Making Sense - Sam Harris - #247 — Constructing Minds

00:00:00.000 Welcome to the Making Sense Podcast.

00:00:08.860 This is Sam Harris.

00:00:10.900 Just a note to say that if you're hearing this,

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00:00:52.280 Okay, some housekeeping today.

00:01:03.740 I have a new podcast to announce.

00:01:07.200 A single episode, which we will be dropping, I believe, Friday of this week,

00:01:13.640 if all goes according to plan.

00:01:16.420 So look for it in your feed on the 23rd of April.

00:01:22.920 The title of this episode is Engineering the Apocalypse.

00:01:26.700 And it was produced by my friend Rob Reed, who is a podcaster and author, also a tech entrepreneur.

00:01:35.120 I met Rob at the TED conference some years ago.

00:01:39.700 And then he started his own podcast, the After On podcast.

00:01:43.700 And he interviewed me, I think, for the first episode there.

00:01:47.640 And I thought it was probably the best interview anyone had ever done of me.

00:01:51.540 So we aired that here on Making Sense.

00:01:54.940 I believe we titled it the After On interview.

00:01:57.300 Anyway, in the intervening years, Rob has gotten very interested in existential risk.

00:02:04.720 And in particular, the risk posed by advances in synthetic biology,

00:02:08.640 which could very well lead to an engineered pandemic.

00:02:12.180 But everything he says in this podcast is relevant to a naturally occurring pandemic,

00:02:17.060 like the one we are currently suffering.

00:02:19.720 Anyway, this is a deeply researched and, by turns,

00:02:23.920 harrowing and hopeful look at advances in synthetic biology.

00:02:30.320 And it's broken into four chapters,

00:02:32.420 which are separated by interstitial conversations that I have with Rob.

00:02:37.200 Anyway, I thought the job he did was fantastic.

00:02:40.920 Pandemic preparedness has to be a huge priority for us going forward.

00:02:45.460 And this is our best effort to argue that it really must be.

00:02:51.400 COVID has been a dress rehearsal.

00:02:53.920 For something far worse.

00:02:57.780 And as such, it has been pretty much an unmitigated disaster.

00:03:02.660 We may have lost sight of this,

00:03:04.280 given how successful our vaccine production has been,

00:03:07.620 and how the rollout has ramped up.

00:03:10.220 But our response to COVID,

00:03:13.160 in particular our failure to organize a globally coherent response,

00:03:17.260 was just a terrifying failure.

00:03:20.220 Terrifying, given how much worse a pandemic can be.

00:03:25.980 And how much worse it's likely to be if it's ever consciously engineered.

00:03:31.040 So anyway, this upcoming podcast will be dropped as a single episode that's nearly four hours in length.

00:03:39.020 And again, the title is Engineering the Apocalypse.

00:03:41.440 And needless to say, we'll be releasing that as yet another PSA,

00:03:46.680 which is to say the whole thing will be freely available.

00:03:50.080 But of course, if you find this work valuable,

00:03:53.820 the way to support it is to subscribe at samharris.org.

00:03:58.140 And to coincide with the release of this podcast,

00:04:02.900 the Waking Up Foundation will be giving two significant grants to relevant organizations

00:04:09.120 that are working on the front lines of pandemic preparedness.

00:04:13.280 As many of you know from my conversations with the philosopher Will McCaskill,

00:04:18.000 I've been thinking more about how to effectively do some good in the world,

00:04:21.880 in addition to just talking about what is good to do.

00:04:25.220 So we formed the Waking Up Foundation for that purpose.

00:04:30.220 And at least 10% of the corporate profits of Waking Up go there,

00:04:34.100 as does a minimum of 10% of my own income.

00:04:37.740 And the foundation works as a pass-through to other organizations.

00:04:42.580 So 100% of the funds leave it and go elsewhere.

00:04:46.720 And so these next donations are focused on this problem of pandemic preparedness.

00:04:51.820 And in this vein, we're supporting the Center for Communicable Disease Dynamics,

00:04:55.220 at Harvard University, which focuses on improving our methods of understanding

00:05:00.020 the data around infectious disease.

00:05:03.300 And it engages policymakers to improve their decision-making,

00:05:07.060 which often leaves a lot to be desired.

00:05:09.780 And the second organization is the Coalition for Epidemic Preparedness Innovations,

00:05:15.340 the CEPI,

00:05:17.000 whose mission is to accelerate the development of vaccine technology.

00:05:20.620 They're funding new platforms so that we can develop vaccines even more quickly than we did

00:05:26.680 for COVID, and really do it just in time in response to a novel pathogen,

00:05:33.080 which is precisely what we're likely to face in the case of a synthetically engineered pandemic.

00:05:38.880 Now, neither of these organizations are set up to take small, individual donations.

00:05:45.560 But if you're a philanthropist,

00:05:47.520 and you want to come along with us in helping to improve our pandemic preparedness,

00:05:51.400 I would certainly encourage you to support these organizations.

00:05:55.440 Once again, that's the Center for Communicable Disease Dynamics

00:05:58.440 at Harvard University and the Coalition for Epidemic Preparedness Innovations.

00:06:04.280 And I should say that the Waking Up Foundation is getting great advice

00:06:07.360 on this front from Natalie Cargill of Longview Philanthropy.

00:06:12.880 This is an organization that advises individuals and foundations

00:06:16.220 who want to deploy significant funds to solve long-term problems.

00:06:21.760 And I was introduced to Natalie through Will McCaskill.

00:06:25.560 And I've been extremely impressed with the research that they've done at Longview

00:06:30.760 and the clarity of their advice, all of which is given free of charge.

00:06:36.080 Longview is independently funded.

00:06:38.580 So if you're running a foundation, or you're a wealthy person who wants free advice

00:06:43.380 about how to give most effectively, I highly recommend that you get in touch

00:06:47.480 with the people at longview.org.

00:06:49.960 Again, this is not a recommendation for small donors.

00:06:53.980 I believe you need to be giving away at least a million dollars a year

00:06:57.120 before Longview can help guide you.

00:07:00.360 But for those of you who are in the philanthropy space,

00:07:03.180 I recommend you get in touch.

00:07:05.800 But if you are an individual donor and you want to ride along with me,

00:07:09.620 we will be detailing all the orgs we support at the Waking Up Foundation

00:07:13.900 once that website is launched.

00:07:16.420 And on that point, I want to say that the Making Sense audience

00:07:18.900 has been fantastically generous in the past.

00:07:24.120 On the occasions where I've discussed specific non-profits on this podcast,

00:07:29.180 the people who run them always come back astonished at the result.

00:07:36.580 To give you just a couple of snapshots here,

00:07:39.980 GiveWell.org reached out recently to say that

00:07:43.280 just by my mentioning their organization a few times on this podcast.

00:07:49.500 This is the group that does exhaustive research on the effectiveness of charities

00:07:53.440 and recommends what they consider to be the most effective ones in several categories.

00:07:58.600 My discussing their work a few times, once with Will McCaskill,

00:08:04.420 resulted in you guys donating $1.8 million through them directly

00:08:10.380 and pledging another $1.8 million in recurring donations.

00:08:15.200 So that's $3.6 million through the end of this year.

00:08:20.060 And Will McCaskill's organization, Giving What We Can,

00:08:23.760 which was started by Toby Ord, who's also been on the podcast,

00:08:27.280 has told me that in response to my discussing their pledge,

00:08:32.580 this is the pledge to give a minimum of 10% of one's lifetime earnings

00:08:36.140 to the most effective charities,

00:08:38.240 which you can do at any level,

00:08:40.460 whether you're making $30,000 a year or $30 billion.

00:08:45.140 I'm told that my discussing this pledge with Will

00:08:48.600 caused hundreds of you to take this pledge yourselves.

00:08:52.320 And after waking up, became the first company to take the pledge,

00:08:57.200 10 more companies soon followed.

00:09:00.000 Now, I don't know how much money to the most effective charities this represents,

00:09:04.360 but it's surely many, many millions of dollars.

00:09:08.880 I believe, Giving What We Can just passed the $2 billion mark

00:09:12.380 in lifetime earnings that have been pledged.

00:09:16.580 Anyway, my point in mentioning this isn't to brag about the influence of this podcast,

00:09:20.320 but rather to convey my gratitude and astonishment, frankly.

00:09:28.140 I mean, it's just amazing to see the knock-on effects of discussing these things.

00:09:33.220 Anyway, I will keep you all informed about this,

00:09:36.080 but this is just to let you know that over at Waking Up and here at Making Sense,

00:09:41.320 we have transitioned into doing more than just talk about specific problems.

00:09:49.140 We're marshalling our own resources to try to do some good directly ourselves.

00:09:55.740 Okay.

00:09:57.340 Today I'm speaking with Lisa Feldman Barrett,

00:10:00.680 who is one of the most cited scientists in the world

00:10:03.380 for her research in psychology and neuroscience.

00:10:06.220 She's a professor at Northeastern University

00:10:09.360 with appointments at Mass General Hospital and Harvard Medical School.

00:10:14.620 Lisa was awarded a Guggenheim Fellowship in Neuroscience in 2019,

00:10:19.200 and she's a member of the American Academy of Arts and Sciences

00:10:22.420 and the Royal Society of Canada.

00:10:25.600 And she's the author, most recently, of a very enjoyable book,

00:10:30.660 Seven and a Half Lessons About the Brain.

00:10:32.640 And we cover a few of those lessons in today's podcast.

00:10:36.620 We talk about how the human brain evolved,

00:10:40.580 the myth of the triune brain, which has been all too influential.

00:10:45.780 We discuss how the brain is organized into networks,

00:10:49.560 the predictive nature of perception and action,

00:10:52.980 the construction of emotion,

00:10:56.340 concepts as prescriptions for action,

00:10:59.280 culture as an operating system,

00:11:01.020 and many other topics.

00:11:04.060 And now, without further delay,

00:11:06.200 I bring you Lisa Feldman Barrett.

00:11:14.880 I am here with Lisa Feldman Barrett.

00:11:17.640 Lisa, thanks for joining me.

00:11:19.900 It's my pleasure.

00:11:20.980 So you've written this wonderful little primer on the brain,

00:11:24.800 Seven and a Half Lessons About the Brain,

00:11:26.640 which I think will be the focus of our discussion,

00:11:30.680 although we'll probably wander to other topics.

00:11:34.000 But I just want our listeners to know that this is a marvelously accessible book,

00:11:42.020 and a short one.

00:11:43.800 It's only 130 pages or so.

00:11:47.160 And, you know, we need more of this kind of thing.

00:11:50.120 There's this kind of awful property of the brain and neuroscience generally,

00:11:57.840 which is, when you get into the details,

00:12:00.840 it becomes just a catalog of anatomical names that are certainly not written by writers,

00:12:09.460 especially ones who wanted to write books for a general audience.

00:12:12.520 And it becomes this blizzard of mnemonic challenges for a reader.

00:12:18.060 And you've managed to avoid all of that

00:12:20.300 and still deliver a very interesting discussion about the brain and the mind.

00:12:25.800 So congratulations.

00:12:26.560 Thank you so much.

00:12:29.780 So before we jump in, perhaps you can summarize your background intellectually.

00:12:36.280 What kinds of questions have you focused on as a scientist?

00:12:39.680 Well, I, you know, I started my training as a clinical psychologist

00:12:44.060 and then very quickly went through a series of retrainings in physiology

00:12:49.700 and then in neuroscience and more recently in engineering,

00:12:54.580 learning something about systems theory and in evolutionary and developmental aspects of neuroscience.

00:13:01.820 So the questions I really think about now relate to, you know, how, how is the brain,

00:13:10.860 how is your brain in constant conversation with your body

00:13:15.100 and the other brains and bodies, you know, that surround you?

00:13:19.480 How is it conjuring the features of your mind?

00:13:23.120 How does it control your, the internal systems of your body at the same time as it's,

00:13:30.160 you know, controlling your behavior and giving you memories and thoughts and feelings and so on?

00:13:35.200 And that may sound like, you know, too big of a question to answer,

00:13:39.920 but I would say I'm really interested in understanding a systems level kind of approach to,

00:13:46.840 to brain function.

00:13:48.680 And that encompasses a lot of things.

00:13:50.460 So I have a large, a large-ish lab and we have a lot of different research projects going on.

00:13:57.700 So it's really hard when someone asks me, so what are you, what is your newest research project?

00:14:01.660 And I'm like, well, we have like probably 40 of them going on.

00:14:04.420 So it's hard to, it's hard to summarize in one sentence.

00:14:07.580 And you're currently a professor as well, right?

00:14:09.660 So do you spend some time teaching or is it all research at the moment?

00:14:13.120 I mean, I know we're talking in COVID land or at the tail end, one hopes of COVID,

00:14:18.200 the COVID pandemic.

00:14:19.140 So nothing seems normal, but what is your general life like as a professor?

00:14:24.820 Yeah.

00:14:25.160 So I run a lab which has 25 full-time people in it.

00:14:31.280 And then usually we have, not during COVID, but usually at other times we have about a hundred,

00:14:39.100 150 undergraduate researchers, researchers in the laboratory in any given year.

00:14:46.040 And the lab is spread out across two different places.

00:14:49.120 So I have personnel at two different places, graduate students, postdocs, and so on,

00:14:53.740 postdoctoral fellows.

00:14:54.660 I teach one course a year for undergraduates.

00:14:57.560 It's a lab course.

00:14:58.640 And then occasionally I will also teach, formally teach graduate seminars, but I also run a weekly

00:15:07.580 or now bi-weekly seminar that I've been running for, I guess about eight or nine years that

00:15:14.180 I don't get any credit for.

00:15:15.520 We just do it out of the love of doing it with engineers and computer scientists and other

00:15:20.360 neuroscientists and psychologists.

00:15:21.840 And so I and another, and my colleague in engineering, we run this seminar for all of our peeps.

00:15:29.600 So it's about 25 people who attend this seminar and it's been going on, like I said, for quite a number

00:15:35.060 of years.

00:15:35.540 And then I also run other reading groups that people attend on particular topics, depending on what we're

00:15:44.120 interested in, for example, on predictive processing or on energetics, which is a word that we use to

00:15:52.860 refer to brain metabolism and the way that the brain is regulating the metabolic functions of the body.

00:15:59.940 So one of the things you do throughout this book, especially at the outset, is debunk a few myths and bad

00:16:10.520 metaphors we've relied on to understand the brain or seem to understand the brain.

00:16:17.720 And this seems like a very useful thing to do.

00:16:20.600 So perhaps we should just start where you start with the larger context of evolution and what we think we

00:16:28.700 understand about the evolution of the human brain.

00:16:31.980 And perhaps this is a good place to part company with Paul McLean.

00:16:37.300 So how do you think about the brain in evolutionary terms?

00:16:42.860 I love this question.

00:16:44.680 This is one of my, I think this is one of the most fun questions, really.

00:16:47.940 It occurred to me at some, at one point, like, why don't we even have a brain?

00:16:52.420 It's, it's a really expensive organ, right?

00:16:55.160 That three pound blob of meat between your ears costs you about 20% of your entire metabolic budget.

00:17:01.740 So it's pretty expensive.

00:17:03.420 And I'll just point out, depending on what you do with it, it can cost you much more than that.

00:17:07.300 It certainly can.

00:17:08.720 Especially on social media.

00:17:11.560 Certainly can.

00:17:13.000 That's absolutely right.

00:17:14.220 And so I'm very fortunate in that I've been, we meeting really weekly with Barbara Finley,

00:17:23.620 who is an evolutionary and developmental neuroscientist.

00:17:25.900 And she's basically, you know, to use her words, she's like downloading all of her knowledge into

00:17:31.560 my brain, which really means that she repeats herself frequently and has to explain things

00:17:36.700 often more than one time.

00:17:38.840 And this is pretty, pretty, you know, not to make a bad pun, but like pretty heavy stuff.

00:17:44.260 It's pretty complicated.

00:17:46.440 You know, I had to learn embryology and I, you know, barely understand what I'm reading,

00:17:51.560 but I understand a little bit now at least.

00:17:53.640 But that, the really cool thing I think is that if you go back, you know, 550 million years

00:18:00.260 ago to a time in the earth's history called the Edicarion, animals didn't have brains.

00:18:06.880 And so I was just really interested to try to understand, well, why, you know, why did

00:18:12.520 brains evolve?

00:18:13.660 And Sam, you know, you know, you can never really answer the why question very easily

00:18:19.620 in evolution, but you certainly can answer what question.

00:18:23.500 So like, what is the brain's most important job?

00:18:26.820 What is a brain really good for?

00:18:28.400 And you can look at the evolutionary, the evolutionary story that, that molecular geneticists and

00:18:37.280 anatomists and so on, ecologists have, have crafted.

00:18:42.320 And it's a really cool and interesting drama.

00:18:45.860 And it, what it suggests is that your brain's most important job isn't thinking or seeing or

00:18:55.260 even feeling.

00:18:56.380 So these are characteristics.

00:18:58.800 These are features that the brain performs or computes, but they're not actually the brain's

00:19:05.940 most important job.

00:19:06.820 It's most important job is regulating the systems of your body, your heart, your lungs, your immune

00:19:14.240 system, your, you know, endocrine system and so on.

00:19:17.880 And of course, you know, we don't experience every delight and, or, you know, every drama

00:19:26.820 in our lives this way.

00:19:28.740 We don't experience every hug that we get or used to get before COVID or every insult that

00:19:34.580 we bear.

00:19:35.600 We don't, we don't experience things this way, this way, but this is actually what is going

00:19:40.680 on under the hood.

00:19:41.660 And when your brain thinks and decides and sees and hears and feels, it's doing this

00:19:49.940 in the service of the regulation of your body.

00:19:53.820 And that turns out to be a really important insight.

00:19:58.220 I would add one piece here.

00:20:00.480 I know you, I don't recall if you put it this way in your book, but it does strike me that

00:20:05.140 just by the logic of evolution, the motor behavior is in some ways primary here, because if you

00:20:13.500 can't move, if you can't do anything with a brain, if there's no way that it can influence

00:20:20.060 the differential success of an organism in the contest for mates or survival, then there

00:20:27.460 would have been no evolutionary pressure in this direction.

00:20:30.340 So it seems to presuppose an ability to do something with respect to the environment.

00:20:36.380 I don't think there's a bright line between that story and the story of regulating the

00:20:41.900 internal states of the body.

00:20:43.620 I think we'll get to that.

00:20:44.920 But don't you see an ability to actually act in some way as being the necessary context

00:20:51.440 for this evolutionary pressure?

00:20:54.480 Absolutely.

00:20:54.740 In fact, really, you know, I guess I'm very persuaded by work in motor neuroscience and

00:21:02.660 certainly in philosophy, the idea that motor action is primary and all sensory processing

00:21:12.300 is in the service of motor action.

00:21:14.040 I think that's absolutely right.

00:21:15.420 The one thing I would say, though, is that, you know, in vertebrates or in all vertebrates,

00:21:23.080 certainly, and in, in, I would maybe hazard to say all animals who have limbs that move

00:21:31.560 or parts that move, there's usually an internal set of systems that support that movement.

00:21:39.620 Now, in vertebrates, you know, like us, that's, you know, a cardiovascular system and a respiratory

00:21:45.580 system and so on.

00:21:48.000 You know, not all animals have the kind of viscera that we have, that vertebrates have.

00:21:52.180 So invertebrates, you know, have their own systems, but there is no external movement

00:21:58.540 of bodies without internal systems to support that.

00:22:02.260 And in motor neuroscience, as much as I respect that work, and I really do, I think they're

00:22:06.780 really ahead of the curve in certain ways.

00:22:09.060 They, they tend to ignore the internal systems of animals' bodies.

00:22:13.460 And I really think that that's an important part of the story that is missing.

00:22:18.180 So when I say, you know, that the brain is regulating the body, I really mean everything

00:22:26.240 motor about the body.

00:22:27.540 That would include what we call visceral motor, which means the beating of your heart and the,

00:22:32.060 you know, contraction of your lungs and so on.

00:22:34.700 But it also means the movement of your skeletal motor system, your muscles, the voluntary movements

00:22:42.160 of your muscles.

00:22:42.800 And in fact, if you look at, for example, primary motor cortex in a monkey brain, a macaque

00:22:49.820 brain, it has visceral motor maps in it.

00:22:53.080 And some of the regions that are considered to be, you know, sort of association regions

00:22:58.900 for the motor system are actually the primary cortical controllers of visceral motor regulation,

00:23:05.220 meaning regulation of the viscera of your lungs and your heart and so on.

00:23:08.280 So in your brain, the internal systems of your body, the, the, the source, the, the neurons

00:23:17.780 that are controlling the internal systems of your body and the neurons that are controlling

00:23:21.480 your skeletal motor system, the, you know, your voluntary muscle movements are really intertwined.

00:23:27.820 And that's not well documented in motor neuroscience work, but it's present in the anatomy.

00:23:39.180 You can just see it.

00:23:40.060 It's there.

00:23:41.960 Yeah, but we'll talk about emotion, but I tend to think about emotion now as a kind of covert

00:23:47.540 behavior, right?

00:23:49.120 So the, the line between emotion and action that is, um, commonsensical, I think can break

00:23:55.860 down if you follow that framing, but, uh, let's, let's not leap to emotion just yet.

00:24:00.880 The evolutionary story we have told ourselves for a long time has been, uh, summarized by this

00:24:08.200 concept given to us by, uh, Paul McLean of the triune brain.

00:24:13.280 And, uh, you know, so people refer to their, their lizard brain, or they think of a stepwise

00:24:20.580 evolution from reptiles to mammals generally, and then to primates as having kind of climbed

00:24:27.560 up from the brainstem to the cortex.

00:24:30.660 What's wrong with this picture?

00:24:33.940 Well, what's wrong with that picture is that it doesn't really match the best available

00:24:40.340 scientific evidence for how brains evolved.

00:24:43.280 I mean, if you look at a lizard brain and say, uh, a mammal brain, like a, like say, um,

00:24:53.040 a rat or like a rodent brain, say, and you look at a monkey brain and a human brain, you

00:24:59.020 know, they look different to the naked eye.

00:25:00.680 It looks like the rat, or I should say, it looks like, it looks like the lizard doesn't

00:25:05.620 really have much of a cerebral cortex.

00:25:07.260 It looks like the rat has, you know, maybe a little bit of, of kind of old cortex and,

00:25:14.980 um, that, that the monkey and the human have quite a bit and the human having, you know,

00:25:19.300 substantially more than a, the monkey.

00:25:21.580 That's how it looks to the naked eye.

00:25:23.180 But, and this, you know, led Paul McLean and others, you know, guided by, I think, certain

00:25:30.800 cultural beliefs to describe brain evolution in, in much the way that you just described

00:25:38.840 it.

00:25:39.080 Although your description, Sam is slightly more lyrical than maybe what McLean wrote, but,

00:25:43.500 you know, the idea that a lizard brain is mostly has parts for instincts, you know, like freezing

00:25:52.300 and fighting and fleeing and copulating, which, you know, neuroscientists make a funny joke,

00:25:59.640 you know, like they refer to it as the four Fs.

00:26:02.040 So that's neuroscience humor for you.

00:26:03.880 And then layered on top of that evolved what's called a limbic system, limbic meaning border,

00:26:11.340 bordering this, you know, these lizard parts for emotion.

00:26:16.100 And then what lay, and then what evolved on top of that is the cerebral cortex or the neocortex,

00:26:23.380 the new part of the cortex, which you only see in what are referred to as higher, uh, mammals,

00:26:32.180 you know, like us.

00:26:33.880 And the idea is that, you know, your lizard brain contains your instincts, your limbic system

00:26:40.100 contains your emotions.

00:26:41.100 And then these are, these make up your inner beast and they are constantly in battle with

00:26:46.540 the more rational side of yourself, which resides in your cerebral cortex.

00:26:52.160 So your brain is a battleground between your inner beast and your rational self for control

00:26:59.480 of your behavior.

00:27:00.160 And the idea is that, you know, when your cortex wins and you behave rationally, you're a moral

00:27:10.780 person and you're healthy.

00:27:12.820 And if your inner beast wins to control your behavior, then you're either immoral because

00:27:18.740 you didn't try hard enough or you're sick because it didn't work.

00:27:24.200 You know, that there's something wrong with your, with your rational cortex.

00:27:28.360 And the problem with this, even though it makes a lot of sense in terms of our, you know, the

00:27:34.700 stories that we tell ourselves about what, what it means to be moral and responsible for

00:27:39.220 behavior.

00:27:39.580 And it's, you know, it's very consistent with Western views of the self.

00:27:43.740 The problem is that it doesn't actually match the evidence that when you peer into neurons

00:27:49.420 and you look at their molecular structure, in particular, you know, the, the genes that

00:27:56.240 guide the formation and function of, of those neurons, you see a really, really different

00:28:03.760 story.

00:28:04.900 And the story is that really all mammals who've, whose brains have ever been studied, actually

00:28:12.720 their brains follow the same developmental plan.

00:28:15.880 Their neurons actually, there are no new neurons, really no new neurotypes.

00:28:21.300 And remarkably, the stages of development, and I'm talking about, you know, embryological development

00:28:26.600 forward, the stages of development in, in all of these mammal brains that have been studied,

00:28:31.680 different species, proceeds in exactly the same order, pretty much it, what's, what changes

00:28:37.780 is the duration of each stage.

00:28:43.440 And there's this really interesting observation that George Streeter, the, the neurobiologist

00:28:51.740 made about brains in his book on brain evolution, by the way, excellent book, if anyone wants a

00:28:56.480 primer on, you know, brain evolution, it's, it's a really fantastic book.

00:29:00.080 But, you know, he says, you know, brains reorganize as they grow larger.

00:29:06.280 And so it can look like there are new structures there, just because there are more of certain

00:29:11.440 neuron types, but actually the, you know, there's nothing new in terms of the neurons.

00:29:17.720 It's just there, they look like they're reorganized and they look like there are miraculously new

00:29:23.880 parts there, but there are really no new parts.

00:29:25.560 It's just that certain types of neurons have certain stages in development have gone on

00:29:30.520 for longer.

00:29:31.020 And so there are certain types of neurons, there's just more of them.

00:29:34.900 And if you go back even further and you look at other animals, you know, other vertebrates,

00:29:40.960 you see that many of them have also really striking similarities to the, to mammalian brains.

00:29:48.180 So for example, birds don't have a cerebral cortex, but they certainly have neurons that

00:29:53.560 are the same as the neurons that make up our cerebral cortex and that seem to perform some

00:29:58.360 very similar functions to what our cerebral cortex, the various functions our cortex performs.

00:30:03.760 So basically there is no, you know, lizard brain.

00:30:09.500 I mean, you don't have a, an ancient beast lurking inside your brain and the only animal who

00:30:15.680 has a lizard brain is a lizard.

00:30:17.860 Are there any exceptions to this?

00:30:19.060 I had thought that, um, Von Economo neurons were an exception that they were just, they were

00:30:26.100 present in great apes and, uh, I think cetaceans and elephants and a few other, you know, charismatic

00:30:33.660 vertebrates, but were not found in, in reptiles or birds or.

00:30:39.740 So Von Economo neurons are very contentious.

00:30:43.520 I mean, there, there are some anatomists who will tell you that Von Economo neurons are not

00:30:49.840 a special class of neurons.

00:30:51.160 They're just really big honking pyramidal cells.

00:30:55.980 So, you know, you find them in large brain animals because, you know, as brains get bigger,

00:31:03.040 sometimes the neurons also get bigger.

00:31:06.580 And, you know, one thing that's happened, for example, in large brain animals, what often

00:31:10.420 happens is that there are certain parts of the cortex in particular that as they grow,

00:31:18.800 what happened, you know, evolutionarily, but also in development, what happens is not that

00:31:24.060 they develop more neurons, but they develop fewer neurons that get much bigger and they

00:31:28.780 have much more connectivity.

00:31:30.540 And the reason for that is, um, I don't know the reason for it, but the, the functional

00:31:36.340 consequence of that is that, which something I explained in essay seven, which is that it

00:31:44.820 means that the animal's brain can summarize information much more efficiently and maybe even

00:31:52.240 do some abstraction, meaning can find similarities in things that look and feel and smell and taste

00:31:59.440 different, find functional similarities.

00:32:01.980 So this is abstraction.

00:32:03.200 This is what we call abstraction, right?

00:32:05.320 And that's really, you know, maybe what these very large pyramidal neurons are for, but there

00:32:11.880 are some anatomists and some neuroscientists who look at von Economo neurons and say, well,

00:32:17.760 these are just ordinary big, you know, neurons.

00:32:21.400 They're not, there's nothing really special about them.

00:32:23.420 Um, and you find them in animals who have large brains relative to their body size.

00:32:28.000 Right.

00:32:29.180 Right.

00:32:30.020 So what is the appropriate picture of the structure of, uh, what we have in there?

00:32:38.640 If it's not this cartoon of descent from reptiles, what picture of complexity and, and, you know,

00:32:48.060 now leading the witness network complexity, uh, should we, uh, should we have in our heads?

00:32:54.680 Yeah, I think I'm going to ask your question, but I just want to take one step back for a

00:32:58.580 minute and say that, you know, we live in a world where we see objects and we, we see boundaries

00:33:06.300 between objects and, you know, like here's a book, here's a purse, here's a computer, here's a glass,

00:33:11.200 whatever.

00:33:12.140 And so we have a tendency to think about things in terms of objects instead of in terms of

00:33:20.020 relationships between features.

00:33:24.140 And so for a really long time, people have thought about the brain as having these distinct parts,

00:33:30.500 you know, like there's this group of neurons called the amygdala, which performs emotion.

00:33:35.080 And there's this other group, you know, called the basal ganglia, which performs, you know,

00:33:40.460 movement.

00:33:40.940 And then there's this other part called the cerebral cortex.

00:33:43.380 And the prefrontal part of that really performs decision-making or rationality or what have you.

00:33:48.060 And that's just, I mean, there are people who still hold to that view and, and it's certainly

00:33:53.460 people have built their whole careers on such notions and, and been very successful.

00:33:59.060 But I think there's also a growing understanding that that's really not how the brain works.

00:34:05.760 It's not how the brain is structured.

00:34:07.340 There are no objects, you know, there are no kind of mental organs in your brain.

00:34:11.720 That's just not really the way, that's just not really the best way to understand the anatomy

00:34:17.680 or the function.

00:34:18.540 And that instead we should be understanding neurons in terms of their relationships to one

00:34:24.040 another and the features that they compute.

00:34:26.400 And so they're really, this can take many forms in published papers on neuroscience, but

00:34:33.400 one that's very popular at the moment is to think about the brain, think about, you know,

00:34:38.040 neurons as in a large dynamically fluctuating network.

00:34:43.520 And so if you think about, you know, instead of thinking about neural signals as being passed

00:34:49.920 from one, you know, region to the other, like a baton in a race, you can think about neural

00:34:57.720 activity and the patterns that are created more like weather patterns or something where,

00:35:03.200 you know, many, many, many neurons are participating in computing an event that has a set of features

00:35:13.260 and some of those features are, you know, very close to the data that you get from your

00:35:19.940 sensory surfaces, like your retina and your cochlea and all the sensory, all the sensors

00:35:25.940 in inside your body.

00:35:27.420 So, you know, like a line, for example, or color, like the color red, your experience of

00:35:32.780 the color red is a feature that your brain computes.

00:35:35.480 It doesn't detect, as you know, and it's computing it using information from not one color

00:35:42.800 detector detector, you know, like as so-called cones.

00:35:47.140 And, you know, you have three, you have cones in these cells in your retina that register

00:35:52.160 three different ranges of wavelengths of light.

00:35:55.400 And you need all three to see red or green or any color.

00:36:00.960 And so your brain computes these features.

00:36:03.340 And it also computes features like, like seeing a face.

00:36:06.960 It computes features like a threat.

00:36:09.740 It computes features like novelty.

00:36:11.860 It computes features, all kinds of features.

00:36:14.240 And in a given event, your brain is sort of computing sequences of events.

00:36:20.080 And in computing an event, what it's doing is computing features in the service of regulating

00:36:24.980 the body, regulating action and the, all the visceral, you know, changes that will support

00:36:30.920 that action.

00:36:31.500 And so the way to think about it is your brain is a single structure with, you know, 128 billion

00:36:39.420 neurons, give or take, and it can take on trillions of patterns.

00:36:43.460 And these patterns are, you know, helped along by the chemical bath that surrounds these neurons.

00:36:52.300 So your neurons are bathed in a chemical system.

00:36:55.860 And, and it's just, your brain is basically dynamically along a trajectory from one pattern

00:37:03.340 to another pattern, to another pattern, to another pattern, and trying to understand what

00:37:07.320 launches those patterns, what maintains those patterns, what features your brain is, is computing.

00:37:12.780 That's really the goal of understanding brain function.

00:37:16.020 Yeah, I would also just point out that the methods we use to understand brain function,

00:37:22.200 like increasingly functional neuroimaging, can also give a, a false picture of the modularity

00:37:29.920 of the brain and therefore the mind.

00:37:32.000 Because, you know, we just, by the nature of the tool that we look at the data in terms

00:37:37.260 of these pretty pictures of certain regions of the brain, so-called lighting up in response

00:37:42.100 to stimuli or tasks, and it can give a sense, you know, not to actual neuroscientists generally,

00:37:49.360 but perhaps in a more subtle way, can even corrupt their thinking.

00:37:53.320 But it certainly can give a sense to the general public that this is a question of other areas

00:37:59.220 of the brain actually not doing anything when they're not part of the illuminated map of,

00:38:06.100 you know, what is most active during a certain function.

00:38:09.240 So it can just give this, this false picture of separate organs in the brain that are, albeit

00:38:17.300 connected, are really independently responsible for an emotion like disgust, say, or a certain

00:38:26.100 kind of perceptual task.

00:38:27.680 And you just can't visualize the network behavior and the fluctuating network behavior and the,

00:38:34.880 and the weighting between nodes in the network as easily as you can, just aggregate the data

00:38:40.620 by subtracting, you know, two states of the brain and showing one to one where these regions were

00:38:47.400 more active than in the other.

00:38:50.600 Yes and no.

00:38:51.180 I think I mostly agree with you, but I would, I would probably just push back maybe a little

00:38:56.020 bit on a couple of points.

00:38:57.160 One, I would say it's not the fault of, of brain imaging techniques.

00:39:00.380 It's really the fault of the analysis techniques that we use and the sample sizes we have.

00:39:06.060 So I would say that with fMRI, you know, fMRI has its problems for sure.

00:39:12.660 It's, it has limitations in terms of its temporal, you know, resolution and also even some spatial

00:39:20.060 resolution issues.

00:39:21.860 But really it, it has, it has much more to do with the kinds of designs that scientists

00:39:27.320 use and the kinds of analytic techniques that they use.

00:39:30.820 And I'll give you a really good example.

00:39:33.600 There's a, what I think of as a really brilliant paper that was published in the proceedings

00:39:41.240 of the National Academy in 2012.

00:39:44.440 The first author is Gonzalez Castillo.

00:39:46.640 And it's this really nice paper where they, you know, compare the sort of standard, you

00:39:56.880 know, experimental design really for a very, very simple task, which is, I believe it was

00:40:04.040 a visual, visual perception task, maybe visual orientation.

00:40:07.420 I think it was, but very, very straightforward task.

00:40:10.740 So visual attention task.

00:40:11.860 And when you run, you know, some subjects and you, you have maybe, you know, 40, 50 to

00:40:20.280 a hundred trials where a trial is, you know, you show something unexpected to the subject

00:40:25.760 and then they, you know, they have to make a judgment of whether, you know, lines are pointing

00:40:30.800 in the left direction or the right direction or what have you.

00:40:33.240 So what you see in the, the way the analysis is done, the way that choices, analytic choices

00:40:39.880 are made to separate signal from noise and so on.

00:40:43.060 You see a couple of islands of, of, of increase in activity that are depicted on a brain, you

00:40:51.760 know, image as like spots that light up, like the light bright, you know, sort of brain.

00:40:57.000 And it's important to really understand here that these images that we see in magazines

00:41:03.380 and in journal articles and so on are curated by scientists.

00:41:06.480 They don't just pop out of the data on their own.

00:41:09.280 They're made contingent that these images are contingent on a bunch of analytic decisions

00:41:15.100 that are made.

00:41:16.640 Now, if you expect that there are islands of activity because, you know, different parts

00:41:23.960 of your brain are responsible for different specific psychological functions and that's

00:41:28.520 what you expect and you've designed your study that way and you've only, you know, tested

00:41:33.420 your subjects on 50 to 100 trials and you threshold, that is, you make decisions about signal versus

00:41:39.980 noise in particular ways, what you get are a couple of islands of activity.

00:41:44.600 However, what this paper showed is that if you run 400 trials for each subject, so you bring

00:41:52.100 them back for multiple scanning sessions and you analyze the data in a slightly different

00:41:59.100 way by instead of assuming that every part of the brain has that the, that the, the shape

00:42:08.200 of the, the response is the same.

00:42:11.940 And instead of assuming that you model, you know, this, the variability and how the different

00:42:16.520 parts are responding, what you see is that 85% of the brain shows an increase in activity.

00:42:25.800 That means 85% of the brain is showing a change to make a very, very simple decision that is

00:42:33.420 considered.

00:42:34.220 Yeah.

00:42:34.460 So the point is that if your, if your studies are designed in a way that is underpowered,

00:42:40.400 you're not going to realize that you're making what we would call a type two error, which

00:42:46.420 is that you're missing a lot of important activity that's there because, you know, you're

00:42:51.580 expecting to see blobs and what you get are blobs.

00:42:54.500 And so, you know, if what you expect is islands of activity, you'll perform your studies, you

00:43:00.420 know, with something I used to call blobology, which is that, you know, you'll identify these

00:43:04.340 blobs of activity.

00:43:05.120 I think people have to realize that these, these images are really curated by humans who

00:43:10.300 have a set of assumptions.

00:43:11.200 I'll just give you one other really quick example.

00:43:13.280 And that is, you know, when people started looking at networks in the brain, so this is

00:43:22.000 regions that are, have correlated that where the brain response is correlated.

00:43:27.700 So, you know, you take a brain and you divide it up into lots of little cubes called voxels.

00:43:33.200 And so you look for sets of voxels that have a similar change in blood flow during an experiment.

00:43:41.540 And you call that a network.

00:43:43.500 And it turns out, you know, this actually does reveal something about the underlying structure

00:43:48.400 of the brain.

00:43:49.780 But when you look at the way that scientists mostly study these networks, they're, they

00:43:56.340 look like Lego blocks, like they're completely unrelated to each other and like, like, you know,

00:44:01.000 pieces of a puzzle and you put them all together and you get a brain, but, you know, that's

00:44:05.300 a decision.

00:44:05.960 Those are computational decisions that are made on, based on analytic, you know, choices that

00:44:10.120 are guided by certain assumptions.

00:44:11.340 If you do the analysis slightly differently, which is what we did.

00:44:14.520 So we took, you know, almost a thousand subjects and we, instead of asking, you know, using kind

00:44:23.260 of standard way of looking for signal and noise, we said, okay, anything which replicates from

00:44:28.860 one subject to another is signal by definition and anything which doesn't is noise.

00:44:34.720 And so let's just try to parse the, you know, networks in the brain by doing this.

00:44:40.500 And what we found was, you know, we found that the sort of networks that people often talk

00:44:45.120 about, but they're really, they overlap.

00:44:48.160 They're not, they're not disconnected.

00:44:50.040 They're actually overlap and they overlap in, in particular regions of the brain, which

00:44:54.120 are known to be, they're called hubs or rich club hubs, meaning densely connected regions

00:44:59.540 that are responsible for really coordinating activity across the whole brain.

00:45:06.020 They're called, you know, these rich club hubs are called the backbone of neural communication

00:45:10.860 in the brain.

00:45:11.480 There's a really nice paper by Olaf Spornes and, and Vanden Heuvel Spornes.

00:45:17.720 I think it's Vanden Heuvel and Spornes in 2013 in the Journal of Neuroscience.

00:45:24.360 And so my point is that these images that you see, they're beautiful and awe-inspiring, but

00:45:31.640 they're curated by humans who have a set of assumptions.

00:45:33.860 Yeah, and it's also easy to see the temptation to think in those terms, because I mean, we

00:45:39.680 have, you know, something like 170 years of neurology attesting to the fact that highly

00:45:47.800 focal lesions, you know, brain damage can lead to very specific deficits.

00:45:55.900 Again, this can be understood in network terms, but it is in fact descriptively true that you

00:46:02.820 can have a small region of the brain damaged and that can dissect out a very specific mental

00:46:11.620 capacity, you know, language use or an ability to recognize faces or, or even to recognize

00:46:18.280 specific classes of objects like, you know, tools versus animals.

00:46:23.820 And that's, that does give you this sort of jigsaw puzzle, like Lego, like intuition about

00:46:31.380 the modularity of the mind.

00:46:34.220 Yeah, you're right.

00:46:35.080 But even there, it's more complicated than it first appears, right?

00:46:38.080 Because when you damage a part, when you damage tissue, you don't really know whether what

00:46:46.900 you've damaged, the critical part, you know, to the function that you've lost are the neurons

00:46:53.360 that are damaged or what are called fibers of passage, which means, you know, axons that

00:46:59.080 run through that area, which are really important.

00:47:01.780 And I just learned about this really, this phenomenon that I, I just, this is the kind

00:47:07.260 of stuff I just love, honestly, where, you know, you can lose, if you damage one part of

00:47:15.160 your primary visual cortex, so this is in animals, they'll ablate a part of the primary visual

00:47:21.420 cortex and the animal will lose the ability to see.

00:47:25.680 And so obviously, you think, oh, well, okay, this, this region must be super important to

00:47:32.080 seeing.

00:47:33.000 And it is important, except that you can recover some of that function by a second lesion in

00:47:39.400 the superior colliculus in the midbrain.

00:47:42.060 So there's information that could make it from your retina to your primary visual cortex,

00:47:48.900 but it's being suppressed by the colliculus, right in a regular fat in a regular neurotypical

00:47:54.240 brain, but you can recover function by a second lesion.

00:47:57.960 And so it's just things like that, right, that make you, or here's another example, another,

00:48:02.980 you know, example, which I find just absolutely fascinating.

00:48:07.280 I find it slightly horrifying as a person, but because of what happens to the animals, but

00:48:12.400 as a scientist, it's really fascinating.

00:48:15.080 So they took these rats and train them to run on a wheel and, you know, recorded directly

00:48:22.760 from neurons in the visual cortex, primary visual cortex, and then they ablate the damage,

00:48:31.500 the retinas, destroy the retinas of these animals so they can't see. 1.00

00:48:36.620 And V1 neurons, primary visual cortex neurons, quieten down.

00:48:42.480 And then over 24 hours, they ramp up again and start firing at normal rates.

00:48:50.560 So what's causing these neurons to fire?

00:48:55.120 You know, you put the rat back on the wheel and its neurons, the pattern of firing looks

00:49:00.360 really similar to what it looked like when the animal was sighted.

00:49:02.860 So what is it exactly that's driving the activity in these neurons?

00:49:08.700 And the answer probably is regions of the anterior cingulate cortex, which have direct connections

00:49:17.500 to V1.

00:49:19.200 And the reason why this is interesting is that this region of the brain is a primary regulator

00:49:26.280 of the systems of your body.

00:49:27.900 Both it is a primary motor area for the viscera of your body, and it's an association region

00:49:34.780 for your skeletal motor system.

00:49:37.660 And what this activity is, essentially, what you can think about it is, are a set of visual

00:49:45.080 predictions that are coming from past experience from that, you know, that these motor regions

00:49:56.080 are able to reinstate.

00:49:59.440 And so it's just trickier, Sam, than, you know, I mean, if you start to just poke at it a little

00:50:06.740 bit, modularity starts to fall apart.

00:50:09.100 Yeah, yeah.

00:50:10.380 Well, I think we found the seminar you can teach at Esalen one day, ablating brainstem nuclei

00:50:16.480 so as to recover a proper vision of the world.

00:50:20.300 Yeah, I really wouldn't recommend that people try that at home.

00:50:22.660 It's not advised.

00:50:24.120 So let's talk about prediction and just this uncanny circumstance we're all in, which very

00:50:33.140 few people realize, and those of us who realize it, I think, rarely think about, which is, we

00:50:40.480 have this venerable philosophical thought experiment of the brain in the vat, and, you know, this

00:50:47.880 is a kind of device to think about many things in the philosophy of mind, but rarely is it

00:50:55.700 pointed out that we really are brains in vats already.

00:50:59.860 The vat is our skull, and we do not have direct contact with the physical environment, much

00:51:07.940 less reality itself, in any straightforward way.

00:51:11.820 It's not like our senses are windows through which we're peering or hearing or sensing directly.

00:51:19.440 There's a very active and even anticipatory, to use your term, predictive activity that

00:51:27.280 is producing a visionary experience, a dreamlike experience of the world.

00:51:33.340 I mean, it's exactly like a dream, except for the ways in which, in the waking state, our

00:51:40.760 envisioning of the world is constrained by sensory input, and, you know, to a different

00:51:47.320 degree.

00:51:47.720 So, how do you think about the situation we're in, you know, just epistemologically, existentially,

00:51:55.340 we are, and this is a phrase you use at some point in the book, we are experiencing a kind

00:52:00.680 of controlled hallucination.

00:52:03.080 It's not to say that nothing is veridical or nothing is, that no statement about the world

00:52:08.160 as it is, is better than any other, you know, or more convergent with facts that we could

00:52:14.380 intersubjectively find credible, but, you know, it's much more like the matrix than we

00:52:21.120 give it credit for most of the time.

00:52:24.100 And so that's, you know, perhaps that can get you going in the direction of how you think

00:52:30.020 about the mind as a, and the brain as a predictive computational system, and not one that's merely

00:52:38.420 passively encountering the world as it is.

00:52:42.020 Well, I think you just did a beautiful job describing it in very poetic terms, actually,

00:52:48.200 calling it a dreamlike, calling the brains, you know, or describing the brain's function

00:52:53.320 as conjuring a dreamlike state is actually something that I just came across in this really

00:53:00.260 wonderful book by Carlo Rovelli.

00:53:04.060 It's his new book called Helgoland.

00:53:06.660 I don't think it's available yet in the US, I had to order it from the UK.

00:53:11.760 And I, and, you know, he's really he what he's doing, he's explaining his understanding

00:53:18.100 of quantum mechanics for a civilian like me, you know, I'm not, I don't, I'm not a physicist.

00:53:24.680 And, but, you know, and with, with very, very little math, and, and then, you know, as often

00:53:31.380 seems to happen, you know, everyone wants to take a shot at explaining what the brain

00:53:35.840 does.

00:53:36.240 And, you know, what consciousness is, doesn't matter if you trained as a, you know, a physicist

00:53:41.900 or what have you, everyone takes their shot.

00:53:44.060 And, but his shot, you know, he's describing, trying to describe prediction based on, you

00:53:50.080 know, I, I'm imagining what he, what he read from the literature in visual neuroscience,

00:53:55.360 where a lot of this work has taken place.

00:53:57.780 I think though, there's much, there's a lot more work, which is very consistent with, you

00:54:03.740 know, your description.

00:54:05.700 And there's a really, really nice paper that was written, actually, which was my review.

00:54:12.560 I was, I reviewed this paper, actually, for behavioral and brain sciences, which is a really

00:54:16.920 great journal.

00:54:18.040 And this is what alerted me to this growing literature.

00:54:23.080 This was back like in 2010, I think, maybe, or 2011, this growing literature on what's called

00:54:29.240 predictive coding or predictive processing.

00:54:31.520 It's a paper by Andy Clark.

00:54:34.180 Philosopher, right?

00:54:34.740 Philosopher, but also, you know, just writes beautifully about, very intuitively and beautifully

00:54:43.060 about the brain as a predictive organ.

00:54:46.600 And, but you know what, for me, I'm, I, you know, I don't know about you, but I am like

00:54:52.140 inherently skeptical person.

00:54:54.900 I, I really, I don't even believe my own data necessarily.

00:55:00.140 It takes me a really long time before I, I don't jump on bandwagons typically.

00:55:04.460 And I also really don't, I mean, scientists, I think in general, wouldn't you agree?

00:55:09.460 We don't really like to use the F word, you know, fact, that's a really scary word.

00:55:13.660 So we try to avoid it.

00:55:15.340 And, but, you know, if you look in the literature, if you look at anatomy and you look at any number

00:55:22.580 of literatures in neuroscience and you look at signal processing literatures and engineering

00:55:28.300 and so on, what you see is that exactly the same discovery is being made over and over

00:55:36.520 and over again by literatures that don't talk to each other.

00:55:40.100 And I found this really compelling.

00:55:42.920 And that is this idea that your brain is trapped in a dark, silent box called your skull.

00:55:52.020 skull and it is constantly receiving sense data from the world, you know, through its sensory

00:56:02.640 surfaces, your retina, your cochlea, whatever, and also in, inside your body.

00:56:06.960 So it's, it's, it's the world to your brain is everything outside of the skull and it's receiving

00:56:15.500 these, this sense data that it has to make sense of, and this is an inverse problem because

00:56:21.180 it, these sense data are the effects that they're the outcomes of some set of changes,

00:56:27.720 but your brain doesn't have access to those changes.

00:56:32.140 It only has access to the outcomes, the consequences of those changes.

00:56:37.220 So how does it, you know, if your brain, if your brain is exposed to a loud bang, how does

00:56:42.520 your brain know what that loud bang is, how does your brain know what to do about it?

00:56:48.280 Um, you know, it, it, it, your, you would do something different if it was, uh, a slamming

00:56:55.200 door or a dropped box or a gunshot.

00:56:57.940 And similarly, you know, when you feel a tug in your chest, how does your brain know, how

00:57:03.300 does your brain know when it detects a tug, right?

00:57:06.080 Whether with, when it's sensing a tug, whether that's, you know, anxiety or,

00:57:11.980 you know, that there's some uncertainty or that you just ate a big meal and, uh, you're

00:57:17.440 having a little trouble digesting it or the beginnings of a heart attack, it has to guess.

00:57:22.340 And what does it use to guess?

00:57:24.340 It uses the only other source of information that it has, which is past experience that

00:57:28.480 it can re-implement, re-instate in its own wiring.

00:57:33.520 So colloquially we would call that memory.

00:57:36.060 So when a brain remembers, when your brain remembers, when my brain remembers, my brains

00:57:41.940 don't store memories and then call them up like files in a file drawer.

00:57:47.240 Basically remembering is reassembling, reassembling the past in the present for the purposes of,

00:57:53.600 of making sense of sense data.

00:57:55.580 And for a number of reasons, some of which are metabolic, your brain is sort of doing this

00:58:01.900 predictively, so it's not waiting to receive the input and then trying to make sense of

00:58:07.640 it.

00:58:08.300 And there are lots of ways to demonstrate this to people.

00:58:11.640 Sometimes when I'm giving talks, you know, I'll use a baseball example and I'll kind of

00:58:15.340 walk people through the timing of the baseball example.

00:58:19.060 You know, baseball couldn't exist as a sport.

00:58:22.540 No actual ball related sport could exist if we had reactive brains.

00:58:27.660 There just isn't physically enough time for, you know, to, for a batter to wait, to see

00:58:33.240 a ball before he swings and actually hit the ball.

00:58:36.260 And there are lots of really, lots of really cool, interesting examples from everyday life.

00:58:40.040 But the point is that metabolically speaking, it's much cheaper for the brain to use past

00:58:45.000 experience, to guess what's going to happen next, where the guess is not some abstraction.

00:58:49.880 It's actually your brain changing the firing of its own neurons to prepare you to see and

00:58:56.740 hear and smell and feel and do something in the next moment.

00:59:00.960 And then it checks those predictions against the incoming sense data from the body and from

00:59:09.300 the world.

00:59:11.420 Scientists call this, you know, running a model of the world.

00:59:18.020 But really what your brain is doing is it's running a model of your body.

00:59:25.100 And it, it's the model of your body in the world, but it only knows the world by virtue

00:59:32.820 of the sense data that it gets from the sensory surfaces of your body.

00:59:38.440 So essentially, every feature that your brain computes, it's computing in relation to your

00:59:44.080 body in a particular moment in time, in a particular context or location relative to or related to

00:59:52.420 the particular shape of your ear and the particular distance of your two eyes from one another and

01:00:00.240 the particular state of your mitochondria and so on and so forth.

01:00:06.120 It's all relative.

01:00:08.120 That doesn't mean some kind of postmodernist morass, but what it does mean is that we really have

01:00:16.820 to realize that everything that we experience, we experience from a particular perspective.

01:00:24.220 And there is nothing really called objectivity.

01:00:30.020 The best we can hope for, according to the historian of science, Naomi Oreskes, is that a bunch of

01:00:39.860 people with their own subjectivity, you know, with different histories and different backgrounds

01:00:45.500 and different experiences in the world, that they can come to consensus over a scientific set of observations.

01:00:53.220 And that's about as close to objective fact as we can get.

01:00:56.420 And it's a pretty, it's pretty darn good.

01:00:58.060 It's worked out pretty, pretty well for us, you know, but the idea that there are universal facts that can

01:01:03.920 be objectively adjudicated by being rational or something is just, it's a fiction that interestingly,

01:01:11.160 that brains tell themselves, even though, you know, brains are completely incapable of doing such things.

01:01:18.060 Well, to say that there's no true objectivity is not the same thing as saying that it's not possible to be wrong, right?

01:01:26.720 And we know certain things are wrong.

01:01:29.600 Oh, for sure.

01:01:30.280 And it's also not saying that anything is possible, right?

01:01:32.920 So, I mean, sometimes when I say, well, there's more than one, you know, there's more than one,

01:01:38.620 you know, when I talk about, you know, variability is the norm, right?

01:01:41.920 That in many places in biology and in psychology, there's much more variation than we often acknowledge

01:01:49.160 or would like.

01:01:50.460 But that doesn't mean that anything is possible.

01:01:54.340 You know, it means that there's just more than one possibility.

01:01:57.640 And similarly, I would say, look, you know, we can all agree, right, that we're going to have

01:02:03.700 ground glass for dinner, but that doesn't necessarily translate into the objective reality

01:02:09.480 that we can actually eat glass, right?

01:02:11.540 It doesn't really matter what we believe.

01:02:12.900 We could all agree that COVID is not infectious and that we don't have to wear masks.

01:02:18.040 But, you know, the virus doesn't care about that.

01:02:22.840 I mean, viruses don't care about anything.

01:02:24.060 But really, all the virus needs is a nice, wet set of lungs.

01:02:28.080 It doesn't matter what that person's brain believes.

01:02:32.420 But there are many, many, but I think, you know, there are many, many cases where what

01:02:38.220 we believe really matters to what we experience.

01:02:42.400 But even if you want to take belief out of the equation, you know, what you experience,

01:02:46.820 what your reality is, how you experience the world is very much relational.

01:02:52.840 It's in relation to the body that you have.

01:02:56.400 And you don't experience yourself that way.

01:03:00.060 I certainly, I mean, I can't tell you what you experience.

01:03:02.160 I don't experience myself that way.

01:03:04.000 And if I wasn't a scientist, and somebody just told me that, I'm not sure that I would

01:03:08.520 believe it, actually.

01:03:09.520 But it is, that is the best available evidence that your brain is constantly cultivating your

01:03:21.440 past for the purposes of predicting your future, which will become your present.

01:03:26.640 Yeah, let's see if we can make this concrete for people, because this is really ground upon

01:03:33.860 which the scientific framing of what's going on can unlock a kind of psychological freedom

01:03:42.980 to just change one's sense of what one is as a subject in the world.

01:03:49.860 And it, and I think it can relieve certain kinds of suffering.

01:03:55.620 In the simplest case, just to take this predictive piece, which can sound spooky, you take something

01:04:01.900 like a voluntary motor action, like so I can decide to reach and pick up a cup on my desk.

01:04:07.940 And this is, this does relate to this controversy that, that I keep resurrecting for myself over

01:04:13.760 the reality or lack thereof of free will.

01:04:17.800 I don't know if you know how far down that rabbit hole I've gone, but...

01:04:21.420 Oh yes, I've enjoyed, I've enjoyed, I guess, following you down that rabbit hole.

01:04:27.620 Yeah.

01:04:28.680 So we can talk about that if it interests you, but people have a sense that they are subjects

01:04:34.760 that have this capacity to freely initiate behavior, and that's different.

01:04:42.360 You know, I would certainly agree that voluntary behavior is different from involuntary behavior,

01:04:46.400 but I just don't think we need the concept of free will to differentiate the two.

01:04:51.740 So one way they're different is when I'm doing something, you know, of my own volition,

01:04:57.960 you know, reaching and picking up a cup, that feels a certain way, and it feels a certain

01:05:01.960 way because there are certain implicit processes that we know must be going on neurophysiologically

01:05:09.440 there that do follow this kind of predictive mapping of things.

01:05:15.440 So when I'm reaching, and I'm not consciously aware of it, but I can be made consciously aware

01:05:20.920 of it, certainly when anything goes wrong.

01:05:24.860 So I'm not aware that I'm a prediction machine when I'm reaching to grasp this cup, but if

01:05:32.820 I reached and my fingers passed through it, right, if it was a hologram of a cup and not

01:05:38.340 a real one, or if it felt, you know, squishy, if it was made of, you know, rubber and I wasn't

01:05:44.740 expecting that, all of those occasions of surprise are built on some set of expectations that I

01:05:53.840 wasn't aware of having until I became disillusioned.

01:05:57.320 So I was not aware of expecting solidity, though of course I was.

01:06:02.540 I mean, everything about the grasping behavior of my hand was anticipatory in a certain way.

01:06:09.160 And you can make those, that predictive program consciously felt, certainly in the moments in

01:06:16.540 which it's violated, but it's just simply neurologically the case, that we are comparing,

01:06:22.720 in order, the only way to detect anomalies in the environment is to have this background

01:06:28.500 modeling going on of what's likely to happen in each moment based on what I'm doing now

01:06:35.680 and what I'm doing next.

01:06:36.960 And this question of what to do next really does cover so much of what we're about as minds.

01:06:44.460 We're constantly deciding what to do next on some level.

01:06:49.140 Oh, absolutely.

01:06:50.000 And there's so much to say about, there's so much to unpack that's interesting about what

01:06:55.780 you just said.

01:06:57.200 I mean, first of all, I would say, it seems to me that, you know, because for whatever

01:07:03.080 reason we've talked about why.

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Making Sense - Sam Harris - April 22, 2021

#247 — Constructing Minds

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