Ep83 "Why Do Your 30 Trillion Cells Feel Like a Self?" Part 2

Does our sense of self emerge from our brain's skill at lumping things into unchanging categories? What can we learn watching a caterpillar brain transition to a butterfly brain? Can we think of a memory as a pattern that stays alive and has its own life? Does an ant colony have a sense of self? Join Eagleman and biologist Michael Levin at Tufts – one of the most energetic and original thinkers in the field -- to dive into new territories of the self.

Episode Transcript

Speaker 1 (00:05):
Why do you think of your thirty trillion cells as
a self? Does an ant colony have a sense of self?
And could you think of all those ants as a
liquid brain? What does any of this have to do
with how the brain of a caterpillar transitions to the
brain of a butterfly, or how we might think of

(00:26):
a memory as a pattern that stays alive and has
its own life. Welcome to Inner Cosmos with me David Eagleman.
I'm a neuroscientist and an author at Stanford and in
these episodes we dive deeply into our three pound universe
to uncover some of the most surprising aspects of our lives.

(00:59):
In the last piso, I talked about who we are
and how we change through time. The pieces and parts
of every single cell in your body degrade and get
replaced continuously, such that you are physically speaking, a totally
new person every few years, and yet we experience the

(01:21):
illusion that we are the same person we've always been.
We have this illusion of constancy. So last week we
explore this by considering the thought experiment of the Ship
of Theseus.

Speaker 2 (01:35):
The story here is that.

Speaker 1 (01:37):
Each plank of a famous ship is replaced one by
one over time, which raises the question, is it's still
the same ship even after every plank has been replaced
and nothing of the original remains. We consider this question,
of course, because like the ship, we too exist with

(01:59):
physical changes that never stop, and yet we perceive ourselves
as constant through time. The planks of theseus's ship map
onto the cells and molecules in our bodies. So what
maintains your sense of self over time? Okay, so maybe
the thing that links our different selves through time is

(02:21):
our memory. But there's a problem here as well, which
is that memory is notoriously unreliable. It's constantly being reshaped
and revised. So this is all very strange. And there's
an added difficulty, which is that we're constantly changing, and
even when we recognize that we have changed, we always

(02:42):
assume that we're not going to change very much into
the future, and that's always incorrect. This is a cognitive
bias called the end of history illusion. We tend to
believe that our current preferences and personalities are fixed, even
though we will in fact continue to evolve. So that's

(03:03):
our situation. We constantly change, but we're reaching back into
our not so good memory to try to understand who
we are, or.

Speaker 2 (03:12):
At least who we were.

Speaker 1 (03:13):
All this encourages us to start looking for new frameworks
when we think about the self. For example, what if
we thought of the memories themselves like their own little
creatures and they are competing to stay alive. So in
this episode, I want to dive into new ways of
thinking about all of this, and there is no one

(03:34):
better to ring up for that than my colleague Michael Levins.
He's one of the most energetic and original thinkers in
the field. Michael is a developmental biologist and a synthetic biologist,
meaning he makes new kinds of organisms. He's a Toughts
University and what I love about Michael is his extremely
broad interests like bioelectrical signals by which cells communicate, and

(04:00):
what cognition looks like across totally different body plans, and
how you get similar forms and functions across all kinds
of different scales and biology. So I'm going to have
Michael back to talk about some of these other topics
on a future episode, but today I want to zoom
in with him on his recent thinking about the self

(04:21):
and memory. So Mike, tell us how you think about
the self.

Speaker 2 (04:31):
I work in diverse intelligence. I'm interested in all different
kinds of implementations of minds, and I think a self
is a useful way to think about selves is as
a system that has goals of some particular size. It's
what the dark hop Statu would call a strange loop.
It's an observer of the outside world, but it's also

(04:51):
an observer of itself. It's something that can loop back
in and interpret what its own memories mean and what
it is doing, and make decision and going forward. Those
are the components of being a self. And so do
selves change? Yeah? Part of the paradox of being a
self is that you have to change in order to
stay alive. You have to change in order to persist.

(05:12):
Every time you learn something, every time you make a
decision that then feeds back and alters the inputs that
you receive and thus changes your own cognitive system and
your own behavior going forward. You've now changed. I you know,
we selves face this this paradox that in order to
to to to persist, they must change and transform over time.

Speaker 1 (05:31):
Why do you suppose we have the illusion of ourselves
when we think about you know yourself and your life,
you have the illusion that it's unchanging.

Speaker 2 (05:40):
What do you suppose that's about. Yeah, I think that,
Uh well, let's let's just think about very primitive, basic, fundamental,
basic agents at the very beginning of of of of
that of that spectrum. If if you're any kind of
a system that's going to survive in the real world,
what you can't afford to do is to be a
kind of placium demon where where you're tracking the micro

(06:01):
states of you know, every single stimulus, every molecular impact
on your membrane or whatever you can. You can't afford
to track all that. If you try to do that,
you will run out of time, You'll run out of energy,
You'll be eaten and dead in no time. And so
I think that what we have in at least at
least in biological evolution, is an incredible pressure to coarse grain,

(06:21):
that is, to look out into the world and to
group inputs and sensations and stimuli and observations that you make,
to group them into categories, and to try to understand
your world by reducing its complexity, and by trying to
make models of the world that have these large things
in it that are themselves agents. You know that do things,

(06:42):
and that you can then make decisions about what they
do and you don't have to pay attention to all
the micro states. If you're going to do that, you
fundamentally need a way to make models of persistent things.
You have to have some way of saying that this
is an object I need to stay away from. More conversely,
this something I need to approach, or this is something
I need to recognize meanwhile, of course, so let's just

(07:05):
look at vision, you know. For example, So you have
these these pixels and impacting onto your retina, these these
visual stimuli, and depending on how you're looking at something,
the details will be completely different, the lighting will be different,
the motion, the way that it's angled. But your job
as a successful cognitive system is to recognize as well
as possible that this is always the same thing, right

(07:27):
that you can, despite the fact that it's now just shifted,
tinted green or whatever it is, that that hey, wait
a minute, I recognize it. I know what this is.
And so I think from the very beginning to be
a successful being that can survive in a world war,
time and energy are extremely precious. These are precious resources.
You have to get good at noticing and in fact

(07:48):
magnifying invariance. You know, things that stay the same. And
then I think you turn that on yourself and you say,
wait a minute, I am also an agent that does things.
And I don't mean consciously, and we don't do this consciously.
But every system, every living system, I think, has an
internal self model, and part of that model is to
recognize you as a persistent thing even though things change.

(08:08):
And then and then you know, those those of us
that have self consciousness then have a have a story
that we tell ourselves about being a persistent thing as
opposed to I think what we much more close to
what we really are, which is a flowing process.

Speaker 1 (08:23):
So so we all walk through life and we have
our notion of self, and we observe the selves in
others that we love, and we try to figure out
what this is all about. You have a very cool approach,
which is that you think about diverse intelligences. Tell us
what that term means.

Speaker 2 (08:42):
Yeah, diverse intelligence is the name of a of a
of a feel that on the one hand is the
emerging now and it's a very exciting kind of feel.
On the other hand, this is a collection of ideas
that have really been around for a very long time.
And it goes back to the efforts of trying to
understand what what do we actually mean by intelligence, by minds,

(09:03):
by cognition, by all all these kinds of all these
kinds of terms. And we have familiar examples of them
in brainy animals and and and you know, in ourselves.
But actually the key thing that that we need to
understand is that all of us, at one point, both
on an evolutionary scale and on a developmental scale, we
were all a single sell once. You know, at one

(09:25):
point we were a little blob of quies and cytoplasm
and unfertilized noocyte. And we would look at that and
we would say, well, there's there's a little a little
blob of chemicals that obeys physics and chemistry. And then
at some point we have something that we would say
has an inner perspective, It has a mind, it has preferences,
it has goals, it has memories, and and you know
it and it has a full blown self conscious kind

(09:46):
of meta cognition and so on. Now, how did we
get there? Right? Because we started out you know, this
this amazing journey from physics to mind. You know, you
start off with with a little blob of chemistry, and
then eventually here we are, so diverse. Intelligence is is
I think, is predicated on the on the idea that
we need to understand how it is that minds are

(10:07):
embodied in the physical world, and that by understanding that
we emerge slowly and gradually, this is we are not.
You know, there's no there's no magical category called human
that suddenly snaps into existence at some particular moment of
embryonic development. But this is a slow, gradual process. It's
it's an effort to understand what those processes are that
give rise to collective intelligence. And by the way, all intelligence,

(10:29):
I think is collective intelligence because all of us are
made of parts, and we have to understand how the
competencies of those parts give rise to whatever the larger
scale system is capable of doing. And as soon as
as soon as you frame the problem that way, to
understand what is it that the components are doing to
scale that cognition from the competencies of a single cell
to that of an animal or a human, then then

(10:50):
immediately you start to ask yourself, okay, so not only
is there a history going backwards where we see increasingly
simpler forms and ask what is their intelligence like? But
then we can really try to shed some of the
limitations that we're given to us by our evolutionary firmware
that make it easy for us to recognize, you know,

(11:11):
large intelligence, large large, medium sized objects moving at medium
speeds through three dimensional space, and we so, okay, that's
an intelligent animal doing whatever it's doing. But to learn
to recognize intelligence and unfamiliar guises, you know, what other
spaces can intelligence be operating and what what else can
it be made of? What other processes give rise to

(11:32):
the scale up of intelligence. That's diverse intelligence. It's the
broad attempt to understand intelligence as it can be. What's
an example of that?

Speaker 1 (11:40):
For example, ants in a colony understanding that as an
intelligence system where they're laying down memories in particular ways.
It's very different than the way we think about the
human brain. Is that an example?

Speaker 2 (11:52):
Yes, I'll give you a few examples and get the
sort of progressively progressively weirder with them. Traditional collective intelligence
is like ants and beehives and termite colonies and blocks
of birds and things like that. Those are typical collective intelligence.
They are what our Carsole calls liquid brains because they're
moving around relative to each other. Our neurons tend to

(12:13):
stand still relative to each other. And yet we too
are a collective intelligence, right, We are a pile of
neurons and other cells that work together to give rise
to an emergent being with memories and preferences and goals
that don't belong to any of the individual cells. So,
no matter what you are, whether you're a collection of
neurons or your collection of birds or of termites or
anything like that, what you need is a kind of

(12:34):
cognitive glue. You need these policies that are implemented by
the pieces that allow the collective to be more than
the sum of its parts in relevant ways. And so
if we define intelligence as problem solving competencies in some space,
so the ability to and so this is kind of
William James's definition of intelligence, same goal by different meats, right,
the ability to navigate some space to reach your objective

(12:57):
and do so despite novel despite perturbations, and and so on.
Then we can get we can get progressively more inclusive
with this. We can say, okay, so we have we
have animals that solve problems. We have unfamiliar things like
slime molds, right, so so so there are people including us,
who study slime mold behavior, in slime mold learning and

(13:20):
things like that. So slime molds are a very unusual organism.
It's a single cell. It can be quite large, and
yet it can it can solve all kinds of problems.
People have studied memory and learning in in bacteria, in
unicellular organisms and plants, and and then you can get
then you can get you know, even even more sort
of broad with this, and you can ask, well, what

(13:41):
happens beyond three dimensional space? For example, the cells that
make up our bodies and our and our tissues and
our organs, they operate in physiological state space, so the
space of all possible physiological conditions. They operate in gene
expression space. Embryonic and regenerative cells operate in anatomical space.
They have to navigate from whatever shape they have now

(14:01):
to some kind of complex organ structure. And in all
of these spaces you can see if you look for
them using the techniques of behavioral science, you can find
them solving problems. You can find them getting to their
goal in very creative ways despite various problems So that's
kind of the range of the diverse intelligences. And then
beyond that you have embodied robotics, and you have maybe

(14:22):
software intelligent agents, and maybe someday exobiological beings and so on.

Speaker 1 (14:28):
Okay, so the challenge is that everything's changing all the
time in our brains and all these other biological systems
and colonies whatever, things are changing all the time. Cells
are dying or aging or getting mutations. And yet somehow,
when it comes to memory of oneself, we tend to

(14:50):
have some sort of memory. It's not perfect, it only
has a few details, things like that, but we retain
this memory through time. Now, what is your framework for
thinking about that?

Speaker 2 (15:02):
Yeah, I'm not sure we retain memory through time as
much as we reconstruct memory through time. And of course
there's a lot of human neuroscience being done about this.
But I want to take a step back and give
you another example that kind of drives my thinking on this.
Consider what happens from a butterfly to a caterpillar. So
in the caterpillar, you have the soft bodied creature that

(15:23):
lives in basically a two dimensional world where it crawls
around and eats leaves. Now, one thing that it has
to do is that has to turn into a butterfly.
During that process of metamorphosis, its brain is massively rebuilt.
Many of the cells die, all the connections are broken,
it's completely refactored, and you get a new brain suitable
for driving a now hard bodied kind of vehicle, completely

(15:45):
different controller you need for that. And now it flies
and it lives in the three dimensional world and so on.
Now it turns out that if you train the caterpillar
the let's say you train it to associate a specific
color to a specific leaf that it wants to eat,
what you will find is that the butterfly, despite having

(16:05):
a totally refactored brain, will remember that information. And for
years I thought the amazing thing here was how do
you hold on to information when your information medium is
being totally ripped apart and refactored. Right, we don't, you know,
our computer technology doesn't doesn't like that. We don't have
anything that has that that robustness to it. But if
you think about it more, what turns out is that

(16:27):
the amazing thing is not holding onto the memory because
actually the memories, the exact memories of the caterpillar, are
of no use to the butterfly whatsoever, because it doesn't
move the same way. It doesn't want the leaves a
drinks nectar, right, And so just holding onto that memory
is of no use. What you need is to actually
transform and remap that memory into a completely novel context

(16:49):
where it's like not leaves but food for example. Right,
So you have to generalize a little bit, and you
have to now remap it on a different on a
different controller. And you know, numerous people who have done
experiments in memory transfer, most recently David Landsman and others
show that that it's it's really wild how you can
move either tissues or extra extracts let's say, RNA extracts

(17:10):
or whatever, and introduce them into a new into a
new creature and and have that creature like take on
that that initial learning.

Speaker 1 (17:18):
So let me interrupt for a second. So tell us
about David Landsman's experiments in these sea slugs. Yeah, Well,
specifically in David's work, what he was doing is training
these c slugs to a particular to a particular task.
I mean it was a simple, you know, simple reflex
and extracting RNA from from the brain. Because the hypothesis

(17:40):
was that memory is stored in some way in this
in this medium of RNA, and then he was injecting
it into a naive animal and showing that they now
have you know, they show a recall of that information
I mean to me, I mean, it's it's a fabulous
body of work.

Speaker 2 (17:55):
To me. One of the most interesting things about it
is that when you've got this RNA, you don't have
to go put it exactly in the right neuron where
it was supposed to be, you know, and that you
just kind of inject it somewhere in the brain of
a second of a second sea slug exactly of the
recipient right of the host seasug, and it somehow gets
taken out. And that's that's a that's a theme that

(18:17):
that is like central to all this because what I
think this is, this is telling us is so now,
so now let's let's walk into this from from the
other end. Whatever you are, human or anything else, you
don't have access to the past. What you have access
to is the end grams, the memory traces that the
past has left in your brain or body or wherever
that were formed by past experience. And so what you

(18:39):
now have to do is to at any given moment
and it's you know, these moments. I don't know how
many milliseconds they are, but but some some number of milliseconds.
You have to reconstruct that memory to be meaningful to
you now because you don't know what it used to mean.
This is you have to do this on the on
the fly. And I think there's a lot of good
neuroscience showing how plastic these memories are and how there

(19:00):
how you know, even even recall of the memories means
they're getting changed. There's no non destructive read. Accessing these
memories changes them and and and so so the way
I think about this is as an architecture in the
shape of a kind of bow tie. And this is
for the computer science listeners. You might imagine like a
like a an auto encoder architecture where there's a funnel

(19:21):
on the left side which receives the primary experiences, the
raw data that come in the sense impressions, and that
the process of learning has to and and and this
is fundamental to intelligence, is abstracting from individual instances of
things you experience to a rule, to some kind of
some kind of pattern, and it's the pattern that you
remember it, so you compress all those experiences, you throw

(19:45):
away all the irrelevant details and you form a memory.
You store that in some sort of enngram. Uh. You know.
Sometimes people think of this as synaptic modifications. Sometimes other
people think it's in the RNA or in a cyber
skeleton wherever. So so you store this but now but
now here comes here comes the really interesting part. When
you need to recall this. You have to know, here
comes the right side of that, on the right side

(20:06):
of that bow tie. You have to sort of reinflate
that compressed memory. And because you've lost all kinds of information,
this process, this, this recall process is creative because you
don't have all the details that were there, and nor
can you really be sure at a later time what
the meaning was to your past self. In other words,

(20:27):
I also think of memories as literally messages from your
past self. So I tend to think of memory and
recall as communication events. And it's just you know, it's
a communication with a past version of you, but it's
the same. They sent you a message. It was encrypted
and compressed in these n grams, and then you try
to reinflate it. And your goal at any given time
is not to have any kind of allegiance to what

(20:51):
the memory meant before. Your goal now is to reinterpret
it the way the butterfly does in whatever is the
most optimal adaptive way that makes sense. Now, so this really,
you know, the first part is kind of algorithmic, which
is the compression. But now here comes a creative process.
It's not really deductive. It's a creative process where you
take that prompt. It's more of a prompt than anything else,

(21:11):
and you say, okay, what does this mean to me? Now?
How can I incorporate this into my current constantly evolving
model of the self, of the outside world and on.
So it's very much And this goes on, goes on
all the time. And this is consistent with the plasticity
of memories. It's consistent with confabulation, which has been seen
in all kinds of experiments with human subjects, you know,

(21:33):
split brain and so on.

Speaker 1 (21:33):
Wait, let's take a second to talk about confabulation. So
this is where brains seem to make something up. You know,
there are these experiments, for example, the cutaneous rabit illusion.
If I tap you twice somewhere on your forearm, and
then I tap you a third time. Let's say, in
a different location, you will feel like you felt three taps,
that we're all that we're moving in the direction. Even

(21:56):
though the second tap was in the same spot, you
feel like you felt it on the way to the
third one. This is one example of lots of confabulation
where the brain is retrospectively making things up. Now, the
interesting part is we look at confabulation generally as something bad.
For example, with large language models, we talk about hallucinations.

(22:20):
But there is another way to look at this, which
is as hypothesis, as generating new creative ideas. So tell
us how that fits into the way you think about confabulation.

Speaker 2 (22:31):
Yeah, I mean so, here's another another example of confabulation
that is similar to what I'm talking about. Two examples.
One is there was a patient that had an electrode
that was placed in their brain for I think aplepsy
was the idea, and it happened to land in a
region of the brain that when you stimulate that electrode,
it makes them laugh, makes the mouth laugh. So the

(22:53):
person will be sitting there thinking about something serious. You
can see and there's a video of this I saw somewhere.
The scientist pushes the button. They start laughing, and then
you ask them why are you laughing, And the answer
is never, Gee, I don't know. I was sitting here
thinking about something serious and then my mouth started laughing.
That's never the answer you get. The answer you get is, well,
I thought of something funny. I thought of a joke,
And you get the same thing out of split brain patients.

(23:14):
When the one hemisphere causes the other side of the
body to do something that the language hemisphere doesn't understand,
what they usually do is make up a story about
it on the spot, and they don't know, you know, consciously,
they won't report that they're making up a story. So
I think what we mean by confabulation is really a
fundamental skill and necessity of sense making of your world.
You have a model of the outside world. You have

(23:35):
a model of yourself and what you are and how
you behave and sometimes that model gets updated, but sometimes
you just incorporate other world events that the go on.
You incorporate them into that model and you interpret them
in a way that makes sense to you. Now, you know,
and something similar to what you said about the tapping,
you've seen the rubber hand illusion.

Speaker 1 (23:55):
Yeah, in the rubber hand illusion, your hand Let's say
your left hand is covered up. You're not seeing it,
but you're seeing a rubber hand, and you see somebody
stroking that rubber hand, and every time that rubber hand
gets stroked, you feel a stroke on your hand too.
The person is stroking them both at the same time,
even though you can't see your own hand, and then

(24:16):
they hit that hand with a hammer and you withdraw
in terror because it feels like it's become your hand.

Speaker 2 (24:23):
Yeah. So, to me, the amazing thing about that that
is the plasticity. Look, we've been tetrapods for I think
almost four hundred million years something like that, and so
for millions of years we had a brain that knows
exactly how many limbs you have, and within what seven
minutes of new experience, you now have decided that you

(24:43):
have this extra hand. It's the plasticity is crazy.

Speaker 1 (25:03):
By the way, One thing that has been extraordinary on
this is experiments in VR, where, for example, you give
somebody a third arm that comes out of their chest,
and you control your natural two arms with two controllers,
and you can see your arms and you also see
this third arm which you control by changing your wrist orientations,
and that can control the third arm, and within a

(25:24):
few minutes people can get very good at controlling this
third arm. And you know they're doing a game where
you pick up boxes of certain colors, and yes, so
you can add limbs and subtract limbs readily. The homunculus,
the little model of your body, is totally flexible in
that way.

Speaker 2 (25:42):
That right there, the ability to adapt to novel situations
in this way that can fabulation to tell a story
that makes sense, not that it's necessarily true relative to
what your past was giving you, but to what makes
sense for you now is fundamental to buyology. And I
think biology doesn't preserve the fidelity of memories. It preserves

(26:04):
the salience of memories. It tries to remap them in
the way that it makes sense to you now, not
necessarily to what it meant. And I think that this
is fundamentally an intelligence ratchet for life. And here's what
I mean by that. Let's look instead of the memories
of a single of a single human or animal, Let's
look on an evolutionary timescale. You come into the world
as an embry Oh, You've been given all of this

(26:26):
genetic and cytoplasmic and other kinds of information that are
the accumulated really the memories of your of your lineage agent, right,
the lineage that's been through that's been through the evolution
and has accumulated all this useful information. There are some
animals and at least as far as we know, maybe
nematodes like C. Elegance, that are extremely hardwired. We know

(26:49):
exactly how many cells are going to have, all the
cells of the same position that's determined by lineage. They're
very hardwired. But the majority of living forms aren't like that.
They take that information and they reinterpret it in novel ways.
I think that now now in normal development, we always
see the same thing happening, so we kind of assume
that it's some kind of hardwired mechanical process, but it's

(27:09):
not that at all. For this reason, we can take
we can make a tadpole which has no eyes in
the head, but it has an eye on its tail,
and that those those animals can see right out of
the box. They don't need new rounds of selection or
evolution or adaptation or any of that. They can see immediately.
We can take cells from an early frog embryo and
they become zenobots, and they do interesting things. There have

(27:31):
never been any zenobots. There's never been any selection to
be a good xenobot, right, so that plasticity can you
can you explain to the audience with zenebot is sure? Yeah,
So so we make we make in our group, we
make zenobots and anthrobots. These are these are living systems
that we call them biobots because we use them for,
among other things, bio robotics kinds of applications. They are

(27:53):
living uh organisms made of cells in the In the
case of xenobots, they come from frog cells, and the
case of anthrobots, they come from adult human tracheal epithelial cells.
They are self motile. They will move around a dish.
They sort of swim around the dish on their own.
And they have lots of interesting capabilities. For example, the anthrobots,
if they find a neural wound, they will heal They

(28:14):
will heal the peripheral innervation by taking the two sides
of the neural wound and kind of connecting them together.
You know, who would have known that your tracheal cells
that sit there for you know, quietly in your body
for decades, have the ability to form a self motile
little creature that runs around and do these things, and
does these things so the plasticity and people have been
noticing this, this kind of thing for a very long time.

(28:36):
Developmental biologists is that living systems will play they hand
their depth. They don't just automatically, at least most of
them don't just automatically do the same thing. They will
try to as much as we try to telecoherence story
in confabulation and linguistic space, they will confabulate in transcriptional
space meaning gene expression space, in physiological space, and in

(28:58):
anatomical space to put together some kind of a coherent lifestyle.
Given novel, novel circumstances, the environment can be novel. You
can interface living tissue with all kinds of weird materials.
They will always try to make something, And I think
that's because they never assume that you can take the
past literally. They have to on the fly put something together.

(29:23):
One of my favorite examples is what happens in the
new to kidney tubules. You have a newt If you
take a section through the kidney tubule, you see eight
or ten cells are making like a circle, and they
make this. They make this two well, one of the
things that the people have found is that you can
treat them in a way that makes multiple copy number
of their chromosomes so they have the genetic material. Instead

(29:44):
of two n they will have four and five and
six end and so on. If you do that, the
cells get bigger, but the nude stays the same size.
So that's kind of amazing. So you take a cross
section through the two bule and you see, oh, the
cells are bigger, but there's fewer of them and they
still form the exact same structure. Well, you can make
a highly polyploid neud like that that has gigantic cells,

(30:05):
and in that case, one single cell will bend around itself,
leaving a hole in the middle, which is a completely
different molecular mechanism. Right, it's not cell to cell communication.
That's some kind of cideoscalarle bending. So now think about
what this means if you're a nude coming into this world.
You can't count on how much genetic material you're going
to have. You can't count on and never mind not
being able to count on the environment. Right, who knows

(30:25):
what the pha your water is and all that. Forget
that you can't even count on your own parts. You
don't know what your chromosome number is going to be,
you don't know how many cells you're going to have,
you don't know the size of your cells. You have
to do something coherent in that case build an actual
neud when everything changes. And that's why I think that
that's the fundamental thing about confabulation is that if you
commit to the idea, which I think biology has to.

(30:47):
Unlike our computer technology, which relies on a highly reliable hardware,
right when you code, you don't worry about your you know,
cpu doing something weird. You just assume it's going to
do what it needs to do. You don't think, you
know your copper is going to go off or something.
In biology, that's not the case. The medium is completely unreliable.
You have no idea what you know, how many proteins
have we given the type you have, or if they're

(31:07):
going to get a little bit teen natured, or you
know what's going to happen. If you assume that your
medium is unreliable, then instead of this kind of hardwired
here's how we do it every single time. Idea, what
evolution is going to produce are sense making problem solving
agents in different spaces. It can be very simple things
bacteria and you know, but already you're off to the

(31:28):
racist because you can't count on your environment being the same,
and you can't count on yourself being the same. You're
going to mutate, right, your parts will mutate, Everything will change.

Speaker 1 (31:38):
So this is one of the first things that I
was absolutely intrigued with in biology when I was very young,
which is, how in the world does a mouse's heart
and an elephant's heart do the same thing? When you
know these two cases, you've got totally different number of
cells making this, and yet it makes the same structure
that does the same thing. So what is the way

(31:59):
to understand how biology can code for these higher order structures.

Speaker 2 (32:06):
Yeah, I think that there are key elements of understanding
what's going on that come from behavioral science. This is
we are not going to get to this purely by
the concepts of chemistry and physics, although those are crucial
to understand. What we have here are problem solving collective intelligences.
So when you have a bunch of molecular networks that

(32:27):
make a sell that's a coherent organism like a like
an amebo or a lachrom area or something that that
do all these interesting things. They are making a next
They are contributing to a next level collective intelligence that
does that, that operates in some kind of space and
has a small cognitive light coone work and do certain
things that have a little bit of predative power forward,
a little bit of memory backward. When those cells come

(32:49):
together and form an organism, once again, you have a
collective intelligence that now projects into a new space. Whereas
the cells we're solving problems in physiology and metabolics and
gene expression, you know, now have a system that solves
problems in anatomy. So so when you take an early embryo,
let's say, an early mammalian memory, and you cut it
in half, you don't get two half embryos. You get
too perfectly normal monozygotic twins because each side has to

(33:11):
figure out, oh way this is missing, why I have
to rebuild and so on. And so my point is
not that we attribute to uh, you know, high order
human level self consciousness to these things. I'm not saying
they have the metacognition to know what they're doing. What
I'm saying is we have a simplified version of intelligence,
which we know there had to be because we came
that that is our origin. We know there has to

(33:34):
be a version of intelligence that is, you know, sort
of on the on the left end of that spectrum
going all the way back to primitive cells and before
that actually, And that's that's how we need to think
about this as as as problem solving, continuous dynamical problem solving.

Speaker 1 (33:47):
Great, So let's take where we are now and return
to the issue of memory. So how does memory work
in a brain?

Speaker 2 (33:55):
So I'll just address to you know, a couple of
things that I can speak to. What one is that
I think the conventional story that memories are some sort
of fine tuning of synaptic connections. I think that story
is very incomplete, and there there are many people, you know,
like Landsmen and sam Gershman and many others that are
that are working on that. I I tend to think

(34:17):
if I had to guess, I would say that there
probably isn't one substrate of memory. I would look at
memory as an interpretation process, which I think neurons are
very good at this of interpreting a reservoir. That reservoir
is everything else the cell is doing. The side of
skeletal states, the molecular networks. I mean, some people pick
up have picked up transcriptional uh signatures of certain memories

(34:40):
that mice have had, and so on. Every everything in
the cell, all the complexity that is going on, can
be used as a reservoir in a sense of reservoir computing,
to be used as prompts to reinterpret these those prompts
as memories that are useful and with so again maximizing salience,
not as early fidelity, but salience useful in their novel context.

(35:04):
So I think I think memory is a lot about creativity.
I think it's a lot about uh, having prompts that
that that push you into new new kinds of problem solving,
you know. And and if if your if your body
and your environments stay extremely constant, then it just looks
like the old version of memory, where you store a
piece of data, you read it out and that's it, right,

(35:25):
That's how it looks like. That's what it looks like
from the outside. But I don't think that's what's going on,
you know.

Speaker 1 (35:30):
In my in my book Live Wire, I make the
argument that even though in Silicon Valley we think about
everything as being a trim and efficient layer of hardware,
and then you build uh trim and efficient software on
top of that. That's not at all how the brain's working. Instead,
you've got this constant reconfiguration. And I know that you
also reject that dichotomy between a computation layer and a

(35:51):
passive code layer.

Speaker 2 (35:53):
So how do you think about that? Yeah, well, I
think I think there's a couple of major differences between
how how we build hardware now you know, comput computational devices,
and what biology is doing. The first thing we've just
talked about, which is the reliability the idea that in
in in the computational where you have levels of abstraction

(36:13):
and you try to screen every layer from all the
vagaries of the level below. So if you're coding in
you know, c or something, you're not worried about what
the copper is doing and what the silicon is doing.
You you you assume that the function calls you have
are going to do what they need to do and
you go from there. Biology isn't like that. All all
the all the layers are somewhat unreliable, and you need

(36:34):
to be interpreting it at all at all times. Josh
Bongard and I are working on a framework called polycomputing,
and the idea and this is this is partially based
on some amazing work that is student to Saparsa had
done showing that the same set of physical events can
be interpreted as different computations by different observers the exact
same set of physical events. So give us an example.

(36:57):
An example is I mean in her work, they were
looking at the vibrations of particles and you look at
them in one way and you see an end gate,
and you look at them a different way and you
see an ore gate. That's one example in biology. What
it means in biology is that. And by the way,
he and I wrote this paper called this plenty of
room right here kind of riffing off of finements, a

(37:18):
comment that there's plenty of room at the bottom because
because biology has this thing where every level is already occupied,
there is no room at the bottom because every level
is occupied. How do you as if your evolution, how
do you put in novel functionality when every level already
has something. And by the way, when you make changes,
you're going to screw up. If you make changes in
the given subsystem, you're going to screw up all the
other systems. That depend on what it's doing. So one

(37:40):
thing that I think happens in biology is this poly
computing where you don't necessarily change the system. You add
other systems that see what's already going on in a
different way and make use of it as a computation
but from a different perspective. So, if you're some kind
of chemical pathway that mitochondria are using as part of
the metabolic path way, some other system can look at

(38:01):
that and say, well, I'm gonna use it as a
as a clock, I'm gonna I'm gonna take I'm gonna
use it to regulate my timing, or I'm going to
use it, you know, in some other, some other signaling capacity.
And so so I think what we have in biology
is not this linear stack first of all, not a
linear stack, but a kind of a super a society
of multiple nested, cooperating and competing agents which all have

(38:24):
their own perspectives and they all interpret everything that goes
on around them in whatever way they can. Uh so,
And and you know, we're used to the fact that
in a computation, we supposedly know what a given algorithm
is doing, right, you can and if you don't know
you can ask the person who wrote it and they'll
tell you this is what this thing is computing. But
in biology, I don't think there is any one fixed

(38:46):
answer to this. It's doing whatever you as an observer
can usefully think it's doing that. That doesn't mean anything goes.
If you have a story that doesn't help you get
around in the world and thrive, then you don't know
what it's doing. But but but multiple observers can have
different stories about the about the same thing.

Speaker 1 (39:01):
So give us if you can't give us another specific
example of that.

Speaker 2 (39:05):
So the cite of skeleton, on the one hand, is
used by the cell to get around, and so you
might say, well, this is this is my my movement machinery.
That's that's that that I'm counting on to maintain certain
cell shapes and so on. But at the same time,
there's other data showing that the site of skeleton can
actually be can be storing memory. It's also serving as
a scaffold for other molecules to find where they need

(39:26):
to go. They're moving around with motor proteins and things
like that, and they're just there's just lots of lots
of different uses that any given mechanism is performing at
any one time, and there are multiple different readout systems
and this is this is why you know, there's the
same molecule induces eyes in one context, that induces UH.

(39:47):
You know, it might induce a kidney rudiment in a
different UH contest. There are transcription factors that have that
have many different roles depending on the context. In our
work on bioelectrics, the exact same stimulus induces a tail
to regenerate on a tackle, but a leg to regenerate
on a froglet, and they never get confused, so that
specificity is not in the in the treatment, it's in

(40:09):
the surrounding cells being able to interpret that exact same
signal in whatever way makes sense for them. The ability
of these subsystems to cooperate in UH, in in in
groups and solve problems together is is really like a
fundamental thing in which biology is is different than the
kind of you know, control systems that we have now

(40:30):
in computer science. You know, Stephen J.

Speaker 1 (40:47):
Gould wrote about exaptation, where you have something that develops
and then it turns out to have a use in
another way. But what you're talking about is even more
sophisticated in that in the sense that it can retain
its first use and be used for a second thing
and the third thing all the same time, just by
reading that data out in different ways.

Speaker 2 (41:06):
Is that right? That's that's exactly right. And you know,
some of some of the latest the stuff that I've
been thinking about really tries to turn this whole thing
on its head. And you know so, so in the
standard touring computing paradigm, you have a machine and you
have the data. Right, so you have a you have
the process. You have this machine that reads the tape
and it and it records, you know, the byproducts of

(41:30):
the computation onto the tape and so on. So typically
we look at this from the perspective of the machine.
That is, we are the whether the where, the cell
or the human or whatever. We're forming memories and we're
writing it down into some memory medium. The memory medium
is passive. The memories themselves are passive. They're just marks
on a tape, and then we can read them out
when we want. Some of the latest work that we've

(41:52):
been doing, it starts out by thinking about it backwards
and saying, well, what does this look like from the
perspective of the data, right, data that are not passive.
They're not passive patterns within some medium. They're actually active patterns.
And from the perspective of the tape, the tape runs
the show that machine is going to do things depending
on what is written on the tape. So if I'm
a pattern on this tape, I can make the machine

(42:14):
do things. From my perspective, I'm in control. And so
now it sounds it sounds a little crazy to say
that that these patterns are doing things and that they're
agential and whatever. But let's keep in mind we are
patterns too. We are temporary metabolic patterns within an excitable
medium the way that people study you know, other temporary
patterns like solitons and whirlpools, and you know, all different

(42:36):
kinds of all different kinds of systems. And from from
that perspective you can you can see that different kinds
of patterns persist in different media, be they cognitive media
or just computational media. And asking what does the world
look like from their perspective and how much, how much
problem solving capacity, how much agency in fact, might those
patterns have has massive implications not only for new computational architectures,

(43:01):
but also, for example, for regenerate medicine, where you want
to understand what are the persistent information structures that cause
cells to do or not do various things in disease states,
and you know, pro regenerative states and so on. So
let's double click on that.

Speaker 1 (43:16):
So what would that mean for a memory to be
like an agent to be doing something.

Speaker 2 (43:22):
I'll tell I'll tell a story that I read. I'm
sure at least part of this was motivated years ago
by a science fiction story that I'm not exactly sure
what it was. I think it was a it was
an Arthur Clock story, but I'm not one hundred percent sure.
So let's just and I'm sure I've also twisted it
in a different way. But let's just let's just let's
just visual it's because your memory is creative to totally.

(43:43):
There may have been no story. I have no idea, so,
you know, I just want to give credit in case
there was. So so let's just let's just visualize this.
So from the center of the earth come these creatures,
these core creatures, right, they live at the center of
the earth. They come out onto the surface. They are
incredibly dense because they live at the core, they have
vision that operates, let's say, in them in gamma rays.

(44:04):
And so they come up to the surface. What do
they see, Well, pretty much nothing, because everything that we
see here is like a fine ethereal plasma. To them,
they are so dense. All of the stuff that we
think of as real objects are basically not even within
their within their ability to perceive directly. So they're walking around,

(44:26):
stomping through everything. And you know the same way that
when we walk past, you know, some kind of flower bed,
there's all kinds of like fine you know, patterns of
ascents and so on, we just sort of walk right
through it, mix that all up. So they're stepping all
over everything, and well, one of them, one of them,
is a scientist, and he's taking some careful readings of
what's going on around him, and he says to the others, Hey,

(44:47):
you know there's this there's this like fine invisible gas
around our planet. This is like plasma around and there's
patterns in this there's regular patterns in this plasma that
kind of hole together. And he say, so, what, well,
I've been watching some of these patterns, and you know,
they seem to be almost like they do things. They
almost seem agential, they almost seem like they have goals,

(45:07):
and like they you know, they're not they're they're they're
sort of like you would see waves or solitons moving
through water, you know, and they look like they hold
together for a period of time. And they say to him, well,
how long do these hold together? Well about one hundred years.
Well that's crazy, nothing, nothing interesting can happen that that quickly.
You know, They're just temporary. They're temporary, fleeting, you know,
sorts of sorts of patterns. And and by the way,
we've been watching the ecosystem here, and some of these

(45:30):
patterns are really like not conducive to the health of
the ecosystem, you know, these patterns are really are really
like screwing things up. So they're they're kind of like
these these recurrent but the unhelpful patterns. So so I
have a blog post which has this this this fictional
dialogue between that creed, that that that core scientist, and
he tries to talk to one of the patterns. We
of course are the patterns, and so the human says

(45:53):
to him, it's really imperative that you guys understand that
we are alive and and we have we are, we matter,
we you know, in a moral sense, we have goals,
we have memories, We persist. And he says, well, I
feel like I'm crazy. I'm talking to a pattern and gas.
You know you can't be real. He says, I'm real.
I'm solid. I live for you know, millions of years.
You're a temporary pattern in this gas. How can I

(46:14):
take you seriously as a coherent intelligence? And so just
thinking about it that way reminds us that all of
this is relative, and that we two are patterns, and
what other patterns around us have a degree of coherence
and live and strive and have different kinds of degrees

(46:34):
of problem solving competency and other kinds of things that
we don't know. And so once we think about that,
once we realize that this distinction between you know, real
solid beings like us and the temporary pattern like we
are all on that spectrum. We are all patterns. So
once you think about it that way, it unlocks the
ability to take the tools that we use to understand

(46:56):
real embodied beings and ask ourselves, how do some of
those tools and concepts from behavioral science and so on,
how would they apply to certain other kinds of patterns
in other media. So what are patterns in media? Well,
thoughts within the cognitive system are patterns. You can have

(47:16):
fleeting thoughts that sort of come and go. You can
have persistent thoughts, you know, thoughts that are hard to
get rid of. Right, then it's all many examples of that,
and some of those thoughts actually do a little bit
of niche construction. Niche construction in biologies, when an animal
modifies its environment that makes it easier for them to persist.
So you're doing something to the environment that makes it
easy for yourself to stick around. Well, there are data

(47:37):
that depressive thoughts, persistent thoughts, those kinds of things actually
modifying brain issue in ways that makes it easier to
keep having those kinds of thoughts. Right, So you got
your fleeting thoughts, you got your kind of persistent thoughts.
Then maybe you have some dissociative personality alters which are
way more coherent than a simple persistent thought and in
fact somewhat agential. So they have a is and they

(48:00):
have memories or whatever, but not a full on human personality.
So then you have you have that, and then who
knows what's beyond that? Right, trans personal psychology will say
that maybe maybe there are there are bigger things past that.
So so I think that, uh, you know, this this
idea of having patterns within a medium and maybe within
a cognitive meaning, but also a computational medium. If you're

(48:22):
data in a database of being being shuffled depending on
that architecture, maybe you can take the perspective of that
data and ask yourself, what does the world look like
from my perspective, right from the perspective of the pattern,
and what is the pattern doing or not to facilitate
its own persistence and to facilitate its own transformation that

(48:43):
usually is required if you're going to persist over long
periods of the time, you may need to change. So
that's that's you know, that's some cutting sort of cutting
edge stuff as far as what we're thinking about to
understand some of what goes on in these kind of
complex biological cases that we want to be able to
control in medicine and so on.

Speaker 1 (48:59):
And presume will you think about that in a Darwinian
context in terms of if I'm a thought and you know,
so I'm some pattern that is a thought and I'm
trying to keep myself alive. There are certain mutations perhaps
that I can have, or certain things that I can
do that give me an advantage in that domain.

Speaker 2 (49:18):
That's part of it. But I think that the bare
bones Darwinian paradigm, which is short term self interest, competition,
and random change, those three things I think are woefully
incomplete as a story both of actual evolutionary change in
biology and the kinds of things that we're talking about here.

(49:40):
The alternative to this, of course, is that a system
that changes with some sort of foresight. Now that doesn't
mean long term purpose. I am not saying that there's
some sort of human or above level of a plan
that is executing the changes that are happening. What I'm
saying is that we cannot necessarily assume that the change
that is happening is completely blinde and we cannot assume

(50:01):
that there isn't some computational process done at the level
of the lineage that is actually guiding the changes that
are that are happening. One way to think about this
is to think about the whole lineage, you know, I
don't know, fifteen million years of alligators or something. Think
about that that whole lineage as a giant, single agent
distributed over time, bigger than we're used to thinking about,
where every each individual animal is a hypothesis of that

(50:25):
agent about the outside world. Some of those hypotheses are good,
some are not. The thinking evolves as time goes on, right,
So again, this is cutting edge stuff, you know, this
is this is I'm not at all saying that we
have all this worked out. This is just these are
things that we're working on and some ideas going forward.
But there are lots of people thinking about how much
and including Richard Watson, how much and what kind of

(50:48):
computation is done by populations like this that is not
captured in this very simple uh competition for resources random
change model. The way that you think about memories in
the brain as being like their own agents, patterns that
stay alive, and the recollection of memory as sort of
the creation from some physical evidence that's there, recreation into

(51:12):
your current world. Does this tell you anything.

Speaker 1 (51:15):
About Ribou's law, which is the oldest rule in neurology,
which is that older memories are more stable than more
recent memories.

Speaker 2 (51:24):
Have you thought about that at all? I've not thought
about that specifically. It sort of makes sense that you're
If you're a pattern that has managed to stick around
for a really long time by interaction with the surrounding
cognitive system in a way that causes it to keep
you around and for you to persist, it makes sense
that you have now picked up on whatever properties, residents, whatever,

(51:49):
that allows you to be pretty stable in the system.
There's this term that people use sometime about a breakthrough
where you reinterpret a number of things that happen in
your life. If you find out that this person, maybe
that you were mad at, had some other problem going on.
They knew that they had cancer, but they didn't tell
you that, and suddenly they're lashing out at you, you

(52:12):
have a totally different interpretation of it. You're going back
through your memory and recasting everything in a different light.
Do you have any interpretation of that in your framework?
I mean that sounds to me like a very sophisticated
human level cognitive version of a process that happens all

(52:32):
the time, going all the way back to our simplest ancestors,
which is that circumstances change and it forces you to
reuse whatever information you had from the past, whatever tools
you had from the past, to make sense of what's
going on now, and that I think is the fundamental
basis of intelligence. That's why I think this requirement to
confabulate because everything changes is an intelligence ratchet. It requires

(52:55):
cells to get good at solving problems in their spaces,
which eventually bubbles up as collective intelligence scales and the
cognitive light gones expand. It then eventually starts to look
like the kind of intelligence that we we're used to seeing.
But that that that fundamental process I think is is
very ancient and fundament and basic. Mike, does this change
anything about how you think about yourself? For me? I

(53:19):
think it's it's very very important to face this, this
this paradox, right, the paradox which which we face this
as cognitive systems, but also species face this as well.
If you don't change, you will likely die out when
when circumstances change. But if you do change to meet
those circumstances, you're no longer the same, You're not you anymore.

(53:42):
So So what does that mean? Right, that's the paradox?
How how can you possibly persist in this idea of
persisting as a as a as a pattern and uh
realizing that because things change all the time and this, this,
I think is is fundamental. What is in our control
are not the thoughts that we have right now. What's
what's in our control is the long term application of

(54:04):
effort to modify our own cognitive system to have different
thoughts in the future, the thoughts you would like to
have more of, and behaviors you would like to have
more of versus something else. So this idea of committing
to a consistent, long term process of self change, you know,
the Buddhists, you also call it the body step of
a vow, This idea of enlarging your cognitive light cones

(54:24):
so that you're able to have the goal of compassion,
you know, beyond our current limited human kind of scale
that we can actually you know, work towards the goals
of a certain size. Yeah, that's that's that's what motivates me.
And the plasticity is really I find it incredibly hopeful
and positive, this idea, this this incredible plasticity that has
intelligence at its core, that every single cell is intelligent

(54:48):
within and it's exerting intelligence in its cooperation and competition
with others to form larger scale structures that can be
molded top down, molded over time, to be better and
to improve over time.

Speaker 1 (55:07):
That was Mike Levin, a professor and biologist at Tufts University.
So wrapping up this two part episode about the self,
we saw that everything in your biology is changing all
the time.

Speaker 2 (55:19):
Your cells are.

Speaker 1 (55:20):
Constantly turning over their pieces and parts, but we have
memory to bind the use together. Now, I've talked in
several episodes about how memories change in their character. They're
not like a file of zeros and ones that are
written down in a computer and then read back out perfectly.

Speaker 2 (55:39):
And the way Michael Levin.

Speaker 1 (55:40):
Thinks about this is that memories get compressed. They get
encoded down into the neurons or the connections between neurons,
or the inner cosmos of proteins and side neuron and
then when these memories get reinflated later they find themselves
in a different world, they get it interpreted by the

(56:02):
new brain that is looking at them. So I want
to make this model clear. So here's my analogy to
capture that. Imagine that the world out there has lots
of things that need to be bolted down, and so
you create a wrench, and your metal wrench is in
some sense a compressed representation of the world out there,

(56:24):
a world full of bolts. So when you see the wrench,
that reminds you.

Speaker 2 (56:28):
Of all the bolts that are out there.

Speaker 1 (56:30):
Okay, Now, imagine that you bury that wrench, and some
other creature, some future human creature, digs it up in
a thousand years and she doesn't see it as a wrench,
but to her it's maybe a weapon, or it's an
instrument for conducting electricity.

Speaker 2 (56:50):
On her spaceship.

Speaker 1 (56:52):
Or she takes it to be a ceremonial artifact, or
she uses it for physical exercise, or she looks at
its clean, balanced design and uses it for a piece
of art. The point is that what you buried is
not what gets exhumed in a new world of the future.
And that's what happens to memories too. You bury something

(57:15):
that has some meaning in the now, but what you
dig up is interpreted through the eyes of.

Speaker 2 (57:22):
The future you.

Speaker 1 (57:23):
And if there's one thing we can count on, it's that,
despite all your intuitions to the contrary, that future you
will not be the same as the you now. It'll
be someone you don't know, who doesn't share all your
values and opinions, and is someone you can't accurately predict
the ship of theseus with all those changes does not

(57:46):
in fact remain the same ship. I was recently talking
with my friend Lisa Joy, and she said she thinks
it's strange that the longevity community cares so much about
extending their lifespan by decades, because that future person will

(58:06):
be somebody potentially very different from who they are now.
So who are you saving if you go through a
lot of trouble now to extend your life. Whoever you're saving,
it's a stranger to you. You're doing all this work
for someone you don't know. So, coming back to the
question of why we have this notion of an unchanging self,

(58:29):
Michael's answer is that the job of the brain is
to make models of consistency, like this is what a
chair is, this is what a backpack is, this is
what a bicycle is. And even though there may be
a lot of variety in the specifics that you come
across and things might change, you nonetheless are good at

(58:49):
summarizing things as objects. You lump them into unchanging categories.
And so Michael argues, the same cognitive machinery is turned
on to our selves. Even though there's a lot of
fluctuation of what that refers to. We lump the self
into one object that we call me, and that high

(59:12):
level cognitive model just doesn't change much. And so as
we close, we are left with this remarkable paradox that
we move through life carrying memories and stories and beliefs
about who we are, and we carefully preserve them like
relics in the soil of our minds, and we expect
them to stay the same. But with each retrieval, every

(59:35):
time we unearth them, they are interpreted afresh. They're reshaped
by the hands of a self that is itself ever shifting.
But the illusion our brains create for us as a
model of the self as unchanging, a fixed point in
a fluctuating world. And it's a comforting thought that we're

(59:56):
a single thread woven through time. Maybe there's also a
beauty in realizing that each of your future selves is
a stranger unto you, an explorer who picks up that
wrench of memory, holding it to the light and interpreting
something new each time.

Speaker 2 (01:00:18):
Who we are, what we hold dear.

Speaker 1 (01:00:20):
Maybe these aren't artifacts that are meant to be saved
or preserved perfectly.

Speaker 2 (01:00:25):
They are living stories.

Speaker 1 (01:00:27):
They're reimagined and repurposed by every future version of us.

Speaker 2 (01:00:33):
So when you.

Speaker 1 (01:00:34):
Think of your future self, who you will be tomorrow
or a month from now or a decade from now,
think of that stranger that future you, and maybe smile
at the mystery of what that person will even remember,
what they'll care about, what they'll let go of. After all,

(01:00:55):
part of the adventure of life is not just holding
on to who we were. It's also about meeting time
and again who we are becoming. Go to eagleman dot
com slash podcast for more information and find further reading.

(01:01:16):
Send me an email at podcast at eagleman dot com
with questions or discussion, and check out and subscribe to
Inner Cosmos on YouTube for videos of each episode and
to leave comments.

Speaker 2 (01:01:29):
Until next time.

Speaker 1 (01:01:30):
I'm David Eagleman and this is Inner Cosmos.