a you could say poetically that your 259 00:18:03,800 --> 00:18:08,400 Speaker 1: car remembers the rock even long after the rock is gone. 260 00:18:08,400 --> 00:18:11,480 Speaker 1: There is a physical mark on your car, and that's 261 00:18:11,520 --> 00:18:14,040 Speaker 1: the way to think about it. With the brain, an 262 00:18:14,040 --> 00:18:18,479 Speaker 1: event happens to you, and it leaves a physical mark, 263 00:18:18,960 --> 00:18:24,200 Speaker 1: and that mark can be read out later. So as 264 00:18:24,200 --> 00:18:28,879 Speaker 1: opposed to short term memory, long term memory involves brain 265 00:18:29,000 --> 00:18:34,000 Speaker 1: systems that in code and store and retrieve information over 266 00:18:34,119 --> 00:18:37,760 Speaker 1: long periods of time, anywhere from minutes to a lifetime, 267 00:18:38,119 --> 00:18:41,800 Speaker 1: and the capacity of these systems is much greater than 268 00:18:41,840 --> 00:18:44,960 Speaker 1: short term memory, as we see with someone like Jill 269 00:18:45,000 --> 00:18:50,040 Speaker 1: Price or Shashevski. Now, amazingly, long term memory is not 270 00:18:50,320 --> 00:18:54,960 Speaker 1: one thing. Instead, we divide it into two groups. On 271 00:18:55,000 --> 00:18:58,520 Speaker 1: the one hand, we have implicit memory and on the 272 00:18:58,600 --> 00:19:03,960 Speaker 1: other explicit memory. Now, implicit memory is the stuff that 273 00:19:04,080 --> 00:19:10,440 Speaker 1: you can't express or articulate or consciously recall, like riding 274 00:19:10,520 --> 00:19:12,959 Speaker 1: a bike. You have no idea how you do it, 275 00:19:13,000 --> 00:19:15,919 Speaker 1: but you learned it and you remember it. You just 276 00:19:15,960 --> 00:19:20,520 Speaker 1: can't articulate how you do it. It is implicit. And this 277 00:19:20,640 --> 00:19:25,600 Speaker 1: category of implicit memory also includes unconscious emotional memories, like 278 00:19:26,080 --> 00:19:30,160 Speaker 1: an aversion towards snowboarding years after an accident, even if 279 00:19:30,160 --> 00:19:33,879 Speaker 1: the accident itself is long forgotten. Okay, so all of 280 00:19:33,920 --> 00:19:38,400 Speaker 1: that is implicit memory. On the other hand, explicit memory 281 00:19:38,520 --> 00:19:42,399 Speaker 1: is the type of information that can be consciously recalled 282 00:19:42,440 --> 00:19:46,920 Speaker 1: and described, like facts and events. So think about something 283 00:19:46,920 --> 00:19:50,640 Speaker 1: that happened to you that you can recall and describe, 284 00:19:50,720 --> 00:19:53,880 Speaker 1: like this is where you went to college, or this 285 00:19:54,000 --> 00:19:56,600 Speaker 1: is the name of that great movie you saw, or 286 00:19:57,119 --> 00:20:00,840 Speaker 1: that's what your neighbor said to you last week. Explicit 287 00:20:00,880 --> 00:20:05,320 Speaker 1: memory also includes memories of facts like the fact that 288 00:20:05,680 --> 00:20:09,760 Speaker 1: Amazon dot Com sells books or that penguins can't fly. 289 00:20:10,880 --> 00:20:14,960 Speaker 1: If you can say some recollection or fact out loud, 290 00:20:15,160 --> 00:20:19,760 Speaker 1: then that is explicit memory. Now, I wanted to clearly 291 00:20:19,880 --> 00:20:23,359 Speaker 1: lay out these two sides of implicit memory and explicit 292 00:20:23,400 --> 00:20:27,440 Speaker 1: memory before taking a deeper dive to see why these 293 00:20:27,480 --> 00:20:47,040 Speaker 1: are understood to be separate systems. So now let's return 294 00:20:47,119 --> 00:20:51,880 Speaker 1: for a closer look at this first one, implicit memory. 295 00:20:52,480 --> 00:20:56,600 Speaker 1: So think about memories involved in how to perform skills 296 00:20:56,680 --> 00:21:01,639 Speaker 1: or habits like reading or typing, or whimming, or juggling 297 00:21:01,840 --> 00:21:06,520 Speaker 1: or playing piano. You acquire these kinds of memory slowly 298 00:21:06,680 --> 00:21:09,879 Speaker 1: through a lot of practice and repetition. The way you 299 00:21:10,000 --> 00:21:13,920 Speaker 1: learn to perfect these actions is from your brain changing 300 00:21:14,040 --> 00:21:18,120 Speaker 1: its structure. It is remembering. So even though you might 301 00:21:18,160 --> 00:21:21,560 Speaker 1: not typically think of things like reading and typing and 302 00:21:21,600 --> 00:21:25,159 Speaker 1: walking and so on as memory, they are indeed a 303 00:21:25,320 --> 00:21:29,000 Speaker 1: form of remembrance, just one that you cannot articulate, And 304 00:21:29,040 --> 00:21:33,240 Speaker 1: there are many forms of implicit memory. When you might 305 00:21:33,320 --> 00:21:36,680 Speaker 1: have heard of is called priming. That's what happens when 306 00:21:36,760 --> 00:21:42,119 Speaker 1: some past experience influences your future response. So imagine I 307 00:21:42,200 --> 00:21:47,840 Speaker 1: show you the letters S, blank, P, and I ask 308 00:21:47,920 --> 00:21:50,159 Speaker 1: you to fill in the missing letter. If you have 309 00:21:50,440 --> 00:21:54,080 Speaker 1: just had a shower, you'll probably put an A in 310 00:21:54,119 --> 00:21:57,520 Speaker 1: there to make the word soap. If you've just seen 311 00:21:57,600 --> 00:22:01,000 Speaker 1: a steaming hot bowl at the table, you'll probably put 312 00:22:01,040 --> 00:22:04,320 Speaker 1: the letter you in there to make the word soup. 313 00:22:05,400 --> 00:22:09,880 Speaker 1: Your brain just recently experienced something, and that memory influences 314 00:22:09,920 --> 00:22:14,000 Speaker 1: your behavior in the next moment. Now what's fascinating is 315 00:22:14,040 --> 00:22:17,600 Speaker 1: that people with amnesia, which means they can't write down 316 00:22:17,720 --> 00:22:23,480 Speaker 1: new explicit memories, can nonetheless show these kinds of priming effects. 317 00:22:24,080 --> 00:22:27,760 Speaker 1: So they have no conscious recollection of having seen this 318 00:22:27,920 --> 00:22:30,919 Speaker 1: steaming hot bowl at the table, and they'll deny that 319 00:22:30,960 --> 00:22:34,800 Speaker 1: they ever saw that, but nonetheless they'll choose soup. I'll 320 00:22:34,800 --> 00:22:36,359 Speaker 1: come back to this point in just a bit, but 321 00:22:36,400 --> 00:22:39,199 Speaker 1: first I want to finish painting the different types of 322 00:22:39,320 --> 00:22:43,880 Speaker 1: implicit memory. So another type of implicit memory is one 323 00:22:43,880 --> 00:22:47,240 Speaker 1: you might have heard of called classical conditioning. This was 324 00:22:47,320 --> 00:22:52,320 Speaker 1: first described by the Russian physiologist Ivon Pavlov at the 325 00:22:52,440 --> 00:22:55,800 Speaker 1: end of the nineteenth century, so you generally remember this story. 326 00:22:56,000 --> 00:23:01,040 Speaker 1: Pavlov saw that dogs respond to food by drooling or salivating, 327 00:23:01,280 --> 00:23:03,720 Speaker 1: so he said Okay, I'm going to teach the dog 328 00:23:03,800 --> 00:23:08,480 Speaker 1: through experience that the ringing of a bell predicts the food. 329 00:23:08,920 --> 00:23:11,560 Speaker 1: The bell itself is totally arbitrary, but it comes to 330 00:23:11,600 --> 00:23:16,200 Speaker 1: tell the dog that some food is coming. Pavlov could 331 00:23:16,240 --> 00:23:18,440 Speaker 1: just as easily have used a pat on the head 332 00:23:18,440 --> 00:23:21,480 Speaker 1: to predict the food, or a purple dot flashing three 333 00:23:21,520 --> 00:23:24,560 Speaker 1: times or whatever. It's just some random thing that you 334 00:23:24,760 --> 00:23:28,400 Speaker 1: link to food arriving a moment later. So he teaches 335 00:23:28,440 --> 00:23:31,879 Speaker 1: the dog to associate the ringing of the bell with 336 00:23:31,960 --> 00:23:35,840 Speaker 1: the impending delivery of food, and after setting up the relationship, 337 00:23:36,080 --> 00:23:39,400 Speaker 1: the dogs salivate when he rings the bell, even though 338 00:23:39,400 --> 00:23:42,959 Speaker 1: there's no food there yet, because the bell now is 339 00:23:43,080 --> 00:23:46,240 Speaker 1: as good as seeing the food. And again, this is 340 00:23:46,560 --> 00:23:49,199 Speaker 1: implicit memory because you don't have to be able to 341 00:23:49,760 --> 00:23:53,760 Speaker 1: tell the story of a connection consciously in order to 342 00:23:53,880 --> 00:23:59,000 Speaker 1: form this kind of learning, this memory. So that's classical conditioning, 343 00:23:59,280 --> 00:24:03,920 Speaker 1: and there's another form of implicit memory, which is operant conditioning. 344 00:24:04,200 --> 00:24:08,280 Speaker 1: Here you learn to associate your own behavior like pressing 345 00:24:08,280 --> 00:24:13,080 Speaker 1: a lever, with something rewarding like food, or something aversive 346 00:24:13,320 --> 00:24:17,119 Speaker 1: like an electric shock, and you gradually do more or 347 00:24:17,200 --> 00:24:22,880 Speaker 1: less of that behavior as a result. Of remembering the consequences. Now, 348 00:24:22,920 --> 00:24:24,639 Speaker 1: I'm not going to go into the details of the 349 00:24:24,680 --> 00:24:27,800 Speaker 1: brain regions that are involved in these different forms of 350 00:24:27,840 --> 00:24:31,000 Speaker 1: implicit learning. If you're curious, to go to my textbook 351 00:24:31,040 --> 00:24:34,520 Speaker 1: called Brain and Behavior. But I'll just mention that learning 352 00:24:34,840 --> 00:24:38,119 Speaker 1: any of these types of responses depends on some brain 353 00:24:38,160 --> 00:24:41,919 Speaker 1: areas like the amygdala and cerebellum, and some structures in 354 00:24:41,960 --> 00:24:44,439 Speaker 1: the brain stem. The key thing I want you to 355 00:24:44,480 --> 00:24:47,639 Speaker 1: appreciate is that all of these forms of memory I 356 00:24:47,800 --> 00:24:52,640 Speaker 1: just described are forms in which your brain changes itself. 357 00:24:52,680 --> 00:24:57,879 Speaker 1: It changes its detailed internal structure in response to things 358 00:24:57,920 --> 00:25:01,480 Speaker 1: that it has experienced so that it be behaves differently 359 00:25:01,600 --> 00:25:04,280 Speaker 1: in the future. And all of these things I just 360 00:25:04,320 --> 00:25:08,960 Speaker 1: told you about, that's just implicit memory. Now let's switch 361 00:25:09,040 --> 00:25:12,280 Speaker 1: back again to explicit memory, the things that you can 362 00:25:12,400 --> 00:25:18,040 Speaker 1: consciously articulate, like events and facts. Even explicit memory can 363 00:25:18,080 --> 00:25:22,240 Speaker 1: be divided into two different forms. The first is called 364 00:25:22,680 --> 00:25:27,840 Speaker 1: episodic memory. This is memory for past events or episodes 365 00:25:28,080 --> 00:25:31,440 Speaker 1: that you've experienced, like a birthday party or a surfing 366 00:25:31,520 --> 00:25:35,720 Speaker 1: trip you took. These are called autobiographical memories. And the 367 00:25:35,800 --> 00:25:40,800 Speaker 1: key point is that, unlike implicit memories, these Episodic memories 368 00:25:40,840 --> 00:25:44,040 Speaker 1: can be consciously recalled and described. They can have a 369 00:25:44,520 --> 00:25:47,879 Speaker 1: sort of cinematic quality to them, and they usually have 370 00:25:47,920 --> 00:25:51,719 Speaker 1: a particular context, like in the living room on New 371 00:25:51,800 --> 00:25:55,920 Speaker 1: Year's Eve. You're also generally able to pick out specific 372 00:25:56,040 --> 00:25:58,840 Speaker 1: objects and features and the surroundings, like oh, yeah, the 373 00:25:58,920 --> 00:26:01,439 Speaker 1: sofa was over here my left, and there was a 374 00:26:01,440 --> 00:26:04,240 Speaker 1: television set over there, and my uncle was sitting over 375 00:26:04,280 --> 00:26:08,080 Speaker 1: here on my right, And you can recall very particular 376 00:26:08,560 --> 00:26:13,040 Speaker 1: sequences of actions like oh, we were wearing party hats 377 00:26:13,080 --> 00:26:16,399 Speaker 1: and the wine was poured and then it clinked glasses 378 00:26:16,440 --> 00:26:22,040 Speaker 1: with everyone. So those are episodic memories. Now there's another 379 00:26:22,200 --> 00:26:27,399 Speaker 1: type of explicit memory, not episodic, but semantic memories, and 380 00:26:27,440 --> 00:26:30,600 Speaker 1: these are things that your brain remembers about the outside world, 381 00:26:30,720 --> 00:26:34,439 Speaker 1: like the fact that a sheep has four legs and 382 00:26:34,480 --> 00:26:37,679 Speaker 1: it makes a noise that sounds like bah, and it 383 00:26:37,760 --> 00:26:42,600 Speaker 1: has a wooly coat. Semantic properties are more general than 384 00:26:42,800 --> 00:26:46,200 Speaker 1: single events that you saw or experienced, and because they're 385 00:26:46,400 --> 00:26:50,400 Speaker 1: independent of any one particular kind of sensory input, they're 386 00:26:50,440 --> 00:26:55,879 Speaker 1: generally useful for organizing the world into categories of related 387 00:26:56,040 --> 00:26:58,879 Speaker 1: things Like these are vegetables and these are animals, and 388 00:26:58,880 --> 00:27:02,880 Speaker 1: these are tools, these vehicles and so on. So that's 389 00:27:02,880 --> 00:27:06,760 Speaker 1: semantic memory. Now, I just told you about a whole 390 00:27:06,800 --> 00:27:12,160 Speaker 1: bunch of different subtypes of memories. But why do neuroscientists 391 00:27:12,600 --> 00:27:16,879 Speaker 1: divide memory into all these different flavors. Well, it's because 392 00:27:16,920 --> 00:27:21,399 Speaker 1: clinicians have observed for many, many decades that sometimes a 393 00:27:21,520 --> 00:27:25,160 Speaker 1: person can damage one part of their brain and lose 394 00:27:25,240 --> 00:27:29,840 Speaker 1: a very specific sort of memory while not losing other sorts. So, 395 00:27:30,000 --> 00:27:34,280 Speaker 1: for example, you might see a patient with severe amnesia 396 00:27:34,400 --> 00:27:39,040 Speaker 1: for personal experiences. She can remember essentially nothing about her 397 00:27:39,080 --> 00:27:43,399 Speaker 1: personal life, but she retains a good general knowledge for 398 00:27:43,640 --> 00:27:47,280 Speaker 1: facts that she learned before her brain injury. So that's 399 00:27:47,320 --> 00:27:51,440 Speaker 1: how we know that episodic memory is different from semantic 400 00:27:51,480 --> 00:27:55,239 Speaker 1: It's actually underpinned by different structures in the brain, and 401 00:27:55,440 --> 00:27:57,800 Speaker 1: by the way, you see this often in forms of 402 00:27:57,800 --> 00:28:03,280 Speaker 1: dementia like early Alzheimer's disease, where a person's episodic memory 403 00:28:03,480 --> 00:28:07,440 Speaker 1: suffers dramatically. They can't remember the details of their own life, 404 00:28:07,760 --> 00:28:12,159 Speaker 1: but this is long before their semantic knowledge begins to fail. 405 00:28:13,000 --> 00:28:16,320 Speaker 1: And on the flip side, in a rarer illness known 406 00:28:16,320 --> 00:28:22,200 Speaker 1: as semantic dementia, episodic memory is preserved, while even basic 407 00:28:22,320 --> 00:28:26,119 Speaker 1: forms of semantic knowledge are lost, like what a sheep 408 00:28:26,280 --> 00:28:29,280 Speaker 1: is or what sound it makes. I won't go into 409 00:28:29,280 --> 00:28:31,240 Speaker 1: details here, but if you want to look up more, 410 00:28:31,520 --> 00:28:36,600 Speaker 1: episodic memory generally depends on the medial temporal lobes, while 411 00:28:36,680 --> 00:28:42,760 Speaker 1: semantic memory involves the anterior temporal lobes. Okay, so now 412 00:28:43,000 --> 00:28:46,560 Speaker 1: how did people start to figure out what brain regions 413 00:28:46,560 --> 00:28:49,640 Speaker 1: were involved in what functions. Well, like many things, the 414 00:28:49,720 --> 00:28:52,360 Speaker 1: answer is that this generally happens when a person gets 415 00:28:52,520 --> 00:28:56,360 Speaker 1: damage to a very specific part of the brain. So 416 00:28:56,480 --> 00:29:00,200 Speaker 1: let's zoom in on a particular example. In nineteen fifty, 417 00:29:00,640 --> 00:29:04,400 Speaker 1: a particular area of the brain called the hippocampus came 418 00:29:04,440 --> 00:29:08,320 Speaker 1: to the center stage in the neuroscience of memory because 419 00:29:08,360 --> 00:29:11,240 Speaker 1: there was a man named Henry Malaison, who, by the way, 420 00:29:11,360 --> 00:29:15,440 Speaker 1: was known for decades in the literature as HM because 421 00:29:15,440 --> 00:29:18,440 Speaker 1: the patient's real name is never used while he's still alive. 422 00:29:19,000 --> 00:29:22,560 Speaker 1: So anyway, Henry had suffered a head injury at the 423 00:29:22,600 --> 00:29:26,760 Speaker 1: age of nine, and after that he had epileptic seizures 424 00:29:27,320 --> 00:29:31,000 Speaker 1: and these got more frequent and severe, and the doctors 425 00:29:31,080 --> 00:29:34,720 Speaker 1: tried to control the seizures, but eventually they couldn't control 426 00:29:34,760 --> 00:29:37,719 Speaker 1: them anymore. So at the age of twenty seven, Henry 427 00:29:37,800 --> 00:29:43,240 Speaker 1: went in for a neurosurgery because of where the seizures 428 00:29:43,280 --> 00:29:48,320 Speaker 1: were originating. The surgeon removed the hippocampus on both sides 429 00:29:48,360 --> 00:29:50,640 Speaker 1: of his brain, on the right and the left, as 430 00:29:50,680 --> 00:29:54,000 Speaker 1: well as some of the regions that surrounded the hippocampus. Now, 431 00:29:54,720 --> 00:30:00,520 Speaker 1: after the surgery, the seizures were all better, but Henry 432 00:30:00,560 --> 00:30:03,280 Speaker 1: became one of the most famous cases in the medical 433 00:30:03,320 --> 00:30:07,600 Speaker 1: literature because while he was otherwise fine, he had a 434 00:30:07,920 --> 00:30:12,680 Speaker 1: severe amnesia that means he couldn't remember things, and specifically, 435 00:30:12,720 --> 00:30:16,880 Speaker 1: he had an antaro grade amnesia, which means he couldn't 436 00:30:16,920 --> 00:30:21,320 Speaker 1: form new episodic memories. And so that meant he would 437 00:30:21,360 --> 00:30:23,760 Speaker 1: function just fine if you were talking with him, but 438 00:30:23,880 --> 00:30:27,800 Speaker 1: after say fifteen minutes, he couldn't recall anything about the 439 00:30:27,840 --> 00:30:34,160 Speaker 1: conversation because he wasn't converting that into new long term memory. 440 00:30:34,680 --> 00:30:38,000 Speaker 1: So just imagine being one of the people working with him. 441 00:30:38,040 --> 00:30:41,520 Speaker 1: You walk into the room and you introduce yourself, and 442 00:30:41,560 --> 00:30:45,000 Speaker 1: you bring a giant Saint Bernard dog with you. Then 443 00:30:45,080 --> 00:30:47,320 Speaker 1: the dog leaves and you chat with him for about 444 00:30:47,360 --> 00:30:50,280 Speaker 1: five minutes and you ask him, hey, when I came 445 00:30:50,280 --> 00:30:54,120 Speaker 1: in here, did I have anything with me? And he says, yeah, 446 00:30:54,160 --> 00:30:57,120 Speaker 1: you had a giant Saint Bernard dog with you. So 447 00:30:57,200 --> 00:30:59,720 Speaker 1: you continue to have a nice conversation about things. He's 448 00:30:59,720 --> 00:31:03,360 Speaker 1: a small guy. Now you distract him for about another 449 00:31:03,400 --> 00:31:06,520 Speaker 1: fifteen minutes and you ask him, hey, when I came 450 00:31:06,560 --> 00:31:09,120 Speaker 1: in here, did I have anything with me? And he 451 00:31:09,240 --> 00:31:12,120 Speaker 1: draws a total blank. He says, I don't think So 452 00:31:13,200 --> 00:31:15,920 Speaker 1: now you leave the room and you come back twenty 453 00:31:15,960 --> 00:31:18,920 Speaker 1: minutes later and you say, hey, when I walked in 454 00:31:18,960 --> 00:31:22,280 Speaker 1: the room before, did I have anything with me? And 455 00:31:22,320 --> 00:31:26,600 Speaker 1: what does he say? He says, who are you? As 456 00:31:26,640 --> 00:31:29,080 Speaker 1: far as he knows, he has never seen you before 457 00:31:29,120 --> 00:31:32,640 Speaker 1: in his life. So the researchers who studied Henry for 458 00:31:32,760 --> 00:31:37,239 Speaker 1: years had to reintroduce themselves each time they walked in 459 00:31:37,240 --> 00:31:39,479 Speaker 1: the room, even if they'd only left the room briefly. 460 00:31:40,120 --> 00:31:44,160 Speaker 1: So the issue was that Henry could not form new 461 00:31:44,440 --> 00:31:50,120 Speaker 1: explicit memories. But here's the interesting part. His implicit memory 462 00:31:50,440 --> 00:31:55,280 Speaker 1: was fine. He could practice and learn new tasks like 463 00:31:55,800 --> 00:31:59,760 Speaker 1: tracing a five pointed star viewed through a mirror, but 464 00:31:59,880 --> 00:32:03,200 Speaker 1: he had no recollection of the practice in learning, and 465 00:32:03,560 --> 00:32:06,880 Speaker 1: he expressed surprise at how well, he could perform this 466 00:32:07,040 --> 00:32:09,640 Speaker 1: task that, as far as he knew, he had never 467 00:32:09,840 --> 00:32:13,840 Speaker 1: seen before. Also, his short term memory was fine, well 468 00:32:13,880 --> 00:32:17,280 Speaker 1: within the range of seven plus or minus two items. 469 00:32:18,320 --> 00:32:22,680 Speaker 1: And so the tragic outcome from Henry's surgery helped to 470 00:32:22,800 --> 00:32:26,719 Speaker 1: define the taxonomy of memory systems that we know today. 471 00:32:29,000 --> 00:32:31,440 Speaker 1: And by the way, we've learned these same lessons from 472 00:32:31,600 --> 00:32:35,840 Speaker 1: hundreds of other patients with amnesia since then. For example, 473 00:32:35,920 --> 00:32:40,240 Speaker 1: somebody who can't form any new explicit memories can nonetheless 474 00:32:40,320 --> 00:32:44,360 Speaker 1: learn an implicit task like the video game Tetris, and 475 00:32:44,400 --> 00:32:46,800 Speaker 1: they can get better and better at the game. But 476 00:32:46,920 --> 00:32:49,480 Speaker 1: each time you place them in front of the game, 477 00:32:50,200 --> 00:32:54,280 Speaker 1: they claim genuinely that they've never seen this before. And 478 00:32:54,320 --> 00:32:57,560 Speaker 1: what's fascinating is that if you wake them up when 479 00:32:57,560 --> 00:32:59,640 Speaker 1: they're sleeping, if you catch them in the middle of 480 00:32:59,640 --> 00:33:02,760 Speaker 1: a dream, they'll say that they were just dreaming about 481 00:33:03,120 --> 00:33:07,920 Speaker 1: colorful falling blocks, but they have no idea why, because 482 00:33:07,960 --> 00:33:12,720 Speaker 1: they have no conscious memory of ever having seen that before. 483 00:33:13,840 --> 00:33:16,600 Speaker 1: By the way, if you saw the movie Memento, you 484 00:33:16,680 --> 00:33:19,440 Speaker 1: know that was about a man who had lost his 485 00:33:19,680 --> 00:33:23,360 Speaker 1: explicit memory. He had had a head injury that gave 486 00:33:23,440 --> 00:33:27,240 Speaker 1: him amnesia. In his case, both in the forward and 487 00:33:27,440 --> 00:33:31,120 Speaker 1: backward direction and tarot grade and retrograde, and so the 488 00:33:31,160 --> 00:33:33,600 Speaker 1: only way that he could keep track of his goals 489 00:33:33,640 --> 00:33:39,160 Speaker 1: through time was to tattoo information onto his skin. And 490 00:33:39,200 --> 00:33:42,160 Speaker 1: I'll be talking more about amnesia in a future episode, 491 00:33:42,480 --> 00:33:44,320 Speaker 1: but all I want to say for now is that 492 00:33:44,520 --> 00:33:47,640 Speaker 1: for most of us, we are lucky enough to tattoo 493 00:33:47,680 --> 00:33:53,040 Speaker 1: the information directly into our vast neural forests, and we 494 00:33:53,080 --> 00:33:56,040 Speaker 1: can do this again and again throughout every day of 495 00:33:56,040 --> 00:34:00,640 Speaker 1: our lives, and we never run out of room. Now, 496 00:34:00,680 --> 00:34:04,080 Speaker 1: we know from Henry and many other patients that normal 497 00:34:04,240 --> 00:34:09,680 Speaker 1: autobiographical memory depends on the integrity of the hippocampus. But 498 00:34:09,760 --> 00:34:13,120 Speaker 1: I mentioned in episode number one of this podcast that 499 00:34:13,200 --> 00:34:17,880 Speaker 1: there's another important player in the medial temporal lob memory system, 500 00:34:17,960 --> 00:34:22,280 Speaker 1: and that's the amygdala, which is an almond sized structure 501 00:34:22,320 --> 00:34:24,880 Speaker 1: that's just in front of the hippocampus, and this is 502 00:34:24,920 --> 00:34:31,360 Speaker 1: involved in emotional memory. The amygdala assigns value positive or 503 00:34:31,440 --> 00:34:35,160 Speaker 1: negative to things that it sees or hears or smells 504 00:34:35,200 --> 00:34:39,680 Speaker 1: based on past experiences. And because it's so well connected, 505 00:34:40,200 --> 00:34:44,200 Speaker 1: it can coordinate all the different prongs of an emotional 506 00:34:44,280 --> 00:34:49,200 Speaker 1: response to something. It can coordinate the autonomic responses like 507 00:34:49,280 --> 00:34:52,920 Speaker 1: an increased heart rate, and the endocrine responses like the 508 00:34:53,000 --> 00:34:59,120 Speaker 1: secretion of stress hormones, and behavioral responses like fear and avoidance. 509 00:35:00,080 --> 00:35:02,799 Speaker 1: So it turns out that while normal memories are just 510 00:35:02,880 --> 00:35:07,320 Speaker 1: taken care of by the hippocampus, emotional situations like something 511 00:35:07,440 --> 00:35:11,600 Speaker 1: very stressful or dangerous, those kick the amigdala into gear 512 00:35:12,160 --> 00:35:15,000 Speaker 1: and memories get written down on what is essentially a 513 00:35:15,120 --> 00:35:20,319 Speaker 1: secondary memory pathway. And that's why emotional events are more 514 00:35:20,520 --> 00:35:23,759 Speaker 1: likely to be remembered, because in a sense, those are 515 00:35:23,800 --> 00:35:29,000 Speaker 1: the most important memories. When something really emotionally important happens, 516 00:35:29,120 --> 00:35:32,200 Speaker 1: that's what you want to write down. That's why memory 517 00:35:32,360 --> 00:35:36,600 Speaker 1: exists to keep a record of the really important stuff. 518 00:35:37,560 --> 00:35:40,040 Speaker 1: By the way, I spoke in the first episode about 519 00:35:40,480 --> 00:35:44,560 Speaker 1: time seeming to run in slow motion when you're in danger, 520 00:35:44,719 --> 00:35:48,040 Speaker 1: when you're in fear for your life, And if you're interested, 521 00:35:48,360 --> 00:35:50,600 Speaker 1: you can go back and listen to the experiments that 522 00:35:50,680 --> 00:35:53,160 Speaker 1: my lab did. But the punchline is that when you're 523 00:35:53,160 --> 00:35:58,320 Speaker 1: in a very stressful situation, your brain writes down denser 524 00:35:58,520 --> 00:36:01,239 Speaker 1: memories than normal, and so when you think back on 525 00:36:01,280 --> 00:36:05,640 Speaker 1: what just happened, your brain pulls up much more detail 526 00:36:05,800 --> 00:36:09,680 Speaker 1: than you would normally have, and you interpret that as 527 00:36:10,280 --> 00:36:13,520 Speaker 1: that must have taken a long time because I hit 528 00:36:13,600 --> 00:36:16,320 Speaker 1: the brakes, but because of all the ice, I couldn't 529 00:36:16,320 --> 00:36:19,320 Speaker 1: get traction, and so my car slid into the intersection. 530 00:36:19,400 --> 00:36:21,839 Speaker 1: And I saw the blue Toyota coming and I saw 531 00:36:21,880 --> 00:36:24,120 Speaker 1: the expression on the other driver, and she hit the 532 00:36:24,200 --> 00:36:26,120 Speaker 1: front of my car, and I saw the hood crumple, 533 00:36:26,160 --> 00:36:28,800 Speaker 1: and the glass spider web, and the rear view mirror 534 00:36:28,880 --> 00:36:32,120 Speaker 1: fall off, and so on and so on, and so 535 00:36:32,160 --> 00:36:34,440 Speaker 1: when you think what just happened, what just happened, your 536 00:36:34,440 --> 00:36:38,479 Speaker 1: brain assumes, Wow, that must have taken a long time. 537 00:36:38,520 --> 00:36:41,120 Speaker 1: That must have taken many many seconds for all of 538 00:36:41,160 --> 00:36:43,880 Speaker 1: that to happen in order for me to see that 539 00:36:43,960 --> 00:36:49,000 Speaker 1: much detail. But in fact, time does not actually run 540 00:36:49,040 --> 00:36:53,440 Speaker 1: in slow motion for you, as we demonstrated with experiments. Instead, 541 00:36:53,440 --> 00:36:57,320 Speaker 1: it's a trick of memory. You just have more memory 542 00:36:57,400 --> 00:37:01,520 Speaker 1: that you're drawing up. As I mentioned in the first episode. 543 00:37:01,800 --> 00:37:04,719 Speaker 1: You can convince yourself of this because if you had 544 00:37:04,760 --> 00:37:07,920 Speaker 1: a passenger on the car seat next to you, you 545 00:37:07,920 --> 00:37:13,640 Speaker 1: don't actually remember his voice as saying, which would have 546 00:37:13,719 --> 00:37:16,160 Speaker 1: to be the case if time were actually slowed down. 547 00:37:16,520 --> 00:37:19,920 Speaker 1: So it's a trick of memory, and the retrospective illusion 548 00:37:20,080 --> 00:37:24,520 Speaker 1: happens because the amygdala has come online because something very 549 00:37:24,600 --> 00:37:30,360 Speaker 1: salient is happening, and it wrote down denser memories than normal. 550 00:37:31,280 --> 00:37:33,719 Speaker 1: So that's what I wanted to say about the amygdala. 551 00:37:33,880 --> 00:37:36,600 Speaker 1: But now we're going to return to the hippocampus, which 552 00:37:36,719 --> 00:37:40,759 Speaker 1: underlies most normal day to day memory. And there was 553 00:37:40,800 --> 00:37:44,279 Speaker 1: a very interesting discovery which won a Nobel Prize some 554 00:37:44,400 --> 00:37:47,600 Speaker 1: years ago, and that is that the hippocampus is involved 555 00:37:47,719 --> 00:37:53,280 Speaker 1: in encoding your location, your position in space. In other words, 556 00:37:53,719 --> 00:37:57,120 Speaker 1: it's involved in saying, I'm in this spot, and the 557 00:37:57,200 --> 00:38:00,000 Speaker 1: room I'm in connects to that hallway which leads to 558 00:38:00,280 --> 00:38:02,880 Speaker 1: the door of that lobby, which has an elevator to 559 00:38:02,880 --> 00:38:05,960 Speaker 1: go up to that other room. Your sense of where 560 00:38:06,000 --> 00:38:11,479 Speaker 1: you are relies on your hippocampus. Your hipp campus, in fact, 561 00:38:11,520 --> 00:38:16,000 Speaker 1: has these specialized neurons that we call place cells, and 562 00:38:16,080 --> 00:38:19,520 Speaker 1: these neurons become active they fire off spikes only when 563 00:38:19,520 --> 00:38:23,160 Speaker 1: you're in a particular spot in your environment. And there 564 00:38:23,200 --> 00:38:26,960 Speaker 1: are other neurons known as grid cells, and these have 565 00:38:27,480 --> 00:38:31,640 Speaker 1: multiple receptive fields that are arranged in a grid pattern 566 00:38:31,719 --> 00:38:35,360 Speaker 1: that covers your local environment anyway. So all these cells 567 00:38:35,400 --> 00:38:38,839 Speaker 1: work together to give you a very precise sense of 568 00:38:38,880 --> 00:38:42,880 Speaker 1: your position in a room. And the hippocampus is not 569 00:38:42,920 --> 00:38:45,719 Speaker 1: only about your current position, but it's also crucial for 570 00:38:46,360 --> 00:38:51,239 Speaker 1: spatial memory. For example, imagine that you're in a labyrinth 571 00:38:51,239 --> 00:38:53,120 Speaker 1: and you have to go down this hallway and come 572 00:38:53,120 --> 00:38:55,080 Speaker 1: back to the center, and then you have to remember 573 00:38:55,120 --> 00:38:57,480 Speaker 1: that you've been there, and then you go down a 574 00:38:57,480 --> 00:38:59,080 Speaker 1: different one and you come back, and then you have 575 00:38:59,160 --> 00:39:01,000 Speaker 1: to figure out a different one and go back and 576 00:39:01,040 --> 00:39:03,080 Speaker 1: so on. It's really easy for you to do this 577 00:39:03,280 --> 00:39:06,640 Speaker 1: because even in a brand new environment, you have a 578 00:39:06,840 --> 00:39:10,680 Speaker 1: spatial memory, as in, I've already been down that hallway. 579 00:39:11,719 --> 00:39:15,000 Speaker 1: But if you have damage to your hippocampus on both sides, 580 00:39:15,280 --> 00:39:19,080 Speaker 1: then you can't do that task. You need your hippocampi 581 00:39:19,400 --> 00:39:25,480 Speaker 1: for spatial memory. Now, interestingly, you find hippocampus like structures 582 00:39:25,600 --> 00:39:28,800 Speaker 1: throughout the animal kingdom, not only in mammals, but also 583 00:39:28,880 --> 00:39:32,319 Speaker 1: in birds and in goldfish, and in all cases this 584 00:39:32,400 --> 00:39:36,640 Speaker 1: is involved in spatial memory. And across the kingdom you 585 00:39:36,719 --> 00:39:40,520 Speaker 1: see that the size of the hippocampus is related to 586 00:39:40,640 --> 00:39:44,320 Speaker 1: the demands of the territory mapping and the spatial memory. 587 00:39:44,760 --> 00:39:49,480 Speaker 1: For example, squirrels spend the autumn months hiding literally thousands 588 00:39:49,560 --> 00:39:53,319 Speaker 1: of seeds and nuts in different locations throughout their territory 589 00:39:53,719 --> 00:39:56,720 Speaker 1: so they can have a steady food supply through the winter. 590 00:39:57,360 --> 00:40:00,520 Speaker 1: And during this period the volume of their hippocampus increases 591 00:40:00,560 --> 00:40:04,560 Speaker 1: by fifteen percent. Or as another example, some species of 592 00:40:04,760 --> 00:40:09,719 Speaker 1: birds hide their food and these species have larger hippocampi 593 00:40:09,920 --> 00:40:12,799 Speaker 1: than the birds that don't do this, and when they're 594 00:40:12,800 --> 00:40:17,760 Speaker 1: doing their food hiding, that stimulates growth of their hippocampus. 595 00:40:18,160 --> 00:40:20,719 Speaker 1: And this is what you find in humans too. Our 596 00:40:20,800 --> 00:40:25,759 Speaker 1: hippocampi give us map like spatial codes, and these regions 597 00:40:25,920 --> 00:40:31,279 Speaker 1: guide navigations. So consider London taxi drivers. I don't know 598 00:40:31,320 --> 00:40:35,839 Speaker 1: if they do this anymore, but pre GPS, they had 599 00:40:35,880 --> 00:40:40,239 Speaker 1: to memorize an unbelievably detailed map of London with thousands 600 00:40:40,280 --> 00:40:45,360 Speaker 1: of destinations encompassing something like twenty five thousand streets, and 601 00:40:45,400 --> 00:40:48,359 Speaker 1: they had to be able to verbally recite the most 602 00:40:48,400 --> 00:40:52,760 Speaker 1: efficient roots between any two points, and just working from memory, 603 00:40:52,920 --> 00:40:55,560 Speaker 1: say points of interest along the way, like the names 604 00:40:55,560 --> 00:40:58,439 Speaker 1: of all the theaters that they'd pass in sequential order. 605 00:40:59,239 --> 00:41:02,000 Speaker 1: And when neu oh imaging studies were done on these 606 00:41:02,080 --> 00:41:06,040 Speaker 1: cab drivers, it could be seen that their hippocampi had 607 00:41:06,160 --> 00:41:11,640 Speaker 1: grown compared with novice taxi drivers. And interestingly, this doesn't 608 00:41:11,680 --> 00:41:16,040 Speaker 1: happen for physicians, who have to master a similarly large 609 00:41:16,080 --> 00:41:36,279 Speaker 1: body of knowledge. But it's not spatial. Okay, So I 610 00:41:36,360 --> 00:41:38,960 Speaker 1: just told you a lot about how the hippocampus is 611 00:41:39,000 --> 00:41:43,439 Speaker 1: involved in understanding this spatial layout of things. But why 612 00:41:43,480 --> 00:41:46,719 Speaker 1: do I mention the hippocampus encoding space in an episode 613 00:41:46,840 --> 00:41:52,200 Speaker 1: on memory? While first, the world is full of spatial things, 614 00:41:52,280 --> 00:41:56,080 Speaker 1: and traditionally our memories had to care about that, just 615 00:41:56,120 --> 00:41:59,360 Speaker 1: like the squirrels remembering where they had buried their seeds 616 00:41:59,400 --> 00:42:02,560 Speaker 1: and nuts. Also, you may have noticed that we are 617 00:42:02,600 --> 00:42:07,040 Speaker 1: generally very good at remembering spatial information, like all the 618 00:42:07,080 --> 00:42:10,680 Speaker 1: important rooms in a very large building. And many people, 619 00:42:10,680 --> 00:42:13,880 Speaker 1: when they're trying to memorize something like a list of items, 620 00:42:14,200 --> 00:42:18,360 Speaker 1: will use what is called a memory palace, which is 621 00:42:18,360 --> 00:42:23,600 Speaker 1: where you associate these different items with different locations. So, 622 00:42:23,719 --> 00:42:26,040 Speaker 1: for example, let's say I had to memorize a long 623 00:42:26,160 --> 00:42:35,000 Speaker 1: list of words like apple, baby, clock, dennis, exhibition, flaggirl, horse, ice, jester, ladder, machine, noos, ocean, pigeon, radio, sheep, theater, 624 00:42:35,560 --> 00:42:37,640 Speaker 1: and so on. What I might do is I might 625 00:42:37,960 --> 00:42:42,720 Speaker 1: picture my house and I visualize the first item apple 626 00:42:42,920 --> 00:42:45,839 Speaker 1: at the front door, and then I imagine walking in 627 00:42:45,880 --> 00:42:49,240 Speaker 1: and I visualize the second item, let's say a baby 628 00:42:49,320 --> 00:42:51,919 Speaker 1: in this case in a crib just inside the door. 629 00:42:52,200 --> 00:42:55,280 Speaker 1: And then just past the baby, I picture a giant 630 00:42:55,320 --> 00:42:58,920 Speaker 1: pendulum clock on the wall, and on the couch over there, 631 00:42:59,000 --> 00:43:02,440 Speaker 1: I visualize a dentist doing his work on a patient, 632 00:43:02,760 --> 00:43:07,120 Speaker 1: and so on and so on. I leverage location. I 633 00:43:07,160 --> 00:43:11,480 Speaker 1: take advantage of these cells in my hippocampus that care 634 00:43:11,520 --> 00:43:16,040 Speaker 1: about place to tie other information to them. And when 635 00:43:16,080 --> 00:43:18,800 Speaker 1: I need to remember this list much later, I simply 636 00:43:18,840 --> 00:43:22,840 Speaker 1: imagine myself walking through the house, and I can recall 637 00:43:22,920 --> 00:43:28,080 Speaker 1: an enormously long list of arbitrary objects this way. This is, 638 00:43:28,080 --> 00:43:32,400 Speaker 1: in fact, the oldest memory technique called a mnemonic device, 639 00:43:32,800 --> 00:43:36,560 Speaker 1: that is on record. The ancient Greek and Roman bards 640 00:43:37,120 --> 00:43:40,400 Speaker 1: used to tell their epic tales this way. This was 641 00:43:40,440 --> 00:43:43,359 Speaker 1: before the invention of the printing press, so they had 642 00:43:43,400 --> 00:43:46,600 Speaker 1: to memorize these things. But happily this was after the 643 00:43:46,640 --> 00:43:53,000 Speaker 1: evolution of the hippocampus. Now, although some people purposely leverage 644 00:43:53,040 --> 00:43:57,000 Speaker 1: these kind of techniques to memorize for a small fraction 645 00:43:57,120 --> 00:44:02,000 Speaker 1: of the population. This happens naturally. In episode four, I 646 00:44:02,040 --> 00:44:05,640 Speaker 1: talked about synesthesia, and one of the most common forms 647 00:44:05,680 --> 00:44:11,840 Speaker 1: of synesthesia involves imagining things with spatial locations. For example, 648 00:44:12,200 --> 00:44:15,680 Speaker 1: you see the days of the week in a circle 649 00:44:15,760 --> 00:44:18,359 Speaker 1: around you physically, like Monday is over here, and then 650 00:44:18,360 --> 00:44:20,560 Speaker 1: tuesdays there, and then Wednesday is up here, and then 651 00:44:20,600 --> 00:44:23,359 Speaker 1: Thursday's over there, and Friday off to the side, and 652 00:44:23,440 --> 00:44:27,840 Speaker 1: so on. And for each person. This is idiosyncratic, meaning 653 00:44:27,880 --> 00:44:31,000 Speaker 1: that it's a different pattern for everyone, and a cinnasthete 654 00:44:31,080 --> 00:44:34,640 Speaker 1: might see the months of the year in spatial locations, 655 00:44:34,920 --> 00:44:39,560 Speaker 1: or the years historical in future, like nineteen seventy one 656 00:44:39,640 --> 00:44:41,200 Speaker 1: is over here, and then it goes up and up 657 00:44:41,239 --> 00:44:43,319 Speaker 1: through nineteen ninety, and then it's flat over to two 658 00:44:43,320 --> 00:44:45,360 Speaker 1: thousand and five, and then it goes a little up 659 00:44:45,440 --> 00:44:47,880 Speaker 1: and down through twenty thirteen, and then cuts to the 660 00:44:47,960 --> 00:44:50,799 Speaker 1: left at twenty twenty, and then it suddenly dives down 661 00:44:50,840 --> 00:44:53,080 Speaker 1: and then curves around, and then the future goes off 662 00:44:53,120 --> 00:44:55,920 Speaker 1: behind you. And it's different for each cynisthete, and I 663 00:44:56,000 --> 00:44:59,200 Speaker 1: gave many more examples in episode four, but the point 664 00:44:59,239 --> 00:45:01,840 Speaker 1: I want to make here is that This form of 665 00:45:01,920 --> 00:45:06,719 Speaker 1: synesthesia is closely tied to memory because all of the 666 00:45:06,760 --> 00:45:10,120 Speaker 1: things that I just mentioned, like weekdays and months and years, 667 00:45:10,600 --> 00:45:13,560 Speaker 1: these are all sequences that you have to learn, you 668 00:45:13,600 --> 00:45:18,040 Speaker 1: have to memorize, and so in many people these sequences 669 00:45:18,480 --> 00:45:24,760 Speaker 1: get tied irreversibly to spatial location. It helps the brain 670 00:45:24,800 --> 00:45:30,719 Speaker 1: to remember something by tagging it with a place. So 671 00:45:30,760 --> 00:45:33,880 Speaker 1: some people suggest that the way to understand the hippocampus 672 00:45:34,080 --> 00:45:37,719 Speaker 1: is as a cognitive map. The idea is that the 673 00:45:37,800 --> 00:45:43,800 Speaker 1: hippocampus originally created and stored territory maps for orientation and 674 00:45:43,920 --> 00:45:48,960 Speaker 1: navigation and finding resources, but eventually the system adapted to 675 00:45:49,040 --> 00:45:55,520 Speaker 1: create and store episodic memories. Also, because events usually have 676 00:45:55,560 --> 00:46:01,120 Speaker 1: a particular setting in space, and because movement cross locations 677 00:46:01,160 --> 00:46:05,240 Speaker 1: also involves movement in time, the hippocampus would be naturally 678 00:46:05,280 --> 00:46:09,839 Speaker 1: well suited to capture sequences of events in time, in 679 00:46:09,840 --> 00:46:13,279 Speaker 1: other words, episodes. And I want to mention that there's 680 00:46:13,360 --> 00:46:17,680 Speaker 1: a cousin theory about the hippocampus that proposes what it's 681 00:46:17,719 --> 00:46:23,520 Speaker 1: doing is it's storing the associations between elements of the events, 682 00:46:23,600 --> 00:46:26,520 Speaker 1: just like what we talked about with the spatial relations 683 00:46:26,560 --> 00:46:33,200 Speaker 1: between objects. So the hippocampus stores the temporal relationship between 684 00:46:33,239 --> 00:46:36,520 Speaker 1: events like this happened after this, or it happened close 685 00:46:36,560 --> 00:46:39,520 Speaker 1: in time or far in time, and it stores the 686 00:46:39,560 --> 00:46:46,560 Speaker 1: relationships between pieces of information. It stores the relationships rather 687 00:46:46,640 --> 00:46:50,520 Speaker 1: than the specific items. So this gives us a way 688 00:46:50,560 --> 00:46:55,640 Speaker 1: to see how the original spatial function of the hippocampus 689 00:46:56,000 --> 00:47:02,920 Speaker 1: could have evolved into episodic memory. Okay, so now I 690 00:47:02,960 --> 00:47:05,600 Speaker 1: want to turn to what's going on at the very 691 00:47:05,800 --> 00:47:09,520 Speaker 1: tiny level to ask how does activity in the brain 692 00:47:09,840 --> 00:47:14,520 Speaker 1: cause lasting changes such that we have learning and memory. 693 00:47:15,640 --> 00:47:19,120 Speaker 1: So almost all theories of brain plasticity, that's what we 694 00:47:19,200 --> 00:47:23,239 Speaker 1: call it, when the brain reorganizes itself. These use the 695 00:47:23,360 --> 00:47:27,960 Speaker 1: idea that the strength of connections between cells, the strength 696 00:47:28,000 --> 00:47:33,120 Speaker 1: of the synapses, can be modified by previous activity. Okay, 697 00:47:33,120 --> 00:47:36,160 Speaker 1: but how does that work? Well, First of all, if 698 00:47:36,160 --> 00:47:40,200 Speaker 1: you imagine one hundred years ago, microscopes didn't have the 699 00:47:40,280 --> 00:47:44,640 Speaker 1: magnification power to actually see neurons in their inner connections. 700 00:47:45,040 --> 00:47:50,160 Speaker 1: So a century ago, scientists believed that brain tissue was 701 00:47:50,239 --> 00:47:54,759 Speaker 1: a continuous network like the blood vessels. Blood vessels are 702 00:47:54,800 --> 00:47:58,080 Speaker 1: a system of tubes where you have stuff running along it, 703 00:47:58,320 --> 00:48:01,640 Speaker 1: and people assumed that was what was happening in the brain. 704 00:48:02,560 --> 00:48:07,160 Speaker 1: But this idea was overturned by a Spanish neuroscientist named 705 00:48:07,200 --> 00:48:10,520 Speaker 1: Santiago Ramonicjl. He spent a lot of time at his 706 00:48:10,719 --> 00:48:14,640 Speaker 1: microscope trying to look at brain tissue, and because he 707 00:48:14,680 --> 00:48:17,960 Speaker 1: was a photographer also, he had all these chemicals in 708 00:48:18,000 --> 00:48:22,239 Speaker 1: his workshop, these stains that he put on to thin 709 00:48:22,440 --> 00:48:25,440 Speaker 1: slices of brain tissue to see if that would allow 710 00:48:25,520 --> 00:48:29,120 Speaker 1: him to see anything better. And some of his stains 711 00:48:29,600 --> 00:48:32,399 Speaker 1: leaped into a few of the neurons and turned them 712 00:48:32,440 --> 00:48:36,280 Speaker 1: all black, and he was able to visualize them under 713 00:48:36,320 --> 00:48:40,240 Speaker 1: the microscope that way. So he put forward this idea, 714 00:48:40,280 --> 00:48:43,680 Speaker 1: which turned out to be right, that the brain, instead 715 00:48:43,719 --> 00:48:47,360 Speaker 1: of being a subway system of tubes, was a massive 716 00:48:47,520 --> 00:48:52,799 Speaker 1: collection of billions of discrete cells. He called this the 717 00:48:53,320 --> 00:48:57,640 Speaker 1: neuron doctrine, and this was a massively important step in neuroscience, 718 00:48:57,640 --> 00:49:00,759 Speaker 1: and it eventually won him the Nobel Prize. Now, this 719 00:49:00,920 --> 00:49:03,960 Speaker 1: idea that the brain is made up of lots of 720 00:49:04,160 --> 00:49:08,880 Speaker 1: individual cells, this ushered in an important new concept because 721 00:49:08,880 --> 00:49:13,399 Speaker 1: people began to realize that these separate cells they have 722 00:49:13,440 --> 00:49:17,080 Speaker 1: to influence each other through these little connections between them, 723 00:49:17,280 --> 00:49:21,720 Speaker 1: these synapses. And Ramonic Cahol was the first to suggest 724 00:49:21,800 --> 00:49:28,960 Speaker 1: that learning and memory might occur by changing these synapses, 725 00:49:29,760 --> 00:49:34,000 Speaker 1: so several decades later, in nineteen forty nine, the neuroscientist 726 00:49:34,080 --> 00:49:39,360 Speaker 1: Donald Hebb made a specific proposal for how synapses should 727 00:49:39,440 --> 00:49:43,920 Speaker 1: adjust to underlie memory. He said that if one cell 728 00:49:44,440 --> 00:49:49,480 Speaker 1: consistently participates in exciting another cell, then the connection between 729 00:49:49,520 --> 00:49:53,800 Speaker 1: them is strengthened, and if the first cell consistently fails 730 00:49:53,840 --> 00:49:56,879 Speaker 1: to excite the second cell, then the connection between them 731 00:49:57,080 --> 00:50:02,080 Speaker 1: is weakened. So this rule is often as cells that 732 00:50:02,280 --> 00:50:07,040 Speaker 1: fire together wire together, and most models of memory formation 733 00:50:07,360 --> 00:50:12,520 Speaker 1: employ this kind of rule. Now, when Hebb proposed this hypothesis, 734 00:50:12,560 --> 00:50:16,440 Speaker 1: there was no experimental evidence to support it, and it 735 00:50:16,480 --> 00:50:21,279 Speaker 1: took until nineteen seventy three for two researchers to discover that, 736 00:50:21,719 --> 00:50:25,040 Speaker 1: in fact, that seems to be what happens between neurons. 737 00:50:25,160 --> 00:50:28,560 Speaker 1: So you stimulate some neuron over here, and you measure 738 00:50:28,600 --> 00:50:31,759 Speaker 1: the very tiny electrical response that it causes in this 739 00:50:31,880 --> 00:50:35,640 Speaker 1: neuron over here. Then you blast the first neural with 740 00:50:35,680 --> 00:50:39,239 Speaker 1: a bunch of electrical stimulation for thirty seconds, and then 741 00:50:39,280 --> 00:50:41,839 Speaker 1: you try that first experiment again, where you give this 742 00:50:41,920 --> 00:50:44,120 Speaker 1: guy a little zapp and you look at the electrical 743 00:50:44,160 --> 00:50:47,239 Speaker 1: signal that it causes in the second neuron, and what 744 00:50:47,280 --> 00:50:50,520 Speaker 1: you find is that you now have a larger signal. 745 00:50:50,600 --> 00:50:57,719 Speaker 1: The synapse has strengthened, and that strengthening lasts. The connection 746 00:50:57,880 --> 00:51:01,120 Speaker 1: strength between these two neurons has changed, and that change 747 00:51:01,200 --> 00:51:05,600 Speaker 1: is held on to through time. So the synaptic connection 748 00:51:05,760 --> 00:51:10,600 Speaker 1: can be modified as a result of the cell's history 749 00:51:10,640 --> 00:51:15,759 Speaker 1: of activity. Now, a typical neuron has ten thousand connections, 750 00:51:15,920 --> 00:51:20,120 Speaker 1: and what's fascinating is that each individual connection can strengthen 751 00:51:20,239 --> 00:51:24,160 Speaker 1: or weaken according to its history. So the way that 752 00:51:24,239 --> 00:51:28,200 Speaker 1: activity flows through a network of billions of neurons can 753 00:51:28,280 --> 00:51:31,640 Speaker 1: be completely changed. By dialing the strength of this connection here, 754 00:51:31,640 --> 00:51:33,880 Speaker 1: in that connection there, and so on for all the 755 00:51:33,920 --> 00:51:38,680 Speaker 1: connections across the brain. By dialing the strengths up and 756 00:51:38,760 --> 00:51:42,520 Speaker 1: down and holding on to those, you can store information 757 00:51:42,760 --> 00:51:45,200 Speaker 1: in the system. And just to give you a sense 758 00:51:45,360 --> 00:51:49,840 Speaker 1: of the size of the parameter space here, the large 759 00:51:49,920 --> 00:51:55,720 Speaker 1: language model GPT four has one point seventy six trillion connections, 760 00:51:56,400 --> 00:52:00,759 Speaker 1: but the human brain has about one hundred times that. 761 00:52:03,840 --> 00:52:06,000 Speaker 1: So there are a lot of parameters that can be 762 00:52:06,040 --> 00:52:09,960 Speaker 1: tweaked in the brain to store information. Now, by the 763 00:52:10,000 --> 00:52:13,320 Speaker 1: way the birth of artificial neural networks like GPT, for 764 00:52:14,160 --> 00:52:17,440 Speaker 1: is rooted in these discoveries from the brain from the 765 00:52:17,480 --> 00:52:20,520 Speaker 1: past century. You have a bunch of units, and you 766 00:52:20,560 --> 00:52:23,840 Speaker 1: have connections between these units, and you change the strength 767 00:52:23,840 --> 00:52:28,480 Speaker 1: of those connections. That's how large language models work because 768 00:52:28,520 --> 00:52:32,400 Speaker 1: making small, subtle changes in the way that units communicate 769 00:52:32,480 --> 00:52:36,919 Speaker 1: a network can change the entire network's output behavior. By 770 00:52:37,120 --> 00:52:41,399 Speaker 1: tuning the parameters of the network just right, or in fact, 771 00:52:41,520 --> 00:52:44,800 Speaker 1: letting a network adjust its own connections according to some algorithm, 772 00:52:45,320 --> 00:52:50,279 Speaker 1: a network learns to associate inputs and remember what it 773 00:52:50,360 --> 00:52:56,120 Speaker 1: has learned. This is how we build artificial learning and memory, 774 00:52:56,200 --> 00:53:01,200 Speaker 1: and modern artificial neural networks are extraordinarily impressive. But I 775 00:53:01,239 --> 00:53:05,400 Speaker 1: think it's really important to note that although these connections 776 00:53:05,440 --> 00:53:09,600 Speaker 1: between neurons have gotten all the attention, both theoretically and experimentally, 777 00:53:10,440 --> 00:53:14,960 Speaker 1: there are lots of other possible ways to store information 778 00:53:15,239 --> 00:53:18,680 Speaker 1: in the brain. For the last century, people have assumed 779 00:53:18,760 --> 00:53:22,960 Speaker 1: that synapses are the key to memory, but the story 780 00:53:23,000 --> 00:53:28,000 Speaker 1: has been complexified by recent decades of research. Where it 781 00:53:28,040 --> 00:53:32,240 Speaker 1: stands now is that synaptic changes are necessary for learning 782 00:53:32,280 --> 00:53:35,560 Speaker 1: and memory, but we really don't know if they are sufficient. 783 00:53:36,120 --> 00:53:39,080 Speaker 1: It's still unclear whether these changes that the synapse is 784 00:53:39,680 --> 00:53:43,480 Speaker 1: will be the only or even the most important mechanisms 785 00:53:43,520 --> 00:53:47,120 Speaker 1: involved in memory, or perhaps whether they're involved at all, 786 00:53:47,320 --> 00:53:51,399 Speaker 1: because the fact is that a dense net of intertwined 787 00:53:51,480 --> 00:53:57,920 Speaker 1: cells has to orchestrate a careful balance between excitation and inhibition, 788 00:53:58,080 --> 00:54:01,279 Speaker 1: otherwise the whole thing blows up into epilepsy or it 789 00:54:01,480 --> 00:54:04,640 Speaker 1: sinks down into non activity. And it could be that 790 00:54:04,680 --> 00:54:08,120 Speaker 1: all these synaptic changes we see are just to keep 791 00:54:08,160 --> 00:54:13,560 Speaker 1: the system away from epileptic overload or synaptic shutdown, and 792 00:54:13,760 --> 00:54:18,520 Speaker 1: memory maybe is stored in an entirely different manner. Our 793 00:54:18,640 --> 00:54:22,440 Speaker 1: science is still young, and it's possible that we're still 794 00:54:22,560 --> 00:54:27,120 Speaker 1: missing the core mechanisms of memory, because after all, there 795 00:54:27,200 --> 00:54:31,880 Speaker 1: are adjustable parameters throughout the brain. You change something here 796 00:54:32,200 --> 00:54:35,240 Speaker 1: and the network behaves differently there. So there are many 797 00:54:35,360 --> 00:54:40,080 Speaker 1: other possible mechanisms involved in memory in the brain. For 798 00:54:40,160 --> 00:54:44,719 Speaker 1: the cognitionanty, this could involve neurogenesis or changes in the 799 00:54:44,800 --> 00:54:48,800 Speaker 1: excitability of the neuron, or the distribution of ion channel 800 00:54:49,239 --> 00:54:52,359 Speaker 1: or the shape of dendritic trees and their spines, where 801 00:54:52,400 --> 00:54:57,000 Speaker 1: the phosphorylation states of intracellular proteins, where the epigenetic codes 802 00:54:57,239 --> 00:54:59,719 Speaker 1: and on and on. Okay, this is all in my 803 00:54:59,800 --> 00:55:01,959 Speaker 1: tech book and we're not going to go into that here, 804 00:55:02,400 --> 00:55:05,080 Speaker 1: but I do want to say that with so many 805 00:55:05,120 --> 00:55:10,200 Speaker 1: degrees of freedom in biological systems, the number of possible 806 00:55:10,200 --> 00:55:15,080 Speaker 1: ways to storm memory in a brain is vast. So 807 00:55:15,239 --> 00:55:18,600 Speaker 1: why do we look at the connection strength of synapse 808 00:55:18,640 --> 00:55:23,040 Speaker 1: as well? It's partially because that's what we can measure 809 00:55:23,080 --> 00:55:28,640 Speaker 1: most easily. It's extremely difficult, essentially impossible currently to measure 810 00:55:29,000 --> 00:55:33,680 Speaker 1: in a living animal changes in channel distribution or dendritic 811 00:55:33,719 --> 00:55:39,239 Speaker 1: spines or epigenetic codes in single neurons. So as a result, 812 00:55:39,719 --> 00:55:44,239 Speaker 1: almost the whole field of neuroscience just measures changes at 813 00:55:44,280 --> 00:55:47,520 Speaker 1: the connections between cells. And that might turn out to 814 00:55:47,560 --> 00:55:51,120 Speaker 1: be right, But if you could read the textbooks one 815 00:55:51,160 --> 00:55:53,680 Speaker 1: hundred years from now, it might turn out to be 816 00:55:54,440 --> 00:55:57,720 Speaker 1: that we were like the drunk, looking for our keys 817 00:55:57,800 --> 00:56:02,200 Speaker 1: under the street light because that's where the lighting was better. 818 00:56:03,480 --> 00:56:07,640 Speaker 1: So while artificial neural networks are awe inspiring and they're 819 00:56:07,719 --> 00:56:10,360 Speaker 1: rapidly changing the world, we don't know for sure that 820 00:56:10,440 --> 00:56:14,680 Speaker 1: they're doing the same things algorithmically that the brain is doing. 821 00:56:15,440 --> 00:56:19,120 Speaker 1: They in a sense simplify everything. They are a clever 822 00:56:19,360 --> 00:56:22,960 Speaker 1: step backwards from biology. In other words, you take the 823 00:56:23,440 --> 00:56:28,480 Speaker 1: complexity of a single cell with the entire human genome 824 00:56:28,520 --> 00:56:31,520 Speaker 1: in it and millions of proteins trafficking around, and you 825 00:56:31,640 --> 00:56:36,080 Speaker 1: just imagine that it's a unit with simple connections to 826 00:56:36,160 --> 00:56:39,800 Speaker 1: other units. And as I said, that has turned out 827 00:56:39,920 --> 00:56:43,880 Speaker 1: in large language models to be shockingly effective. But we 828 00:56:44,080 --> 00:56:47,440 Speaker 1: really have no way of knowing right now how much 829 00:56:47,560 --> 00:56:50,839 Speaker 1: more we could get out of artificial neural networks if 830 00:56:50,840 --> 00:56:56,600 Speaker 1: we included the rich complexity of actual biology. Maybe it 831 00:56:56,640 --> 00:57:00,759 Speaker 1: wouldn't add anything, but maybe it would unlock in entirely 832 00:57:00,920 --> 00:57:06,160 Speaker 1: new levels of function, making artificial neural networks more like 833 00:57:06,360 --> 00:57:11,160 Speaker 1: a human with a sense of what information is relevant 834 00:57:11,160 --> 00:57:16,200 Speaker 1: to learn and incorporating needs and goals and strategies and 835 00:57:16,920 --> 00:57:21,800 Speaker 1: enjoying the experience of consciousness. The thing to keep in 836 00:57:21,840 --> 00:57:25,720 Speaker 1: mind is that Mother Nature has had billions of years 837 00:57:25,760 --> 00:57:30,320 Speaker 1: to try quadrillions of experiments, and we've only been making 838 00:57:30,440 --> 00:57:33,960 Speaker 1: artificial neural networks for a few decades. So if I 839 00:57:34,000 --> 00:57:36,800 Speaker 1: were a betting man, I would say there's probably a 840 00:57:36,920 --> 00:57:41,800 Speaker 1: lot more to be discovered. It is certainly possible that 841 00:57:41,880 --> 00:57:48,120 Speaker 1: we have not yet found memories Rosetta Stone and I 842 00:57:48,200 --> 00:57:51,560 Speaker 1: want to point to one more thing about real brains 843 00:57:51,680 --> 00:57:55,040 Speaker 1: that's definitely not captured in artificial neural networks, and that 844 00:57:55,200 --> 00:57:59,720 Speaker 1: is the concept of forgetting. Remember I mentioned at the 845 00:57:59,720 --> 00:58:04,080 Speaker 1: beginning about the mneminist named Sharyshevski, It turns out that 846 00:58:04,480 --> 00:58:10,400 Speaker 1: Sherishevski's enviable memory went hand in hand with a surprisingly 847 00:58:10,600 --> 00:58:14,600 Speaker 1: handicapped personality. He was crippled by the fact that he 848 00:58:14,640 --> 00:58:18,640 Speaker 1: could not forget. Because when you have a memory like his, 849 00:58:19,680 --> 00:58:24,680 Speaker 1: all the moments in life are retained. Past vandettas and 850 00:58:24,760 --> 00:58:28,880 Speaker 1: things people did to slight you, and embarrassing moments and 851 00:58:29,080 --> 00:58:34,200 Speaker 1: situations you've outgrown, and heartbreaks you would rather let slip 852 00:58:34,240 --> 00:58:38,480 Speaker 1: into the past. All of these remain present and emotionally 853 00:58:38,560 --> 00:58:43,120 Speaker 1: salient for you. And remember I mentioned the mnemenist Jill Price, 854 00:58:43,200 --> 00:58:46,120 Speaker 1: who also had an untaxable memory. Just think about what 855 00:58:46,320 --> 00:58:49,240 Speaker 1: drove her to seek help in the first place. Her 856 00:58:49,280 --> 00:58:53,479 Speaker 1: memories were deeply emotional and sprang up throughout her day. 857 00:58:54,240 --> 00:59:00,240 Speaker 1: Jill is constantly pouring over her past, an obsessive detailing 858 00:59:00,360 --> 00:59:05,560 Speaker 1: mementos from childhood onward and becoming distressed by changes like 859 00:59:05,640 --> 00:59:08,760 Speaker 1: moving to a new home. So for both Sharashevski and 860 00:59:08,840 --> 00:59:13,640 Speaker 1: Jill Price. Having a perfect memory impaired them as much 861 00:59:13,640 --> 00:59:17,640 Speaker 1: as equipped them, and this unmasks one of the great 862 00:59:17,800 --> 00:59:21,120 Speaker 1: values of the way memory typically exists in most people. 863 00:59:21,560 --> 00:59:27,840 Speaker 1: We don't retain everything, but instead items fade. One of 864 00:59:27,840 --> 00:59:31,360 Speaker 1: my favorite quotations is from the French novelist and playwright 865 00:59:31,640 --> 00:59:37,160 Speaker 1: Honore de Balzac, who wrote, memories beautify life, but the 866 00:59:37,240 --> 00:59:42,360 Speaker 1: capacity to forget makes it bearable. So when it comes 867 00:59:42,440 --> 00:59:45,640 Speaker 1: to thinking about human memory, remember that of all the 868 00:59:45,680 --> 00:59:49,160 Speaker 1: things we talked about that differentiate us from digital computers, 869 00:59:49,440 --> 00:59:54,480 Speaker 1: a very important one is our capacity to not remember everything. 870 00:59:55,320 --> 00:59:59,280 Speaker 1: And even that which we do remember doesn't last too long. 871 01:00:00,080 --> 01:00:03,080 Speaker 1: So let's wrap up. We've seen that human memory is 872 01:00:03,120 --> 01:00:05,680 Speaker 1: not quite the same as a computer's memory. We don't 873 01:00:05,720 --> 01:00:09,720 Speaker 1: store zeros and ones, but instead we hold memories about 874 01:00:09,800 --> 01:00:13,480 Speaker 1: the gist of things, the relationships between things, and we 875 01:00:13,640 --> 01:00:18,240 Speaker 1: forget through time and memory for us is stored in 876 01:00:18,320 --> 01:00:22,480 Speaker 1: lots and lots of subsystems. We have different mechanisms for 877 01:00:22,920 --> 01:00:26,160 Speaker 1: short term memory and for long term memory, and within 878 01:00:26,240 --> 01:00:31,920 Speaker 1: each of those categories we have subcategories. For example, implicit 879 01:00:32,000 --> 01:00:34,360 Speaker 1: memories that you can't articulate, like how to ride a 880 01:00:34,400 --> 01:00:38,440 Speaker 1: bike and explicit memories like facts and events that you 881 01:00:38,440 --> 01:00:41,400 Speaker 1: can talk about. And in all of these cases you 882 01:00:41,440 --> 01:00:44,919 Speaker 1: can lose some of these types of memory without any 883 01:00:44,960 --> 01:00:48,919 Speaker 1: effect on the other types. And now that we've seen 884 01:00:48,960 --> 01:00:52,280 Speaker 1: an overview of memory in the brain, we will now 885 01:00:52,360 --> 01:00:57,520 Speaker 1: be ready to turn to the main reason we have memory, 886 01:00:57,720 --> 01:01:03,600 Speaker 1: and that is to predict the future. We retain information 887 01:01:04,120 --> 01:01:08,080 Speaker 1: in the detailed configuration of our forest of billions of 888 01:01:08,160 --> 01:01:12,000 Speaker 1: cells and trillions of connections, and the point is to 889 01:01:12,080 --> 01:01:15,840 Speaker 1: develop a better understanding of the world so that we 890 01:01:16,040 --> 01:01:21,440 Speaker 1: can know what will happen next. And for that, please 891 01:01:21,560 --> 01:01:24,800 Speaker 1: join me in the next episode where we look at 892 01:01:25,080 --> 01:01:30,760 Speaker 1: prediction how we simulate possible future worlds. This is one 893 01:01:30,800 --> 01:01:33,680 Speaker 1: of the main jobs of brains, and this is the 894 01:01:33,720 --> 01:01:38,000 Speaker 1: reason they retain memory. So right now, if you're imagining 895 01:01:38,080 --> 01:01:42,000 Speaker 1: tuning into the next episode and feeling the emotional joy 896 01:01:42,360 --> 01:01:44,640 Speaker 1: of what you will learn and what you will see there, 897 01:01:45,200 --> 01:01:48,280 Speaker 1: this is your brain doing what it was meant to do, 898 01:01:48,880 --> 01:01:56,640 Speaker 1: simulating the future. I will see you there. Go to 899 01:01:56,680 --> 01:01:59,800 Speaker 1: Eagleman dot com slash podcast for more information and to 900 01:02:00,080 --> 01:02:04,960 Speaker 1: find further reading. Send me any questions to podcast at 901 01:02:05,000 --> 01:02:08,240 Speaker 1: eagleman dot com and I'll be making episodes in which 902 01:02:08,280 --> 01:02:13,840 Speaker 1: I address those until next time. I'm David Eagleman, and 903 01:02:13,880 --> 01:02:15,640 Speaker 1: this is Inner Cosmos.