Speaker 1: Why do you think of your thirty trillion cells as
Speaker 1: a self? Does an ant colony have a sense of self?
Speaker 1: And could you think of all those ants as a
Speaker 1: liquid brain? What does any of this have to do
Speaker 1: with how the brain of a caterpillar transitions to the
Speaker 1: brain of a butterfly, or how we might think of
Speaker 1: a memory as a pattern that stays alive and has
Speaker 1: its own life. Welcome to Inner Cosmos with me David Eagleman.
Speaker 1: I'm a neuroscientist and an author at Stanford and in
Speaker 1: these episodes we dive deeply into our three pound universe
Speaker 1: to uncover some of the most surprising aspects of our lives.
Speaker 1: In the last piso, I talked about who we are
Speaker 1: and how we change through time. The pieces and parts
Speaker 1: of every single cell in your body degrade and get
Speaker 1: replaced continuously, such that you are physically speaking, a totally
Speaker 1: new person every few years, and yet we experience the
Speaker 1: illusion that we are the same person we've always been.
Speaker 1: We have this illusion of constancy. So last week we
Speaker 1: explore this by considering the thought experiment of the Ship
Speaker 1: of Theseus.
Speaker 2: The story here is that.
Speaker 1: Each plank of a famous ship is replaced one by
Speaker 1: one over time, which raises the question, is it's still
Speaker 1: the same ship even after every plank has been replaced
Speaker 1: and nothing of the original remains. We consider this question,
Speaker 1: of course, because like the ship, we too exist with
Speaker 1: physical changes that never stop, and yet we perceive ourselves
Speaker 1: as constant through time. The planks of theseus's ship map
Speaker 1: onto the cells and molecules in our bodies. So what
Speaker 1: maintains your sense of self over time? Okay, so maybe
Speaker 1: the thing that links our different selves through time is
Speaker 1: our memory. But there's a problem here as well, which
Speaker 1: is that memory is notoriously unreliable. It's constantly being reshaped
Speaker 1: and revised. So this is all very strange. And there's
Speaker 1: an added difficulty, which is that we're constantly changing, and
Speaker 1: even when we recognize that we have changed, we always
Speaker 1: assume that we're not going to change very much into
Speaker 1: the future, and that's always incorrect. This is a cognitive
Speaker 1: bias called the end of history illusion. We tend to
Speaker 1: believe that our current preferences and personalities are fixed, even
Speaker 1: though we will in fact continue to evolve. So that's
Speaker 1: our situation. We constantly change, but we're reaching back into
Speaker 1: our not so good memory to try to understand who
Speaker 1: we are, or.
Speaker 2: At least who we were.
Speaker 1: All this encourages us to start looking for new frameworks
Speaker 1: when we think about the self. For example, what if
Speaker 1: we thought of the memories themselves like their own little
Speaker 1: creatures and they are competing to stay alive. So in
Speaker 1: this episode, I want to dive into new ways of
Speaker 1: thinking about all of this, and there is no one
Speaker 1: better to ring up for that than my colleague Michael Levins.
Speaker 1: He's one of the most energetic and original thinkers in
Speaker 1: the field. Michael is a developmental biologist and a synthetic biologist,
Speaker 1: meaning he makes new kinds of organisms. He's a Toughts
Speaker 1: University and what I love about Michael is his extremely
Speaker 1: broad interests like bioelectrical signals by which cells communicate, and
Speaker 1: what cognition looks like across totally different body plans, and
Speaker 1: how you get similar forms and functions across all kinds
Speaker 1: of different scales and biology. So I'm going to have
Speaker 1: Michael back to talk about some of these other topics
Speaker 1: on a future episode, but today I want to zoom
Speaker 1: in with him on his recent thinking about the self
Speaker 1: and memory. So Mike, tell us how you think about
Speaker 1: the self.
Speaker 2: I work in diverse intelligence. I'm interested in all different
Speaker 2: kinds of implementations of minds, and I think a self
Speaker 2: is a useful way to think about selves is as
Speaker 2: a system that has goals of some particular size. It's
Speaker 2: what the dark hop Statu would call a strange loop.
Speaker 2: It's an observer of the outside world, but it's also
Speaker 2: an observer of itself. It's something that can loop back
Speaker 2: in and interpret what its own memories mean and what
Speaker 2: it is doing, and make decision and going forward. Those
Speaker 2: are the components of being a self. And so do
Speaker 2: selves change? Yeah? Part of the paradox of being a
Speaker 2: self is that you have to change in order to
Speaker 2: stay alive. You have to change in order to persist.
Speaker 2: Every time you learn something, every time you make a
Speaker 2: decision that then feeds back and alters the inputs that
Speaker 2: you receive and thus changes your own cognitive system and
Speaker 2: your own behavior going forward. You've now changed. I you know,
Speaker 2: we selves face this this paradox that in order to
Speaker 2: to to to persist, they must change and transform over time.
Speaker 1: Why do you suppose we have the illusion of ourselves
Speaker 1: when we think about you know yourself and your life,
Speaker 1: you have the illusion that it's unchanging.
Speaker 2: What do you suppose that's about. Yeah, I think that,
Speaker 2: Uh well, let's let's just think about very primitive, basic, fundamental,
Speaker 2: basic agents at the very beginning of of of of
Speaker 2: that of that spectrum. If if you're any kind of
Speaker 2: a system that's going to survive in the real world,
Speaker 2: what you can't afford to do is to be a
Speaker 2: kind of placium demon where where you're tracking the micro
Speaker 2: states of you know, every single stimulus, every molecular impact
Speaker 2: on your membrane or whatever you can. You can't afford
Speaker 2: to track all that. If you try to do that,
Speaker 2: you will run out of time, You'll run out of energy,
Speaker 2: You'll be eaten and dead in no time. And so
Speaker 2: I think that what we have in at least at
Speaker 2: least in biological evolution, is an incredible pressure to coarse grain,
Speaker 2: that is, to look out into the world and to
Speaker 2: group inputs and sensations and stimuli and observations that you make,
Speaker 2: to group them into categories, and to try to understand
Speaker 2: your world by reducing its complexity, and by trying to
Speaker 2: make models of the world that have these large things
Speaker 2: in it that are themselves agents. You know that do things,
Speaker 2: and that you can then make decisions about what they
Speaker 2: do and you don't have to pay attention to all
Speaker 2: the micro states. If you're going to do that, you
Speaker 2: fundamentally need a way to make models of persistent things.
Speaker 2: You have to have some way of saying that this
Speaker 2: is an object I need to stay away from. More conversely,
Speaker 2: this something I need to approach, or this is something
Speaker 2: I need to recognize meanwhile, of course, so let's just
Speaker 2: look at vision, you know. For example, So you have
Speaker 2: these these pixels and impacting onto your retina, these these
Speaker 2: visual stimuli, and depending on how you're looking at something,
Speaker 2: the details will be completely different, the lighting will be different,
Speaker 2: the motion, the way that it's angled. But your job
Speaker 2: as a successful cognitive system is to recognize as well
Speaker 2: as possible that this is always the same thing, right
Speaker 2: that you can, despite the fact that it's now just shifted,
Speaker 2: tinted green or whatever it is, that that hey, wait
Speaker 2: a minute, I recognize it. I know what this is.
Speaker 2: And so I think from the very beginning to be
Speaker 2: a successful being that can survive in a world war,
Speaker 2: time and energy are extremely precious. These are precious resources.
Speaker 2: You have to get good at noticing and in fact
Speaker 2: magnifying invariance. You know, things that stay the same. And
Speaker 2: then I think you turn that on yourself and you say,
Speaker 2: wait a minute, I am also an agent that does things.
Speaker 2: And I don't mean consciously, and we don't do this consciously.
Speaker 2: But every system, every living system, I think, has an
Speaker 2: internal self model, and part of that model is to
Speaker 2: recognize you as a persistent thing even though things change.
Speaker 2: And then and then you know, those those of us
Speaker 2: that have self consciousness then have a have a story
Speaker 2: that we tell ourselves about being a persistent thing as
Speaker 2: opposed to I think what we much more close to
Speaker 2: what we really are, which is a flowing process.
Speaker 1: So so we all walk through life and we have
Speaker 1: our notion of self, and we observe the selves in
Speaker 1: others that we love, and we try to figure out
Speaker 1: what this is all about. You have a very cool approach,
Speaker 1: which is that you think about diverse intelligences. Tell us
Speaker 1: what that term means.
Speaker 2: Yeah, diverse intelligence is the name of a of a
Speaker 2: of a feel that on the one hand is the
Speaker 2: emerging now and it's a very exciting kind of feel.
Speaker 2: On the other hand, this is a collection of ideas
Speaker 2: that have really been around for a very long time.
Speaker 2: And it goes back to the efforts of trying to
Speaker 2: understand what what do we actually mean by intelligence, by minds,
Speaker 2: by cognition, by all all these kinds of all these
Speaker 2: kinds of terms. And we have familiar examples of them
Speaker 2: in brainy animals and and and you know, in ourselves.
Speaker 2: But actually the key thing that that we need to
Speaker 2: understand is that all of us, at one point, both
Speaker 2: on an evolutionary scale and on a developmental scale, we
Speaker 2: were all a single sell once. You know, at one
Speaker 2: point we were a little blob of quies and cytoplasm
Speaker 2: and unfertilized noocyte. And we would look at that and
Speaker 2: we would say, well, there's there's a little a little
Speaker 2: blob of chemicals that obeys physics and chemistry. And then
Speaker 2: at some point we have something that we would say
Speaker 2: has an inner perspective, It has a mind, it has preferences,
Speaker 2: it has goals, it has memories, and and you know
Speaker 2: it and it has a full blown self conscious kind
Speaker 2: of meta cognition and so on. Now, how did we
Speaker 2: get there? Right? Because we started out you know, this
Speaker 2: this amazing journey from physics to mind. You know, you
Speaker 2: start off with with a little blob of chemistry, and
Speaker 2: then eventually here we are, so diverse. Intelligence is is
Speaker 2: I think, is predicated on the on the idea that
Speaker 2: we need to understand how it is that minds are
Speaker 2: embodied in the physical world, and that by understanding that
Speaker 2: we emerge slowly and gradually, this is we are not.
Speaker 2: You know, there's no there's no magical category called human
Speaker 2: that suddenly snaps into existence at some particular moment of
Speaker 2: embryonic development. But this is a slow, gradual process. It's
Speaker 2: it's an effort to understand what those processes are that
Speaker 2: give rise to collective intelligence. And by the way, all intelligence,
Speaker 2: I think is collective intelligence because all of us are
Speaker 2: made of parts, and we have to understand how the
Speaker 2: competencies of those parts give rise to whatever the larger
Speaker 2: scale system is capable of doing. And as soon as
Speaker 2: as soon as you frame the problem that way, to
Speaker 2: understand what is it that the components are doing to
Speaker 2: scale that cognition from the competencies of a single cell
Speaker 2: to that of an animal or a human, then then
Speaker 2: immediately you start to ask yourself, okay, so not only
Speaker 2: is there a history going backwards where we see increasingly
Speaker 2: simpler forms and ask what is their intelligence like? But
Speaker 2: then we can really try to shed some of the
Speaker 2: limitations that we're given to us by our evolutionary firmware
Speaker 2: that make it easy for us to recognize, you know,
Speaker 2: large intelligence, large large, medium sized objects moving at medium
Speaker 2: speeds through three dimensional space, and we so, okay, that's
Speaker 2: an intelligent animal doing whatever it's doing. But to learn
Speaker 2: to recognize intelligence and unfamiliar guises, you know, what other
Speaker 2: spaces can intelligence be operating and what what else can
Speaker 2: it be made of? What other processes give rise to
Speaker 2: the scale up of intelligence. That's diverse intelligence. It's the
Speaker 2: broad attempt to understand intelligence as it can be. What's
Speaker 2: an example of that?
Speaker 1: For example, ants in a colony understanding that as an
Speaker 1: intelligence system where they're laying down memories in particular ways.
Speaker 1: It's very different than the way we think about the
Speaker 1: human brain. Is that an example?
Speaker 2: Yes, I'll give you a few examples and get the
Speaker 2: sort of progressively progressively weirder with them. Traditional collective intelligence
Speaker 2: is like ants and beehives and termite colonies and blocks
Speaker 2: of birds and things like that. Those are typical collective intelligence.
Speaker 2: They are what our Carsole calls liquid brains because they're
Speaker 2: moving around relative to each other. Our neurons tend to
Speaker 2: stand still relative to each other. And yet we too
Speaker 2: are a collective intelligence, right, We are a pile of
Speaker 2: neurons and other cells that work together to give rise
Speaker 2: to an emergent being with memories and preferences and goals
Speaker 2: that don't belong to any of the individual cells. So,
Speaker 2: no matter what you are, whether you're a collection of
Speaker 2: neurons or your collection of birds or of termites or
Speaker 2: anything like that, what you need is a kind of
Speaker 2: cognitive glue. You need these policies that are implemented by
Speaker 2: the pieces that allow the collective to be more than
Speaker 2: the sum of its parts in relevant ways. And so
Speaker 2: if we define intelligence as problem solving competencies in some space,
Speaker 2: so the ability to and so this is kind of
Speaker 2: William James's definition of intelligence, same goal by different meats, right,
Speaker 2: the ability to navigate some space to reach your objective
Speaker 2: and do so despite novel despite perturbations, and and so on.
Speaker 2: Then we can get we can get progressively more inclusive
Speaker 2: with this. We can say, okay, so we have we
Speaker 2: have animals that solve problems. We have unfamiliar things like
Speaker 2: slime molds, right, so so so there are people including us,
Speaker 2: who study slime mold behavior, in slime mold learning and
Speaker 2: things like that. So slime molds are a very unusual organism.
Speaker 2: It's a single cell. It can be quite large, and
Speaker 2: yet it can it can solve all kinds of problems.
Speaker 2: People have studied memory and learning in in bacteria, in
Speaker 2: unicellular organisms and plants, and and then you can get
Speaker 2: then you can get you know, even even more sort
Speaker 2: of broad with this, and you can ask, well, what
Speaker 2: happens beyond three dimensional space? For example, the cells that
Speaker 2: make up our bodies and our and our tissues and
Speaker 2: our organs, they operate in physiological state space, so the
Speaker 2: space of all possible physiological conditions. They operate in gene
Speaker 2: expression space. Embryonic and regenerative cells operate in anatomical space.
Speaker 2: They have to navigate from whatever shape they have now
Speaker 2: to some kind of complex organ structure. And in all
Speaker 2: of these spaces you can see if you look for
Speaker 2: them using the techniques of behavioral science, you can find
Speaker 2: them solving problems. You can find them getting to their
Speaker 2: goal in very creative ways despite various problems So that's
Speaker 2: kind of the range of the diverse intelligences. And then
Speaker 2: beyond that you have embodied robotics, and you have maybe
Speaker 2: software intelligent agents, and maybe someday exobiological beings and so on.
Speaker 1: Okay, so the challenge is that everything's changing all the
Speaker 1: time in our brains and all these other biological systems
Speaker 1: and colonies whatever, things are changing all the time. Cells
Speaker 1: are dying or aging or getting mutations. And yet somehow,
Speaker 1: when it comes to memory of oneself, we tend to
Speaker 1: have some sort of memory. It's not perfect, it only
Speaker 1: has a few details, things like that, but we retain
Speaker 1: this memory through time. Now, what is your framework for
Speaker 1: thinking about that?
Speaker 2: Yeah, I'm not sure we retain memory through time as
Speaker 2: much as we reconstruct memory through time. And of course
Speaker 2: there's a lot of human neuroscience being done about this.
Speaker 2: But I want to take a step back and give
Speaker 2: you another example that kind of drives my thinking on this.
Speaker 2: Consider what happens from a butterfly to a caterpillar. So
Speaker 2: in the caterpillar, you have the soft bodied creature that
Speaker 2: lives in basically a two dimensional world where it crawls
Speaker 2: around and eats leaves. Now, one thing that it has
Speaker 2: to do is that has to turn into a butterfly.
Speaker 2: During that process of metamorphosis, its brain is massively rebuilt.
Speaker 2: Many of the cells die, all the connections are broken,
Speaker 2: it's completely refactored, and you get a new brain suitable
Speaker 2: for driving a now hard bodied kind of vehicle, completely
Speaker 2: different controller you need for that. And now it flies
Speaker 2: and it lives in the three dimensional world and so on.
Speaker 2: Now it turns out that if you train the caterpillar
Speaker 2: the let's say you train it to associate a specific
Speaker 2: color to a specific leaf that it wants to eat,
Speaker 2: what you will find is that the butterfly, despite having
Speaker 2: a totally refactored brain, will remember that information. And for
Speaker 2: years I thought the amazing thing here was how do
Speaker 2: you hold on to information when your information medium is
Speaker 2: being totally ripped apart and refactored. Right, we don't, you know,
Speaker 2: our computer technology doesn't doesn't like that. We don't have
Speaker 2: anything that has that that robustness to it. But if
Speaker 2: you think about it more, what turns out is that
Speaker 2: the amazing thing is not holding onto the memory because
Speaker 2: actually the memories, the exact memories of the caterpillar, are
Speaker 2: of no use to the butterfly whatsoever, because it doesn't
Speaker 2: move the same way. It doesn't want the leaves a
Speaker 2: drinks nectar, right, And so just holding onto that memory
Speaker 2: is of no use. What you need is to actually
Speaker 2: transform and remap that memory into a completely novel context
Speaker 2: where it's like not leaves but food for example. Right,
Speaker 2: So you have to generalize a little bit, and you
Speaker 2: have to now remap it on a different on a
Speaker 2: different controller. And you know, numerous people who have done
Speaker 2: experiments in memory transfer, most recently David Landsman and others
Speaker 2: show that that it's it's really wild how you can
Speaker 2: move either tissues or extra extracts let's say, RNA extracts
Speaker 2: or whatever, and introduce them into a new into a
Speaker 2: new creature and and have that creature like take on
Speaker 2: that that initial learning.
Speaker 1: So let me interrupt for a second. So tell us
Speaker 1: about David Landsman's experiments in these sea slugs. Yeah, Well,
Speaker 1: specifically in David's work, what he was doing is training
Speaker 1: these c slugs to a particular to a particular task.
Speaker 1: I mean it was a simple, you know, simple reflex
Speaker 1: and extracting RNA from from the brain. Because the hypothesis
Speaker 1: was that memory is stored in some way in this
Speaker 1: in this medium of RNA, and then he was injecting
Speaker 1: it into a naive animal and showing that they now
Speaker 1: have you know, they show a recall of that information
Speaker 1: I mean to me, I mean, it's it's a fabulous
Speaker 1: body of work.
Speaker 2: To me. One of the most interesting things about it
Speaker 2: is that when you've got this RNA, you don't have
Speaker 2: to go put it exactly in the right neuron where
Speaker 2: it was supposed to be, you know, and that you
Speaker 2: just kind of inject it somewhere in the brain of
Speaker 2: a second of a second sea slug exactly of the
Speaker 2: recipient right of the host seasug, and it somehow gets
Speaker 2: taken out. And that's that's a that's a theme that
Speaker 2: that is like central to all this because what I
Speaker 2: think this is, this is telling us is so now,
Speaker 2: so now let's let's walk into this from from the
Speaker 2: other end. Whatever you are, human or anything else, you
Speaker 2: don't have access to the past. What you have access
Speaker 2: to is the end grams, the memory traces that the
Speaker 2: past has left in your brain or body or wherever
Speaker 2: that were formed by past experience. And so what you
Speaker 2: now have to do is to at any given moment
Speaker 2: and it's you know, these moments. I don't know how
Speaker 2: many milliseconds they are, but but some some number of milliseconds.
Speaker 2: You have to reconstruct that memory to be meaningful to
Speaker 2: you now because you don't know what it used to mean.
Speaker 2: This is you have to do this on the on
Speaker 2: the fly. And I think there's a lot of good
Speaker 2: neuroscience showing how plastic these memories are and how there
Speaker 2: how you know, even even recall of the memories means
Speaker 2: they're getting changed. There's no non destructive read. Accessing these
Speaker 2: memories changes them and and and so so the way
Speaker 2: I think about this is as an architecture in the
Speaker 2: shape of a kind of bow tie. And this is
Speaker 2: for the computer science listeners. You might imagine like a
Speaker 2: like a an auto encoder architecture where there's a funnel
Speaker 2: on the left side which receives the primary experiences, the
Speaker 2: raw data that come in the sense impressions, and that
Speaker 2: the process of learning has to and and and this
Speaker 2: is fundamental to intelligence, is abstracting from individual instances of
Speaker 2: things you experience to a rule, to some kind of
Speaker 2: some kind of pattern, and it's the pattern that you
Speaker 2: remember it, so you compress all those experiences, you throw
Speaker 2: away all the irrelevant details and you form a memory.
Speaker 2: You store that in some sort of enngram. Uh. You know.
Speaker 2: Sometimes people think of this as synaptic modifications. Sometimes other
Speaker 2: people think it's in the RNA or in a cyber
Speaker 2: skeleton wherever. So so you store this but now but
Speaker 2: now here comes here comes the really interesting part. When
Speaker 2: you need to recall this. You have to know, here
Speaker 2: comes the right side of that, on the right side
Speaker 2: of that bow tie. You have to sort of reinflate
Speaker 2: that compressed memory. And because you've lost all kinds of information,
Speaker 2: this process, this, this recall process is creative because you
Speaker 2: don't have all the details that were there, and nor
Speaker 2: can you really be sure at a later time what
Speaker 2: the meaning was to your past self. In other words,
Speaker 2: I also think of memories as literally messages from your
Speaker 2: past self. So I tend to think of memory and
Speaker 2: recall as communication events. And it's just you know, it's
Speaker 2: a communication with a past version of you, but it's
Speaker 2: the same. They sent you a message. It was encrypted
Speaker 2: and compressed in these n grams, and then you try
Speaker 2: to reinflate it. And your goal at any given time
Speaker 2: is not to have any kind of allegiance to what
Speaker 2: the memory meant before. Your goal now is to reinterpret
Speaker 2: it the way the butterfly does in whatever is the
Speaker 2: most optimal adaptive way that makes sense. Now, so this really,
Speaker 2: you know, the first part is kind of algorithmic, which
Speaker 2: is the compression. But now here comes a creative process.
Speaker 2: It's not really deductive. It's a creative process where you
Speaker 2: take that prompt. It's more of a prompt than anything else,
Speaker 2: and you say, okay, what does this mean to me? Now?
Speaker 2: How can I incorporate this into my current constantly evolving
Speaker 2: model of the self, of the outside world and on.
Speaker 2: So it's very much And this goes on, goes on
Speaker 2: all the time. And this is consistent with the plasticity
Speaker 2: of memories. It's consistent with confabulation, which has been seen
Speaker 2: in all kinds of experiments with human subjects, you know,
Speaker 2: split brain and so on.
Speaker 1: Wait, let's take a second to talk about confabulation. So
Speaker 1: this is where brains seem to make something up. You know,
Speaker 1: there are these experiments, for example, the cutaneous rabit illusion.
Speaker 1: If I tap you twice somewhere on your forearm, and
Speaker 1: then I tap you a third time. Let's say, in
Speaker 1: a different location, you will feel like you felt three taps,
Speaker 1: that we're all that we're moving in the direction. Even
Speaker 1: though the second tap was in the same spot, you
Speaker 1: feel like you felt it on the way to the
Speaker 1: third one. This is one example of lots of confabulation
Speaker 1: where the brain is retrospectively making things up. Now, the
Speaker 1: interesting part is we look at confabulation generally as something bad.
Speaker 1: For example, with large language models, we talk about hallucinations.
Speaker 1: But there is another way to look at this, which
Speaker 1: is as hypothesis, as generating new creative ideas. So tell
Speaker 1: us how that fits into the way you think about confabulation.
Speaker 2: Yeah, I mean so, here's another another example of confabulation
Speaker 2: that is similar to what I'm talking about. Two examples.
Speaker 2: One is there was a patient that had an electrode
Speaker 2: that was placed in their brain for I think aplepsy
Speaker 2: was the idea, and it happened to land in a
Speaker 2: region of the brain that when you stimulate that electrode,
Speaker 2: it makes them laugh, makes the mouth laugh. So the
Speaker 2: person will be sitting there thinking about something serious. You
Speaker 2: can see and there's a video of this I saw somewhere.
Speaker 2: The scientist pushes the button. They start laughing, and then
Speaker 2: you ask them why are you laughing, And the answer
Speaker 2: is never, Gee, I don't know. I was sitting here
Speaker 2: thinking about something serious and then my mouth started laughing.
Speaker 2: That's never the answer you get. The answer you get is, well,
Speaker 2: I thought of something funny. I thought of a joke,
Speaker 2: And you get the same thing out of split brain patients.
Speaker 2: When the one hemisphere causes the other side of the
Speaker 2: body to do something that the language hemisphere doesn't understand,
Speaker 2: what they usually do is make up a story about
Speaker 2: it on the spot, and they don't know, you know, consciously,
Speaker 2: they won't report that they're making up a story. So
Speaker 2: I think what we mean by confabulation is really a
Speaker 2: fundamental skill and necessity of sense making of your world.
Speaker 2: You have a model of the outside world. You have
Speaker 2: a model of yourself and what you are and how
Speaker 2: you behave and sometimes that model gets updated, but sometimes
Speaker 2: you just incorporate other world events that the go on.
Speaker 2: You incorporate them into that model and you interpret them
Speaker 2: in a way that makes sense to you. Now, you know,
Speaker 2: and something similar to what you said about the tapping,
Speaker 2: you've seen the rubber hand illusion.
Speaker 1: Yeah, in the rubber hand illusion, your hand Let's say
Speaker 1: your left hand is covered up. You're not seeing it,
Speaker 1: but you're seeing a rubber hand, and you see somebody
Speaker 1: stroking that rubber hand, and every time that rubber hand
Speaker 1: gets stroked, you feel a stroke on your hand too.
Speaker 1: The person is stroking them both at the same time,
Speaker 1: even though you can't see your own hand, and then
Speaker 1: they hit that hand with a hammer and you withdraw
Speaker 1: in terror because it feels like it's become your hand.
Speaker 2: Yeah. So, to me, the amazing thing about that that
Speaker 2: is the plasticity. Look, we've been tetrapods for I think
Speaker 2: almost four hundred million years something like that, and so
Speaker 2: for millions of years we had a brain that knows
Speaker 2: exactly how many limbs you have, and within what seven
Speaker 2: minutes of new experience, you now have decided that you
Speaker 2: have this extra hand. It's the plasticity is crazy.
Speaker 1: By the way, One thing that has been extraordinary on
Speaker 1: this is experiments in VR, where, for example, you give
Speaker 1: somebody a third arm that comes out of their chest,
Speaker 1: and you control your natural two arms with two controllers,
Speaker 1: and you can see your arms and you also see
Speaker 1: this third arm which you control by changing your wrist orientations,
Speaker 1: and that can control the third arm, and within a
Speaker 1: few minutes people can get very good at controlling this
Speaker 1: third arm. And you know they're doing a game where
Speaker 1: you pick up boxes of certain colors, and yes, so
Speaker 1: you can add limbs and subtract limbs readily. The homunculus,
Speaker 1: the little model of your body, is totally flexible in
Speaker 1: that way.
Speaker 2: That right there, the ability to adapt to novel situations
Speaker 2: in this way that can fabulation to tell a story
Speaker 2: that makes sense, not that it's necessarily true relative to
Speaker 2: what your past was giving you, but to what makes
Speaker 2: sense for you now is fundamental to buyology. And I
Speaker 2: think biology doesn't preserve the fidelity of memories. It preserves
Speaker 2: the salience of memories. It tries to remap them in
Speaker 2: the way that it makes sense to you now, not
Speaker 2: necessarily to what it meant. And I think that this
Speaker 2: is fundamentally an intelligence ratchet for life. And here's what
Speaker 2: I mean by that. Let's look instead of the memories
Speaker 2: of a single of a single human or animal, Let's
Speaker 2: look on an evolutionary timescale. You come into the world
Speaker 2: as an embry Oh, You've been given all of this
Speaker 2: genetic and cytoplasmic and other kinds of information that are
Speaker 2: the accumulated really the memories of your of your lineage agent, right,
Speaker 2: the lineage that's been through that's been through the evolution
Speaker 2: and has accumulated all this useful information. There are some
Speaker 2: animals and at least as far as we know, maybe
Speaker 2: nematodes like C. Elegance, that are extremely hardwired. We know
Speaker 2: exactly how many cells are going to have, all the
Speaker 2: cells of the same position that's determined by lineage. They're
Speaker 2: very hardwired. But the majority of living forms aren't like that.
Speaker 2: They take that information and they reinterpret it in novel ways.
Speaker 2: I think that now now in normal development, we always
Speaker 2: see the same thing happening, so we kind of assume
Speaker 2: that it's some kind of hardwired mechanical process, but it's
Speaker 2: not that at all. For this reason, we can take
Speaker 2: we can make a tadpole which has no eyes in
Speaker 2: the head, but it has an eye on its tail,
Speaker 2: and that those those animals can see right out of
Speaker 2: the box. They don't need new rounds of selection or
Speaker 2: evolution or adaptation or any of that. They can see immediately.
Speaker 2: We can take cells from an early frog embryo and
Speaker 2: they become zenobots, and they do interesting things. There have
Speaker 2: never been any zenobots. There's never been any selection to
Speaker 2: be a good xenobot, right, so that plasticity can you
Speaker 2: can you explain to the audience with zenebot is sure? Yeah,
Speaker 2: So so we make we make in our group, we
Speaker 2: make zenobots and anthrobots. These are these are living systems
Speaker 2: that we call them biobots because we use them for,
Speaker 2: among other things, bio robotics kinds of applications. They are
Speaker 2: living uh organisms made of cells in the In the
Speaker 2: case of xenobots, they come from frog cells, and the
Speaker 2: case of anthrobots, they come from adult human tracheal epithelial cells.
Speaker 2: They are self motile. They will move around a dish.
Speaker 2: They sort of swim around the dish on their own.
Speaker 2: And they have lots of interesting capabilities. For example, the anthrobots,
Speaker 2: if they find a neural wound, they will heal They
Speaker 2: will heal the peripheral innervation by taking the two sides
Speaker 2: of the neural wound and kind of connecting them together.
Speaker 2: You know, who would have known that your tracheal cells
Speaker 2: that sit there for you know, quietly in your body
Speaker 2: for decades, have the ability to form a self motile
Speaker 2: little creature that runs around and do these things, and
Speaker 2: does these things so the plasticity and people have been
Speaker 2: noticing this, this kind of thing for a very long time.
Speaker 2: Developmental biologists is that living systems will play they hand
Speaker 2: their depth. They don't just automatically, at least most of
Speaker 2: them don't just automatically do the same thing. They will
Speaker 2: try to as much as we try to telecoherence story
Speaker 2: in confabulation and linguistic space, they will confabulate in transcriptional
Speaker 2: space meaning gene expression space, in physiological space, and in
Speaker 2: anatomical space to put together some kind of a coherent lifestyle.
Speaker 2: Given novel, novel circumstances, the environment can be novel. You
Speaker 2: can interface living tissue with all kinds of weird materials.
Speaker 2: They will always try to make something, And I think
Speaker 2: that's because they never assume that you can take the
Speaker 2: past literally. They have to on the fly put something together.
Speaker 2: One of my favorite examples is what happens in the
Speaker 2: new to kidney tubules. You have a newt If you
Speaker 2: take a section through the kidney tubule, you see eight
Speaker 2: or ten cells are making like a circle, and they
Speaker 2: make this. They make this two well, one of the
Speaker 2: things that the people have found is that you can
Speaker 2: treat them in a way that makes multiple copy number
Speaker 2: of their chromosomes so they have the genetic material. Instead
Speaker 2: of two n they will have four and five and
Speaker 2: six end and so on. If you do that, the
Speaker 2: cells get bigger, but the nude stays the same size.
Speaker 2: So that's kind of amazing. So you take a cross
Speaker 2: section through the two bule and you see, oh, the
Speaker 2: cells are bigger, but there's fewer of them and they
Speaker 2: still form the exact same structure. Well, you can make
Speaker 2: a highly polyploid neud like that that has gigantic cells,
Speaker 2: and in that case, one single cell will bend around itself,
Speaker 2: leaving a hole in the middle, which is a completely
Speaker 2: different molecular mechanism. Right, it's not cell to cell communication.
Speaker 2: That's some kind of cideoscalarle bending. So now think about
Speaker 2: what this means if you're a nude coming into this world.
Speaker 2: You can't count on how much genetic material you're going
Speaker 2: to have. You can't count on and never mind not
Speaker 2: being able to count on the environment. Right, who knows
Speaker 2: what the pha your water is and all that. Forget
Speaker 2: that you can't even count on your own parts. You
Speaker 2: don't know what your chromosome number is going to be,
Speaker 2: you don't know how many cells you're going to have,
Speaker 2: you don't know the size of your cells. You have
Speaker 2: to do something coherent in that case build an actual
Speaker 2: neud when everything changes. And that's why I think that
Speaker 2: that's the fundamental thing about confabulation is that if you
Speaker 2: commit to the idea, which I think biology has to.
Speaker 2: Unlike our computer technology, which relies on a highly reliable hardware,
Speaker 2: right when you code, you don't worry about your you know,
Speaker 2: cpu doing something weird. You just assume it's going to
Speaker 2: do what it needs to do. You don't think, you
Speaker 2: know your copper is going to go off or something.
Speaker 2: In biology, that's not the case. The medium is completely unreliable.
Speaker 2: You have no idea what you know, how many proteins
Speaker 2: have we given the type you have, or if they're
Speaker 2: going to get a little bit teen natured, or you
Speaker 2: know what's going to happen. If you assume that your
Speaker 2: medium is unreliable, then instead of this kind of hardwired
Speaker 2: here's how we do it every single time. Idea, what
Speaker 2: evolution is going to produce are sense making problem solving
Speaker 2: agents in different spaces. It can be very simple things
Speaker 2: bacteria and you know, but already you're off to the
Speaker 2: racist because you can't count on your environment being the same,
Speaker 2: and you can't count on yourself being the same. You're
Speaker 2: going to mutate, right, your parts will mutate, Everything will change.
Speaker 1: So this is one of the first things that I
Speaker 1: was absolutely intrigued with in biology when I was very young,
Speaker 1: which is, how in the world does a mouse's heart
Speaker 1: and an elephant's heart do the same thing? When you
Speaker 1: know these two cases, you've got totally different number of
Speaker 1: cells making this, and yet it makes the same structure
Speaker 1: that does the same thing. So what is the way
Speaker 1: to understand how biology can code for these higher order structures.
Speaker 2: Yeah, I think that there are key elements of understanding
Speaker 2: what's going on that come from behavioral science. This is
Speaker 2: we are not going to get to this purely by
Speaker 2: the concepts of chemistry and physics, although those are crucial
Speaker 2: to understand. What we have here are problem solving collective intelligences.
Speaker 2: So when you have a bunch of molecular networks that
Speaker 2: make a sell that's a coherent organism like a like
Speaker 2: an amebo or a lachrom area or something that that
Speaker 2: do all these interesting things. They are making a next
Speaker 2: They are contributing to a next level collective intelligence that
Speaker 2: does that, that operates in some kind of space and
Speaker 2: has a small cognitive light coone work and do certain
Speaker 2: things that have a little bit of predative power forward,
Speaker 2: a little bit of memory backward. When those cells come
Speaker 2: together and form an organism, once again, you have a
Speaker 2: collective intelligence that now projects into a new space. Whereas
Speaker 2: the cells we're solving problems in physiology and metabolics and
Speaker 2: gene expression, you know, now have a system that solves
Speaker 2: problems in anatomy. So so when you take an early embryo,
Speaker 2: let's say, an early mammalian memory, and you cut it
Speaker 2: in half, you don't get two half embryos. You get
Speaker 2: too perfectly normal monozygotic twins because each side has to
Speaker 2: figure out, oh way this is missing, why I have
Speaker 2: to rebuild and so on. And so my point is
Speaker 2: not that we attribute to uh, you know, high order
Speaker 2: human level self consciousness to these things. I'm not saying
Speaker 2: they have the metacognition to know what they're doing. What
Speaker 2: I'm saying is we have a simplified version of intelligence,
Speaker 2: which we know there had to be because we came
Speaker 2: that that is our origin. We know there has to
Speaker 2: be a version of intelligence that is, you know, sort
Speaker 2: of on the on the left end of that spectrum
Speaker 2: going all the way back to primitive cells and before
Speaker 2: that actually, And that's that's how we need to think
Speaker 2: about this as as as problem solving, continuous dynamical problem solving.
Speaker 1: Great, So let's take where we are now and return
Speaker 1: to the issue of memory. So how does memory work
Speaker 1: in a brain?
Speaker 2: So I'll just address to you know, a couple of
Speaker 2: things that I can speak to. What one is that
Speaker 2: I think the conventional story that memories are some sort
Speaker 2: of fine tuning of synaptic connections. I think that story
Speaker 2: is very incomplete, and there there are many people, you know,
Speaker 2: like Landsmen and sam Gershman and many others that are
Speaker 2: that are working on that. I I tend to think
Speaker 2: if I had to guess, I would say that there
Speaker 2: probably isn't one substrate of memory. I would look at
Speaker 2: memory as an interpretation process, which I think neurons are
Speaker 2: very good at this of interpreting a reservoir. That reservoir
Speaker 2: is everything else the cell is doing. The side of
Speaker 2: skeletal states, the molecular networks. I mean, some people pick
Speaker 2: up have picked up transcriptional uh signatures of certain memories
Speaker 2: that mice have had, and so on. Every everything in
Speaker 2: the cell, all the complexity that is going on, can
Speaker 2: be used as a reservoir in a sense of reservoir computing,
Speaker 2: to be used as prompts to reinterpret these those prompts
Speaker 2: as memories that are useful and with so again maximizing salience,
Speaker 2: not as early fidelity, but salience useful in their novel context.
Speaker 2: So I think I think memory is a lot about creativity.
Speaker 2: I think it's a lot about uh, having prompts that
Speaker 2: that that push you into new new kinds of problem solving,
Speaker 2: you know. And and if if your if your body
Speaker 2: and your environments stay extremely constant, then it just looks
Speaker 2: like the old version of memory, where you store a
Speaker 2: piece of data, you read it out and that's it, right,
Speaker 2: That's how it looks like. That's what it looks like
Speaker 2: from the outside. But I don't think that's what's going on,
Speaker 2: you know.
Speaker 1: In my in my book Live Wire, I make the
Speaker 1: argument that even though in Silicon Valley we think about
Speaker 1: everything as being a trim and efficient layer of hardware,
Speaker 1: and then you build uh trim and efficient software on
Speaker 1: top of that. That's not at all how the brain's working. Instead,
Speaker 1: you've got this constant reconfiguration. And I know that you
Speaker 1: also reject that dichotomy between a computation layer and a
Speaker 1: passive code layer.
Speaker 2: So how do you think about that? Yeah, well, I
Speaker 2: think I think there's a couple of major differences between
Speaker 2: how how we build hardware now you know, comput computational devices,
Speaker 2: and what biology is doing. The first thing we've just
Speaker 2: talked about, which is the reliability the idea that in
Speaker 2: in in the computational where you have levels of abstraction
Speaker 2: and you try to screen every layer from all the
Speaker 2: vagaries of the level below. So if you're coding in
Speaker 2: you know, c or something, you're not worried about what
Speaker 2: the copper is doing and what the silicon is doing.
Speaker 2: You you you assume that the function calls you have
Speaker 2: are going to do what they need to do and
Speaker 2: you go from there. Biology isn't like that. All all
Speaker 2: the all the layers are somewhat unreliable, and you need
Speaker 2: to be interpreting it at all at all times. Josh
Speaker 2: Bongard and I are working on a framework called polycomputing,
Speaker 2: and the idea and this is this is partially based
Speaker 2: on some amazing work that is student to Saparsa had
Speaker 2: done showing that the same set of physical events can
Speaker 2: be interpreted as different computations by different observers the exact
Speaker 2: same set of physical events. So give us an example.
Speaker 2: An example is I mean in her work, they were
Speaker 2: looking at the vibrations of particles and you look at
Speaker 2: them in one way and you see an end gate,
Speaker 2: and you look at them a different way and you
Speaker 2: see an ore gate. That's one example in biology. What
Speaker 2: it means in biology is that. And by the way,
Speaker 2: he and I wrote this paper called this plenty of
Speaker 2: room right here kind of riffing off of finements, a
Speaker 2: comment that there's plenty of room at the bottom because
Speaker 2: because biology has this thing where every level is already occupied,
Speaker 2: there is no room at the bottom because every level
Speaker 2: is occupied. How do you as if your evolution, how
Speaker 2: do you put in novel functionality when every level already
Speaker 2: has something. And by the way, when you make changes,
Speaker 2: you're going to screw up. If you make changes in
Speaker 2: the given subsystem, you're going to screw up all the
Speaker 2: other systems. That depend on what it's doing. So one
Speaker 2: thing that I think happens in biology is this poly
Speaker 2: computing where you don't necessarily change the system. You add
Speaker 2: other systems that see what's already going on in a
Speaker 2: different way and make use of it as a computation
Speaker 2: but from a different perspective. So, if you're some kind
Speaker 2: of chemical pathway that mitochondria are using as part of
Speaker 2: the metabolic path way, some other system can look at
Speaker 2: that and say, well, I'm gonna use it as a
Speaker 2: as a clock, I'm gonna I'm gonna take I'm gonna
Speaker 2: use it to regulate my timing, or I'm going to
Speaker 2: use it, you know, in some other, some other signaling capacity.
Speaker 2: And so so I think what we have in biology
Speaker 2: is not this linear stack first of all, not a
Speaker 2: linear stack, but a kind of a super a society
Speaker 2: of multiple nested, cooperating and competing agents which all have
Speaker 2: their own perspectives and they all interpret everything that goes
Speaker 2: on around them in whatever way they can. Uh so,
Speaker 2: And and you know, we're used to the fact that
Speaker 2: in a computation, we supposedly know what a given algorithm
Speaker 2: is doing, right, you can and if you don't know
Speaker 2: you can ask the person who wrote it and they'll
Speaker 2: tell you this is what this thing is computing. But
Speaker 2: in biology, I don't think there is any one fixed
Speaker 2: answer to this. It's doing whatever you as an observer
Speaker 2: can usefully think it's doing that. That doesn't mean anything goes.
Speaker 2: If you have a story that doesn't help you get
Speaker 2: around in the world and thrive, then you don't know
Speaker 2: what it's doing. But but but multiple observers can have
Speaker 2: different stories about the about the same thing.
Speaker 1: So give us if you can't give us another specific
Speaker 1: example of that.
Speaker 2: So the cite of skeleton, on the one hand, is
Speaker 2: used by the cell to get around, and so you
Speaker 2: might say, well, this is this is my my movement machinery.
Speaker 2: That's that's that that I'm counting on to maintain certain
Speaker 2: cell shapes and so on. But at the same time,
Speaker 2: there's other data showing that the site of skeleton can
Speaker 2: actually be can be storing memory. It's also serving as
Speaker 2: a scaffold for other molecules to find where they need
Speaker 2: to go. They're moving around with motor proteins and things
Speaker 2: like that, and they're just there's just lots of lots
Speaker 2: of different uses that any given mechanism is performing at
Speaker 2: any one time, and there are multiple different readout systems
Speaker 2: and this is this is why you know, there's the
Speaker 2: same molecule induces eyes in one context, that induces UH.
Speaker 2: You know, it might induce a kidney rudiment in a
Speaker 2: different UH contest. There are transcription factors that have that
Speaker 2: have many different roles depending on the context. In our
Speaker 2: work on bioelectrics, the exact same stimulus induces a tail
Speaker 2: to regenerate on a tackle, but a leg to regenerate
Speaker 2: on a froglet, and they never get confused, so that
Speaker 2: specificity is not in the in the treatment, it's in
Speaker 2: the surrounding cells being able to interpret that exact same
Speaker 2: signal in whatever way makes sense for them. The ability
Speaker 2: of these subsystems to cooperate in UH, in in in
Speaker 2: groups and solve problems together is is really like a
Speaker 2: fundamental thing in which biology is is different than the
Speaker 2: kind of you know, control systems that we have now
Speaker 2: in computer science. You know, Stephen J.
Speaker 1: Gould wrote about exaptation, where you have something that develops
Speaker 1: and then it turns out to have a use in
Speaker 1: another way. But what you're talking about is even more
Speaker 1: sophisticated in that in the sense that it can retain
Speaker 1: its first use and be used for a second thing
Speaker 1: and the third thing all the same time, just by
Speaker 1: reading that data out in different ways.
Speaker 2: Is that right? That's that's exactly right. And you know,
Speaker 2: some of some of the latest the stuff that I've
Speaker 2: been thinking about really tries to turn this whole thing
Speaker 2: on its head. And you know so, so in the
Speaker 2: standard touring computing paradigm, you have a machine and you
Speaker 2: have the data. Right, so you have a you have
Speaker 2: the process. You have this machine that reads the tape
Speaker 2: and it and it records, you know, the byproducts of
Speaker 2: the computation onto the tape and so on. So typically
Speaker 2: we look at this from the perspective of the machine.
Speaker 2: That is, we are the whether the where, the cell
Speaker 2: or the human or whatever. We're forming memories and we're
Speaker 2: writing it down into some memory medium. The memory medium
Speaker 2: is passive. The memories themselves are passive. They're just marks
Speaker 2: on a tape, and then we can read them out
Speaker 2: when we want. Some of the latest work that we've
Speaker 2: been doing, it starts out by thinking about it backwards
Speaker 2: and saying, well, what does this look like from the
Speaker 2: perspective of the data, right, data that are not passive.
Speaker 2: They're not passive patterns within some medium. They're actually active patterns.
Speaker 2: And from the perspective of the tape, the tape runs
Speaker 2: the show that machine is going to do things depending
Speaker 2: on what is written on the tape. So if I'm
Speaker 2: a pattern on this tape, I can make the machine
Speaker 2: do things. From my perspective, I'm in control. And so
Speaker 2: now it sounds it sounds a little crazy to say
Speaker 2: that that these patterns are doing things and that they're
Speaker 2: agential and whatever. But let's keep in mind we are
Speaker 2: patterns too. We are temporary metabolic patterns within an excitable
Speaker 2: medium the way that people study you know, other temporary
Speaker 2: patterns like solitons and whirlpools, and you know, all different
Speaker 2: kinds of all different kinds of systems. And from from
Speaker 2: that perspective you can you can see that different kinds
Speaker 2: of patterns persist in different media, be they cognitive media
Speaker 2: or just computational media. And asking what does the world
Speaker 2: look like from their perspective and how much, how much
Speaker 2: problem solving capacity, how much agency in fact, might those
Speaker 2: patterns have has massive implications not only for new computational architectures,
Speaker 2: but also, for example, for regenerate medicine, where you want
Speaker 2: to understand what are the persistent information structures that cause
Speaker 2: cells to do or not do various things in disease states,
Speaker 2: and you know, pro regenerative states and so on. So
Speaker 2: let's double click on that.
Speaker 1: So what would that mean for a memory to be
Speaker 1: like an agent to be doing something.
Speaker 2: I'll tell I'll tell a story that I read. I'm
Speaker 2: sure at least part of this was motivated years ago
Speaker 2: by a science fiction story that I'm not exactly sure
Speaker 2: what it was. I think it was a it was
Speaker 2: an Arthur Clock story, but I'm not one hundred percent sure.
Speaker 2: So let's just and I'm sure I've also twisted it
Speaker 2: in a different way. But let's just let's just let's
Speaker 2: just visual it's because your memory is creative to totally.
Speaker 2: There may have been no story. I have no idea, so,
Speaker 2: you know, I just want to give credit in case
Speaker 2: there was. So so let's just let's just visualize this.
Speaker 2: So from the center of the earth come these creatures,
Speaker 2: these core creatures, right, they live at the center of
Speaker 2: the earth. They come out onto the surface. They are
Speaker 2: incredibly dense because they live at the core, they have
Speaker 2: vision that operates, let's say, in them in gamma rays.
Speaker 2: And so they come up to the surface. What do
Speaker 2: they see, Well, pretty much nothing, because everything that we
Speaker 2: see here is like a fine ethereal plasma. To them,
Speaker 2: they are so dense. All of the stuff that we
Speaker 2: think of as real objects are basically not even within
Speaker 2: their within their ability to perceive directly. So they're walking around,
Speaker 2: stomping through everything. And you know the same way that
Speaker 2: when we walk past, you know, some kind of flower bed,
Speaker 2: there's all kinds of like fine you know, patterns of
Speaker 2: ascents and so on, we just sort of walk right
Speaker 2: through it, mix that all up. So they're stepping all
Speaker 2: over everything, and well, one of them, one of them,
Speaker 2: is a scientist, and he's taking some careful readings of
Speaker 2: what's going on around him, and he says to the others, Hey,
Speaker 2: you know there's this there's this like fine invisible gas
Speaker 2: around our planet. This is like plasma around and there's
Speaker 2: patterns in this there's regular patterns in this plasma that
Speaker 2: kind of hole together. And he say, so, what, well,
Speaker 2: I've been watching some of these patterns, and you know,
Speaker 2: they seem to be almost like they do things. They
Speaker 2: almost seem agential, they almost seem like they have goals,
Speaker 2: and like they you know, they're not they're they're they're
Speaker 2: sort of like you would see waves or solitons moving
Speaker 2: through water, you know, and they look like they hold
Speaker 2: together for a period of time. And they say to him, well,
Speaker 2: how long do these hold together? Well about one hundred years.
Speaker 2: Well that's crazy, nothing, nothing interesting can happen that that quickly.
Speaker 2: You know, They're just temporary. They're temporary, fleeting, you know,
Speaker 2: sorts of sorts of patterns. And and by the way,
Speaker 2: we've been watching the ecosystem here, and some of these
Speaker 2: patterns are really like not conducive to the health of
Speaker 2: the ecosystem, you know, these patterns are really are really
Speaker 2: like screwing things up. So they're they're kind of like
Speaker 2: these these recurrent but the unhelpful patterns. So so I
Speaker 2: have a blog post which has this this this fictional
Speaker 2: dialogue between that creed, that that that core scientist, and
Speaker 2: he tries to talk to one of the patterns. We
Speaker 2: of course are the patterns, and so the human says
Speaker 2: to him, it's really imperative that you guys understand that
Speaker 2: we are alive and and we have we are, we matter,
Speaker 2: we you know, in a moral sense, we have goals,
Speaker 2: we have memories, We persist. And he says, well, I
Speaker 2: feel like I'm crazy. I'm talking to a pattern and gas.
Speaker 2: You know you can't be real. He says, I'm real.
Speaker 2: I'm solid. I live for you know, millions of years.
Speaker 2: You're a temporary pattern in this gas. How can I
Speaker 2: take you seriously as a coherent intelligence? And so just
Speaker 2: thinking about it that way reminds us that all of
Speaker 2: this is relative, and that we two are patterns, and
Speaker 2: what other patterns around us have a degree of coherence
Speaker 2: and live and strive and have different kinds of degrees
Speaker 2: of problem solving competency and other kinds of things that
Speaker 2: we don't know. And so once we think about that,
Speaker 2: once we realize that this distinction between you know, real
Speaker 2: solid beings like us and the temporary pattern like we
Speaker 2: are all on that spectrum. We are all patterns. So
Speaker 2: once you think about it that way, it unlocks the
Speaker 2: ability to take the tools that we use to understand
Speaker 2: real embodied beings and ask ourselves, how do some of
Speaker 2: those tools and concepts from behavioral science and so on,
Speaker 2: how would they apply to certain other kinds of patterns
Speaker 2: in other media. So what are patterns in media? Well,
Speaker 2: thoughts within the cognitive system are patterns. You can have
Speaker 2: fleeting thoughts that sort of come and go. You can
Speaker 2: have persistent thoughts, you know, thoughts that are hard to
Speaker 2: get rid of. Right, then it's all many examples of that,
Speaker 2: and some of those thoughts actually do a little bit
Speaker 2: of niche construction. Niche construction in biologies, when an animal
Speaker 2: modifies its environment that makes it easier for them to persist.
Speaker 2: So you're doing something to the environment that makes it
Speaker 2: easy for yourself to stick around. Well, there are data
Speaker 2: that depressive thoughts, persistent thoughts, those kinds of things actually
Speaker 2: modifying brain issue in ways that makes it easier to
Speaker 2: keep having those kinds of thoughts. Right, So you got
Speaker 2: your fleeting thoughts, you got your kind of persistent thoughts.
Speaker 2: Then maybe you have some dissociative personality alters which are
Speaker 2: way more coherent than a simple persistent thought and in
Speaker 2: fact somewhat agential. So they have a is and they
Speaker 2: have memories or whatever, but not a full on human personality.
Speaker 2: So then you have you have that, and then who
Speaker 2: knows what's beyond that? Right, trans personal psychology will say
Speaker 2: that maybe maybe there are there are bigger things past that.
Speaker 2: So so I think that, uh, you know, this this
Speaker 2: idea of having patterns within a medium and maybe within
Speaker 2: a cognitive meaning, but also a computational medium. If you're
Speaker 2: data in a database of being being shuffled depending on
Speaker 2: that architecture, maybe you can take the perspective of that
Speaker 2: data and ask yourself, what does the world look like
Speaker 2: from my perspective, right from the perspective of the pattern,
Speaker 2: and what is the pattern doing or not to facilitate
Speaker 2: its own persistence and to facilitate its own transformation that
Speaker 2: usually is required if you're going to persist over long
Speaker 2: periods of the time, you may need to change. So
Speaker 2: that's that's you know, that's some cutting sort of cutting
Speaker 2: edge stuff as far as what we're thinking about to
Speaker 2: understand some of what goes on in these kind of
Speaker 2: complex biological cases that we want to be able to
Speaker 2: control in medicine and so on.
Speaker 1: And presume will you think about that in a Darwinian
Speaker 1: context in terms of if I'm a thought and you know,
Speaker 1: so I'm some pattern that is a thought and I'm
Speaker 1: trying to keep myself alive. There are certain mutations perhaps
Speaker 1: that I can have, or certain things that I can
Speaker 1: do that give me an advantage in that domain.
Speaker 2: That's part of it. But I think that the bare
Speaker 2: bones Darwinian paradigm, which is short term self interest, competition,
Speaker 2: and random change, those three things I think are woefully
Speaker 2: incomplete as a story both of actual evolutionary change in
Speaker 2: biology and the kinds of things that we're talking about here.
Speaker 2: The alternative to this, of course, is that a system
Speaker 2: that changes with some sort of foresight. Now that doesn't
Speaker 2: mean long term purpose. I am not saying that there's
Speaker 2: some sort of human or above level of a plan
Speaker 2: that is executing the changes that are happening. What I'm
Speaker 2: saying is that we cannot necessarily assume that the change
Speaker 2: that is happening is completely blinde and we cannot assume
Speaker 2: that there isn't some computational process done at the level
Speaker 2: of the lineage that is actually guiding the changes that
Speaker 2: are that are happening. One way to think about this
Speaker 2: is to think about the whole lineage, you know, I
Speaker 2: don't know, fifteen million years of alligators or something. Think
Speaker 2: about that that whole lineage as a giant, single agent
Speaker 2: distributed over time, bigger than we're used to thinking about,
Speaker 2: where every each individual animal is a hypothesis of that
Speaker 2: agent about the outside world. Some of those hypotheses are good,
Speaker 2: some are not. The thinking evolves as time goes on, right,
Speaker 2: So again, this is cutting edge stuff, you know, this
Speaker 2: is this is I'm not at all saying that we
Speaker 2: have all this worked out. This is just these are
Speaker 2: things that we're working on and some ideas going forward.
Speaker 2: But there are lots of people thinking about how much
Speaker 2: and including Richard Watson, how much and what kind of
Speaker 2: computation is done by populations like this that is not
Speaker 2: captured in this very simple uh competition for resources random
Speaker 2: change model. The way that you think about memories in
Speaker 2: the brain as being like their own agents, patterns that
Speaker 2: stay alive, and the recollection of memory as sort of
Speaker 2: the creation from some physical evidence that's there, recreation into
Speaker 2: your current world. Does this tell you anything.
Speaker 1: About Ribou's law, which is the oldest rule in neurology,
Speaker 1: which is that older memories are more stable than more
Speaker 1: recent memories.
Speaker 2: Have you thought about that at all? I've not thought
Speaker 2: about that specifically. It sort of makes sense that you're
Speaker 2: If you're a pattern that has managed to stick around
Speaker 2: for a really long time by interaction with the surrounding
Speaker 2: cognitive system in a way that causes it to keep
Speaker 2: you around and for you to persist, it makes sense
Speaker 2: that you have now picked up on whatever properties, residents, whatever,
Speaker 2: that allows you to be pretty stable in the system.
Speaker 2: There's this term that people use sometime about a breakthrough
Speaker 2: where you reinterpret a number of things that happen in
Speaker 2: your life. If you find out that this person, maybe
Speaker 2: that you were mad at, had some other problem going on.
Speaker 2: They knew that they had cancer, but they didn't tell
Speaker 2: you that, and suddenly they're lashing out at you, you
Speaker 2: have a totally different interpretation of it. You're going back
Speaker 2: through your memory and recasting everything in a different light.
Speaker 2: Do you have any interpretation of that in your framework?
Speaker 2: I mean that sounds to me like a very sophisticated
Speaker 2: human level cognitive version of a process that happens all
Speaker 2: the time, going all the way back to our simplest ancestors,
Speaker 2: which is that circumstances change and it forces you to
Speaker 2: reuse whatever information you had from the past, whatever tools
Speaker 2: you had from the past, to make sense of what's
Speaker 2: going on now, and that I think is the fundamental
Speaker 2: basis of intelligence. That's why I think this requirement to
Speaker 2: confabulate because everything changes is an intelligence ratchet. It requires
Speaker 2: cells to get good at solving problems in their spaces,
Speaker 2: which eventually bubbles up as collective intelligence scales and the
Speaker 2: cognitive light gones expand. It then eventually starts to look
Speaker 2: like the kind of intelligence that we we're used to seeing.
Speaker 2: But that that that fundamental process I think is is
Speaker 2: very ancient and fundament and basic. Mike, does this change
Speaker 2: anything about how you think about yourself? For me? I
Speaker 2: think it's it's very very important to face this, this
Speaker 2: this paradox, right, the paradox which which we face this
Speaker 2: as cognitive systems, but also species face this as well.
Speaker 2: If you don't change, you will likely die out when
Speaker 2: when circumstances change. But if you do change to meet
Speaker 2: those circumstances, you're no longer the same, You're not you anymore.
Speaker 2: So So what does that mean? Right, that's the paradox?
Speaker 2: How how can you possibly persist in this idea of
Speaker 2: persisting as a as a as a pattern and uh
Speaker 2: realizing that because things change all the time and this, this,
Speaker 2: I think is is fundamental. What is in our control
Speaker 2: are not the thoughts that we have right now. What's
Speaker 2: what's in our control is the long term application of
Speaker 2: effort to modify our own cognitive system to have different
Speaker 2: thoughts in the future, the thoughts you would like to
Speaker 2: have more of, and behaviors you would like to have
Speaker 2: more of versus something else. So this idea of committing
Speaker 2: to a consistent, long term process of self change, you know,
Speaker 2: the Buddhists, you also call it the body step of
Speaker 2: a vow, This idea of enlarging your cognitive light cones
Speaker 2: so that you're able to have the goal of compassion,
Speaker 2: you know, beyond our current limited human kind of scale
Speaker 2: that we can actually you know, work towards the goals
Speaker 2: of a certain size. Yeah, that's that's that's what motivates me.
Speaker 2: And the plasticity is really I find it incredibly hopeful
Speaker 2: and positive, this idea, this this incredible plasticity that has
Speaker 2: intelligence at its core, that every single cell is intelligent
Speaker 2: within and it's exerting intelligence in its cooperation and competition
Speaker 2: with others to form larger scale structures that can be
Speaker 2: molded top down, molded over time, to be better and
Speaker 2: to improve over time.
Speaker 1: That was Mike Levin, a professor and biologist at Tufts University.
Speaker 1: So wrapping up this two part episode about the self,
Speaker 1: we saw that everything in your biology is changing all
Speaker 1: the time.
Speaker 2: Your cells are.
Speaker 1: Constantly turning over their pieces and parts, but we have
Speaker 1: memory to bind the use together. Now, I've talked in
Speaker 1: several episodes about how memories change in their character. They're
Speaker 1: not like a file of zeros and ones that are
Speaker 1: written down in a computer and then read back out perfectly.
Speaker 2: And the way Michael Levin.
Speaker 1: Thinks about this is that memories get compressed. They get
Speaker 1: encoded down into the neurons or the connections between neurons,
Speaker 1: or the inner cosmos of proteins and side neuron and
Speaker 1: then when these memories get reinflated later they find themselves
Speaker 1: in a different world, they get it interpreted by the
Speaker 1: new brain that is looking at them. So I want
Speaker 1: to make this model clear. So here's my analogy to
Speaker 1: capture that. Imagine that the world out there has lots
Speaker 1: of things that need to be bolted down, and so
Speaker 1: you create a wrench, and your metal wrench is in
Speaker 1: some sense a compressed representation of the world out there,
Speaker 1: a world full of bolts. So when you see the wrench,
Speaker 1: that reminds you.
Speaker 2: Of all the bolts that are out there.
Speaker 1: Okay, Now, imagine that you bury that wrench, and some
Speaker 1: other creature, some future human creature, digs it up in
Speaker 1: a thousand years and she doesn't see it as a wrench,
Speaker 1: but to her it's maybe a weapon, or it's an
Speaker 1: instrument for conducting electricity.
Speaker 2: On her spaceship.
Speaker 1: Or she takes it to be a ceremonial artifact, or
Speaker 1: she uses it for physical exercise, or she looks at
Speaker 1: its clean, balanced design and uses it for a piece
Speaker 1: of art. The point is that what you buried is
Speaker 1: not what gets exhumed in a new world of the future.
Speaker 1: And that's what happens to memories too. You bury something
Speaker 1: that has some meaning in the now, but what you
Speaker 1: dig up is interpreted through the eyes of.
Speaker 2: The future you.
Speaker 1: And if there's one thing we can count on, it's that,
Speaker 1: despite all your intuitions to the contrary, that future you
Speaker 1: will not be the same as the you now. It'll
Speaker 1: be someone you don't know, who doesn't share all your
Speaker 1: values and opinions, and is someone you can't accurately predict
Speaker 1: the ship of theseus with all those changes does not
Speaker 1: in fact remain the same ship. I was recently talking
Speaker 1: with my friend Lisa Joy, and she said she thinks
Speaker 1: it's strange that the longevity community cares so much about
Speaker 1: extending their lifespan by decades, because that future person will
Speaker 1: be somebody potentially very different from who they are now.
Speaker 1: So who are you saving if you go through a
Speaker 1: lot of trouble now to extend your life. Whoever you're saving,
Speaker 1: it's a stranger to you. You're doing all this work
Speaker 1: for someone you don't know. So, coming back to the
Speaker 1: question of why we have this notion of an unchanging self,
Speaker 1: Michael's answer is that the job of the brain is
Speaker 1: to make models of consistency, like this is what a
Speaker 1: chair is, this is what a backpack is, this is
Speaker 1: what a bicycle is. And even though there may be
Speaker 1: a lot of variety in the specifics that you come
Speaker 1: across and things might change, you nonetheless are good at
Speaker 1: summarizing things as objects. You lump them into unchanging categories.
Speaker 1: And so Michael argues, the same cognitive machinery is turned
Speaker 1: on to our selves. Even though there's a lot of
Speaker 1: fluctuation of what that refers to. We lump the self
Speaker 1: into one object that we call me, and that high
Speaker 1: level cognitive model just doesn't change much. And so as
Speaker 1: we close, we are left with this remarkable paradox that
Speaker 1: we move through life carrying memories and stories and beliefs
Speaker 1: about who we are, and we carefully preserve them like
Speaker 1: relics in the soil of our minds, and we expect
Speaker 1: them to stay the same. But with each retrieval, every
Speaker 1: time we unearth them, they are interpreted afresh. They're reshaped
Speaker 1: by the hands of a self that is itself ever shifting.
Speaker 1: But the illusion our brains create for us as a
Speaker 1: model of the self as unchanging, a fixed point in
Speaker 1: a fluctuating world. And it's a comforting thought that we're
Speaker 1: a single thread woven through time. Maybe there's also a
Speaker 1: beauty in realizing that each of your future selves is
Speaker 1: a stranger unto you, an explorer who picks up that
Speaker 1: wrench of memory, holding it to the light and interpreting
Speaker 1: something new each time.
Speaker 2: Who we are, what we hold dear.
Speaker 1: Maybe these aren't artifacts that are meant to be saved
Speaker 1: or preserved perfectly.
Speaker 2: They are living stories.
Speaker 1: They're reimagined and repurposed by every future version of us.
Speaker 2: So when you.
Speaker 1: Think of your future self, who you will be tomorrow
Speaker 1: or a month from now or a decade from now,
Speaker 1: think of that stranger that future you, and maybe smile
Speaker 1: at the mystery of what that person will even remember,
Speaker 1: what they'll care about, what they'll let go of. After all,
Speaker 1: part of the adventure of life is not just holding
Speaker 1: on to who we were. It's also about meeting time
Speaker 1: and again who we are becoming. Go to eagleman dot
Speaker 1: com slash podcast for more information and find further reading.
Speaker 1: Send me an email at podcast at eagleman dot com
Speaker 1: with questions or discussion, and check out and subscribe to
Speaker 1: Inner Cosmos on YouTube for videos of each episode and
Speaker 1: to leave comments.
Speaker 2: Until next time.
Speaker 1: I'm David Eagleman and this is Inner Cosmos.