WEBVTT

00:00:00.480 --> 00:00:01.600
I'm David Pinsov.

00:00:01.840 --> 00:00:03.120
I'm David Petrozowski.

00:00:03.440 --> 00:00:07.360
And this is Evolutionary Psychology, the podcast, Actual Evolutionary Psychology.

00:00:07.680 --> 00:00:10.320
By actual evolutionary psychologists.

00:00:11.920 --> 00:00:15.520
Wow, that was worthy of an Oscar.

00:00:15.760 --> 00:00:16.000
Okay.

00:00:30.890 --> 00:00:36.570
David today's guest is uh Tidek Quillian and he studies really

00:00:36.810 --> 00:00:42.650
complicated things that are abstract and difficult to grasp from an adaptationist

00:00:42.730 --> 00:00:43.210
perspective.

00:00:43.840 --> 00:00:47.360
Well yeah, you make it sound really exciting and appealing for the listener.

00:00:47.840 --> 00:00:52.080
Well I I honestly this is if you if you're if you're if you feel like you want a mental

00:00:52.080 --> 00:00:54.080
Rubik's Cube, this is the episode for you.

00:00:54.320 --> 00:00:57.600
This this is uh a a mentally challenging episode for sure.

00:00:57.680 --> 00:01:00.640
It certainly um expanded both of our minds.

00:01:00.880 --> 00:01:05.920
Um so yeah maybe if you're sleepy this isn't the best time to to dive into this meaty

00:01:05.920 --> 00:01:06.160
podcast.

00:01:06.560 --> 00:01:12.450
I did I did uh uh I did rant about uh what a domain is though so there's yeah we

00:01:12.610 --> 00:01:17.490
we had some good rants in there about uh simplicity and domain specificity and what it

00:01:17.490 --> 00:01:18.850
means to be computational.

00:01:19.250 --> 00:01:25.010
Uh though this this one if if you're you know alert, you got a cup of coffee with you and

00:01:25.010 --> 00:01:31.010
you're and you think you can tackle this, this is a really um fascinating, insightful, mind

00:01:31.170 --> 00:01:35.410
expanding episode about the nature of causality, yeah.

00:01:35.570 --> 00:01:40.020
Uh which touches on literally everything the mind does, which is a point we get to in the

00:01:40.020 --> 00:01:40.500
episode.

00:01:40.820 --> 00:01:44.260
We could not do anything if we had no understanding of causality.

00:01:44.500 --> 00:01:50.100
Uh and and Taydeg just has a really uh elegant, insightful way of

00:01:50.100 --> 00:01:53.140
thinking about this complex topic.

00:01:53.300 --> 00:01:57.550
Um and I I found a lot of value and learned a lot from uh talking to Tay Deg.

00:01:57.790 --> 00:01:57.950
Yeah.

00:01:58.030 --> 00:02:03.710
Uh it's a really cool example of cool uh work at the cutting edge of uh computational

00:02:03.950 --> 00:02:08.510
modeling and um sort of philosophy of causation.

00:02:08.910 --> 00:02:12.750
Um and uh I think it's a challenge.

00:02:12.830 --> 00:02:17.710
It's this is you should take this as a challenge to um uh grapple with these issues.

00:02:17.790 --> 00:02:20.110
These are these are chewy issues to deal with.

00:02:20.840 --> 00:02:21.160
Absolutely.

00:02:21.240 --> 00:02:24.120
Very chewy, very crunchy, um, very meaty.

00:02:24.280 --> 00:02:26.600
Uh I'm just gonna run through the food metaphors.

00:02:27.400 --> 00:02:28.520
Uh this is great.

00:02:28.680 --> 00:02:33.800
Um but uh but yeah, without further ado, here is Tidek uh and uh David and I talking about

00:02:33.800 --> 00:02:35.880
all things easy like causality.

00:02:40.680 --> 00:02:44.520
Tadek, tell us about where you are and how long you've been there.

00:02:45.610 --> 00:02:46.970
I'm in Scotland.

00:02:47.290 --> 00:02:48.730
I'm at the University of Edinburgh.

00:02:48.890 --> 00:02:51.050
And it's been about five years now.

00:02:51.690 --> 00:02:56.490
There's like a storied history of Edinburgh and um computer science.

00:02:56.650 --> 00:02:59.450
And um yeah, is that true?

00:02:59.930 --> 00:03:05.130
So the I think the the first center for co cognitive science uh maybe in the world was in

00:03:05.130 --> 00:03:06.170
Edinburgh.

00:03:07.120 --> 00:03:13.120
So we had uh people like Nick Chater were here, uh people like Jeff Hinton, Peter

00:03:13.120 --> 00:03:14.000
Diane.

00:03:14.800 --> 00:03:18.880
Why Edinburgh and then they chose to dissolve the center for some reason.

00:03:20.080 --> 00:03:21.920
Why why Edinburgh?

00:03:22.000 --> 00:03:27.440
I mean not to be US centric about it or continental centric about it, but like why

00:03:27.440 --> 00:03:28.480
Edinburgh?

00:03:29.410 --> 00:03:34.610
Um how do you mean why uh such a what is the why the

00:03:35.250 --> 00:03:41.170
the the the answer I like is that maybe in the it's a city that has a long intellectual

00:03:41.170 --> 00:03:46.530
history and we have people like um people like David Hume that's yeah I was gonna say David

00:03:46.610 --> 00:03:48.930
I was gonna do David Hume joke actually.

00:03:50.450 --> 00:03:54.610
So I I like to think that that is where the cognitive science energy is coming from.

00:03:54.850 --> 00:03:55.170
Yeah.

00:03:55.560 --> 00:03:56.440
David Hume.

00:03:57.880 --> 00:03:59.320
Have you read David Hume?

00:04:00.200 --> 00:04:00.760
I have.

00:04:01.240 --> 00:04:05.800
It's actually I started as a political science major.

00:04:06.600 --> 00:04:10.680
And there was a political philosophy class I had to take.

00:04:11.240 --> 00:04:17.080
And almost kind of randomly, I trust you to read a book by David Hume because we had to

00:04:17.080 --> 00:04:19.400
kind of pick a book and write a report about it.

00:04:19.720 --> 00:04:24.610
And that's, I think that was my gateway direct towards uh cognitive science.

00:04:24.850 --> 00:04:25.250
Yeah.

00:04:25.570 --> 00:04:31.410
Um just to be on this for a second, um, there are two opinions about David Hume, and I

00:04:31.410 --> 00:04:32.050
think both are right.

00:04:32.130 --> 00:04:33.330
And I'm curious where you stand.

00:04:33.490 --> 00:04:37.250
One is that he was not an empiricist, and one is that he was.

00:04:37.410 --> 00:04:38.450
Where do you stand?

00:04:39.970 --> 00:04:45.170
Um I think I I stand with the kind of classical view that he was an empiricist.

00:04:45.730 --> 00:04:51.280
Um but I mean I can see the the pull of the opposing argument

00:04:51.840 --> 00:04:57.840
because uh so Immanuel Kant said that he was woken from his dogmatic slumber

00:04:57.840 --> 00:05:01.840
by reading David Hume, and Kant is famously not an empiricist.

00:05:02.000 --> 00:05:02.160
Yeah.

00:05:02.320 --> 00:05:08.130
Um and so I kind of read David Hume as um like someone who

00:05:08.130 --> 00:05:13.970
pushes empiricism to to its logical limits, and you find that if you push empiricism

00:05:13.970 --> 00:05:19.570
to its limits, then you're forced to uh quite an extreme degree of skepticism.

00:05:19.730 --> 00:05:23.970
Uh and then that's a reason in itself to be skeptical of empire of empiricism.

00:05:24.450 --> 00:05:29.570
For people who might not know, can we just make this kind of grounded and concrete?

00:05:29.730 --> 00:05:35.700
Like um just like you know, what what was the basic argument This is gonna turn into uh

00:05:35.780 --> 00:05:40.340
uh um uh what what do you know about David Hume dial a friend?

00:05:40.420 --> 00:05:44.740
Uh well I mean at least for for my purposes, yeah.

00:05:44.980 --> 00:05:49.780
Um what I find interesting is his argument about about causality.

00:05:49.940 --> 00:05:50.180
Yeah.

00:05:50.420 --> 00:05:56.260
Um because um so David Hume was concerned with why it is that

00:05:56.260 --> 00:05:58.740
we can come to have causal knowledge at all.

00:05:59.060 --> 00:06:04.830
Okay, and so he observed that well, we can come to know that there is an

00:06:04.830 --> 00:06:10.590
association between uh the presence of clouds in the sky and then

00:06:10.590 --> 00:06:11.230
rain.

00:06:11.550 --> 00:06:17.550
And I know day after day we see that when there is when there are clouds in the

00:06:17.550 --> 00:06:19.150
sky, rain follows.

00:06:19.870 --> 00:06:24.750
And so we form by by induction this kind of sort of a strong association between those two

00:06:24.750 --> 00:06:25.150
things.

00:06:25.790 --> 00:06:31.710
But then it seems that in order to really uh come to the conclusion that there is a

00:06:32.320 --> 00:06:38.320
causal relationship between those things, that the the rain causes the that the cloud

00:06:38.320 --> 00:06:42.400
is the cause of the rain, then there's an extra step that we need to take.

00:06:43.120 --> 00:06:48.000
And it is not clear that this take can be justified in in a rational way.

00:06:48.560 --> 00:06:53.520
So the idea is that so I've always seen clouds, then rain, clouds and rain.

00:06:53.680 --> 00:06:57.280
And then I say, look, clouds cause rain, and somebody asks me, Well, how do you know that?

00:06:57.440 --> 00:07:00.880
And I say, Well, they've always preceded that.

00:07:01.130 --> 00:07:06.650
And the argument is that well, yeah, that's your data, but where do you get

00:07:07.130 --> 00:07:09.370
where do you get the conclusion that that caused that?

00:07:09.450 --> 00:07:10.330
Maybe it was something else.

00:07:10.730 --> 00:07:11.210
Yeah, exactly.

00:07:11.290 --> 00:07:13.850
And it's quite difficult to justify that.

00:07:15.050 --> 00:07:17.930
Is this just the correlation causation fallacy?

00:07:18.170 --> 00:07:22.410
Because you could say, well, no, clouds don't cause rain, rain causes clouds.

00:07:22.570 --> 00:07:27.610
Or maybe some other thing, maybe uh Zeus getting angry causes both, right?

00:07:27.900 --> 00:07:28.700
Um right.

00:07:29.020 --> 00:07:33.180
So how do we know that that it's called the causal arrow is going from clouds to rain?

00:07:33.260 --> 00:07:36.540
Is it is that it or is is is the the skepticism deeper than that?

00:07:36.940 --> 00:07:42.700
So I think this might be pushing beyond my ex philosophical expertise, but uh I I would say

00:07:42.700 --> 00:07:45.580
that maybe the worry is even a little bit deeper.

00:07:45.820 --> 00:07:51.570
Um the idea is that even if you can um sort of control exclude those

00:07:51.570 --> 00:07:52.450
alternative explanations.

00:07:52.530 --> 00:07:57.410
So even if you can definitely exclude that the causal direction is not from the rain to the

00:07:57.410 --> 00:07:57.890
clouds.

00:07:58.450 --> 00:08:04.450
Then you still have the question of what justifies me to think that there is a causal

00:08:04.450 --> 00:08:05.250
relationship.

00:08:06.210 --> 00:08:12.140
And uh we're kind of getting into the territory of um it is

00:08:12.140 --> 00:08:15.900
a bit weird to posit something like a causal relationship.

00:08:16.060 --> 00:08:17.980
This is not something that we can see.

00:08:18.380 --> 00:08:18.700
Yes.

00:08:19.260 --> 00:08:20.460
It's not so there is a data.

00:08:20.860 --> 00:08:23.660
Yes, yes, there is a lip of safe that we have to take regardless.

00:08:23.980 --> 00:08:28.140
And I think like this is something I was worried about before we had this conversation, and

00:08:28.140 --> 00:08:33.180
I want to like clear away because when I when I I remember when I first started learning

00:08:33.180 --> 00:08:38.430
about like causation and things like that as an early student, I found it very obtuse, like

00:08:38.430 --> 00:08:40.830
this kind of thing where I'd be like, okay, yeah, whatever.

00:08:40.910 --> 00:08:42.670
Like, of course, clouds, clouds, rain, whatever.

00:08:42.750 --> 00:08:43.870
Why guys this is important?

00:08:44.030 --> 00:08:49.710
And I think it's worth like just taking a step back to just dispelling like why this is not

00:08:49.710 --> 00:08:51.470
obtuse and why it's actually really important.

00:08:51.630 --> 00:08:57.520
And I think the cloud thing is a good example where my understanding is and like why

00:08:57.520 --> 00:09:03.280
I now find it not at all obtuse, but like it's fundamentally like the same non-bullshitty

00:09:03.280 --> 00:09:05.680
thing as like kids asking, well, why, why, why?

00:09:05.760 --> 00:09:08.720
Like that's a simple non-bullshitty thing.

00:09:08.960 --> 00:09:14.400
And my understanding is that it is kind of like David's thing where it's like clouds rain,

00:09:14.560 --> 00:09:15.520
clouds rain.

00:09:15.760 --> 00:09:19.110
You've always seen that relationship, but you don't know.

00:09:19.350 --> 00:09:21.190
It might be something else.

00:09:21.430 --> 00:09:26.630
And the problem of induction is really just that it's always correlation.

00:09:26.790 --> 00:09:30.950
Like that was kind of my read of Hume was like, it's always correlation.

00:09:31.430 --> 00:09:35.830
And it's weird that people don't represent that.

00:09:35.910 --> 00:09:41.190
Well, I'm I have a tentative guess that it is a correlation, but I should reserve in my

00:09:41.190 --> 00:09:45.000
mind the fact that maybe that's just a correlation.

00:09:45.080 --> 00:09:50.760
And I don't have this idea that no matter what, and especially in the future,

00:09:51.160 --> 00:09:53.080
it's going to be the same way.

00:09:53.240 --> 00:09:59.240
Like I'm really taking a leap of faith in saying the way it is today is the same it's

00:09:59.240 --> 00:10:04.200
going to be tomorrow because I don't have all the information I need to make that leap of

00:10:04.200 --> 00:10:04.520
faith.

00:10:04.600 --> 00:10:06.440
And then therefore you could be wrong.

00:10:06.680 --> 00:10:12.190
And to my mind, like that's the problem of induction, is it's always

00:10:12.190 --> 00:10:18.190
correlation because there's an infinite number of causal relationships that the

00:10:18.430 --> 00:10:24.350
the correlation you've seen so far is not determinative of what the

00:10:24.350 --> 00:10:27.470
actual underlying causal thing, and that's the obtuse way.

00:10:27.630 --> 00:10:31.790
But the simple way of putting it is like if you don't know why something happens, you might

00:10:31.790 --> 00:10:32.750
be wrong in the future.

00:10:32.830 --> 00:10:34.430
And that that's my read.

00:10:34.670 --> 00:10:40.020
And that sort of grounds it in like non-bullshit, like this is a fitness problem because

00:10:40.180 --> 00:10:46.020
like you gotta know like did the tiger eat the my friend for X reason or Y reason

00:10:46.100 --> 00:10:48.260
and can I be go back there tomorrow?

00:10:48.420 --> 00:10:49.700
Like that's not bullshit.

00:10:49.780 --> 00:10:52.980
That's not like, you know, well is it causation or correlation?

00:10:53.060 --> 00:10:58.500
But we know it always like the this is like really like um real um stuff here.

00:10:58.660 --> 00:11:02.180
I just so that's my um yeah clearing of that.

00:11:02.580 --> 00:11:07.540
Yeah the the often I mean not often hopefully but sometimes it will be a a life or death

00:11:07.540 --> 00:11:07.780
matter.

00:11:08.030 --> 00:11:08.990
Yeah, exactly.

00:11:09.150 --> 00:11:09.470
Yeah.

00:11:09.710 --> 00:11:10.190
I don't know.

00:11:11.070 --> 00:11:11.230
Yeah.

00:11:11.710 --> 00:11:16.830
One one example to you, one reason why it's important to represent

00:11:17.070 --> 00:11:22.510
causality instead of just a pattern of correlations, is that um storing

00:11:22.990 --> 00:11:27.870
having a a predictive model of the world that is just a pattern of correlations um is not a

00:11:27.870 --> 00:11:30.190
very stable way of representing the world.

00:11:30.750 --> 00:11:36.350
So I mean you might um notice that there is a high correlation between um there being

00:11:36.350 --> 00:11:40.060
rain and then the grass in your in your garden being wet.

00:11:40.460 --> 00:11:43.260
And you store the fact that the correlation is maybe 0.3.

00:11:44.780 --> 00:11:45.980
So far so good.

00:11:46.540 --> 00:11:51.660
But maybe you install a sprinkler in your garden and to to to water the grass.

00:11:52.140 --> 00:11:56.300
Now once you do that, then the correlation between the rain and the grass being wet is

00:11:56.300 --> 00:11:57.740
going to be much lower.

00:11:57.980 --> 00:11:58.220
Yeah.

00:11:59.180 --> 00:12:03.370
And this is not something you know this is not something where you have a nice procedure

00:12:03.370 --> 00:12:08.250
where we can say, Well, I can just adjust the correlation based on this new information.

00:12:08.490 --> 00:12:08.810
Yeah.

00:12:09.130 --> 00:12:12.490
There's nothing in the correlation that really tells you how you sh you're supposed to do

00:12:12.490 --> 00:12:12.650
that.

00:12:13.370 --> 00:12:18.970
Instead, if what's if your representation is a representation of the is a causal

00:12:18.970 --> 00:12:24.970
relationship between the rain and the and the the grass being wet, then you can

00:12:24.970 --> 00:12:29.850
incorporate this new information that there is a sprinkler in a much easier way.

00:12:30.400 --> 00:12:30.640
Yeah.

00:12:30.880 --> 00:12:35.680
It's like Paul Smaldino calls it uh being brittle, and he uses the example of science,

00:12:35.760 --> 00:12:36.000
right?

00:12:36.080 --> 00:12:39.840
So, like if you have this thing of like, I think we use the example, I think Noam Chomsky

00:12:39.920 --> 00:12:45.680
talks about this, is you could have a model, uh a correlational non-causal model of leaves

00:12:45.680 --> 00:12:47.920
falling, just describing the data.

00:12:48.000 --> 00:12:51.680
And that's what like a lot of science is, like, especially social science, it's just

00:12:51.680 --> 00:12:52.640
describing the data.

00:12:52.800 --> 00:12:55.840
But then that's not going to work for bowling balls, how they fall.

00:12:56.010 --> 00:12:58.090
Um, that's not going to work for a lot of other things.

00:12:58.170 --> 00:12:58.970
It's very brittle.

00:12:59.050 --> 00:13:00.330
It only applies to that context.

00:13:00.410 --> 00:13:03.850
And you don't even know what the context is because you don't have a theory, because you

00:13:03.850 --> 00:13:07.610
don't have like um coefficients of drag and accelerations of gravity.

00:13:07.690 --> 00:13:10.650
You just have the descriptions of the relationships.

00:13:10.810 --> 00:13:16.490
And so the whole replication crisis, or like the this effect holds in this context, but not

00:13:16.650 --> 00:13:20.820
another one, is the scientific equivalent of exactly what you're talking about.

00:13:20.980 --> 00:13:25.620
Um that it's this, as Paul Smaldino says, it's this brittle thing that as soon as you try

00:13:25.620 --> 00:13:29.700
to pick it up and move it, it's like a tower of blocks and it just falls apart.

00:13:30.580 --> 00:13:35.380
Yeah, and I mean to be clear, uh having a description is a a good starting point.

00:13:35.860 --> 00:13:41.830
Sometimes you sometimes you can't really start by having a nice theory, you need to gather

00:13:41.830 --> 00:13:42.870
a lot of data.

00:13:43.030 --> 00:13:48.150
Um, but it certainly cannot be the end goal of uh of a science.

00:13:48.310 --> 00:13:53.830
Um and yeah, and because we we should ideally be extracting the invariants, the things

00:13:53.910 --> 00:13:59.510
such that if uh if you look at a different context, you know that the invariants are still

00:13:59.510 --> 00:14:04.550
there and you understand how they work, um, and then you can make new new predictions.

00:14:07.660 --> 00:14:13.660
So I uh to me this is uh a lovely example of uh instinct blindness and

00:14:13.660 --> 00:14:16.460
and making the natural seem strange.

00:14:16.620 --> 00:14:22.060
Uh this idea that causality is not in the input itself, it's something that the mind

00:14:22.060 --> 00:14:26.780
constructs in the same way that you know uh the mental states of other people, there's

00:14:26.940 --> 00:14:32.830
there's nothing in you looking uh in one direction that necessarily says

00:14:32.830 --> 00:14:35.710
that you are that you know what's over in that direction, right?

00:14:35.790 --> 00:14:37.710
That's something that I'm projecting onto you.

00:14:37.870 --> 00:14:42.830
Uh, if you're moving really uh quickly toward a particular object, I'm projecting the fact

00:14:42.830 --> 00:14:44.270
that you want that object, right?

00:14:44.350 --> 00:14:45.870
That that's not in the data.

00:14:46.030 --> 00:14:49.950
Um, in the same way as you know, I'm picking up this coffee mug and drinking it, and when I

00:14:49.950 --> 00:14:55.680
put it down on the table, there's nothing in the sensory stimulation on the coffee cup that

00:14:55.680 --> 00:14:57.680
says it's my coffee cup, that it belongs to me.

00:14:57.840 --> 00:15:01.040
Ownership is another thing that we project onto the world.

00:15:01.200 --> 00:15:06.480
And what's uh lovely about your work is that it's we're doing the same thing with

00:15:06.480 --> 00:15:07.440
causality, right?

00:15:07.600 --> 00:15:10.080
And in some sense, we're constructing it.

00:15:10.320 --> 00:15:14.880
Um so to me, that leaves the question of how we are constructing.

00:15:15.120 --> 00:15:20.160
So, given that all we're getting is just uh, you know, photons hitting the retina,

00:15:20.320 --> 00:15:26.090
vibrations hitting our eardrums, how how do we take that information and

00:15:26.090 --> 00:15:29.770
construct causal relationships out of that data?

00:15:30.250 --> 00:15:30.410
Yeah.

00:15:30.490 --> 00:15:32.410
And can I add to that question really quick?

00:15:32.570 --> 00:15:34.090
What is it that we're constructing?

00:15:34.250 --> 00:15:35.050
Like what is it?

00:15:35.130 --> 00:15:38.090
Uh software, like what does it mean that we have a causal reper?

00:15:38.490 --> 00:15:39.050
That would be great.

00:15:39.130 --> 00:15:39.450
Thank you.

00:15:39.530 --> 00:15:39.770
Yeah.

00:15:39.850 --> 00:15:40.010
Yeah.

00:15:40.330 --> 00:15:40.650
Yeah.

00:15:40.730 --> 00:15:41.050
Yeah.

00:15:41.130 --> 00:15:45.610
And that's uh that's that's last question is the first question that we should ask,

00:15:45.770 --> 00:15:46.410
obviously.

00:15:46.650 --> 00:15:52.080
Um so I mean I I take it and and a lot of people who are working in the field of causal

00:15:52.080 --> 00:15:57.920
cognition take it that the target, the goal is to construct a causal model uh of

00:15:57.920 --> 00:16:02.640
the world, or at least a causal model of the particular system that we're interested in.

00:16:03.120 --> 00:16:08.960
And so this causal model uh will be made up of of variables uh and then the

00:16:08.960 --> 00:16:10.880
causal relationships between them.

00:16:11.200 --> 00:16:16.720
So um if I have a a causal model of of the rain then maybe

00:16:17.190 --> 00:16:20.710
one of my variables will be the presence of clouds in the sky.

00:16:21.190 --> 00:16:24.710
Another variable will be the presence of rain.

00:16:24.950 --> 00:16:29.270
And then we will have another variable that is is the grass wet, for example.

00:16:30.310 --> 00:16:34.550
And then we can express the causal relationships between them.

00:16:34.790 --> 00:16:40.310
So what is the color the causal relationships between the rain and the clouds?

00:16:40.470 --> 00:16:46.100
And then what is the causal relationships between the whether the grass is wet

00:16:46.260 --> 00:16:47.940
uh and and the rain.

00:16:48.340 --> 00:16:53.220
And there's various formalisms for how you might represent those causal relationships.

00:16:53.540 --> 00:16:56.740
And we don't need to get into the the details.

00:16:57.620 --> 00:17:03.300
But basically something that is quite um important and that is kind of foundational to the

00:17:03.300 --> 00:17:08.870
way that we think about causality is that the relationship between two variables is

00:17:08.870 --> 00:17:10.150
asymmetric, right?

00:17:10.310 --> 00:17:16.230
So it's not just that we know that there is an association between uh clouds and

00:17:16.230 --> 00:17:16.630
rain.

00:17:16.950 --> 00:17:21.830
It's just that there's this asymmetry that it is the clouds that are causing the rain and

00:17:21.830 --> 00:17:23.270
and not vice versa.

00:17:23.990 --> 00:17:29.960
Um and so one way to actually represent that pact concretely is by

00:17:30.520 --> 00:17:35.640
building into that the model this commitment that if we were to intervene on the on the

00:17:35.640 --> 00:17:39.400
clouds, then this will have a downstream effect on rain.

00:17:39.800 --> 00:17:45.400
So if by some if I had some magical powers and I was able to to

00:17:45.400 --> 00:17:49.480
prevent the clouds from forming, then there would not be any rain.

00:17:50.040 --> 00:17:55.550
But if I were to kind of uh if I were to build a giant dome over the city that

00:17:55.790 --> 00:17:59.870
shelters us from the rain, this would not make the clouds go away.

00:18:00.350 --> 00:18:06.030
And so that is the way that we kind of build in this important distinction between

00:18:06.350 --> 00:18:09.870
something like X causing Y and Y causing X.

00:18:10.670 --> 00:18:14.430
So just to we can keep going on, but just to clarify.

00:18:14.590 --> 00:18:18.910
So what I'm hearing is because it's hard to talk about this, because this is again where

00:18:18.910 --> 00:18:23.720
natural language fails us, is that if we snip out the word causal, if we're trying to

00:18:23.720 --> 00:18:27.640
explain what a causal relationship is and we snip out because we don't want to use the term

00:18:27.640 --> 00:18:28.760
in the explanation.

00:18:29.000 --> 00:18:34.920
It's a relationship that excuse me is um it's a relationship

00:18:34.920 --> 00:18:39.000
that has like directionality and then also

00:18:40.920 --> 00:18:46.830
it sort of picks out um what what is likely

00:18:46.830 --> 00:18:52.430
to if one thing is present and another thing will be present in sort of a compositional

00:18:52.590 --> 00:18:54.110
order-dependent way.

00:18:54.510 --> 00:19:00.350
So it's sort of saying, you know, like when this when this bit of the world is there.

00:19:01.230 --> 00:19:03.470
I don't even know how to talk about it, but yeah, right?

00:19:03.630 --> 00:19:04.830
Like, is that is that fair?

00:19:04.990 --> 00:19:08.270
Like it's sort of that, you know, this this is necessary.

00:19:08.840 --> 00:19:14.360
I don't even like using the word necessary, but it's just it's these placeholders for

00:19:15.080 --> 00:19:20.840
um you can't change the order, and then if you

00:19:21.080 --> 00:19:25.400
manipulate this thing, this other thing, they're kind of attached to each other in terms of

00:19:25.560 --> 00:19:26.040
how comes of the thing.

00:19:26.760 --> 00:19:30.280
This this notion of manip manipulability is quite important.

00:19:30.360 --> 00:19:33.960
So sometimes we talk about interventions of manipulate manipulability, etc.

00:19:34.280 --> 00:19:40.150
But it's this idea that if if someone could kind of reach into the system from the outside

00:19:40.630 --> 00:19:46.150
and change the state of change what happens to uh at some node in the

00:19:46.150 --> 00:19:49.510
network, then you will have particular effects somewhere.

00:19:49.830 --> 00:19:49.990
Right.

00:19:50.070 --> 00:19:53.590
I like that we not example yeah, I like the example of moon and tides.

00:19:53.750 --> 00:19:56.950
Like nobody's ever nobody's ever grabbed the moon.

00:19:57.110 --> 00:20:02.070
But it but if but the theory is the the causal theory of moon and tides is if somebody

00:20:02.070 --> 00:20:03.910
grabbed the moon and moved it, the tides would be different.

00:20:04.150 --> 00:20:08.960
Yes, and it's quite remarkable that we can entertain this despite the obvious imp

00:20:09.120 --> 00:20:11.680
impracticality of actually moving the moon.

00:20:12.080 --> 00:20:15.040
It's something that's quite easy to imagine to think about.

00:20:15.360 --> 00:20:20.560
And if you were to explain to someone how tides work, then that would be one way to to do

00:20:20.560 --> 00:20:20.640
that.

00:20:20.800 --> 00:20:25.280
You can say, well yes, if we move the moon then the tide would happen in a different way.

00:20:25.760 --> 00:20:30.720
And I guess you're the person that you're trying to teach to would not be bewildered at at

00:20:30.720 --> 00:20:31.360
the idea.

00:20:32.690 --> 00:20:33.010
I don't know.

00:20:33.090 --> 00:20:37.810
Maybe maybe in certain cultures this this is a bit strange to teach things in that way.

00:20:38.290 --> 00:20:41.170
But I guess the general yeah, the general idea is quite intuitive.

00:20:41.650 --> 00:20:46.850
So it seems it seems like what you're saying is that causality is ultimately about

00:20:46.850 --> 00:20:48.370
hypothetical actions.

00:20:48.770 --> 00:20:54.210
And that if I think A causes B, what that means is just uh I'm just translating that into

00:20:54.210 --> 00:20:56.130
the language of hypothetical things I can do.

00:20:56.370 --> 00:20:58.290
If I add A, I'm gonna get B.

00:20:58.530 --> 00:21:01.490
If I take away A, uh then B goes away.

00:21:01.740 --> 00:21:02.060
Right?

00:21:02.460 --> 00:21:07.420
These are hypothetical, but e even if I'm in incapable of taking away A or adding A, if I

00:21:07.500 --> 00:21:10.540
you know, if if I don't have magical powers, I can't do anything about the clouds.

00:21:10.620 --> 00:21:16.460
Uh merely the the ability to entertain uh the the possible action of adding or

00:21:16.460 --> 00:21:19.180
removing A will have an effect.

00:21:19.420 --> 00:21:21.900
I'm using up the word effect, but will change B.

00:21:22.220 --> 00:21:24.530
Um, that is ultimately what causality is.

00:21:24.610 --> 00:21:29.170
It's framed in terms of hypothetical interventions or actions or impacts you can have on

00:21:29.170 --> 00:21:33.010
the world, uh, and what is going to be changed in the world as a result.

00:21:33.330 --> 00:21:36.850
Again, I'm using the word as a result, which gets us back into causality.

00:21:37.010 --> 00:21:40.050
But what so it's a it's about hypothetical actions.

00:21:40.450 --> 00:21:40.770
Right?

00:21:41.090 --> 00:21:41.410
Yes.

00:21:41.650 --> 00:21:42.610
Uh yes, yes.

00:21:42.690 --> 00:21:45.330
That's uh it's a great way of explaining it.

00:21:45.490 --> 00:21:51.440
Um And I think that the the fact that we are we we

00:21:51.440 --> 00:21:55.840
are quite uh able to entertain those hypothetical actions.

00:21:56.080 --> 00:22:00.720
Um I think this is one of the best psychological evidence we have that people are

00:22:00.720 --> 00:22:02.640
constructing causal models of the world.

00:22:03.120 --> 00:22:08.880
They're not just passively trying to store patterns of correlations in in

00:22:09.670 --> 00:22:12.070
that that results from their experience, right?

00:22:12.310 --> 00:22:17.910
Because I have never experienced a state of the world where the moon has

00:22:18.150 --> 00:22:20.550
been kind of changed course.

00:22:21.510 --> 00:22:24.390
But I can imagine a manipulation that would do so.

00:22:24.870 --> 00:22:30.150
Maybe a better example is I have never imagined the state of the world where the sun

00:22:30.150 --> 00:22:31.750
suddenly disappears.

00:22:31.910 --> 00:22:37.420
But I can quite easily imagine what would happen if a sort of an action, a

00:22:37.420 --> 00:22:40.380
hypothetical action, was taken and the sun goes out.

00:22:41.580 --> 00:22:46.700
And this, I mean, if you are if you are looking at the mind from a sort of behaviorist

00:22:46.700 --> 00:22:52.620
perspective, where all you're doing is keeping track of associations, maybe keeping

00:22:52.620 --> 00:22:56.940
track of the results of your own actions, you would not really predict that people have the

00:22:56.940 --> 00:22:58.940
ability to engage in that kind of thinking.

00:22:59.340 --> 00:23:04.050
And you'd you don't you would even less predict the fact that people actually quite like

00:23:04.050 --> 00:23:05.810
thinking about those those sorts of things.

00:23:06.130 --> 00:23:06.290
Yeah.

00:23:06.530 --> 00:23:10.370
It's also a point like I I don't know who made this point, or maybe other whatever.

00:23:10.530 --> 00:23:15.890
Um, is uh the associationism never really talked about what is done with those

00:23:15.890 --> 00:23:17.090
representations either.

00:23:17.250 --> 00:23:21.570
Like it was very much uh um explain the data given the study.

00:23:21.810 --> 00:23:26.210
But but yeah, like the problem, like a good example also is the desert ant where you know

00:23:26.290 --> 00:23:31.180
it goes out, it has polar light sensitive thing, it does dead reckoning where it

00:23:31.980 --> 00:23:37.180
encodes distance by steps and uh angle of the sun as it moves.

00:23:37.340 --> 00:23:41.820
And then it does that integration when it finds prey and goes back to the nest.

00:23:42.620 --> 00:23:47.820
You know, if you just say, well, it's just associating, um, it's just associating stuff,

00:23:47.980 --> 00:23:51.100
like there's no notion of like what the association is for.

00:23:51.180 --> 00:23:54.700
But the second you have this thing of like, well, the whole point of doing the association

00:23:54.700 --> 00:24:00.590
is creating some representation of where the nest is, That's very much like a model, right?

00:24:00.750 --> 00:24:06.190
Um, that's integrating stuff for a purpose and it allows you to predict and go on a

00:24:06.190 --> 00:24:11.950
novel d dead point uh line to where the nest is so you don't fry

00:24:11.950 --> 00:24:14.990
yourself in the desert, which is why it's a selection pressure.

00:24:15.150 --> 00:24:20.940
But no, I like that that this is very evolutionary um um in

00:24:20.940 --> 00:24:26.380
a way is in is this causal model uh literature

00:24:26.620 --> 00:24:32.380
evolutionary and what does evolution buy you that may not be in the literature

00:24:32.540 --> 00:24:37.980
or or is it i don't know yeah is that a coherent yeah no that that that's that's a great

00:24:37.980 --> 00:24:43.910
question i i think um i would say that the causal cognition literature is implicitly

00:24:43.910 --> 00:24:44.950
evolutionary.

00:24:45.350 --> 00:24:47.590
So it definitely has a functionalist band.

00:24:48.150 --> 00:24:53.430
So people who look at causal cognition, they they ask, well no, what is the purpose of the

00:24:53.430 --> 00:24:53.670
system?

00:24:53.990 --> 00:24:55.830
Why why should we have those computations?

00:24:55.910 --> 00:25:00.070
And and it's really coming committed to this idea that people are building representations.

00:25:00.950 --> 00:25:06.390
And so uh there's also this uh very strong commitment to to optimality,

00:25:07.060 --> 00:25:10.660
at least as a as a sort of methodological heuristic.

00:25:11.140 --> 00:25:16.340
So because I mean once you have this machinery of causal models, you can ask, well,

00:25:16.900 --> 00:25:20.020
what is the best way to work with a causal model?

00:25:20.100 --> 00:25:25.940
So for example, you're trying to learn well, given all the data that I have,

00:25:26.340 --> 00:25:32.180
what is the probability that um the what is the probability that it is

00:25:32.180 --> 00:25:36.420
raining right now, given that all I can observe is that the grass is wet.

00:25:36.670 --> 00:25:42.190
And this is something that you can solve using the mathematics of uh of

00:25:42.510 --> 00:25:43.550
probability theory.

00:25:43.950 --> 00:25:46.590
So it's basically a Bayesian inference problem.

00:25:46.670 --> 00:25:52.590
So you have to set up um Bayes' theorem, and then you can work out what is the probability

00:25:52.590 --> 00:25:54.430
of a cause given the effect.

00:25:54.670 --> 00:26:00.480
Um and so we can look at whether people actually um Compute,

00:26:00.720 --> 00:26:02.640
engage in causal reasoning in that way.

00:26:03.120 --> 00:26:08.800
To what extent is it the case that when people sorry, when people make those

00:26:08.800 --> 00:26:13.600
inferences, there are actually reasoning in the normative way that is described by

00:26:13.760 --> 00:26:14.640
probability theory.

00:26:28.310 --> 00:26:30.550
have hypotheses that can be tested.

00:26:31.350 --> 00:26:36.790
And from my own point of view, as an evolutionary psychologist, I think that the

00:26:36.950 --> 00:26:42.870
another justification for the optimality analysis is that you should expect

00:26:43.030 --> 00:26:48.870
to have first order of approximation that people will solve those problems in uh in the

00:26:48.870 --> 00:26:53.830
normatively correct way because they have been a relatively high selection pressure for

00:26:53.830 --> 00:26:56.950
people to really um be good at causal reasoning.

00:26:57.800 --> 00:27:00.200
So that's the first part of the of the answer.

00:27:01.000 --> 00:27:06.840
The second part of the answer is that people who do causal recognition, they are

00:27:06.840 --> 00:27:12.840
often not um they're not quite concerned about domain specificity in the way

00:27:12.840 --> 00:27:15.160
that most of us would be concerned.

00:27:16.520 --> 00:27:21.560
So we're not going to to ask well no what are the the

00:27:22.200 --> 00:27:27.010
principles that people draw on when they think about biology, for example.

00:27:27.490 --> 00:27:33.250
And one, I guess, one motivation behind this choice is that there are

00:27:33.250 --> 00:27:37.010
general principles of causality that are supposed to apply across the board.

00:27:37.170 --> 00:27:43.010
And so this idea that causality is a matter of intervention, and that you need to

00:27:43.010 --> 00:27:47.170
engage in some sort of Bayesian reasoning in order to engage in causal reasoning, those are

00:27:47.170 --> 00:27:52.460
things that will apply regardless of whether you are thinking about psychological reasoning

00:27:52.460 --> 00:27:56.300
or biological biological reasoning or physical physical reasoning.

00:27:56.620 --> 00:28:02.460
And so I mean to us at least from the outside this can appear as

00:28:02.460 --> 00:28:04.300
something that is very domain general.

00:28:04.780 --> 00:28:08.060
But I would argue that there is a justification to that.

00:28:09.020 --> 00:28:14.940
Because you can think of the domain as being causality and you have certain

00:28:14.940 --> 00:28:20.110
invariants of uh of good causal reasoning that will apply across the board.

00:28:20.350 --> 00:28:20.590
Yeah.

00:28:20.990 --> 00:28:22.510
Yeah, don't yeah, yeah that's great.

00:28:22.590 --> 00:28:22.910
Yeah, yeah.

00:28:23.230 --> 00:28:24.590
It's I mean it's I mean it's interesting.

00:28:24.670 --> 00:28:30.590
If if um causality is ultimately about things I can do, like what will

00:28:30.590 --> 00:28:36.430
happen if I do this, uh well that's basically almost pretty much all of what the brain does

00:28:36.510 --> 00:28:38.350
is trying to figure out what I should do.

00:28:38.510 --> 00:28:39.470
What will happen if I do this.

00:28:39.630 --> 00:28:39.950
Yeah, you can't do it.

00:28:40.110 --> 00:28:42.350
Like it's a it's a machine for figuring out what to do.

00:28:42.510 --> 00:28:46.400
And it seems like causality should be literally a part of every system in the mind, because

00:28:46.400 --> 00:28:49.840
every system in the mind is ultimately about what should I do next, right?

00:28:50.000 --> 00:28:51.040
What's gonna happen if I do this?

00:28:51.200 --> 00:28:52.560
What's gonna happen if I do that?

00:28:52.720 --> 00:28:58.400
Um so I think you could think about it as you know ultra-domain general generality, but I

00:28:58.400 --> 00:28:59.840
don't think that's a really helpful way to think about it.

00:28:59.920 --> 00:29:02.480
I think it's it's better to think about in terms of functional specificity.

00:29:02.640 --> 00:29:06.880
We have systems that are functionally designed for the purpose of picking out causal

00:29:06.880 --> 00:29:07.760
relationships.

00:29:07.920 --> 00:29:13.890
Um, and that should by design affect every part of the mind that is involved in

00:29:13.890 --> 00:29:15.250
figuring out what to do next.

00:29:15.490 --> 00:29:15.650
Yeah.

00:29:15.730 --> 00:29:17.570
And and can I just like elaborate too?

00:29:17.650 --> 00:29:22.610
Like I think like domain generality is or just the whole issue of domain, like the word

00:29:22.610 --> 00:29:27.970
domain, is if you look at how people use it, I think it's exceedingly

00:29:28.210 --> 00:29:29.250
unclear.

00:29:29.410 --> 00:29:35.400
Um if you look at how Fodor uses it, it's very folk psychological, where like a domain is

00:29:35.400 --> 00:29:36.600
like a thing out in the world.

00:29:36.680 --> 00:29:39.160
Like, is it like cheese versus like cows?

00:29:39.320 --> 00:29:41.080
Right, like that's a domain.

00:29:41.640 --> 00:29:46.520
There's remarkably little like notion of like once you start thinking about the psychology,

00:29:46.680 --> 00:29:48.440
what do you mean by domain at all?

00:29:48.680 --> 00:29:54.570
And I would put it as like what David just described, which is exactly right, is

00:29:54.890 --> 00:29:59.210
you have all these systems for representing all the stuff out in the world.

00:29:59.610 --> 00:30:04.890
And all of those systems are all very specific for making all those representations

00:30:04.890 --> 00:30:10.010
possible while you're representing objects or the social world or physical relationships or

00:30:10.010 --> 00:30:13.690
emotion, mental states, like all these things in specialized machinery.

00:30:14.000 --> 00:30:20.000
once all those very specific systems are all working, they're also generating formats in

00:30:20.000 --> 00:30:25.840
this like event system that can take many of these inputs in because of all this very

00:30:25.840 --> 00:30:27.440
specific machinery working.

00:30:27.600 --> 00:30:33.200
And it's that event system being fed all of this content where

00:30:33.520 --> 00:30:39.410
that that has this rule, these rules of sort of like, um, you know, well, if I

00:30:39.410 --> 00:30:41.730
do this, this is the other thing that's going to come along with it.

00:30:41.890 --> 00:30:46.290
And then these other things don't matter, which applies to lots of relationships in the

00:30:46.290 --> 00:30:46.610
world.

00:30:46.770 --> 00:30:51.970
Yeah, that's common to all of them, but it's that there's more and more specialized

00:30:51.970 --> 00:30:52.450
machinery.

00:30:52.530 --> 00:30:54.290
There's even machinery for that job.

00:30:54.370 --> 00:30:57.090
I mean, it might get replicated across systems, who knows?

00:30:57.250 --> 00:31:00.930
But I think this is a thing where, like, if you say it's domain general in the sense of it

00:31:00.930 --> 00:31:05.660
applies to cows and cheese, it sort of sounds like, well, one mechanism does cows and

00:31:05.660 --> 00:31:10.060
cheese, but it's actually there's a ton of mechan, there's even more mechanisms than just

00:31:10.060 --> 00:31:12.940
if there was one mechanism for cows and a different mechanism for cheese.

00:31:13.020 --> 00:31:17.900
It's actually there's more mechanisms that allow something to be domain general in the in

00:31:17.900 --> 00:31:19.820
the unclear way than the domain specific way.

00:31:19.980 --> 00:31:23.980
I don't know if that's helpful for this, but I just always have this rant of like, my God,

00:31:24.140 --> 00:31:29.410
domain general versus domain specific is like it's it's I think it's even worse than innate

00:31:29.410 --> 00:31:32.210
and learned um or it's just as bad.

00:31:32.370 --> 00:31:33.330
Um yeah.

00:31:33.730 --> 00:31:36.050
Yeah yeah I mean yeah I I agree with that.

00:31:36.210 --> 00:31:42.130
Um I think to some extent this this was a thing that um it's

00:31:42.130 --> 00:31:48.050
a it's a nice it's a nice set of concepts to use if you're not already being

00:31:48.290 --> 00:31:52.160
uh in the in the process of thinking about things in uh in a functional way.

00:31:52.480 --> 00:31:53.200
Yes, hundred percent.

00:31:53.280 --> 00:31:54.960
It's kind of a gateway drug.

00:31:55.120 --> 00:31:56.960
Yeah, it's a gateway functionality.

00:31:58.000 --> 00:32:01.200
Where you can use people's intuitive understanding about the world 100%.

00:32:01.600 --> 00:32:02.000
Yeah, yeah.

00:32:03.200 --> 00:32:08.960
So uh you mentioned that um uh uh we uh have looked at how

00:32:09.270 --> 00:32:14.070
well people conform to normative standards of causal reasoning or Bayesian reasoning.

00:32:14.230 --> 00:32:18.310
And you mentioned that um there are some cases where we fall short of the normative

00:32:18.310 --> 00:32:18.790
standard.

00:32:19.030 --> 00:32:23.670
What what are some of those cases and and and what do you think might be going on there?

00:32:24.310 --> 00:32:25.190
Uh yeah.

00:32:25.350 --> 00:32:29.350
So there are many things we can could say about that.

00:32:29.430 --> 00:32:31.430
And this this could be its own discussion.

00:32:31.510 --> 00:32:35.830
But I think maybe I can talk about two case studies that I think are quite interesting.

00:32:35.990 --> 00:32:40.920
Um and so the first case study actually illustrates the fact that

00:32:41.720 --> 00:32:47.720
we are our brain is uh wired up to reason to represent the world in

00:32:47.720 --> 00:32:48.760
causal terms.

00:32:48.920 --> 00:32:54.840
Um so if you if you read psychology from the 70s, 80s, um there's a bit of

00:32:54.840 --> 00:32:59.800
a pessimistic picture that emerges about uh how good people are at at reasoning.

00:32:59.960 --> 00:33:03.400
Uh so I know there are studies by people like Tversky and Kahneman.

00:33:03.640 --> 00:33:09.390
Um, and one classic classic example of that is the um the diagnostic test problem.

00:33:10.030 --> 00:33:15.550
And so in that problem, you're told that there's this disease that maybe one in a thousand

00:33:15.870 --> 00:33:16.510
people have.

00:33:16.830 --> 00:33:19.550
And there's this test to see if you have the disease.

00:33:19.950 --> 00:33:25.550
And you're told that the test has a certain false positive rate and it has a certain false

00:33:25.550 --> 00:33:26.350
negative rate.

00:33:26.990 --> 00:33:32.140
And putting all this information together, you can in principle compute what is the

00:33:32.140 --> 00:33:37.180
probability that you have the disease, given that you were tested positive to the to the

00:33:37.180 --> 00:33:37.740
disease.

00:33:37.900 --> 00:33:43.020
Um and when you ask participants uh you know what is this probability, um

00:33:43.340 --> 00:33:46.380
typically they will say something like oh like 80%.

00:33:47.180 --> 00:33:52.570
They will say something pretty high, even if the numbers are set up in such a way that the

00:33:52.570 --> 00:33:55.130
answer is actually something like seven percent.

00:33:55.690 --> 00:34:01.050
And so that is a huge gap between the normative Bayesian answer and the correct answer.

00:34:01.210 --> 00:34:05.770
Um and so when you look at that, you can maybe conclude, well, it looks like people are

00:34:05.770 --> 00:34:08.250
actually terrible at probabilistic reasoning.

00:34:08.410 --> 00:34:14.330
Um, but then if you look at the way this problem is set up, you are never really given

00:34:14.330 --> 00:34:17.370
an explanation for why there might be false positives.

00:34:17.620 --> 00:34:21.380
You kind of just told that well, sometimes the test doesn't work very well.

00:34:22.260 --> 00:34:27.700
And so it is difficult to kind of for the participants to construct a causal model of the

00:34:27.700 --> 00:34:29.620
situation that they have to reason about.

00:34:30.180 --> 00:34:35.940
And so one hypothesis that that is just the explanation for why this is a

00:34:35.940 --> 00:34:36.740
hard problem.

00:34:37.380 --> 00:34:43.150
And so some psychologists have done an experiment where modify this

00:34:43.150 --> 00:34:47.790
problem in a slight way, and the only thing that they do is that they say, well, sometimes

00:34:47.790 --> 00:34:53.470
people, you know, like I don't know, maybe 70% of people have a benign uh keist

00:34:54.110 --> 00:34:56.750
that will cause a false positive.

00:34:57.070 --> 00:35:01.150
And when you reformulate this problem in that way, then people suddenly they they have a

00:35:01.150 --> 00:35:05.950
sort of a causal explanation that is available to account for the presence of false

00:35:05.950 --> 00:35:06.430
positives.

00:35:06.590 --> 00:35:10.030
And now their answers get much closer to the Bayesian optimum.

00:35:10.720 --> 00:35:16.720
And so it seems that people are able to engage in in good Bayesian reasoning, but they

00:35:16.720 --> 00:35:19.440
need to form a causal representation in order to do that.

00:35:20.000 --> 00:35:20.880
And so yeah.

00:35:21.200 --> 00:35:26.800
Yeah, sorry to in that example too, just really quick, I think there's also additional work

00:35:26.800 --> 00:35:31.920
showing that if you do this with frequencies, so you show people this is the number of

00:35:31.920 --> 00:35:36.690
people who have false positives, this is the number of people who have the correct hits, it

00:35:36.690 --> 00:35:42.050
also goes away in the sense of the Tversky and Kahneman results was actually very

00:35:42.050 --> 00:35:44.610
improbable and it was really designed.

00:35:45.170 --> 00:35:51.010
My friend and colleague Tomas Leharaga has a paper in Psych Bulletin about how people used

00:35:51.010 --> 00:35:56.770
to see people say that people were good statisticians, and Tversky Kahneman reversed that,

00:35:56.930 --> 00:36:02.290
but they were using very abstract, uh non-evolutionarily

00:36:02.500 --> 00:36:07.780
valid sort of cues of like, you know, not using whole objects or frequencies and sort of

00:36:07.780 --> 00:36:09.940
giving people uh counterintuitive things.

00:36:10.100 --> 00:36:14.980
So I just want to say like that's really interesting that even just changing the uh adding

00:36:14.980 --> 00:36:19.220
the causal model is like another problem with that methodology.

00:36:19.300 --> 00:36:20.100
So yeah, yeah.

00:36:20.660 --> 00:36:26.590
Um I mean and and to some extent I I think that First Ken Ganneman um they still did

00:36:26.590 --> 00:36:32.030
a service to the field by pointing out that there are those cases where people do really

00:36:32.110 --> 00:36:33.150
really badly, right?

00:36:33.230 --> 00:36:36.110
Because then see something that is crying for explanation.

00:36:36.350 --> 00:36:41.550
And then when we explain why it is it that people do very badly, we learn new things about

00:36:41.550 --> 00:36:42.910
how the mind works.

00:36:51.260 --> 00:36:56.220
principle, it's almost um well, it is actually an axiom of causal reasoning that is called

00:36:56.220 --> 00:36:57.740
the the Markov condition.

00:36:57.820 --> 00:37:03.500
So it's named after this mathematician uh from the early 20th century called

00:37:03.500 --> 00:37:09.340
Andrei Markov, who studied very simple systems where uh the state of

00:37:09.340 --> 00:37:15.180
the system in uh in a particular uh time just depends on the previous state of

00:37:15.180 --> 00:37:15.740
the system.

00:37:16.370 --> 00:37:21.570
And this is relevant to causality because if you have a causal system where

00:37:22.050 --> 00:37:27.490
suppose you have the again the clouds cause the rain and the rain causes the grass to be

00:37:27.490 --> 00:37:27.890
wet.

00:37:28.930 --> 00:37:34.850
If that is all there is to your causal system, then the clouds

00:37:34.850 --> 00:37:37.090
can have an influence on the grass being wet.

00:37:37.560 --> 00:37:40.840
So if if there are clouds, the the grass are more likely to be wet.

00:37:41.240 --> 00:37:47.080
But this the whole causal influence of the clouds can only go uh via

00:37:47.160 --> 00:37:48.120
the the rain.

00:37:48.280 --> 00:37:53.880
Okay so the the the only reason that the clouds can make the rain wet is can make the the

00:37:53.880 --> 00:37:56.920
grass wet is is via the is by making it rain.

00:37:57.560 --> 00:38:03.450
And so there's um from um like from from the sort of perspective of

00:38:03.450 --> 00:38:09.130
of causal reasoning um then the the Markov condition is just a fancy way of saying that

00:38:09.290 --> 00:38:15.210
once you know whether it has rained or not, then anything that you learn about whether the

00:38:15.210 --> 00:38:19.930
clouds are there or not should not give you any more information about whether the grass is

00:38:19.930 --> 00:38:20.170
wet.

00:38:21.210 --> 00:38:25.370
You got the most important piece of information you get all the information right.

00:38:25.610 --> 00:38:31.320
And sometimes we say that the the rain screens off the influence of the clouds.

00:38:31.880 --> 00:38:36.600
And so what this means is that if you asked well, what is the probability that the grass is

00:38:36.600 --> 00:38:41.640
wet, given that we know that it's it's been raining, you should give some answer, right?

00:38:41.960 --> 00:38:47.000
And then if later on the experimenter asks you, Well, what is the probability that the

00:38:47.000 --> 00:38:51.150
grass is wet given that it has rain and there are clouds.

00:38:51.310 --> 00:38:53.470
You should give the exact same answer.

00:38:55.150 --> 00:38:56.910
But people don't really do that.

00:38:57.310 --> 00:39:02.350
People will give a higher probability if you tell them that, in addition to the fact that

00:39:02.350 --> 00:39:05.230
there is rain, there are also clouds.

00:39:05.710 --> 00:39:11.470
So it seems like a blatant violation of the rules of how you should reason with causal

00:39:11.470 --> 00:39:12.110
networks.

00:39:12.560 --> 00:39:16.160
And so this has gotten people in the field quite interested because

00:39:21.680 --> 00:39:26.160
is consistent with the fact that people are actually reasoning with causal models.

00:39:35.070 --> 00:39:40.830
when they need to engage in probabilistic reasoning, um they make uh use of approximations.

00:39:41.470 --> 00:39:47.150
Um and the the reason why you need to engage in approximations is that

00:39:47.150 --> 00:39:52.750
computing a probability will in general be relatively um expensive in computational

00:39:52.990 --> 00:39:53.630
terms.

00:39:53.950 --> 00:39:59.950
Um right you need to uh you need to engage in quite complex uh computations um and

00:39:59.950 --> 00:40:05.900
there are actually shortcuts that people who do computational statistics and

00:40:05.900 --> 00:40:09.980
machine learning typically use in order to make the computations easier.

00:40:10.140 --> 00:40:15.980
And it turns out that if you assume that people are engaging in the same kind of shortcuts,

00:40:16.780 --> 00:40:20.540
then you predict that they will make that kind of mistake.

00:40:20.780 --> 00:40:25.580
And so the picture that emerges from that is not that people are don't know the rules of

00:40:25.580 --> 00:40:30.380
causal models, but they they use some approximations in order to compute things in an

00:40:30.380 --> 00:40:31.500
efficient way.

00:40:32.990 --> 00:40:35.310
I'm I'm a little bit confused.

00:40:35.710 --> 00:40:40.590
I mean I hear what you're saying, but let me just let me be devil's advocate for the um so

00:40:40.670 --> 00:40:45.310
like with the rain and the clouds, is the is the study designed that like you tell the same

00:40:45.310 --> 00:40:49.870
person rain and then the same person rain in clouds and which one do you think is more

00:40:49.870 --> 00:40:51.790
likely that the grass is wet?

00:40:52.830 --> 00:40:56.670
Um so there are various ways that the experiment has been done.

00:40:56.750 --> 00:41:01.380
And of course uh when people do those experiments, they they use a more controlled setup

00:41:01.460 --> 00:41:04.900
where you say, well, there's this machine, and then there's this component of the machine

00:41:04.980 --> 00:41:08.260
that can influence this component that then can influence that component.

00:41:08.980 --> 00:41:14.740
But why is that so why is it wrong for me to be more confidently

00:41:14.980 --> 00:41:20.580
grass is wet if I say uh clouds uh and rain as opposed to rain?

00:41:21.780 --> 00:41:26.510
Um so if I ask you, you know, suppose you know that there is rain, yeah, what is the

00:41:26.510 --> 00:41:28.110
probability that the grass is wet?

00:41:28.270 --> 00:41:34.110
Um, then the the answer you give to that should be the same as if I ask you, suppose you

00:41:34.110 --> 00:41:36.270
know that there is rain and there are clouds.

00:41:36.590 --> 00:41:42.270
Yeah, but I but I would push back on that because like that ignores that like is it's very

00:41:42.350 --> 00:41:46.750
like um uh I mean I don't know, I'm not saying you're doing this, but I'm saying like the

00:41:46.750 --> 00:41:50.590
causal model people, like there's a difference between like there's a little bit of rain

00:41:50.590 --> 00:41:52.430
versus like there's a storm, right?

00:41:52.590 --> 00:41:56.480
So like there's differing I mean, in some sense, people Yeah.

00:41:56.640 --> 00:42:00.880
I mean, just to take a step back, like causality doesn't exist.

00:42:01.360 --> 00:42:06.880
It's a representation of the mind of like how certain am I likely that this event will

00:42:06.880 --> 00:42:08.080
produce this event?

00:42:08.320 --> 00:42:14.240
And if you tell me rain versus rain and clouds, um, how likely is

00:42:14.240 --> 00:42:17.040
it that the r the water, the grass is drenched?

00:42:17.230 --> 00:42:18.430
how much water is there?

00:42:18.590 --> 00:42:22.910
How likely is it that that was rain and not like a bird peeing or something?

00:42:23.070 --> 00:42:25.550
You know, like that's r all relevant.

00:42:25.790 --> 00:42:30.350
And if you tell me like there there's a there was a little bit of rain versus like there's

00:42:30.430 --> 00:42:36.430
rain in clouds, like you just if there's all this common sense knowledge that I

00:42:36.430 --> 00:42:42.360
think a smart computer would say it's much more likely that whatever

00:42:42.360 --> 00:42:47.160
you're trying to communicate of like how likely is it the grass is wet, how wet it got, how

00:42:47.160 --> 00:42:49.960
confident you were that like, you know, it was a rainstorm.

00:42:50.280 --> 00:42:51.160
All these things make sense.

00:42:51.480 --> 00:42:51.640
Yeah.

00:42:51.720 --> 00:42:55.720
Or or even like the person who's telling you that it's raining without clouds, like maybe

00:42:55.720 --> 00:42:56.760
you should not trust that person.

00:42:57.000 --> 00:42:57.640
Maybe they were wrong.

00:42:57.800 --> 00:43:01.480
Like if you've ever spelt rain when there's no clouds, you're like, what is this?

00:43:01.640 --> 00:43:02.680
And it does happen, right?

00:43:02.760 --> 00:43:04.200
Because like the wind is strong.

00:43:04.440 --> 00:43:09.910
So like I I I don't I mean maybe maybe we're we're hanging a lot on this example, but like

00:43:10.550 --> 00:43:12.710
it seems what you're saying scientists are wrong.

00:43:13.910 --> 00:43:18.790
I think that those are those are good objections to sort of the experiment in the way that

00:43:18.790 --> 00:43:19.350
I described it.

00:43:19.590 --> 00:43:19.910
Yeah, exactly.

00:43:20.230 --> 00:43:24.470
Um of course in in in practice when people do those experiments you have a much more

00:43:24.470 --> 00:43:28.870
controlled setting where you really make it clear to people that well those are just binary

00:43:28.870 --> 00:43:34.620
variables and really the only way that this like the the end end variable,

00:43:34.860 --> 00:43:39.340
the only way that this could be affected is by the state of the previous variable.

00:43:39.500 --> 00:43:44.220
And so that way you you bypass that kind of concern about well what inferences people are

00:43:44.220 --> 00:43:47.980
making from the fact that you tell them many things and so on and so forth.

00:43:48.300 --> 00:43:53.340
But isn't it still rational in the sense of like shouldn't an evolved psychology represent

00:43:53.470 --> 00:43:58.110
because, like, I mean, from an evolved psychology perspective, it should acknowledge that

00:43:58.110 --> 00:44:02.030
all it is, all it can do is pick up on correlations, right?

00:44:02.190 --> 00:44:07.950
Like, like the evolved psychology is is only ever

00:44:08.110 --> 00:44:14.110
detecting co occurrences and is using the fact that some co occurrences

00:44:14.750 --> 00:44:20.500
are actionable for intervention and actually generally produce the right

00:44:20.500 --> 00:44:21.540
outcome, right?

00:44:21.860 --> 00:44:26.820
Like, so that's what we mean by causation, but it's all a guess.

00:44:27.220 --> 00:44:32.260
Um so I it strikes me as like a I mean maybe this is I maybe I should get more concrete,

00:44:32.340 --> 00:44:32.820
but I don't know.

00:44:32.900 --> 00:44:37.140
I just I feel like the the scientists are wrong in this case and that it's a smart system

00:44:37.300 --> 00:44:43.230
to to be uh if more if more predictors are present, you should

00:44:43.230 --> 00:44:44.350
increase your likelihood.

00:44:44.670 --> 00:44:44.830
Yeah.

00:44:45.070 --> 00:44:50.910
I mean I have I have a a version of arguments that I've made in in print, which is that um

00:44:51.390 --> 00:44:56.990
so I mean I I buy the this idea that I described that kind of if people engage with in in

00:44:56.990 --> 00:45:00.830
shortcuts in their computations, then they will make the kind of mistakes that have been

00:45:00.830 --> 00:45:01.470
observed.

00:45:01.630 --> 00:45:07.400
Um but a different way that those kinds of mistakes will be produced is that

00:45:07.400 --> 00:45:11.800
if people don't completely buy the story that has been given to them by the experimenter.

00:45:11.960 --> 00:45:15.800
So maybe they think, well, you know, the experimenter tells me what those variables are

00:45:15.800 --> 00:45:20.040
causally related, but I don't know, maybe they're not, or maybe they're related but in a

00:45:20.040 --> 00:45:22.920
different way than has been told to me.

00:45:23.160 --> 00:45:28.920
And so if that's what you think, then the smart move is to just maintain those different

00:45:28.920 --> 00:45:32.360
hypotheses in mind and then and then to average over them.

00:45:34.650 --> 00:45:38.090
So you're not you're telling me that people aren't assuming that experimenters and

00:45:38.090 --> 00:45:41.210
psychology experiments are infallible and uh omniscient.

00:45:41.450 --> 00:45:43.610
What if you let people do the experiment themselves?

00:45:43.690 --> 00:45:44.810
Like, has anybody ever done that?

00:45:44.890 --> 00:45:48.650
Where like you actually give people the ability to actually test the relationships

00:45:48.650 --> 00:45:49.130
themselves?

00:45:49.290 --> 00:45:54.650
Like the experience, the experiential format of teasing apart what causes what, as opposed

00:45:54.650 --> 00:45:56.440
to somebody telling you, because actually, thank you.

00:45:56.520 --> 00:45:59.160
I f I felt like I was humming and hawing, but that's exactly what I was doing.

00:45:59.240 --> 00:46:00.680
I was like, I don't believe you.

00:46:00.840 --> 00:46:02.120
That's exactly what I was doing.

00:46:02.600 --> 00:46:06.680
And and and what what we what we've have shown in in that paper is that well if you if you

00:46:06.680 --> 00:46:11.400
assume that people have this uncertainty and that they are averaging over that uncertainty,

00:46:11.560 --> 00:46:17.490
then you predict that the that they will make the kind of quote unquote mistakes

00:46:17.490 --> 00:46:19.650
that have been observed in these experiments.

00:46:20.050 --> 00:46:24.930
But to answer your question, I don't think that that experiment has been run where you

00:46:24.930 --> 00:46:30.370
really allow participants to manipulate the causal structure themselves.

00:46:30.530 --> 00:46:34.530
That's it, yeah, it would be interesting to perform that experiment, that's for sure.

00:46:34.850 --> 00:46:35.490
Yeah.

00:46:35.970 --> 00:46:41.360
So is your take that um this idea that humans are bad at causal reasoning or they're

00:46:41.360 --> 00:46:44.800
irrational, uh, that we deviate from normative standards.

00:46:44.880 --> 00:46:49.760
Is your take that this idea is overblown and that actually, if you dig into the details and

00:46:49.760 --> 00:46:54.080
operationalize things in a sensible way and in an ecologically valid way, that actually

00:46:54.080 --> 00:46:58.080
people are pretty good at causal reasoning and and and fit pretty closely to what you might

00:46:58.080 --> 00:47:00.880
expect uh an optimal system to look like?

00:47:02.800 --> 00:47:03.440
Yeah, yeah.

00:47:03.600 --> 00:47:07.550
And uh I think that's a good summary of of what I think.

00:47:07.790 --> 00:47:13.630
Um and if you were to try to to come up with a taxonomy of

00:47:13.630 --> 00:47:19.150
possible sources of mistakes that people make, um I would say that there's maybe

00:47:19.390 --> 00:47:24.750
two main sources of mistakes that people can for why people might make mistakes.

00:47:24.910 --> 00:47:30.510
Um and the first is this kind of representational issue where people just either don't

00:47:30.510 --> 00:47:36.320
really believe you or they don't uh they're not able to construct a causal

00:47:36.320 --> 00:47:42.160
model that's that would actually allow them to to solve the the to solve the

00:47:42.160 --> 00:47:43.360
statistical problem.

00:47:43.520 --> 00:47:47.520
Um there's a different way that they could be wrong is that if the problem is super

00:47:47.520 --> 00:47:52.480
complicated then then of course you I mean it's it's kind of it's a bit of a trivial point

00:47:52.560 --> 00:47:52.960
in a way.

00:47:53.120 --> 00:47:58.320
It's like um asking people if I if I ask you you know what is the 17th digit of pi

00:47:58.990 --> 00:48:03.550
and you give me an answer that is no better than chance, there's a sense in which you're

00:48:03.550 --> 00:48:04.510
not being normative.

00:48:05.150 --> 00:48:08.910
But it's just that the computational demands that I put on you are too high.

00:48:09.070 --> 00:48:11.790
And so that that is one way that that you can make mistakes.

00:48:12.510 --> 00:48:18.110
And I mean one one other domain in which I have shown uh that people are relatively

00:48:18.270 --> 00:48:24.110
quite they're quite good at following the normative principles of probabilistic inference

00:48:24.350 --> 00:48:27.200
is um to in the social domain.

00:48:27.360 --> 00:48:32.480
So when they have to infer the welfare trade-off ratio of other people, then they have to

00:48:32.480 --> 00:48:35.280
solve a problem that is actually quite sophisticated.

00:48:35.600 --> 00:48:40.400
If you were to think of, you know, they have to make probabilistic computations over a

00:48:40.560 --> 00:48:43.920
causal model of how other people make decisions.

00:48:44.800 --> 00:48:50.800
And yes, despite the complexity of the problem, they're actually quite good at

00:48:50.880 --> 00:48:54.000
uh inferring how much someone else values them.

00:48:54.170 --> 00:49:00.090
So this is a a series of experiments that we've done with uh with John and Lida, um, where

00:49:00.730 --> 00:49:06.730
the simplified version of this experiment is that you see someone kind of uh sharing a

00:49:06.730 --> 00:49:12.250
pot of money uh in a particular context uh and you see the decision that they have made.

00:49:12.330 --> 00:49:17.690
Uh and then there's a slightly different context where maybe the cost to them of giving you

00:49:17.690 --> 00:49:19.130
money is a bit higher.

00:49:19.380 --> 00:49:21.300
And I um and I asked.

00:49:25.300 --> 00:49:26.020
Something, yeah, yeah.

00:49:26.420 --> 00:49:27.220
It's a good picture.

00:49:27.700 --> 00:49:31.380
And so in that in that context, so you know what they have done in that previous situation.

00:49:31.540 --> 00:49:36.020
And I ask you, what is the probability that they will give the food to you?

00:49:36.660 --> 00:49:42.500
And so you you can compute what is the like given some assumptions, what

00:49:42.500 --> 00:49:47.190
is the like the normative probability that uh a Bayesian model would give.

00:49:47.350 --> 00:49:52.790
And what we find is that first people are quite good, the people's answers are quite

00:49:52.790 --> 00:49:56.470
aligned with the answers that are given by the Bayesian model.

00:49:57.270 --> 00:50:03.270
And the second cool thing that we have found is that we also asked people well

00:50:03.510 --> 00:50:08.710
how grateful are you when the person actually because you actually at the end you you learn

00:50:08.710 --> 00:50:10.230
what they have what they have done.

00:50:10.390 --> 00:50:15.840
And so if the if the person in the experiment gives you uh gives you something.

00:50:16.000 --> 00:50:19.360
We ask people, well how grateful are you that you are given that thing?

00:50:19.520 --> 00:50:24.080
And if they decide to take the selfish action, we ask, well how angry do you feel?

00:50:24.400 --> 00:50:29.760
And then what we found is that the best predictor of how angry or grateful the people feel

00:50:29.920 --> 00:50:34.000
is not the best predictor is not how much they get or how much they fail to get.

00:50:34.560 --> 00:50:36.480
Because we control for those things tightly.

00:50:36.560 --> 00:50:42.160
So like the the kind of the payoffs that are involved or controlled across trials um

00:50:42.400 --> 00:50:45.630
and the sort of opportunity cost is also controlled.

00:50:45.950 --> 00:50:51.870
But what we can do is we can actually compute the welfare trade-off ratio that

00:50:51.870 --> 00:50:57.710
an ideal Bayesian observer would infer the other person has on the basis of the decisions

00:50:57.710 --> 00:50:58.670
that you observe.

00:50:59.230 --> 00:51:03.710
And then this inferred welfare trade-off ratio is actually quite a good predictor of

00:51:03.710 --> 00:51:05.070
gratitude and anger.

00:51:05.380 --> 00:51:11.220
So basically if you infer that if even if a if an uh an ideal Bayesian observer infers that

00:51:11.300 --> 00:51:15.620
yes, this person does not care at all about me, then I'm going to be quite hang angry.

00:51:16.020 --> 00:51:18.020
Even if you end up getting a lot.

00:51:18.740 --> 00:51:19.300
Yes, yeah.

00:51:19.700 --> 00:51:19.860
Yeah.

00:51:20.180 --> 00:51:23.940
But yeah, so it's kind of the the intuition is that suppose at some point they give me a

00:51:23.940 --> 00:51:26.740
lot, but because it was trivial for them to give me.

00:51:26.900 --> 00:51:27.060
Yeah.

00:51:27.940 --> 00:51:30.100
The cost of was super super low.

00:51:30.270 --> 00:51:33.310
And then they were gonna throw it in the garbage or whatever, and you just happen to be

00:51:33.310 --> 00:51:33.390
there.

00:51:33.470 --> 00:51:33.870
Yeah, whatever.

00:51:34.030 --> 00:51:34.350
Yeah, exactly.

00:51:34.430 --> 00:51:34.910
Yeah, exactly.

00:51:35.230 --> 00:51:40.830
And then on the other occasion, they kind of they could have benefited me by a little bit,

00:51:40.990 --> 00:51:44.270
uh, at again a trivial cost to themselves.

00:51:44.430 --> 00:51:48.030
Um, and they decide, oh yeah, you know what, I don't really feel like giving it to you.

00:51:48.190 --> 00:51:52.190
I'm going to take the two cents or whatever, instead of giving you ten dollars.

00:51:52.430 --> 00:51:58.080
Uh, and here, you know, you just lost ten dollars, it's not that much, but like, ah, you

00:51:58.080 --> 00:52:00.480
did it just to save two cents.

00:52:00.640 --> 00:52:03.040
Like that, that's you just you just don't care about me at all.

00:52:03.200 --> 00:52:05.200
And so that is what elicits anger.

00:52:05.600 --> 00:52:10.480
And of course, this is not this is not my own theory, this is the work of uh of Aaron Cell.

00:52:10.720 --> 00:52:16.000
But what we were able to show in that paper is actually you can uh create a computational

00:52:16.000 --> 00:52:21.830
account of of how anger uh follows from people's

00:52:21.830 --> 00:52:25.830
from observations of of what the other person uh has done.

00:52:26.310 --> 00:52:28.150
What what does that mean?

00:52:28.390 --> 00:52:31.750
I mean, I think people might have two questions, and this is both hard.

00:52:31.910 --> 00:52:34.950
Um we we we you know you and I have talked about this today.

00:52:35.030 --> 00:52:41.030
Like what does it mean to offer a computational um account because you straddle two

00:52:41.030 --> 00:52:46.940
communities we've talked a lot about functionalism or like a software level description Um,

00:52:47.100 --> 00:52:52.940
but then there's also like a technical methodological use of the term

00:52:52.940 --> 00:52:54.300
computationalism.

00:52:54.620 --> 00:52:59.580
Uh and then there's like computers, like a laptop, like can for people, because there's

00:52:59.580 --> 00:53:04.140
different audiences, can you explain what what does that mean, both to like a general

00:53:04.140 --> 00:53:09.260
audience, but also like evolutionary psychologists who might be used to like functionalism,

00:53:09.580 --> 00:53:10.300
functionalism.

00:53:10.380 --> 00:53:12.060
Yeah, what does that mean, computational?

00:53:12.430 --> 00:53:13.230
Yeah, yeah.

00:53:13.390 --> 00:53:16.030
Um that's I think a very important point.

00:53:16.190 --> 00:53:20.910
And it's a bit of an unfortunate fact about the history of psychology that you have just

00:53:21.150 --> 00:53:23.230
two different meanings of computational.

00:53:24.030 --> 00:53:29.550
So the first meaning of computational is maybe the one that will be most familiar to people

00:53:29.550 --> 00:53:30.830
across the sciences.

00:53:30.910 --> 00:53:35.710
Um so it's the idea that we are going to use uh we're going to use mathematics or we're

00:53:35.710 --> 00:53:39.070
going to use computers in order to to help us do our science.

00:53:39.700 --> 00:53:45.460
And so within that meaning of computational, uh, you are engaging in in

00:53:45.460 --> 00:53:50.180
computational work every time you compute a linear regression, or you're when you compute a

00:53:50.180 --> 00:53:54.580
t-test or something like that, because you're using a computer, you're using mathematical

00:53:54.580 --> 00:53:56.900
principles in order to make inferences.

00:53:57.220 --> 00:53:57.380
Right.

00:53:57.540 --> 00:54:03.410
It was sort of just standard, it's just sort of like rules of deduction and just

00:54:03.410 --> 00:54:08.210
sort of force like you know if you know what the inputs are just some deterministic process

00:54:08.370 --> 00:54:13.890
produces some outputs that's that's compute that that's that you know right and it's just

00:54:13.890 --> 00:54:18.050
certain ones work for whatever reason you don't have to do it on computer to you can also

00:54:18.290 --> 00:54:20.690
exactly you can you could compute your T test on on paper.

00:54:20.850 --> 00:54:26.290
Yeah exactly dinosaurs roam the earth yeah and to do that.

00:54:26.370 --> 00:54:28.290
I see the I don't remember anything from it.

00:54:28.450 --> 00:54:34.400
Yes exactly sorry yes Uh I wrote a horse to the schoolhouse and played with

00:54:34.400 --> 00:54:35.120
an advocate.

00:54:35.200 --> 00:54:35.520
Yeah, exactly.

00:54:35.760 --> 00:54:40.080
When there was it to CSB, actually we had to compute matrix multiplications by hand in the

00:54:40.080 --> 00:54:40.080
case.

00:54:40.240 --> 00:54:42.480
Yeah, we it's probably the Greg Ashby.

00:54:47.040 --> 00:54:47.280
Okay, yeah.

00:54:47.440 --> 00:54:49.120
Uh for that one semester because he wasn't live.

00:54:49.360 --> 00:54:52.800
But yeah, I mean and we'll we were giving given no explanations for why it was.

00:54:53.120 --> 00:54:53.360
Exactly.

00:54:53.440 --> 00:54:55.040
I did the same thing, yes, exactly.

00:54:55.360 --> 00:54:56.590
Um anyway.

00:54:56.750 --> 00:55:02.350
But yeah, so that's the everyone understands computation in this sort of sort of way of

00:55:02.350 --> 00:55:08.350
using either math or for more complicated systems, uh computer code, but it's still

00:55:08.350 --> 00:55:14.190
the same principle where it's just deterministic rules, um, it's just it's just deduction

00:55:14.190 --> 00:55:17.550
in sort of a scripted way that is sort of known.

00:55:17.630 --> 00:55:17.870
Yeah.

00:55:18.190 --> 00:55:18.430
Yeah.

00:55:18.590 --> 00:55:24.460
And obviously this is a super powerful tool, uh and it's it it's helpful

00:55:24.460 --> 00:55:26.060
in in in quite a lot of ways.

00:55:26.140 --> 00:55:31.820
Um but the second meaning of computational that is more specific to I guess psychologists

00:55:31.820 --> 00:55:37.500
and neuroscientists is uh being interested in the computations that are performed by an

00:55:37.500 --> 00:55:41.740
organism like typically the computations that are performed in someone's brain.

00:55:42.140 --> 00:55:45.900
So it's really making theories about information processing.

00:55:46.540 --> 00:55:51.150
And the interestingly, the two meanings can be dissociated.

00:55:51.630 --> 00:55:57.550
So I can have a theory of the computation that is that someone is performing, or

00:55:57.550 --> 00:56:02.830
that a theory of the computation that a given part of the brain is performing, uh, that can

00:56:02.830 --> 00:56:05.150
be expressed in in verbal terms.

00:56:07.600 --> 00:56:12.080
the like uh another version of that is that I can have a very simple computational theory.

00:56:12.240 --> 00:56:18.160
So if you think uh if we go back to the theory of of anger uh that Aaron Cell gave, the the

00:56:18.160 --> 00:56:20.080
equation is is quite simple.

00:56:20.480 --> 00:56:26.000
It's just the I mean for those who don't know, it's an equation that determines how um

00:56:26.320 --> 00:56:30.640
someone decides whether to help based on this parameter that's called their wealth fair

00:56:30.720 --> 00:56:31.680
trade-off ratio.

00:56:32.230 --> 00:56:38.070
and then you you decide to help given that your welfare trade-off ratio is above uh

00:56:38.630 --> 00:56:44.550
so if the costs and the benefits of your action of the potential actions are related to

00:56:44.550 --> 00:56:46.630
the welfare trade-off ratio in a certain way.

00:56:46.790 --> 00:56:50.150
Um so this is a computational theory in both senses, right?

00:56:50.230 --> 00:56:52.630
Because it you can express it with an equation.

00:56:52.710 --> 00:56:58.440
Um, and this is you're also telling something about the the computation that is that is

00:56:58.440 --> 00:56:59.800
taking place in someone's brain.

00:57:00.040 --> 00:57:00.200
Right.

00:57:00.280 --> 00:57:05.320
So if Weber Trade F ratio is one, it's like, well, if you would get $10 and I would get um

00:57:05.320 --> 00:57:08.520
nine, I would just give it to you because that's more.

00:57:08.760 --> 00:57:10.200
They're that's just the same.

00:57:10.360 --> 00:57:14.120
But then it's like a par it's just it very it changes the cost and benefits.

00:57:14.200 --> 00:57:20.040
So like for my son, I might have a a a positive one where like even if if if

00:57:20.120 --> 00:57:26.090
even if I get uh ten but he gets seven, I still might give it to him because I care

00:57:26.090 --> 00:57:28.010
about him more than I care about me.

00:57:28.250 --> 00:57:32.010
And so it's it's like a very positive of our trade-off ratio.

00:57:32.170 --> 00:57:37.770
But then if it's like a negative one and yeah or a very negative value, then um

00:57:38.010 --> 00:57:43.930
I might, you know, um be willing to incur a cost to make sure you don't get a benefit.

00:57:44.090 --> 00:57:46.250
So just to make it slightly more concrete.

00:57:46.330 --> 00:57:46.490
Yeah.

00:57:46.650 --> 00:57:50.010
Sorry, but that's just Yeah no no of course that's a yeah that's a great summary.

00:57:50.250 --> 00:57:55.980
And so yeah and so uh uh I think and I mean a lot of evolutionary psychologists think that

00:57:55.980 --> 00:58:00.620
having those computational theories, you know, those theories of what is the computation is

00:58:00.620 --> 00:58:02.140
extremely valuable.

00:58:02.380 --> 00:58:07.660
Um and and this is valuable even when those computational theories are actually not

00:58:07.660 --> 00:58:10.540
expressed in uh in computational terms.

00:58:10.620 --> 00:58:15.260
I don't uh even if you don't have equations or computer code in order to explain the

00:58:15.260 --> 00:58:15.660
theory.

00:58:15.900 --> 00:58:16.060
Yeah.

00:58:16.590 --> 00:58:19.710
Of course, maybe the the ideal case is when you have both, right?

00:58:19.870 --> 00:58:24.910
When you have a computational theory that is expressed in equations or in computer code.

00:58:25.390 --> 00:58:29.070
But then there's another quadrant in the space of possibilities.

00:58:29.710 --> 00:58:34.350
There's a quadrant where you're using very complicated mathematics, but you're not saying

00:58:34.350 --> 00:58:35.710
anything about information processing.

00:58:35.950 --> 00:58:36.270
Exactly.

00:58:36.590 --> 00:58:39.070
And and that is, I mean, it's often very useful, right?

00:58:39.150 --> 00:58:44.220
Sometimes you might be doing sociology and don't care at all about how the mind works.

00:58:44.380 --> 00:58:48.780
Uh it's a very um valuable thing to do.

00:58:49.020 --> 00:58:49.260
Yeah.

00:58:49.900 --> 00:58:54.220
But then the the sort of problematic part of this quadrant is that when you are a

00:58:54.220 --> 00:58:59.260
psychologist and you say, look, I have a computational theory of X, and your computational

00:58:59.260 --> 00:59:03.180
theory is indeed there's a lot of very complicated mathematics.

00:59:03.260 --> 00:59:06.380
You can implement it, you can run nice simulations.

00:59:06.780 --> 00:59:11.490
But when you look at it a little bit better, then you're not actually really saying

00:59:11.490 --> 00:59:13.970
anything about information processing in the mind.

00:59:14.290 --> 00:59:20.290
You're just modeling how the observed variables are

00:59:20.290 --> 00:59:21.970
actually related to each other.

00:59:22.530 --> 00:59:25.010
Yeah, I think that's a really important profound point.

00:59:25.170 --> 00:59:26.290
Yeah, you and I were talked about this.

00:59:26.450 --> 00:59:29.730
We really, what does it mean to be computational is like a paper we should really do.

00:59:30.050 --> 00:59:33.250
I think a good concrete example of this would be just describing, like to make it super

00:59:33.250 --> 00:59:36.740
concrete for people, just a bicycle, like to the extent people know how bicycle works.

00:59:36.820 --> 00:59:40.100
Like you could have a verbal model of how bicycle works, like you know, you push the

00:59:40.100 --> 00:59:43.380
pedals, it turns the crank, it pulls the chain, it pulls the cassette, it turns the wheel.

00:59:43.540 --> 00:59:44.500
That's all verbal.

00:59:44.740 --> 00:59:49.620
I could have a very nice mathematical description of like how fast the tire rotates, and

00:59:49.700 --> 00:59:54.660
like sort of some other parameter, like I don't know how often the chain vibrates, that has

00:59:54.660 --> 00:59:58.500
nothing to do with explaining how the thing works.

00:59:58.970 --> 01:00:04.730
And if you just like if I typed up the functional description and if I made a beautiful

01:00:04.730 --> 01:00:10.650
graph of the of the uh um fluff and you just

01:00:10.650 --> 01:00:15.370
did superficially like which one looks science or people would be like oh the beautiful

01:00:15.370 --> 01:00:19.450
graph you have numbers like there's a pragmatic effect that like once you know the function

01:00:19.450 --> 01:00:25.050
of something so well you you can avoid being qualitative and you can become quantitative.

01:00:25.210 --> 01:00:30.980
But I believe there's been an enormous often cynical, frankly, thing where it's like

01:00:31.060 --> 01:00:34.980
I if I make something quantitative, I can b lead people into believing that we know

01:00:35.140 --> 01:00:39.780
understand it so well that we're beyond the qualitative phase and now we're doing

01:00:39.780 --> 01:00:40.500
quantitative science.

01:00:40.580 --> 01:00:43.780
It's the equivalent of like putting on a lab coat and being like I'm a scientist.

01:00:44.260 --> 01:00:49.860
Um and yeah, it's not a straw man to say like I have sat in on many, many, many, many,

01:00:49.940 --> 01:00:53.380
many, many, many, many, many talks that were this quadrant.

01:00:53.540 --> 01:00:54.180
Yeah.

01:00:54.930 --> 01:00:55.490
Yeah, yeah.

01:00:55.730 --> 01:01:00.850
And there's a the there's a version of that that ends up connecting back to the issue of

01:01:00.850 --> 01:01:06.370
correlation and causation, which is this kind of industry of mediation analysis and

01:01:06.370 --> 01:01:07.970
structural equation models.

01:01:08.050 --> 01:01:13.570
Uh you can have increasingly complicated mathematical models where um someone

01:01:13.890 --> 01:01:19.880
collects a lot of data um and then they put them inside the computer and then the

01:01:19.880 --> 01:01:21.960
computer crunches the numbers for hours.

01:01:22.120 --> 01:01:27.480
And then what comes back is a chart where you have uh you have little causal errors between

01:01:27.480 --> 01:01:28.520
one variable and another.

01:01:28.600 --> 01:01:30.440
And you can have a quite a complicated graph.

01:01:30.520 --> 01:01:35.240
And again, it looks like you have produced a description, a mechanistic description of the

01:01:35.240 --> 01:01:35.560
system.

01:01:35.800 --> 01:01:41.570
Um but very often those things um or kind of increasingly sophisticated

01:01:41.570 --> 01:01:46.610
ways of convincing yourself that you have causal insights, whereas you're you're just

01:01:46.610 --> 01:01:48.530
describing a pattern of correlations.

01:01:49.090 --> 01:01:49.410
Yeah.

01:01:51.010 --> 01:01:55.970
And we shouldn't, you know, I mean, oftentimes the the computational aspect of it can be

01:01:55.970 --> 01:01:57.890
quite simple, as in the case of anger.

01:01:57.970 --> 01:02:02.210
Like it's actually when you think about the design of anger, it's shockingly simple.

01:02:02.370 --> 01:02:07.730
You know, you're trying to figure out what uh cost a person would be willing to inflict on

01:02:07.730 --> 01:02:11.300
you if i if in exchange for whatever benefit to them.

01:02:11.460 --> 01:02:12.980
It's just a ratio, literally a ratio.

01:02:13.060 --> 01:02:14.020
You're trying to figure that out.

01:02:14.180 --> 01:02:17.620
And then your anger and gratitude should be calibrated in response to that.

01:02:17.780 --> 01:02:20.580
Like that's a pretty simple computational model.

01:02:20.740 --> 01:02:24.660
It doesn't require a huge page of equations or graphs.

01:02:25.060 --> 01:02:28.660
And so we should be open to the possibility that whatever system we're trying to explain in

01:02:28.660 --> 01:02:33.430
the mind should be as simple or as complicated as it needs to be for solving the particular

01:02:33.430 --> 01:02:34.150
adaptive problem.

01:02:34.230 --> 01:02:38.310
And we shouldn't have a prior that it necessarily needs to be very sophisticated.

01:02:38.630 --> 01:02:42.070
Or we should at least, you know, be open to the possibility that some parts of it might be

01:02:42.070 --> 01:02:44.870
actually very simple and some parts of it might be complicated depending on what

01:02:44.870 --> 01:02:46.950
perspective you're taking or what part you're looking at.

01:02:47.670 --> 01:02:52.070
In terms of the the possible inputs to the welfare trade-off ratio calculation, there might

01:02:52.070 --> 01:02:55.750
be many different inputs and many different contextual cues that that go into that

01:02:55.750 --> 01:02:56.230
calculation.

01:02:56.390 --> 01:02:57.830
And that part might be very complicated.

01:02:57.990 --> 01:03:02.060
But what what what is spit out in the end is actually very simple uh it's just a ratio of

01:03:02.060 --> 01:03:02.620
two numbers.

01:03:03.260 --> 01:03:09.180
Yeah um I think uh I think you could even go further than that and and say that

01:03:09.340 --> 01:03:13.420
uh even if you start with the baseline assumption that's everything will be quite

01:03:13.420 --> 01:03:17.820
complicated, um there is often value in having a simplified model.

01:03:17.980 --> 01:03:22.780
Yeah where you sort of say well of course we know that the system doesn't really work

01:03:22.780 --> 01:03:28.610
exactly like that but um it's actually the case that we have a very simple

01:03:28.610 --> 01:03:30.690
model that gives us a lot of traction.

01:03:30.850 --> 01:03:35.330
Um and I mean this is actually the if we look at the history of science, this is the way

01:03:35.330 --> 01:03:36.850
that that things have worked, right?

01:03:36.930 --> 01:03:42.530
I mean uh kepp Kepler's the Kepler's law of laws of motion, they're not they're not a

01:03:42.530 --> 01:03:45.570
complete description of kind of the way that the solar system works.

01:03:45.810 --> 01:03:50.840
We we didn't like it's not like the general theory of of

01:03:50.840 --> 01:03:55.320
gravitation uh and relativity sprung up fully formed.

01:03:55.480 --> 01:03:59.560
Um we had to start with very simple models and then we added a lot of complexity.

01:03:59.720 --> 01:04:04.360
And so yeah, probably in the sciences of the mind, there's no reason that things should

01:04:04.360 --> 01:04:05.880
work in the uh otherwise.

01:04:06.280 --> 01:04:08.600
This was a long oh sorry, David, go ahead.

01:04:09.000 --> 01:04:12.920
I I feel like we often confuse which parts should be simple and which parts should be

01:04:12.920 --> 01:04:13.320
complicated.

01:04:13.400 --> 01:04:15.880
The deeper principles should be simple.

01:04:16.770 --> 01:04:20.770
What results from the operation of those simple principles can be mind bogglingly

01:04:20.770 --> 01:04:21.090
complicated.

01:04:21.170 --> 01:04:24.290
I mean, the best example of this is the theory of evolution itself.

01:04:24.450 --> 01:04:25.810
You know, it's extremely simple.

01:04:25.970 --> 01:04:27.970
You can lay it out in a couple sentences.

01:04:28.130 --> 01:04:30.690
Um, Darwin laid it out in a couple sentences.

01:04:30.770 --> 01:04:35.970
Uh, and yet that simple idea, that simple principle, gives rise to the most mind bogglingly

01:04:35.970 --> 01:04:40.370
complex forms of biological organization, including the brain that's producing these words,

01:04:40.450 --> 01:04:42.930
is a is a product of the of that of those simple principles.

01:04:43.220 --> 01:04:47.940
So that to me is the best example of an extreme difference between the simplicity of the

01:04:47.940 --> 01:04:53.860
underlying causal process and the complexity of what root emerges from that causal

01:04:53.860 --> 01:04:54.340
process.

01:04:54.740 --> 01:04:58.740
And I think we should be trying to have the same kind of insights about the mind.

01:04:58.820 --> 01:05:03.220
We should be trying to hone in on what the simple principles are that are producing the

01:05:03.220 --> 01:05:05.700
complexity of mental and behavioral phenomena.

01:05:05.860 --> 01:05:10.410
And I think evolution helps you focus your lens on what those simple principles ought to

01:05:10.410 --> 01:05:10.570
be.

01:05:10.730 --> 01:05:13.370
What are the underlying goals of an evolved organism?

01:05:13.450 --> 01:05:17.370
Uh and and what what are the the parameters that we need to be looking for in order to

01:05:17.370 --> 01:05:19.050
guide behavior adaptively.

01:05:19.370 --> 01:05:23.450
Yeah, Tedek, I think I derailed I think you triggered me when you said computational.

01:05:23.530 --> 01:05:25.290
I think this is like a long digression.

01:05:25.450 --> 01:05:29.530
You were saying you have a computational model like ten minutes ago.

01:05:29.770 --> 01:05:31.290
Do you remember what you were talking about?

01:05:31.370 --> 01:05:34.000
If not, I have we can go on to something else.

01:05:34.640 --> 01:05:40.160
I think I was just talking about the the computational model uh and the instantiation of

01:05:40.160 --> 01:05:45.840
the the model of how anger follows from a sort of a Bayesian inference about

01:05:46.000 --> 01:05:49.360
the um uh about the the person's welfare threat of ratio.

01:05:49.600 --> 01:05:49.680
Right.

01:05:49.840 --> 01:05:55.120
Because I guess in in previous work by Aaron Cell, there was this idea that yes, if you if

01:05:55.360 --> 01:06:01.210
the cost and benefit ratio is such that um this gives you information about the person's

01:06:01.210 --> 01:06:04.730
welfare total ratio, then you will calibrate your anger accordingly.

01:06:05.290 --> 01:06:11.130
And so what what we did in that paper is kind of to really flesh out uh like how this would

01:06:11.130 --> 01:06:12.410
work in in Bayesian terms.

01:06:12.730 --> 01:06:12.890
Yeah.

01:06:13.050 --> 01:06:16.410
In Bayesian terms, just so it's not like jargon or fancy.

01:06:16.570 --> 01:06:21.450
Let me may let me see if I do a simple like roughly six year old level description that you

01:06:21.450 --> 01:06:22.570
tell me if it's accurate.

01:06:22.810 --> 01:06:27.940
So like one version of sort of like let's use the the cloud rain example, right?

01:06:28.100 --> 01:06:31.460
You could do something where you're sort of like, well, every time it rains, how often are

01:06:31.460 --> 01:06:32.260
there clouds?

01:06:32.500 --> 01:06:35.940
And then you could just sort of say, Well, that's how I'm going to figure out how likely it

01:06:35.940 --> 01:06:37.220
is it's going to rain.

01:06:37.380 --> 01:06:38.820
That's one version.

01:06:38.900 --> 01:06:43.860
Um, but a smarter version would you would say, Well, look, how how when it's raining, when

01:06:43.860 --> 01:06:44.820
are there clouds?

01:06:44.980 --> 01:06:48.980
But you should look at another category too, which is how often are there clouds and it's

01:06:48.980 --> 01:06:49.940
not raining?

01:06:50.180 --> 01:06:52.260
And then you should combine those two.

01:06:52.470 --> 01:06:56.070
And roughly speaking, that's what being Bayesian is.

01:06:56.310 --> 01:06:58.470
It's looking at both things and not just the one.

01:06:58.550 --> 01:07:01.030
Is that a fair, super simple gloss?

01:07:01.270 --> 01:07:01.910
Yeah, yeah, yeah.

01:07:02.070 --> 01:07:08.070
If we want to go even more at a more foundational level, maybe the idea behind

01:07:08.070 --> 01:07:13.960
Bayesian thinking is that um there's a lot of uncertainty in the world, And so,

01:07:14.120 --> 01:07:20.040
I mean, with some exception, with some exceptions, we can never be certain of most

01:07:20.040 --> 01:07:22.840
things because of the uncertainty.

01:07:23.400 --> 01:07:29.320
And so the best that we can do is to assign some number to our belief that something

01:07:29.320 --> 01:07:30.200
is the case.

01:07:30.520 --> 01:07:36.120
So maybe I look out the window and it's I see clouds in the sky, and

01:07:36.120 --> 01:07:40.150
maybe maybe it's going to rain, but I'm not quite sure.

01:07:40.230 --> 01:07:44.470
Maybe I will say that there's a 0.9 chance that it's going to rain.

01:07:44.630 --> 01:07:50.150
And so this idea is that we can quantify our belief in something by assigning

01:07:50.150 --> 01:07:50.710
numbers.

01:07:51.190 --> 01:07:57.030
And then the the good way to do this, if we are going to engage in sound reasoning, is for

01:07:57.030 --> 01:07:58.870
those numbers to be probabilities.

01:07:59.580 --> 01:08:04.300
Because when we have probabilities, then we can work with them and we can combine them in

01:08:04.300 --> 01:08:05.260
particular ways.

01:08:12.780 --> 01:08:17.420
Is that you should really uh work with probabilities in certain instead of just truth

01:08:17.420 --> 01:08:17.980
values.

01:08:18.300 --> 01:08:23.900
And then this and this kind of framework is called uh Bayesianism after Bayes theorem,

01:08:23.980 --> 01:08:29.890
because Bayes theorem tells you what is the right way to update your the number that you

01:08:29.890 --> 01:08:34.050
assign to a particular proposition based on the evidence that you have.

01:08:34.450 --> 01:08:34.930
Yeah.

01:08:35.170 --> 01:08:39.330
It's uh we can do yeah, we could do a whole two hours on what is a probability.

01:08:40.050 --> 01:08:40.770
We should not.

01:08:41.250 --> 01:08:44.530
Um I wanted to I wanted to slightly move direct, change direction.

01:08:44.690 --> 01:08:45.650
Oh, David, do you have something?

01:08:45.730 --> 01:08:50.530
I don't want to of just like we've talked a lot about causation versus correlation, and

01:08:50.530 --> 01:08:53.490
we've sort of been like, well, look, the mind is actually pretty good at like picking out

01:08:53.490 --> 01:08:56.680
causation, But there's another sense in which causation doesn't matter at all.

01:08:56.760 --> 01:09:00.680
And I think this gets into your free will work, which is imagine you have a causal account.

01:09:00.760 --> 01:09:03.640
Like, I don't know, let's imagine you're up on a stage, it's made out of wood, and you slip

01:09:03.640 --> 01:09:05.720
on a banana and you hit your head really hard.

01:09:05.800 --> 01:09:09.240
And you're like, what caused me to hit my head?

01:09:09.640 --> 01:09:14.120
There's a long stretch of causal sequences, including like the people who chopped down the

01:09:14.120 --> 01:09:17.400
tree that made the stage, the person that grew the banana, right?

01:09:17.640 --> 01:09:23.160
Like there, there's a long stretch of causality that all led up to you hitting your head.

01:09:23.470 --> 01:09:29.390
But we, we meaning like psychological design, focuses in on

01:09:30.030 --> 01:09:36.030
not all of that causation, but the part of the causation that has the highest effective

01:09:36.030 --> 01:09:40.110
sort of turnover, where it's sort of like, where's the biggest bang for my buck?

01:09:40.270 --> 01:09:44.350
Like, if I don't want to slip, should I like go to where they grow the trees and like

01:09:44.430 --> 01:09:46.110
convince them to not grow the trees?

01:09:46.350 --> 01:09:49.790
Should I like, you know, uh go to the banana grower and not do that?

01:09:49.870 --> 01:09:52.430
No, it's like, well, that banana shouldn't have been on the ground there.

01:09:52.510 --> 01:09:57.720
And so, like a notion of causation is also nar

01:09:58.200 --> 01:10:03.240
it's not just all actual causation, it narrows down to like as David describes, sort of

01:10:03.240 --> 01:10:07.400
like points of intervention or things I could do something about, which is what you would

01:10:07.400 --> 01:10:09.400
expect an organism to do.

01:10:09.880 --> 01:10:12.280
And I that's coming from your work, correct?

01:10:12.440 --> 01:10:17.000
That like I think you're also making this point that sort of an interventionist view of

01:10:17.000 --> 01:10:21.530
causation, I think there's other people doing it too, is also very evolutionary in the

01:10:21.530 --> 01:10:27.370
sense of even if you grant causal views, it really is a a a shrinking

01:10:27.370 --> 01:10:32.170
down of all possible causal sequences to those things you can functionally do something

01:10:32.170 --> 01:10:32.490
about.

01:10:33.290 --> 01:10:33.610
Yeah.

01:10:33.690 --> 01:10:39.450
Uh and actually the first time I heard about this problem was uh a talk by by John

01:10:39.690 --> 01:10:41.220
Toby at HBS.

01:10:41.620 --> 01:10:47.540
And I don't think he ever published anything of the sort, but I

01:10:47.540 --> 01:10:49.460
mean I think this is one of those things.

01:10:49.860 --> 01:10:52.660
It's a great example of of instinct blindness.

01:10:53.220 --> 01:10:56.580
This is a problem that we never think about.

01:10:57.060 --> 01:11:02.980
Basically, I mean it's um it's it's something that's has been it has

01:11:02.980 --> 01:11:08.020
taken uh quite a long time before people actually started to realize that well this is

01:11:08.020 --> 01:11:10.110
actually an important psychological problem.

01:11:10.350 --> 01:11:13.150
What is going on in people's brain uh when they do that?

01:11:13.390 --> 01:11:18.670
Because this is one of those problems that we solve so effortlessly and unconsciously that

01:11:18.670 --> 01:11:21.150
it just does not register that that we are doing it.

01:11:21.870 --> 01:11:22.510
That sounds weird.

01:11:22.670 --> 01:11:26.030
You have to like make effort to be like, well, it's the growers of the trees.

01:11:26.110 --> 01:11:27.390
It's like weird to come up with that.

01:11:28.270 --> 01:11:33.840
It's I mean that's why when I sometimes talk about my work, people look at me funny because

01:11:34.400 --> 01:11:36.080
why is this kind of thing?

01:11:36.880 --> 01:11:42.800
Um another kind of a classical example of of this kind of situation,

01:11:43.040 --> 01:11:48.800
this kind of problem, is that suppose that you want to know what started a forest fire,

01:11:48.880 --> 01:11:53.440
uh what was the cause, and that you know that well there were a lot of dry leaves on on the

01:11:53.440 --> 01:11:58.080
ground and it was very hot on that day, there was a lot of wind, um, there was a lightning

01:11:58.080 --> 01:12:02.550
bolt that struck a tree, there was some oxygen in the air, and so on and so forth.

01:12:02.710 --> 01:12:08.230
Um it seems almost obvious that it was the lightning bolt that was the cause of the forest

01:12:08.230 --> 01:12:08.630
fire.

01:12:08.790 --> 01:12:11.270
Even though there are many things that also contributed.

01:12:11.670 --> 01:12:17.590
So um the oxygen was in a sense also the the the cause of the fire because if we had

01:12:17.590 --> 01:12:21.030
magically removed the oxygen from the air, then the fire would not have happened.

01:12:21.320 --> 01:12:25.560
But it feels almost ludicrous to kind of ask the question why do we think it's the

01:12:25.560 --> 01:12:27.240
lightning bolt and not the oxygen?

01:12:27.400 --> 01:12:30.280
It's like, of course it's the it's not the oxygen.

01:12:30.680 --> 01:12:36.520
This is um an intuition that our brain is giving is giving to us in such a vivid way that

01:12:36.520 --> 01:12:41.720
it doesn't even feel that we had to do any computations in in the first place.

01:12:42.600 --> 01:12:48.430
And so and I think that that's why people didn't really think a lot about

01:12:48.430 --> 01:12:51.550
that problem, uh, and a lot of work that went into this.

01:12:51.710 --> 01:12:54.110
So, this is called the problem of causal selection.

01:12:54.270 --> 01:12:58.510
Sometimes people talk about the problem of causal responsibility and so on and so forth.

01:12:58.670 --> 01:13:04.670
Um, so a lot of the work on this uh sprung up from issues in moral psychology.

01:13:05.150 --> 01:13:10.270
So people say, Well, I know sometimes we have an intuition that some people are more

01:13:10.590 --> 01:13:13.710
morally responsible for a bad outcome.

01:13:13.950 --> 01:13:19.840
Um, and when we try to analyze this concept of moral responsibility, then we find

01:13:19.840 --> 01:13:25.520
that well, causality has a lot to do with it, uh, which is itself uh its own

01:13:25.600 --> 01:13:26.720
interesting thing, right?

01:13:26.880 --> 01:13:32.800
Because uh, you know, you can imagine uh a thought experiment where I have an intent to do

01:13:32.800 --> 01:13:38.790
something bad, I want to I don't I want to harm David, And then David

01:13:38.790 --> 01:13:42.070
just gets harmed in exactly the way that I wanted, but actually, there was no connection,

01:13:42.310 --> 01:13:44.950
causal connection between my intent and his harm.

01:13:46.550 --> 01:13:52.310
People will not punish me as much because there was no causal connection between the bad

01:13:52.310 --> 01:13:54.390
intent and the bad outcome.

01:13:55.590 --> 01:14:01.110
And so this is a bit of a tangent to say that in the process of analyzing how people uh

01:14:01.110 --> 01:14:06.620
think about moral responsibility, uh philosophers and social psychologists and experimental

01:14:06.620 --> 01:14:12.300
philosophers came to wonder well, why how is it that we attribute

01:14:12.300 --> 01:14:14.620
causal responsibility in the first place?

01:14:14.940 --> 01:14:20.220
Um, and so yeah, I think that is interesting because it's we had to

01:14:20.460 --> 01:14:24.700
recognize that there is an information processing to be solved here, but we did not show in

01:14:24.780 --> 01:14:28.220
the field did not really come to it in a very direct way.

01:14:33.350 --> 01:14:39.030
So, um, how how do we figure out when someone is morally

01:14:39.030 --> 01:14:39.510
responsible?

01:14:39.670 --> 01:14:45.670
I mean, certainly, you know, the the causal part of things is is relevant, but

01:14:45.670 --> 01:14:50.470
it do we figure it out in the same way that we figure out that the lightning bolt caused

01:14:50.470 --> 01:14:53.990
the fire, or is it there's something different going on in the realm of moral

01:14:53.990 --> 01:14:55.110
responsibility?

01:14:57.080 --> 01:15:02.840
So uh maybe it it might be useful to talk about the the information

01:15:02.840 --> 01:15:08.440
processing logic of causal responsibility in the first place before we extend it to the the

01:15:08.440 --> 01:15:10.440
the realm of of moral responsibility.

01:15:10.680 --> 01:15:15.880
Um so so one one one sort of obvious idea for how we

01:15:16.120 --> 01:15:21.160
assign responsibility is that we engage in in counterfactual thinking.

01:15:28.190 --> 01:15:33.550
Is that we imagine a counterfactual possibility where we remove the lightning bolt and see

01:15:33.710 --> 01:15:35.470
and we see what happens to the forest fire.

01:15:36.030 --> 01:15:39.550
And then we say, oh without the lightning bolt, then there's no forest fire.

01:15:40.990 --> 01:15:43.870
And in intuitively it produces the correct result.

01:15:44.350 --> 01:15:50.180
But one problem with with that is that we are now kind of forced to judge that the

01:15:50.180 --> 01:15:54.100
presence of oxygen in the air was also a cause of the forest fire, right?

01:15:54.180 --> 01:15:57.700
Because well, if there was no oxygen, then there was a forest fire.

01:15:57.860 --> 01:16:03.780
Um an example that people often use is so you can have this situation where um you

01:16:03.780 --> 01:16:07.140
ask me to water your plants while you're away on a trip.

01:16:07.220 --> 01:16:11.700
Okay, so I'm the one that's supposed to water you, and I kind of forget uh, and then your

01:16:11.700 --> 01:16:13.380
plants wither and die.

01:16:13.860 --> 01:16:19.850
So you can say, well, um my my my omission from watering

01:16:19.850 --> 01:16:24.010
the plants was the cause of your plants dying, which seems like a fine thing to say.

01:16:24.170 --> 01:16:29.290
And this is because if I actually had decided to water your plants, then they would have

01:16:29.290 --> 01:16:29.850
survived.

01:16:30.170 --> 01:16:33.370
So in that counterfactual uh the outcome has changed.

01:16:33.530 --> 01:16:39.050
But then you can also say, well, no, I think it's the Pope that caused your plants to die.

01:16:39.130 --> 01:16:44.170
Because if the Pope had come to your flat and watered your plants, then they would not have

01:16:44.170 --> 01:16:44.490
died.

01:16:44.810 --> 01:16:46.520
Yeah, there's a lot of counterfactuals.

01:16:46.600 --> 01:16:49.240
If you hadn't plant if you didn't have plants, you wouldn't have killed them.

01:16:49.320 --> 01:16:53.080
I mean if there was no water, you wouldn't have killed the space of possible

01:16:53.080 --> 01:16:55.880
counterfactuals that are relevant is is just infinite, right?

01:16:56.120 --> 01:17:02.040
And so and so people need to have a way of just focusing on the counterfactuals that

01:17:02.040 --> 01:17:06.120
are actually relevant to the problem of causal attribution.

01:17:06.280 --> 01:17:11.850
Um and so a lot of what is going on is this that some counterfactual possibilities are just

01:17:11.850 --> 01:17:13.370
more relevant than others.

01:17:14.090 --> 01:17:19.850
And there is some work that has looked at what makes a counterfactual relevant for the

01:17:19.850 --> 01:17:21.690
purpose of causal attribution.

01:17:21.930 --> 01:17:27.930
And so roughly um a counterfactual is relevant if it it was kind of high

01:17:27.930 --> 01:17:29.530
probability to to begin with.

01:17:29.850 --> 01:17:35.290
So the pope coming to your to your flat and watering your plants is just was so unlikely

01:17:35.290 --> 01:17:38.260
that this would happen that this is not a relevant possibility.

01:17:38.420 --> 01:17:44.020
And in the same way, the present like the absence of oxygen in the air is such a remote

01:17:44.020 --> 01:17:46.020
possibility that we're not going to consider it.

01:17:46.260 --> 01:17:52.020
Um, also, people don't really focus on counterfactual possibilities that deviate too much

01:17:52.020 --> 01:17:53.540
from what actually happened.

01:17:54.740 --> 01:18:00.420
So the idea is sorry, like just to make so like in an alien world where oxygen was variable

01:18:00.580 --> 01:18:06.260
but lightning was not, those aliens would think that oxygen, the oxygen caused the fire.

01:18:06.670 --> 01:18:08.430
Yeah, yeah, yeah, absolutely.

01:18:08.670 --> 01:18:14.430
Um and then I mean where it gets maybe a little bit more spooky is that um

01:18:14.590 --> 01:18:20.190
kind of social and and moral norms also seem to have an influence on what we think

01:18:20.350 --> 01:18:23.950
what or the the relevant kind of kind of actual possibilities.

01:18:24.270 --> 01:18:29.940
So it might be that I don't know, um I I every day

01:18:30.020 --> 01:18:34.340
like uh Dave and I kind of come to water your plants.

01:18:34.420 --> 01:18:37.380
But you kind of repeatedly ask Dave not to come in.

01:18:37.540 --> 01:18:40.820
So it's kind of like he's not supposed to be here, but Dave comes in anyway.

01:18:41.220 --> 01:18:44.420
And I'm really the one that's supposed to come in and water your plants.

01:18:44.660 --> 01:18:50.660
Um so then if kind of suddenly none of us come, neither of us come and your plants

01:18:50.660 --> 01:18:55.380
die, it seems that I like I'm the person that is responsible for your plants dying because

01:18:55.380 --> 01:18:57.220
I'm the one that was supposed to be here.

01:18:57.470 --> 01:18:59.550
That's like the counterfactual possibilities.

01:19:00.030 --> 01:19:05.630
The counterfactual possibility where I am I actually show a patcher apartment seems to be

01:19:05.630 --> 01:19:07.550
the most relevant counterfactual.

01:19:09.070 --> 01:19:14.510
And this is this is something for which I kind of I kind of wish there was a strong

01:19:14.510 --> 01:19:19.710
adaptationist answer for why this should be a functional requirement of causal attribution.

01:19:20.590 --> 01:19:25.320
But I don't I don't have super well formed ideas about why this is the case.

01:19:25.720 --> 01:19:30.360
Well, wouldn't it be that I mean what unpacking relevant it comes back to interventionism,

01:19:30.440 --> 01:19:30.600
right?

01:19:30.760 --> 01:19:36.680
So like if the point is what is what's the thing that David can do

01:19:37.000 --> 01:19:42.920
going forward, it's gonna be it's gonna be weird to say, well,

01:19:43.080 --> 01:19:44.120
Dave, I was wrong.

01:19:44.280 --> 01:19:46.360
You should have been coming all along.

01:19:47.010 --> 01:19:52.370
Uh uh like he has to recalibrate himself and me, but just you, right?

01:19:52.530 --> 01:19:56.850
So, like, you know, he asked you to water the plants, but you didn't.

01:19:57.090 --> 01:19:59.650
He asked me to not be there, but I was there.

01:19:59.970 --> 01:20:02.050
And we didn't water the plants.

01:20:02.290 --> 01:20:08.130
The the most efficient, the the biggest bang for your buck causal interventionist thing is

01:20:08.130 --> 01:20:14.120
to recalibrate how much you are taking into account what he's saying

01:20:14.120 --> 01:20:17.160
about um the right about that.

01:20:17.320 --> 01:20:20.200
Like you you just fail to do something you heard him say.

01:20:20.440 --> 01:20:25.480
So that's like the I mean it's not exactly couldn't it couldn't it just be reframed in

01:20:25.480 --> 01:20:31.330
terms of the more likely counterfactual um being a

01:20:31.330 --> 01:20:36.450
more being more relevant because if you think about people responding to social

01:20:36.450 --> 01:20:41.970
expectations or me doing what you request of me, like the probability that I will do what

01:20:41.970 --> 01:20:44.210
you request of me is very high, right?

01:20:44.770 --> 01:20:48.850
Um or or the the probability that I I'd forget is also pretty high, right?

01:20:49.010 --> 01:20:55.010
Um and so it may it may be that um responding to requests or social expectations

01:20:55.010 --> 01:21:01.000
is just is very relevant because um the uh the the degree to which we take them into

01:21:01.000 --> 01:21:05.720
account or not is just at baseline a very high likelihood thing to uh consider.

01:21:06.120 --> 01:21:07.160
Is that yeah, yeah.

01:21:07.240 --> 01:21:09.400
I think that that's that's one possible explanation.

01:21:09.480 --> 01:21:14.120
It's a little bit of a a deflationary explanation that all people are actually just

01:21:14.120 --> 01:21:15.320
tracking probability.

01:21:15.560 --> 01:21:20.120
Um I think I think that there is some evidence against this where people have done

01:21:20.120 --> 01:21:24.890
experiments where they really emphasize well no like the the kind of frequency of those

01:21:24.890 --> 01:21:26.650
things is is really the same.

01:21:26.810 --> 01:21:31.930
Um, but then you could counter argue that you know to what extent the participants actually

01:21:31.930 --> 01:21:37.610
believe what you're telling them, and maybe they're still um using those social

01:21:37.610 --> 01:21:41.690
norms uh as a as a QT probability, that's for sure.

01:21:41.850 --> 01:21:47.240
Um but I I'm I'm I'm kind of inclined to believe that there is There is

01:21:47.240 --> 01:21:52.680
still an effect of kind of social expectations on the relevance of

01:21:52.680 --> 01:21:53.720
countervactuals.

01:21:53.800 --> 01:21:59.240
And one possible explanation to that is what Dave suggested about identifying good points

01:21:59.240 --> 01:22:00.120
of intervention.

01:22:00.520 --> 01:22:05.720
Maybe another possibility is that when we engage in causal attribution, we are trying to

01:22:05.720 --> 01:22:11.720
communicate things about our beliefs, about our values, about our

01:22:11.720 --> 01:22:13.000
own model of the world.

01:22:13.240 --> 01:22:19.190
And so one way that we can communicate what we expect of others is

01:22:19.190 --> 01:22:24.950
to actually uh make causal attributions that will contain cues about

01:22:25.190 --> 01:22:27.430
what's what we expect them to do.

01:22:27.910 --> 01:22:28.150
Yeah.

01:22:28.390 --> 01:22:33.270
So that would predict, for example, like let's say there's um I don't know, we're holding

01:22:33.270 --> 01:22:35.190
up a I'm trying to think of a good example.

01:22:35.270 --> 01:22:39.030
This is a dumb example, but let's imagine like we're holding up a wall that might fall

01:22:39.030 --> 01:22:39.350
over.

01:22:40.620 --> 01:22:43.020
and or or we need to, right?

01:22:43.100 --> 01:22:45.740
Like we're we've noticed this wall we've all worked hard on it.

01:22:45.900 --> 01:22:46.300
We need to.

01:22:46.380 --> 01:22:52.380
And I I tell like 10 people, the wall's falling over, it's gonna like um cause our uh

01:22:52.460 --> 01:22:56.060
it's gonna like hit, it's gonna ru hurt us in some way, right?

01:22:56.220 --> 01:22:58.620
And then another 10 people I don't tell.

01:22:59.020 --> 01:23:01.740
And all else equal, the wall falls over.

01:23:01.900 --> 01:23:05.980
And then you just say, like, well, who's responsible for the wall falling over?

01:23:06.220 --> 01:23:08.940
My intuition is it's the 10 people I told.

01:23:09.100 --> 01:23:15.030
And that's just a readout of like who's more likely to if I add a little bit

01:23:15.030 --> 01:23:19.510
of information or who's easier to convince um, you know, like the

01:23:20.630 --> 01:23:23.430
that's the that's more and more responsibility.

01:23:23.510 --> 01:23:26.790
So I think it comes back to the that's the interventionist account right there.

01:23:27.190 --> 01:23:32.390
That um that it's going to be easier to intervene if I've already told somebody and there's

01:23:32.390 --> 01:23:37.840
common knowledge that there's a cost and there's a problem versus I haven't even said

01:23:37.840 --> 01:23:39.280
anything yet to you.

01:23:39.360 --> 01:23:42.960
Um that's enough to tip it towards the first 10 and not the second 10.

01:23:43.600 --> 01:23:49.120
Yeah and and it is definitely the case that the the counterfactual where those people show

01:23:49.120 --> 01:23:54.160
up is much more relevant than the counterfactual where it is the 10 people who have not

01:23:54.160 --> 01:23:56.160
heard the information that that show up.

01:23:56.650 --> 01:23:56.970
Yeah.

01:23:57.050 --> 01:23:59.690
I mean, I I personally, like this is my own bias.

01:23:59.770 --> 01:24:02.730
I don't like it relevance language because I find that obscuring.

01:24:03.050 --> 01:24:08.410
Because I think it's sort of an outcome of the interventionist calculation.

01:24:08.730 --> 01:24:14.730
Um, it doesn't like the relevance is sort of a placeholder for like this is what you think.

01:24:15.290 --> 01:24:20.940
Not that relevance is calculated and then that's what causes it to be uh the outcome.

01:24:21.020 --> 01:24:25.820
It's that it's relevant because it's the outcome calculated based on interventionist logic.

01:24:26.380 --> 01:24:28.300
That's m that's my own personal bias.

01:24:28.460 --> 01:24:33.420
But um interestingly the sort of the the relevance of something is not

01:24:34.540 --> 01:24:39.820
there is not always a direct a direct mapping uh between the relevance of a counterfactual

01:24:39.820 --> 01:24:41.740
and whether it is selected as the cause.

01:24:41.990 --> 01:24:47.270
which was like I think one of the most interesting discoveries in this research on causal

01:24:47.270 --> 01:24:48.070
attribution.

01:24:48.230 --> 01:24:54.150
Um, and I can so I can first give a like an example of an experiment that shows this:

01:24:54.310 --> 01:25:00.070
that the the most relevant thing is not always the um the what people

01:25:00.070 --> 01:25:02.710
pick as a a causal explanation.

01:25:02.870 --> 01:25:07.430
Um and then I can maybe say a few words about my own preferred explanation for for this

01:25:07.430 --> 01:25:07.830
pattern.

01:25:08.230 --> 01:25:13.760
And so you can imagine that there's this person, call them Bob, they're trying to

01:25:14.000 --> 01:25:14.560
graduate.

01:25:14.720 --> 01:25:18.480
And they're almost past all their requirements for graduating.

01:25:18.640 --> 01:25:24.640
They just need to pass two different exams in order to graduate.

01:25:24.880 --> 01:25:29.680
So they need to pass the history exam, which is famously very easy, and they need to pass

01:25:29.680 --> 01:25:32.160
the math exam, which is famously very hard.

01:25:32.320 --> 01:25:35.040
Everyone, almost everyone fails at the math exam, right?

01:25:35.680 --> 01:25:38.560
And so they really need to pass both of them in order to graduate.

01:25:38.830 --> 01:25:44.670
And suppose that Bob goes, he and he passes the math exam and the history exam.

01:25:44.830 --> 01:25:46.510
Um, and therefore he graduates.

01:25:46.590 --> 01:25:51.710
Uh, when you tell people that story, um, they will say, Well, I think Bob graduated because

01:25:51.710 --> 01:25:53.310
he passed the math exam.

01:25:53.950 --> 01:25:59.950
And so here we have a situation where um it seems that the most relevant counterfactual

01:26:00.190 --> 01:26:05.560
is kind of failing the math exam because it was very likely in the first place that he

01:26:05.560 --> 01:26:07.480
would fail this very difficult exam.

01:26:07.720 --> 01:26:11.960
And also people think that yes, it was passing the math exam that was the cause.

01:26:12.120 --> 01:26:17.480
So here we have we have an alignment between the what counterfactual is relevant and causal

01:26:17.480 --> 01:26:18.200
attribution.

01:26:18.440 --> 01:26:24.200
But you can also modify the story in a very uh slight way, in such a way that actually

01:26:24.600 --> 01:26:26.360
you will get the reverse pattern.

01:26:26.630 --> 01:26:32.390
So now if you say, well, um, and this is Alice, and she also needs to graduate.

01:26:32.470 --> 01:26:37.910
Um, but Alice will graduate if she passes either the history exam or the math exam.

01:26:38.390 --> 01:26:42.790
And again, the math exam is the most difficult thing, but she only needs one of them.

01:26:42.950 --> 01:26:47.430
Um then again, Alice passes the math exam and the history exam and she graduates.

01:26:47.510 --> 01:26:53.430
Uh, when you ask participants uh why did Alice graduate, they will tell you, Well, it is

01:26:53.430 --> 01:26:55.750
because she passed the history exam.

01:26:56.080 --> 01:27:00.000
I don't know, I don't know if you to what extent you share this intuition, but maybe it was

01:27:00.080 --> 01:27:05.600
you know that's the she only needed to pass one of these.

01:27:05.760 --> 01:27:09.840
Uh and since the history exam was the the easy one, that's kind of that's the one that

01:27:09.840 --> 01:27:12.080
actually explains why she graduated.

01:27:12.480 --> 01:27:17.520
Um and and in that case, it is still the case that if we think about what is the most

01:27:17.520 --> 01:27:23.280
relevant counterfactual, um, the one that is easiest to come to mind in a way, um,

01:27:23.440 --> 01:27:26.990
that is the counterfactual where she fails the mathematics exam.

01:27:27.070 --> 01:27:31.230
Because again, the math exam is super difficult, and so it's quite likely that she would

01:27:31.230 --> 01:27:32.270
have failed it.

01:27:32.990 --> 01:27:38.670
But despite the fact that the most relevant

01:27:38.670 --> 01:27:42.750
counterfactual is the one where she fails the math exam, people have this intuition that

01:27:42.750 --> 01:27:46.350
it's really it was passing the history exam that caused her to graduate.

01:27:47.080 --> 01:27:51.000
And really all that we did is change one word in the story.

01:27:59.400 --> 01:28:01.400
I have intuitions about what might explain it.

01:28:01.800 --> 01:28:02.360
I I do.

01:28:02.680 --> 01:28:05.800
But m maybe Dave had a methodological comment.

01:28:06.040 --> 01:28:06.440
No, no, no.

01:28:06.520 --> 01:28:06.760
No, yeah.

01:28:06.920 --> 01:28:07.400
Um okay.

01:28:12.600 --> 01:28:18.210
Uh it might be that what we're simulating counterfactually is how

01:28:18.210 --> 01:28:23.730
likely um she would be to study for the history exam versus the math exam.

01:28:23.810 --> 01:28:27.010
Like whether she would she would really invest in trying to pass the history exam because

01:28:27.010 --> 01:28:29.250
that's gonna get her the most bang for her buck.

01:28:29.490 --> 01:28:35.490
And so um we uh so we're imagining that and that it would be really unlikely if she uh

01:28:35.650 --> 01:28:39.650
if she invested all of her effort studying for the math exam.

01:28:41.100 --> 01:28:42.860
So it's interventionless, sort of again.

01:28:43.100 --> 01:28:46.540
Yeah, and it's again it goes back to this notion of what is the optimal point of

01:28:46.540 --> 01:28:47.100
intervention.

01:28:47.580 --> 01:28:49.420
Um yeah, I like this explanation.

01:28:49.500 --> 01:28:52.940
Um however, I don't think it can explain like all possible cases.

01:28:53.100 --> 01:28:53.260
Okay.

01:28:53.580 --> 01:28:59.260
So you can you can run the same version of this experiment where the the two events are

01:28:59.260 --> 01:29:01.020
completely out of your control.

01:29:01.450 --> 01:29:06.650
So you have no in order to you're playing some sort of casino like chance game.

01:29:06.810 --> 01:29:11.930
And in order to win the game you need to kind of draw a a ball from an urn.

01:29:12.090 --> 01:29:13.930
And you kind of you're going to draw at random.

01:29:14.170 --> 01:29:19.370
And so maybe you're going to win the game if you get a a green ball from

01:29:20.650 --> 01:29:25.360
either one of two boxes in that would be one condition.

01:29:25.520 --> 01:29:31.040
Um and in the other condition, you win if you get a successful draw from the earning

01:29:31.280 --> 01:29:33.600
from uh from both both boxes.

01:29:33.840 --> 01:29:37.600
And so in that kind of setup, you still find the same effects.

01:29:37.840 --> 01:29:43.280
And then since the outcome of a given draw is a random one, you can't really uh

01:29:43.920 --> 01:29:46.960
give an explanation in terms of like how much someone's going to prepare.

01:29:47.570 --> 01:29:47.730
Okay.

01:29:48.050 --> 01:29:48.370
That makes sense.

01:29:48.690 --> 01:29:51.330
But isn't it still an intervention in the sense of like what's the outcome that has the

01:29:51.330 --> 01:29:52.530
biggest bang for the buck?

01:29:52.610 --> 01:29:55.090
Or like what do you need to what what goal do you need to do?

01:29:56.210 --> 01:30:01.490
If if you were like, well, how was I going to do if I can manipulate the system, what's the

01:30:01.490 --> 01:30:03.970
thing I need to do to get the outcome I want?

01:30:04.450 --> 01:30:06.930
Yes, that that's definitely one one explanation.

01:30:07.010 --> 01:30:12.580
And people have tried to formalize this insight in computational terms and it explains some

01:30:12.580 --> 01:30:14.580
of the data but not all of it.

01:30:17.540 --> 01:30:23.380
But I mean the the most parsimonious explanation that I came up with for that is

01:30:23.380 --> 01:30:29.140
that um when people make a causal judgment they they imagine not

01:30:29.140 --> 01:30:34.970
just one um counterfactual possibility but they they simulate many of them.

01:30:36.250 --> 01:30:41.690
So they they kind of sample, simulate some number of counterfactuals.

01:30:42.810 --> 01:30:48.490
And each counterfactual is simulated with a probability that is

01:30:49.370 --> 01:30:53.610
that is proportional to kind of its what we would call its relevance.

01:30:53.850 --> 01:30:59.600
So it is simulated in proportion to how uh to its prior probability, simulated

01:30:59.760 --> 01:31:04.240
in proportion to how close it was to the the actual event and so on and so forth.

01:31:04.720 --> 01:31:09.280
And then once you have this collection of different counterfactual possibilities, you can

01:31:09.280 --> 01:31:14.400
compute the correlation between the cause and the effect across those counterfactuals.

01:31:14.560 --> 01:31:20.110
Yeah, so if you're trying to think, oh is it the passing the history exam that caused Alice

01:31:20.270 --> 01:31:21.150
to win?

01:31:21.310 --> 01:31:27.230
Then you compute what is the correlation between passing the history exam and uh

01:31:27.230 --> 01:31:30.510
Alice graduating in the worlds that you have simulated.

01:31:30.750 --> 01:31:36.110
And so when you do that, you find that well, there is in almost all worlds, she she

01:31:36.110 --> 01:31:37.070
graduates, right?

01:31:37.150 --> 01:31:39.150
And she passes the history exam.

01:31:39.230 --> 01:31:43.310
And it kind of doesn't really matter whether she passes the math exam or not.

01:31:43.470 --> 01:31:49.040
And so the correlation between passing the history exam and graduating is quite high.

01:31:49.280 --> 01:31:53.680
And that's why you think that it was passing the history exam that was the cause of her

01:31:53.840 --> 01:31:54.720
graduating.

01:31:54.960 --> 01:31:58.160
And now we can apply the same logic to the case of Bob.

01:31:58.320 --> 01:32:04.080
So just as a reminder, Bob needed to pass both the math exam and the history exam in order

01:32:04.080 --> 01:32:04.640
to graduate.

01:32:04.800 --> 01:32:06.800
Like he really had to have both.

01:32:07.280 --> 01:32:12.730
And so you kind of engage in the same process of simulating counterfactual possibilities.

01:32:12.890 --> 01:32:18.570
And now you see that well, there's going to be many worlds in which Bob passes the

01:32:18.890 --> 01:32:20.970
history exam, but he fails the math exam.

01:32:21.050 --> 01:32:22.250
And so he graduates.

01:32:30.540 --> 01:32:35.580
So it means that now you have a high correlation between passing the math exam and

01:32:35.580 --> 01:32:36.300
graduating.

01:32:36.540 --> 01:32:41.820
And this then explains why we think that, well, in the case of Bob, it was passing the math

01:32:41.820 --> 01:32:43.020
exam that caused him to graduate.

01:32:43.420 --> 01:32:43.980
Let me make sure.

01:32:44.060 --> 01:32:48.140
So in other words, it's the crucial difference of all these counterfactuals are run

01:32:48.220 --> 01:32:48.380
through.

01:32:48.540 --> 01:32:54.140
It's sort of what's the crucial difference that leads to the outcome

01:32:54.140 --> 01:32:56.380
desired versus not desired.

01:32:57.350 --> 01:32:57.670
Yeah.

01:32:57.910 --> 01:32:58.870
Yes, yes.

01:32:59.350 --> 01:33:05.190
But this idea of, I think this idea of a crucial difference is a nice intuition for what is

01:33:05.190 --> 01:33:05.830
going on.

01:33:07.030 --> 01:33:12.310
And this kind of process of counterfactual simulation and computing correlation is an

01:33:12.310 --> 01:33:15.590
explicit information processing description of what is going on.

01:33:15.830 --> 01:33:16.070
Right.

01:33:16.150 --> 01:33:19.270
It's a crucial difference right across those counterfactuals.

01:33:19.540 --> 01:33:20.180
Yeah, yeah.

01:33:20.340 --> 01:33:20.980
Yeah.

01:33:21.860 --> 01:33:22.740
Interesting.

01:33:22.980 --> 01:33:28.100
Um presumably these counterfactual simulations are happening unconsciously.

01:33:28.180 --> 01:33:32.900
It it seems like it introspectively when when I was thinking about the the Bob problem or

01:33:33.060 --> 01:33:37.940
or the the math test problem, like I didn't have a vivid, you know, simulation in my head

01:33:38.020 --> 01:33:41.540
where you know he he passed in this case, he did he didn't in this case, he graduated in

01:33:41.540 --> 01:33:41.940
this case.

01:33:42.100 --> 01:33:43.620
It's just kind of I just had an intuition.

01:33:43.700 --> 01:33:47.300
So presumably these simulations are happening unconsciously.

01:33:47.970 --> 01:33:53.090
Yeah, and I I think I assume that's yeah, it is the case for most people.

01:33:53.410 --> 01:33:58.450
I myself don't really have uh the phenomenology of running those simulations.

01:33:58.610 --> 01:34:04.610
Um and so from uh from a cognitive perspective, there are two different

01:34:04.610 --> 01:34:06.370
possible accounts of what is going on.

01:34:06.610 --> 01:34:12.370
There's is an account where we can we do run those simulations uh but it's just implicit

01:34:12.370 --> 01:34:13.730
and unconscious.

01:34:13.890 --> 01:34:19.120
Um there is maybe another account where we don't even need to

01:34:20.160 --> 01:34:22.080
to really engage in simulation.

01:34:22.160 --> 01:34:28.080
It instead we our brain has found a way, kind of kind of a shortcut that allows you

01:34:28.080 --> 01:34:33.120
to compute what would be the result of those um of those simulations.

01:34:33.520 --> 01:34:39.440
Kind of maybe as an analogy for what this possibility might look like, you might think

01:34:39.440 --> 01:34:42.800
about you know the process, the much simpler process of computing probabilities.

01:34:43.450 --> 01:34:48.170
So I might want one way that you could compute the probability that

01:34:50.490 --> 01:34:56.330
a dice is going to come up for is to just simulate many rows, many rolls of the

01:34:56.330 --> 01:34:58.330
dice, and you count up the number of fours.

01:34:59.690 --> 01:35:02.170
But you don't necessarily have to do that.

01:35:02.250 --> 01:35:05.130
You can use the laws of probability theory to compute that.

01:35:05.290 --> 01:35:10.650
And so there might be a way that our brain has also evolved some shortcut way of computing

01:35:10.650 --> 01:35:13.920
what would be the outcome of such counterfactual simulations.

01:35:14.400 --> 01:35:20.240
Would an example of that just be that like there's a representation of high variability and

01:35:20.240 --> 01:35:25.920
low variability, for example, like associated with like you know doing good in

01:35:25.920 --> 01:35:26.960
math versus this?

01:35:27.120 --> 01:35:33.040
And so even just that common sense knowledge about the variability um

01:35:33.840 --> 01:35:37.200
curtails some of the needing to run through the simulations.

01:35:37.870 --> 01:35:43.630
Yeah, I mean it might be that if I kind of give you like a

01:35:43.950 --> 01:35:49.870
novel scenario where you have an unfamiliar thing, then uh your brain

01:35:49.870 --> 01:35:51.870
needs to engage in simulation.

01:35:52.030 --> 01:35:57.870
Um but if I ask you about something that is much more common common knowledge,

01:35:58.030 --> 01:36:02.910
I mean in the sense that those are familiar things, then you can um you can use a sort of a

01:36:02.910 --> 01:36:08.520
pre-computed uh Yeah, or even just like if you have like imagine like a physics is a really

01:36:08.520 --> 01:36:08.920
good example.

01:36:09.080 --> 01:36:14.920
Like let's just say the physics, your folk the folk physics um system in your head, the

01:36:14.920 --> 01:36:20.520
physics engine in your head, um just calculates based on certain

01:36:20.520 --> 01:36:23.960
experiences, how precarious something is.

01:36:24.200 --> 01:36:28.770
Um And then there's principles that then just can get extended automatically to new

01:36:28.770 --> 01:36:32.290
instances based on the extracted cues.

01:36:32.450 --> 01:36:36.690
So, like, you know, is a beam precarious, like when it's up straight up and down versus

01:36:36.690 --> 01:36:37.810
like laying on the ground.

01:36:37.970 --> 01:36:43.490
And there in that has been based on past runs of the of the

01:36:43.490 --> 01:36:46.210
physics and seeing these things fall.

01:36:46.370 --> 01:36:51.320
But now there's sort of that gets represented and attributed in new instances without

01:36:51.320 --> 01:36:52.600
running the simulation.

01:36:52.840 --> 01:36:58.840
That on its own, if you see a new instance where the the counterfactual

01:36:58.840 --> 01:37:04.840
depends on the likelihood of stability versus falling, that involves no

01:37:04.840 --> 01:37:07.720
running of the counterfactuals.

01:37:07.800 --> 01:37:13.450
It's just that you already there's already a system there that plugs into one is more

01:37:13.450 --> 01:37:18.330
likely, one is less likely, and you don't have to run which the counterfactuals.

01:37:18.410 --> 01:37:20.410
It would be a concrete example of that.

01:37:20.810 --> 01:37:23.690
Yeah, yeah, I definitely think that something like that could happen.

01:37:23.850 --> 01:37:28.810
Uh I think yeah, I think it's it's an example, it's uh an illustration of the fact that the

01:37:28.810 --> 01:37:34.800
model itself uh is really couched at the kind of the David Mars computational level of

01:37:34.800 --> 01:37:40.080
of analysis where like okay, here is the computation that is that is that the system is

01:37:40.080 --> 01:37:41.200
trying to implement.

01:37:41.360 --> 01:37:46.160
Um when it comes to how the s the the mind is doing that, yeah, uh concretely it kind of

01:37:46.160 --> 01:37:47.120
all backs are off current.

01:37:47.360 --> 01:37:47.920
Yeah, yeah, exactly.

01:37:48.000 --> 01:37:52.000
And the I think intuitions about what's easier and harder are really bad then too, because

01:37:52.080 --> 01:37:57.920
you know, it's yeah, it's what it feels like to me to do something is not how hard

01:37:57.920 --> 01:38:00.590
or easy it is for a computational system.

01:38:00.750 --> 01:38:00.990
Yeah.

01:38:01.870 --> 01:38:02.190
Yeah.

01:38:02.350 --> 01:38:07.230
I mean, cause what's interesting to me is that there are cases where this is conscious that

01:38:07.230 --> 01:38:08.830
I can think of pretty easily.

01:38:08.910 --> 01:38:13.550
And it seems like when I do have a phenomenology of causal reasoning, it tends to fit your

01:38:13.550 --> 01:38:14.110
model pretty well.

01:38:14.270 --> 01:38:18.510
So like the example I'm thinking of is I'm playing chess and I really want to get

01:38:18.510 --> 01:38:19.390
checkmate.

01:38:19.620 --> 01:38:23.060
I might actually consciously simulate all the different moves and counter moves.

01:38:23.140 --> 01:38:24.820
If I do this, you might do this in response.

01:38:24.980 --> 01:38:27.140
If you do if you do that, I'll do this in response.

01:38:27.300 --> 01:38:29.540
Uh and I might simulate a bunch of different possibilities.

01:38:29.620 --> 01:38:34.100
You know, if I move this piece versus this piece, and I might look at the correlation it

01:38:34.100 --> 01:38:37.860
consciously in my head of moving this piece and checkmate further down the line.

01:38:38.020 --> 01:38:41.860
And if that correlation is high, like, oh, and all these different simulated possibilities,

01:38:42.020 --> 01:38:45.860
moving this piece helps me get to checkmate, then I'll move that piece.

01:38:45.940 --> 01:38:50.470
So it seems like uh there are some cases where we we are doing this uh in in in a

01:38:50.470 --> 01:38:52.070
full-fledged conscious way.

01:38:52.150 --> 01:38:56.070
And I'm curious if you if you have any thoughts about when we would need to do that

01:38:56.070 --> 01:38:57.830
consciously and when we wouldn't.

01:38:58.470 --> 01:39:03.670
Yeah, something that is related to to that point is that uh even though even if we assume

01:39:03.670 --> 01:39:09.590
that the system is kind of mostly implicit, um it's not um I don't I I don't want to use

01:39:09.590 --> 01:39:14.900
the term encapsulated here in in in Dave's presence because this we I'll leave.

01:39:19.220 --> 01:39:23.380
You can definitely like uh like a verbally uh intervene on that system.

01:39:23.620 --> 01:39:28.740
Um so you know it's people have done this experiment where you know again you have the case

01:39:28.740 --> 01:39:31.460
of um Alice who needs to graduate.

01:39:31.620 --> 01:39:35.460
And this time I I don't tell you anything about the difficulty of the exams, I just tell

01:39:35.460 --> 01:39:39.170
you there's the history exam and the the the the math exam, right?

01:39:39.330 --> 01:39:45.010
And I tell you, okay, like so she she passes the math exam, she passes the history exam.

01:39:45.090 --> 01:39:50.210
And I tell you, well, just try to imagine you know what would have happened if she had

01:39:50.210 --> 01:39:51.410
failed the math exam.

01:39:51.650 --> 01:39:54.050
Kind of try to really simulate the counterfactual.

01:39:54.130 --> 01:39:59.010
And then after you have done that, I ask you, well, what do you think was the cause of her

01:39:59.010 --> 01:39:59.490
passing?

01:39:59.740 --> 01:40:02.620
And then people will say, oh, this was the history exam.

01:40:03.100 --> 01:40:03.340
Right.

01:40:03.500 --> 01:40:09.340
And so you kind of sort of can artificially push people to to simulate one

01:40:09.340 --> 01:40:10.700
counterfactual more.

01:40:11.420 --> 01:40:14.380
And then this will have an effect on their causal attribution.

01:40:15.020 --> 01:40:20.300
So you can sort of even if the process is implicit, you can sort of um use explicit

01:40:20.300 --> 01:40:23.420
communication in order to influence what people are going to do.

01:40:24.530 --> 01:40:25.970
That's fascinating.

01:40:26.690 --> 01:40:32.290
And I think then the other part of your question is when are you going to engage in sort of

01:40:32.290 --> 01:40:34.290
more conscious simulation?

01:40:35.890 --> 01:40:41.810
I think that at least one part of the answer is that if you are in a novel

01:40:41.810 --> 01:40:46.290
situation, maybe this is the first time that you've been playing this chess, this

01:40:46.290 --> 01:40:49.020
particular kind of chess game.

01:40:49.100 --> 01:40:52.780
I you I guess you you were never in that position before, then you need to engage in

01:40:52.780 --> 01:40:54.140
explicit simulation.

01:40:54.380 --> 01:40:59.900
Um but maybe if this is a situation that you're very familiar with, then all the

01:40:59.900 --> 01:41:05.340
computation has just been done and you can rely on schemas that have been pre computed on

01:41:05.340 --> 01:41:06.780
the basis of your past experience.

01:41:07.100 --> 01:41:07.500
Interesting.

01:41:07.580 --> 01:41:11.340
So maybe like the opening moves are just gonna be wrote, but like the end game, that's

01:41:11.340 --> 01:41:13.340
where my conscious reasoning might kick in.

01:41:13.740 --> 01:41:14.220
Yeah, yeah.

01:41:14.530 --> 01:41:20.450
And I I think I think that uh to a large extent, you know, the people who develop uh AI

01:41:20.450 --> 01:41:26.130
systems that play chess or go, they have this kind of hybrid architecture where they will

01:41:26.370 --> 01:41:32.050
rely a lot on those kinds of uh cached pre-computed um

01:41:32.770 --> 01:41:35.890
sort of calculations of what is the value of a particular move.

01:41:36.050 --> 01:41:40.130
And then they will com combine this with more explicit simulations.

01:41:40.370 --> 01:41:46.080
Um and it's quite likely that the human brain is designed along more or less the same

01:41:46.080 --> 01:41:46.960
principles.

01:41:48.640 --> 01:41:49.360
Interesting.

01:41:49.920 --> 01:41:52.160
Um, let's talk about theory of mind.

01:41:52.320 --> 01:41:57.600
Uh I mean, talking about nebula is hard to understand, um, obtuse stuff.

01:41:57.760 --> 01:42:02.560
Um uh I I I have a very soft spot in my heart for theory of mind.

01:42:02.640 --> 01:42:05.120
Theory of mind like the ability to attribute mental states.

01:42:05.450 --> 01:42:10.090
Mental states are just like causation, as we talked about, like where in some sense they do

01:42:10.090 --> 01:42:10.650
exist, right?

01:42:10.730 --> 01:42:15.210
Like in the sense of it's a convention of like, of course, I have a desire and a belief.

01:42:15.290 --> 01:42:21.050
And um, but there's another sense in which they're placeholders for common

01:42:21.050 --> 01:42:22.170
outcomes in the world.

01:42:22.330 --> 01:42:26.170
Um, so there it's very confusing and weird and abstract too.

01:42:26.410 --> 01:42:31.160
So um, so where does theory of mind come in?

01:42:31.240 --> 01:42:35.000
Um and is essentialism part of it, or is that a separate part?

01:42:35.080 --> 01:42:39.640
Is is uh because because in addition to causation, you've tackled theory of mind and

01:42:39.640 --> 01:42:40.680
essentialism.

01:42:40.840 --> 01:42:46.360
Um uh so that's like the trifecta of of of of of abstraction.

01:42:46.680 --> 01:42:51.560
Uh what what is the what is up with theory of mind and um and essentialism and are they

01:42:51.560 --> 01:42:52.040
related?

01:42:52.280 --> 01:42:52.520
Yeah.

01:42:52.760 --> 01:42:57.690
Well, I think that the nice thing is as we could do like a a two hour discussion on each.

01:42:58.090 --> 01:42:58.410
Exactly.

01:42:58.570 --> 01:42:59.850
Uh yeah.

01:43:00.490 --> 01:43:01.290
Get comfortable.

01:43:01.450 --> 01:43:02.010
Get comfortable.

01:43:02.650 --> 01:43:08.650
The bad news I maybe for the sake of kind of uh uh for the sake of tractability,

01:43:08.810 --> 01:43:11.690
uh I'll focus on theory of mind uh for now.

01:43:12.010 --> 01:43:12.170
Sure.

01:43:12.410 --> 01:43:17.130
Um so theory of mind uh is yeah, I mean as you said it's quite interesting because there's

01:43:17.130 --> 01:43:21.880
that's a lot of abstraction that's we can't really see someone's desires or someone's

01:43:21.880 --> 01:43:27.720
beliefs um yet we have to attribute beliefs and desires and and so on and so

01:43:27.720 --> 01:43:32.360
forth to other people in order to even start understanding their behavior.

01:43:32.760 --> 01:43:37.720
If I were to just try to model you in physical terms

01:43:38.850 --> 01:43:40.690
Then it would be very difficult.

01:43:40.850 --> 01:43:46.370
I mean, I would predict maybe you're going to say to stay roughly at the same place over

01:43:46.370 --> 01:43:51.730
time, that there you will be some sort of spatial temporal continuity to your behavior, but

01:43:51.730 --> 01:43:54.130
that's roughly how far I'm going to go.

01:43:54.370 --> 01:44:00.130
Whereas when I attribute to you mental states, then um the richness of my explanation of

01:44:00.130 --> 01:44:05.330
your behavior is going to be much uh much uh much more effective.

01:44:06.400 --> 01:44:11.840
And so this is something that has been of quite a lot of interest to psychologists because

01:44:11.840 --> 01:44:16.400
obviously we want to know what is the functional logic of this system that has been so

01:44:16.400 --> 01:44:16.720
important.

01:44:17.760 --> 01:44:23.600
Evolutionarily, this is something that social cognition is, especially in a

01:44:23.600 --> 01:44:26.080
species like ours, it is super important.

01:44:26.160 --> 01:44:30.880
If you cannot predict the behavior of other people and if you cannot exert some degree of

01:44:30.880 --> 01:44:34.160
influence on them, then you're not going to go very far.

01:44:34.590 --> 01:44:34.990
Right.

01:44:35.950 --> 01:44:41.710
And so a lot of a lot of research and theory of mind has looked at um our

01:44:41.710 --> 01:44:43.790
ability to attribute beliefs.

01:44:44.510 --> 01:44:47.070
And there are good reasons to to that.

01:44:48.510 --> 01:44:54.270
Because in order to really predict

01:44:54.430 --> 01:45:00.030
um the someone's behavior you need to engage in what people call uh a meta

01:45:00.190 --> 01:45:01.070
representation.

01:45:01.320 --> 01:45:06.840
A r meta representation is just a fancy term to say a representation of a representation.

01:45:07.000 --> 01:45:07.240
Okay.

01:45:07.640 --> 01:45:13.000
Um and it's because if I represent the representations that are inside your mind, then I

01:45:13.000 --> 01:45:14.920
can I will know how you behave.

01:45:15.160 --> 01:45:21.080
Uh and uh a standard test for whether someone has meta representations

01:45:21.240 --> 01:45:26.840
is whether they are able to imagine that someone might uh represent the world in a

01:45:26.840 --> 01:45:28.520
different way than than than your own.

01:45:28.670 --> 01:45:28.910
Okay.

01:45:29.070 --> 01:45:34.670
So and then to give a some simple example, um, there is a mug on on the table on my on my

01:45:34.670 --> 01:45:36.430
office, and I assume you can see it.

01:45:36.590 --> 01:45:42.590
And so I can represent that um that maybe I'll okay I can represent that

01:45:42.830 --> 01:45:48.510
Dave has a representation of the fact that there is a mug on the table.

01:45:49.220 --> 01:45:55.140
But to really have a handle on to really be able to prove that you have a representation, I

01:45:55.140 --> 01:46:00.820
need to show that you are able to represent the world as being different from it actually

01:46:00.820 --> 01:46:01.140
is.

01:46:01.540 --> 01:46:07.460
And so maybe you might have uh I might have a sophisticated hologram of a

01:46:07.460 --> 01:46:10.820
table, of a of a mug on the table instead of an actual mug.

01:46:11.300 --> 01:46:15.620
If that is the case, I know that there is in fact no mug on the table.

01:46:16.660 --> 01:46:20.490
But I know that you believe that there is a mug on the table.

01:46:20.730 --> 01:46:26.730
Um and so a lot of research and theory of mind has been looking at this

01:46:26.970 --> 01:46:30.490
ability to attribute false beliefs to to others.

01:46:30.730 --> 01:46:36.410
Um and in in some of my recent work, I've been trying to to see how

01:46:36.570 --> 01:46:41.860
uh maybe uh how we can think about theory of mind in that

01:46:42.020 --> 01:46:47.780
involves uh representations that are not as uh as demanding as as that

01:46:49.540 --> 01:46:55.380
what makes that demanding uh so so in in the

01:46:55.380 --> 01:47:01.220
case of like representing that you know maybe you think that there is a a

01:47:01.220 --> 01:47:05.810
mug on the table whereas Yeah, like let's say you move the mug and I didn't know that.

01:47:05.890 --> 01:47:06.610
Like I just have a false.

01:47:07.170 --> 01:47:11.090
So like you told me you showed me the mugs on the table, it's out of frame.

01:47:11.410 --> 01:47:16.930
You are uh like a false belief, naughty character, you move the mug, I didn't know it.

01:47:17.090 --> 01:47:22.130
So now you know where the mug is, uh, but I don't, but I so I have a false belief.

01:47:22.210 --> 01:47:22.530
Yeah.

01:47:22.850 --> 01:47:23.170
Yeah.

01:47:23.330 --> 01:47:27.970
Um intuitively it's not necessarily demanding, but I mean if if you think about the the

01:47:27.970 --> 01:47:33.960
logic of meta representation, uh it's if you really take it to the extreme

01:47:34.040 --> 01:47:39.560
that in then it means that when i represent your mind then i will

01:47:39.560 --> 01:47:45.320
construct a whole model of your own of your model of the mind

01:47:45.800 --> 01:47:51.400
right and that's obviously that's a lot to represent right so i need to represent like

01:47:51.560 --> 01:47:57.290
how i think the world is and then i need to have a another

01:47:57.290 --> 01:48:03.050
representation that is basically the the world as I think uh Dave sees

01:48:03.050 --> 01:48:03.370
it.

01:48:04.490 --> 01:48:10.090
And so if you take on to the extreme it it's really um this idea of meta representation

01:48:10.410 --> 01:48:16.410
really uh involves the commitments that I'm going to have one model of the world

01:48:16.410 --> 01:48:22.140
for each person that I'm trying to to uh to as you interact with

01:48:22.700 --> 01:48:28.540
so just to make sure i understand um the the you're arguing against a sort of an argument

01:48:29.100 --> 01:48:34.860
which is the way that we represent other people's mental states is we create

01:48:35.180 --> 01:48:40.700
an additional model of an entire other human brain

01:48:41.500 --> 01:48:47.150
and what it currently is representing in addition to our own, and that we

01:48:47.150 --> 01:48:51.710
multiply that every time for new states of the world and for new agents.

01:48:52.190 --> 01:48:57.070
And that uh that sort of naive model is unlikely to be true.

01:48:57.550 --> 01:48:57.950
Yes.

01:48:58.110 --> 01:48:59.790
Yeah, that's uh that's a good summary.

01:48:59.950 --> 01:49:03.470
And I don't think that's that anyone is really claiming that we do that.

01:49:04.360 --> 01:49:10.360
But if you if you kind of look at the disclaim that theory of mind involves

01:49:10.360 --> 01:49:15.240
meta-representation and that you run all the way with it, then it seems to be the logical

01:49:15.240 --> 01:49:15.880
consequence.

01:49:16.760 --> 01:49:22.600
And so what is your what's your claim that that is instead of that claim, uh what is your

01:49:22.600 --> 01:49:23.000
argument?

01:49:23.160 --> 01:49:23.800
What's going on?

01:49:23.880 --> 01:49:24.120
Yeah.

01:49:24.520 --> 01:49:24.760
Right.

01:49:24.840 --> 01:49:29.990
And so an interesting question is indeed now no what are ways that the system could

01:49:30.070 --> 01:49:34.710
actually work and not completely run out of memory uh every time you're trying to predict

01:49:34.710 --> 01:49:35.590
someone's behavior.

01:49:35.990 --> 01:49:41.430
Um and here the the idea that we have been putting forward is that you don't always need

01:49:41.430 --> 01:49:42.710
meta representation.

01:49:42.870 --> 01:49:46.790
And in fact in in most cases you you don't actually need it.

01:49:46.950 --> 01:49:52.870
So in the case where I'm not playing any tricks on you and there is actually a mug on my

01:49:52.870 --> 01:49:58.060
table and you see the mug and now you know that there is a mug on the table,

01:49:59.020 --> 01:50:04.460
then I don't need to attribute to you the necessarily the a meta representation that there

01:50:04.460 --> 01:50:08.860
is a mug on the table, that a kind of a belief that there is a mug on a table.

01:50:09.020 --> 01:50:14.060
Instead I can just note that well you know that there is a mug on the table.

01:50:14.220 --> 01:50:19.930
And so what this does um is that I have a physical description and a

01:50:19.930 --> 01:50:22.330
physical I have my own physical model of the world.

01:50:22.410 --> 01:50:25.930
So in my physical model of the world, there is a mug on the table.

01:50:26.570 --> 01:50:32.330
And then in addition to that, I just add a very simple binary representation that oh Dave

01:50:32.570 --> 01:50:34.650
knows this fact.

01:50:35.290 --> 01:50:41.040
So now instead of having to duplicate my own model of the world in sort of the

01:50:41.360 --> 01:50:44.160
meta representation that I attribute to you.

01:50:44.400 --> 01:50:50.240
I can just uh have this one binary, one like on or off tag about whether you

01:50:50.240 --> 01:50:50.560
know this.

01:50:50.880 --> 01:50:56.320
And that the def what that on off tag means is uh all the

01:50:56.320 --> 01:51:02.240
inferences and consequences that I have for the mug, the other agent has as well.

01:51:02.630 --> 01:51:06.470
I'm not saying that that's uh the full model because of course it's not right, because like

01:51:06.550 --> 01:51:09.430
I might see it from a different angle and you would need that additional.

01:51:09.590 --> 01:51:14.870
But but just to be clear, like what it means is that there's no um

01:51:15.830 --> 01:51:21.750
there's no difference between what you all the things that follow about the mug for you

01:51:21.910 --> 01:51:24.950
are applied to me, but they don't have to get applied to me.

01:51:25.030 --> 01:51:30.900
It's just that there's representation of everything that is represented in me

01:51:30.900 --> 01:51:34.500
about the mug also applies to this other agent.

01:51:34.740 --> 01:51:35.460
Yes, yes, exactly.

01:51:35.860 --> 01:51:39.140
So it's like one piece of information instead of duplicating a brain.

01:51:39.300 --> 01:51:39.380
Yeah.

01:51:39.540 --> 01:51:39.620
Yeah.

01:51:39.940 --> 01:51:40.020
Yeah.

01:51:40.180 --> 01:51:44.260
In the same way that, you know, we scan the the the physical environment and we tag

01:51:44.500 --> 01:51:46.420
physical objects with various properties.

01:51:46.580 --> 01:51:52.020
We might say this, you know, coffee cup tag belongs to Dave, uh, contains hot liquid, um

01:51:52.500 --> 01:51:55.540
is, you know, this sh shade of blue or whatever.

01:51:55.620 --> 01:51:58.170
Um uh you know uh whatever.

01:51:58.250 --> 01:52:00.970
And in addition to that, another tag is Dave knows it.

01:52:01.130 --> 01:52:03.930
And we just oh or didn't Dave knows it's there.

01:52:04.010 --> 01:52:08.570
And we just have that tag to, you know, everything we know, um, Dave also knows.

01:52:08.730 --> 01:52:09.050
Yeah.

01:52:09.210 --> 01:52:09.610
Right.

01:52:09.930 --> 01:52:10.410
Yeah, yeah.

01:52:10.490 --> 01:52:13.610
That that's yeah, it's such a great description of of the proposal.

01:52:13.770 --> 01:52:13.930
Yeah.

01:52:14.170 --> 01:52:16.810
Just one more feature of the of the object.

01:52:17.660 --> 01:52:23.180
Um yeah so I mean this is this was just an idea um this this is not an idea that actually

01:52:23.180 --> 01:52:29.180
came from from us this existed in in the literature um but this had been proposed as

01:52:29.180 --> 01:52:33.420
just like one possibility for how theory of mind might work.

01:52:33.740 --> 01:52:39.690
And what we we did actually in in our project was to try to take a

01:52:39.690 --> 01:52:42.730
more adaptationist stance towards this proposal.

01:52:42.970 --> 01:52:48.890
And we said, well, can we actually formalize what is the adaptive problem

01:52:48.890 --> 01:52:49.130
here?

01:52:49.770 --> 01:52:55.610
And we started with this fact that, well, we obviously we need to predict and explain the

01:52:55.610 --> 01:53:01.460
behavior of others, but we also have a constraint on on

01:53:01.460 --> 01:53:03.220
our information processing capacity.

01:53:03.460 --> 01:53:07.300
Like we don't have infinite computation, we don't have infinite memory and so on and so

01:53:07.300 --> 01:53:08.180
forth and so forth.

01:53:08.580 --> 01:53:14.580
And so the design of our theory of mind system needs to somehow trade off between those

01:53:14.580 --> 01:53:15.940
two different considerations.

01:53:16.500 --> 01:53:21.780
We want to have a lot of predictive power while at the same time like not run out of

01:53:21.780 --> 01:53:23.940
computation every time we interact with someone.

01:53:24.330 --> 01:53:29.450
And this is this idea that computation is a constraint is really salient in the context of

01:53:29.450 --> 01:53:34.410
theory of mind where it's very easy to run out of memory when you're predicting the

01:53:34.410 --> 01:53:40.010
behavior of an entity that is as complex or even more complex as than than you are.

01:53:41.130 --> 01:53:46.330
And so once you you set up this optimization problem where you try to trade off between

01:53:46.330 --> 01:53:51.760
those two different things, The nice thing is that you can you can solve for it.

01:53:52.640 --> 01:53:58.400
You can ask, well, what would be the optimal cognitive strategy for solving

01:53:58.400 --> 01:53:59.040
that problem?

01:53:59.440 --> 01:54:04.000
And we did that in a in a by setting up a toy model.

01:54:04.560 --> 01:54:10.560
So we had sort of a simple agent-based model, and in that agent-based model,

01:54:10.800 --> 01:54:16.610
the the agent, there's one agent that is basically doing a series of very

01:54:16.610 --> 01:54:18.050
simple theory of mind tasks.

01:54:18.130 --> 01:54:23.090
So there's a like a false belief task, there's a task when you need to predict what someone

01:54:23.730 --> 01:54:27.090
who has knowledge of a fact is going to do and so on and so forth.

01:54:27.570 --> 01:54:33.570
And then and this you can sort of uh then optimize the cognitive strategy that this

01:54:33.570 --> 01:54:35.090
agent is going to use.

01:54:36.320 --> 01:54:42.000
And when we did that, we find that indeed if your agent is sufficiently constrained in

01:54:42.000 --> 01:54:47.840
terms of its information processing capacities, then it will use a strategy that is very

01:54:47.840 --> 01:54:53.360
similar to the one that I just described, where it will kind of abandon any pretension of

01:54:53.360 --> 01:54:54.880
creating meta representations.

01:54:55.040 --> 01:55:00.670
Instead, it will just have its own physical description of the world, and then it will add

01:55:00.910 --> 01:55:06.270
a kind of a binary tag about whether or like this fact is known or not known by the other

01:55:06.270 --> 01:55:06.590
person.

01:55:08.830 --> 01:55:14.350
Is one way to think about it then that um so like I don't I'm not a um

01:55:15.470 --> 01:55:17.630
uh I don't know, I'm not like left-handed.

01:55:17.710 --> 01:55:22.110
So like let's say there's a guitar a right-handed guitar and a left-handed guitar, and I

01:55:22.110 --> 01:55:27.180
reach for the right-handed guitar and then I I somebody else, I see somebody else making

01:55:27.180 --> 01:55:30.140
that same decision, but they're left-handed, they reach for the left-handed guitar.

01:55:30.380 --> 01:55:36.220
My model of predicting what they would have done is wrong when I just use what I would

01:55:36.220 --> 01:55:36.540
do.

01:55:36.860 --> 01:55:37.180
Right.

01:55:37.420 --> 01:55:42.780
So then I have to say, I have to have an entry for what's the minimal extra information I

01:55:42.780 --> 01:55:48.690
need to add to still use me as the machine for

01:55:48.690 --> 01:55:53.650
predicting behavior, but then change one variable to generate the right prediction.

01:55:53.890 --> 01:55:59.570
Is one idea that, like, all these kinds of things of like what a false belief actually is,

01:55:59.730 --> 01:56:05.650
or what a motivation is, or a personality variable, they're all just sort of settings

01:56:05.810 --> 01:56:11.660
on what's the minimal thing I need to add to my own inference engine about

01:56:11.660 --> 01:56:15.420
what I would do next to then just predict the behavior of others.

01:56:15.580 --> 01:56:21.180
That in that sort of all these, all these representations that we think of are just sort of

01:56:21.180 --> 01:56:26.140
uh just slight tweaks to this otherwise very complicated system that we don't have to

01:56:26.140 --> 01:56:28.380
repeat and duplicate.

01:56:28.620 --> 01:56:34.540
Um so it's just that for for for Tedig and for David, I just have my own pre,

01:56:35.090 --> 01:56:39.570
it's like an equalizer, you know, and it's sort of like I have the presets for David such

01:56:39.570 --> 01:56:45.490
that when I think of David or I see David, I can pull those up and I know my own

01:56:45.490 --> 01:56:47.170
self-simulation system.

01:56:48.130 --> 01:56:53.730
I need to add these 20 or 10,000 tweaks to all those little inference

01:56:53.730 --> 01:56:57.250
engines, but that's only little 10 10,000 bits, right?

01:56:57.330 --> 01:57:00.050
It's just small tweaks to the the big machine.

01:57:00.290 --> 01:57:01.500
Is that a fair?

01:57:01.980 --> 01:57:05.180
I mean, I realize it's sort of abstraction, but is that a fair way to characterize the

01:57:05.740 --> 01:57:06.140
picture here?

01:57:06.620 --> 01:57:11.740
I think it's it's uh what you are describing is is a more general extension of the general

01:57:11.740 --> 01:57:17.020
principle that we should like to what the extent that we can, the the system should be

01:57:17.340 --> 01:57:22.860
minimizing the computational complexity uh by using all tricks that it has available.

01:57:23.260 --> 01:57:29.020
But the the the coincide behind our model is that um even before we are trying to predict

01:57:29.020 --> 01:57:34.750
the behavior of others, We have our own model of the physical world

01:57:34.910 --> 01:57:36.990
because it is, I mean, it's super useful.

01:57:37.710 --> 01:57:41.630
It's very useful to know what the world looks like.

01:57:43.070 --> 01:57:48.110
And the nice thing about other people is that they also live in the same world.

01:57:48.590 --> 01:57:53.470
And so by virtue of the fact that you're living in the same world, you will end up creating

01:57:53.470 --> 01:57:55.950
representations that are actually quite similar.

01:57:56.680 --> 01:58:01.400
And so this is a kind of a nice fact about the problem of social prediction is that you're

01:58:01.400 --> 01:58:06.040
predicting the behavior of agents that also live in the same the s the same world.

01:58:06.440 --> 01:58:12.440
And so it means that to a large extent you can use your own understanding of w of what

01:58:12.440 --> 01:58:15.480
the world looks like in order to predict their their behavior.

01:58:16.040 --> 01:58:18.730
It also means you can tell when an error occurs, right?

01:58:18.810 --> 01:58:22.890
Because that's one of the other problems, is like a lot of the machine learning, what the

01:58:22.890 --> 01:58:27.050
pro the trick is to know when an error occurs, and that's a discrepancy between what they

01:58:27.050 --> 01:58:31.050
do, which you have data for, and what you would do, which you also have data for.

01:58:31.530 --> 01:58:32.090
Yes, yes.

01:58:32.330 --> 01:58:37.130
And so really like really from this point of view, yes, the the main challenge is to uh um

01:58:37.370 --> 01:58:43.130
to compute what what are those other those aspects of the world as I represent it that

01:58:43.130 --> 01:58:45.130
other people also are aware of.

01:58:45.980 --> 01:58:51.980
And this is a so I mean an interesting thing about that is that um this this model, even

01:58:51.980 --> 01:58:55.900
though it is relatively simple, it makes quite interesting predictions.

01:58:56.300 --> 01:59:01.740
Because you can look at the for a particular setting of the the parameters, you can look at

01:59:01.740 --> 01:59:05.260
the optimal cognitive strategy that an agent comes up with.

01:59:05.660 --> 01:59:11.260
And you can see well to what extent does this actually describe the behavior of

01:59:11.420 --> 01:59:13.020
actual organisms.

01:59:13.310 --> 01:59:18.510
And here a particularly interesting case is when we look at non-human primates.

01:59:18.670 --> 01:59:24.670
Okay, because non-human primates um they are much more uh constrained in their

01:59:24.670 --> 01:59:27.310
information processing capacities as we are.

01:59:27.710 --> 01:59:33.070
And maybe they don't have to use theory of mind to such an extent that we do because um

01:59:33.470 --> 01:59:39.080
the the sort of social demands of their ecology are are not as as

01:59:39.080 --> 01:59:39.720
strong.

01:59:41.000 --> 01:59:46.680
And so you can you can sort of set up an agent

01:59:46.920 --> 01:59:52.040
that has not a lot of information processing capacity for the purpose of theory of mind.

01:59:57.190 --> 02:00:02.150
And then you can look at the strategy that this simulated agent is using.

02:00:03.110 --> 02:00:08.710
And one prediction that follows from that is that they will behave in a certain way

02:00:09.270 --> 02:00:14.950
in tasks that are a little bit like a false belief task, except that you have

02:00:14.950 --> 02:00:15.910
changed one thing.

02:00:15.990 --> 02:00:19.510
So this task is called the accidental true belief task.

02:00:19.750 --> 02:00:25.660
So this is a task where I so suppose I I hide

02:00:25.660 --> 02:00:28.700
an apple in front of Dave.

02:00:28.780 --> 02:00:34.540
So like you're aware that I'm hiding an apple in this in this one box.

02:00:35.980 --> 02:00:36.940
And then you go away.

02:00:37.340 --> 02:00:41.260
And while you're away, I will remove the apple from that box.

02:00:41.420 --> 02:00:43.980
But then I put it back in the same box.

02:00:44.190 --> 02:00:44.430
Okay.

02:00:44.590 --> 02:00:49.870
So what it means is that you you have a belief that the apple is in that box.

02:00:50.670 --> 02:00:54.670
But the there's a sense in which a belief is kind of lucky.

02:00:55.230 --> 02:00:59.070
Like because it was just uh a chance event that I put it back in the same box.

02:00:59.310 --> 02:01:03.550
Or um and to make it, let's say you know you you you remove it from the box and put it on

02:01:03.550 --> 02:01:06.910
the windowsill and then a bird magically flies in and picks it up and puts it back in the

02:01:06.910 --> 02:01:06.990
box.

02:01:07.230 --> 02:01:07.390
Yes.

02:01:09.380 --> 02:01:10.180
Another tree that's inner.

02:01:12.500 --> 02:01:12.740
Yeah.

02:01:12.900 --> 02:01:15.220
So we just really got lucky.

02:01:15.700 --> 02:01:17.940
A forest fire that was caused by a lightning sunway.

02:01:18.420 --> 02:01:18.660
Yeah.

02:01:18.900 --> 02:01:23.060
Um and so in that situation something surprising happened, which was that kind of the

02:01:23.060 --> 02:01:26.980
simulated agent um sort of stops making any prediction.

02:01:27.220 --> 02:01:30.180
It was like, oh, like the situation is too complicated to me for me.

02:01:30.260 --> 02:01:32.980
I'm just going to throw my hands up in the air.

02:01:33.190 --> 02:01:38.950
and basically uh i'm going to predict that you you will look that i that dave will look in

02:01:38.950 --> 02:01:44.790
any any of the boxes i'm not going to make any bet about where dave is going to look um

02:01:44.950 --> 02:01:50.550
and it turns out that this is exactly how monkeys behave if you give them

02:01:50.790 --> 02:01:56.680
uh that that kind of task And so so it so it seems in that

02:01:56.680 --> 02:02:02.520
there's a lot of similar experiments that are done on non-human primates, uh, where they do

02:02:02.520 --> 02:02:07.480
things that look a little bit weird, um, but that are quite well predicted by this kind of

02:02:07.720 --> 02:02:11.800
version of the model where you assume that the agent does not have a lot of information

02:02:11.800 --> 02:02:12.920
processing capacity.

02:02:14.430 --> 02:02:15.390
Interesting.

02:02:16.670 --> 02:02:18.510
Well, fascinating stuff.

02:02:18.590 --> 02:02:22.990
I feel like uh I could keep picking your brain on this for hours, but we are um approaching

02:02:22.990 --> 02:02:23.870
the end of our time.

02:02:23.950 --> 02:02:28.590
And it and uh is there any topic that you wish we would have talked about that we that we

02:02:28.590 --> 02:02:29.550
should have asked you about?

02:02:29.790 --> 02:02:35.790
Anything that uh Oh yeah, um I think yeah, I think the another thing on which we could

02:02:35.790 --> 02:02:41.640
have spent a lot of time is this concept of uh intentional action that is also related to

02:02:41.640 --> 02:02:45.560
theory of mind that it's its own uh its own thing.

02:02:46.200 --> 02:02:51.080
Well here's here's I would I'd like to ask how did you uh discover we haven't done origin

02:02:51.240 --> 02:02:52.120
stories in a while.

02:02:52.200 --> 02:02:58.200
Um how did you come across of psychological Yeah um so I've always been quite interested

02:02:58.200 --> 02:03:00.840
in the general question of how the mind works.

02:03:01.080 --> 02:03:05.990
Um but I I didn't really know it back then when I was in high school.

02:03:06.230 --> 02:03:10.150
Um and so I decided to because I was also interested in in history.

02:03:10.230 --> 02:03:14.310
Um and so I decided to to do political science as a major.

02:03:14.550 --> 02:03:20.550
Uh and that's when in the process of majoring in in political science, I I read this

02:03:20.550 --> 02:03:21.670
book by David Hume.

02:03:21.990 --> 02:03:23.990
And back to Edinburgh.

02:03:24.470 --> 02:03:25.190
Full circle.

02:03:25.270 --> 02:03:25.430
Yeah.

02:03:25.910 --> 02:03:31.200
And so I was kind of blown away by the fact that oh this guy was asking just deep questions

02:03:31.200 --> 02:03:35.440
about about the mind and it made me realize well uh this is actually something that I find

02:03:35.440 --> 02:03:41.280
super interesting and so I ended up double majoring in in philosophy um and

02:03:41.280 --> 02:03:46.720
this was this is what led me to do uh then a master's in cognitive science in in Lyon.

02:03:47.280 --> 02:03:52.480
Uh and and the the master's was was interesting there was a lot of interesting stuff about

02:03:52.560 --> 02:03:56.640
you know behavioral neuroscience, cognitive psychology uh and so on and so forth.

02:03:56.800 --> 02:04:02.790
Um but while taking those courses, I always had a nagging feeling that there was

02:04:02.790 --> 02:04:05.910
a set of important questions that were not asked.

02:04:06.310 --> 02:04:12.230
Um and then of course like now I know that I was um internally

02:04:12.390 --> 02:04:16.230
complaining about the fact that there were there was not much of an evolutionary

02:04:16.230 --> 02:04:18.230
perspective being being uh offered.

02:04:18.390 --> 02:04:23.030
Um and then it came time to find uh an internship, like a research internship for my

02:04:23.190 --> 02:04:23.590
master.

02:04:23.740 --> 02:04:27.660
and there was this new researcher uh at the university called Pascal Boyer.

02:04:28.140 --> 02:04:31.980
Uh so I reached out to him and like can I attend your lab meeting?

02:04:32.140 --> 02:04:38.140
Um and then during the lab meeting he's sort of like laid out the perspective

02:04:38.140 --> 02:04:42.700
for what his research over the next few few years what was going to be and I had this kind

02:04:42.700 --> 02:04:46.220
of the this click of oh this this is what I want to do.

02:04:47.180 --> 02:04:51.580
And and and uh a year later I was at UCSV working with John Anita.

02:04:52.270 --> 02:04:53.070
That's fantastic.

02:04:53.230 --> 02:04:55.150
So wait, Pascal was where?

02:04:55.790 --> 02:04:57.790
So he he was in Lyon at the time.

02:05:00.190 --> 02:05:02.430
He had gotten a grant to stay there for a few years.

02:05:02.750 --> 02:05:03.310
Fantastic.

02:05:03.470 --> 02:05:03.870
Yeah.

02:05:04.110 --> 02:05:05.950
We have to have Pascal on.

02:05:06.430 --> 02:05:07.870
Oh, I can't believe you haven't had it.

02:05:08.270 --> 02:05:08.830
I know, right?

02:05:08.910 --> 02:05:09.390
Yes.

02:05:10.590 --> 02:05:10.910
Yeah.

02:05:11.150 --> 02:05:14.430
Well, Latik, thank you so much for being on.

02:05:14.750 --> 02:05:17.240
Um my brain is full.

02:05:18.920 --> 02:05:22.600
I've I've exhausted the computational capacity in my head.

02:05:25.320 --> 02:05:26.040
Yeah, this is great.

02:05:26.440 --> 02:05:27.240
This is our pleasure, David.

02:05:27.320 --> 02:05:29.000
It was a really fun conversation.

02:05:32.680 --> 02:05:35.240
David, what causes a good podcast?

02:05:35.640 --> 02:05:36.600
Uh well, hold on.

02:05:36.680 --> 02:05:40.280
Let me run through all the counterfactual worlds in my head.

02:05:40.590 --> 02:05:41.630
Uh computing.

02:05:42.350 --> 02:05:42.910
Copper wires.

02:05:43.150 --> 02:05:44.030
Copper wires.

02:05:44.750 --> 02:05:45.070
Yes.

02:05:45.230 --> 02:05:49.310
Oxygen in the atmosphere is definitely a a critical component to a good podcast.

02:05:49.390 --> 02:05:52.110
If there was no oxygen, then uh we wouldn't be alive.

02:05:52.190 --> 02:05:58.190
So that so um yeah uh so leave us a kind review and

02:05:58.190 --> 02:06:04.190
talk about um all the things all the things that could have gone wrong such that you

02:06:04.190 --> 02:06:05.790
wouldn't have discovered this podcast.

02:06:06.270 --> 02:06:09.300
Yeah that's a great content for your kind review.

02:06:09.460 --> 02:06:15.060
Um, you can also visit us on Patreon and that might have a causal impact on the

02:06:15.060 --> 02:06:16.900
continuation of this podcast.

02:06:17.140 --> 02:06:18.260
Maybe we can afford some.

02:06:19.700 --> 02:06:25.380
This is very uh it feels very Well, we're just giving you ways you can

02:06:25.540 --> 02:06:26.580
intervene on the world.

02:06:26.740 --> 02:06:27.300
Yeah, exactly.

02:06:29.990 --> 02:06:32.470
you have you have free will to do any of these things.

02:06:32.710 --> 02:06:35.990
Yeah, but it there's we unintended consequences to things too.

02:06:36.150 --> 02:06:38.070
So you know, be careful what you do.

02:06:38.470 --> 02:06:39.190
That's true.

02:06:39.350 --> 02:06:40.070
That's true.

02:06:40.310 --> 02:06:43.590
Um uh I'll see you next week, David.

02:06:43.830 --> 02:06:44.790
Yep, sounds good.

02:06:44.950 --> 02:06:46.310
All right, well see everybody.

02:06:46.390 --> 02:06:46.550
Okay.

02:06:46.950 --> 02:06:47.270
See ya.