Mechanism, Function, and Philsophy of Science with Lauren Ross
2026-06-30 12:00:00.000 +00:00

I'm David Petrozuski. I'm David Pinsov, and this is Evolutionary Psychology, the podcast, Actual Evolutionary Psychology by Actual Evolutionary Psychologists. David, today we have another wonderful episode with a philosopher. Yes, I believe our second philosopher on the podcast. I love talking to philosophers, and we actually reiter reiterate this in the episode. Uh, just because I find that they have um uh really precise ways of communicating ideas and a really good handle on concepts and distinctions that I think is sorely lacking in in the social sciences. I think we need to be better about uh defining our terms precisely and making uh more fine-grained distinctions and being clear about what we mean.

And I think philosophers are specialists in that task. I completely disagree, David. I think we should be more muddled and confused. I'm kidding. Um No, of course, yeah. Um and uh I think so. This is Lauren Ross, who's at UC Irvine, uh uh close to us, um, Southern California. And I think what is exciting about this conversation is I came across Lauren's work and work when I was working on my own struggles dealing with things like mechanisms and modules, um, and the whole confusion about the things that we're postulating as evolutionary scientists. Um, and it was very validating. And it was really great to have this conversation because I feel like it was a real-time example of uh here's a philosopher of science who's been thinking about these issues,

not at all uh from the perspective of what we're doing, but then is hearing what we're doing and seeing commonalities with things that she's doing. And it was just very helpful. And I feel like there's much more to do. I I don't know how you feel, David, but I thought I felt like it was a very cathartic conversation to have to be seen and heard. Yes, totally. Yeah, we joked about how philosophers should be the therapists of scientists. Exactly. And yeah, this was a therapeutic conversation in that regard where we got to voice our frustrations with the uh conceptual confusions and difficulties in our field, and how we struggle to communicate our explanatory frameworks to people who are not familiar with that way of thinking and and what it means to you know be at a different

level of analysis or a different level of explanation, uh how low we should go in terms of our explanations and our level of reduction. This was a really fun and enriching conversation. Yeah, and Lauren was really great about also articulating what is sort of state of the art for philosophy of science, including a very practical functional approach to well, what is a science explanation for? And if you, you know, not that there's a one-size or grand theory uh of this is what an explanation should be. So this was just great. Uh I I really had a good time. And hopefully, if you're a embattled evolutionary behavioral scientist or know someone who is, this will also be a uh therapeutic um episode for you.

Yep. Enjoy. Lauren, thank you for being here. Um, I want to just uh start with a preface, which is one of the reasons why I wanted to have you on this podcast, which is about evolutionary behavioral sciences, is that a lot of us evolutionary psychologists or evolutionary anthropologists, we are working with colleagues who sometimes react to us talking about mechanisms in a way that makes them uh maybe to just put it bluntly, sort of grumpy or angry. Like I've literally heard people say, well, why do you have to talk about a mechanism for that? Or why do you have to postulate that there's some system that does something? Why can't you just say there's this experimental effect and I'm interested in that?

And so this is all my preface. Um, oh we lost David, uh, but hopefully he'll come back. Uh that happens occasionally. But this is my uh preface of saying when I came across your work, I was really excited because you're doing um from a very different perspective, an idea of well, what is a mechanism, for example, what's a pathway? And one of the reasons why I wanted to have you on was to sort of at least maybe buck us up or um uh oh David's internet cut out but we'll just we'll have him come back in a in a moment here. Um either buck us up or sort of help us understand from your perspective how did you get into this idea of mechanisms or pathways or causation in biology. Um maybe we can start with that and we can see to what extent what we're doing either needs

correction or we can sort of feel somewhat vindicated, if that makes sense, as we are, as evolutionary behavioral scientists, sort of among empirical scientists, trying to postulate mechanisms, and we're often sort of crying in the wilderness about mechanisms, I would say, to put it in a way. Great. This is a nice concrete example of questions about mechanism and perhaps also the importance of it in science. So I'm a philosopher of science, and in particular, I work on causation and explanation, how scientists are providing explanations, the standards their explanations need to meet, or the standards that need to be met to have a good explanation. And then causation often shows up here because we think many explanations in science are

causal, although, of course, we're often interested in varieties of explanation, some of which we might think are non-causal. But explanation is, you know, a very important project in science. We sometimes think of it as one of the most important projects where you're getting deep understanding of the world. And so mechanism is showing up in many cases where we're interested in A explanation or B causation. And they're both sort of related to each other because we often think that a main way we get explanations is by getting causal information about the world, or we're getting some kind of causal information about the brain, some causal structure, or the causes that matter for and that account for some kind of outcome of interest.

So I think an important question to ask when mechanism is coming up is what's the point of this concept in science? What is it supposed to be doing for us? And what are we using it for? What is it what is it, you know, being used in order to do in terms of like scientific goals, but also if it's referring to causal systems in the world, you know, what types of causal systems is it referring to? So if we're interested in you know getting explanations, it makes sense that mechanism is coming up in those discussions. Um And I think it's important to focus on if it if we're interested in giving explanations, kind of how mechanisms are are serving that goal, and if they're giving us a causal explanation,

essentially what kind of causal stuff in the world are they referring to. I guess some other points to note are that mechanism is a status term in the life sciences? And you often see this in the calls for grants that scientists are applying to, and you see it in the publication guidelines for journals. It's often suggested in many life sciences that if you want to get your work funded and published, you need to identify mechanisms and provide mechanistic insights. So part of what that shows is that it's a status term. um, the challenge is there isn't agreement on how to define a mechanism or what it means. And so this sort of connects to and nicely supports this situation you're referring to, where one scientist says they have a mechanism

and the other scientist is sort of like, well, wait a minute, do you really? That's a debate that shows up in many different domains. And it's an important debate to discuss because mechanism is viewed as a standard in science. And if we don't have a clear notion of what it means, then part of what that implies is that the standard we're using isn't very clear and that there isn't kind of consensus on what that is. So if a journal is using that as their standard for publishing a paper, but you have two reviewers that have very different conceptions of mechanism in mind, um, you know, we're going to get different types of suggestions for whether this paper is meeting our standards or not. So Um so yeah, I think

part of what this shows is the importance of asking this question of what are we interested in when we're bringing up the notion of mechanism and what do we want it to do and what do we think it is doing? And maybe that's a start for unpacking these debates and and topics in this space. Yeah, that's great. And one thing, I mean, so I can to have a dialogue about sort of what we do as evolutionary behavioral scientists, one thing I think many of us experience, and I can make this with a concrete example, is when we use mechanism, we're often doing sort of design stance level, like Dan Dennett's sort of design stance level. So we really want to say what a mechanism is, is not just a sequence of causation

out in the world, but it's a packet of evolved systems at an abstract functionalist level. And I think many of us come up against an idea where people will use the word mechanism. And I would talk about like social psychologists would often do this. Well they'll say something like you know, the number of ice cream cones correlates with number of shark attacks. And then they'll say, well, the mechanism behind that. And when they say the mechanism behind that, they'll say, well, it's not a direct causal link. It's that when it's warmer out, there's more people at the beach, and therefore there's more people at the beach and it's hot and they have more ice cream, and also there's more people swimming.

And that's the mechanism behind that correlation. And I think for many of us, uh evolutionary behavioral scientists, I at least, and David, you can correct me, I kind of feel like, oh, that's not a mechanism, that's a a causal pathway. I would prefer to use mechanism as in our case, something that was designed by natural selection and that um is in some sense a you can make predictions about what it's going to be like and how it's going to work in future situations, um, in a kind of functional teleological perspective. And um, so that's like an example concretely of one of the uh uh we find each other at cross purposes among us behavioral scientists talking about mechanism. I'm curious what your thoughts are on that example, um one way or the other.

Oh yeah, absolutely. So part of what I think it reveals is the sense in which there are different types of causal systems and causes and systems in general that we're interested in referring to, studying, that we think matter in our work. And we need a way to systematically talk about what those are and to distinguish them. And those differences, you know, those differences matter for all sorts of reasons. And sometimes the term mechanism is getting in the way of our ability to do that well. So if we have a type of causal system that involves functional considerations and it's maybe goal-directed, that could be different from a causal system that's sort of mere maybe domino causation, where it's just a sequence of

causes that in succession lead to an effect. We need a way to talk about the differences between those two types of causal systems. That's one example. This is a situation that you see just all across the sciences where there are different terms that are used to refer to different types of causes and different types of causal systems, right? A scientist will call a cause deterministic or probabilistic, it's more proximal or distal, it's operating on different timescales. That's those are distinctions with respect to a single cause, but causal systems also, if we call them pathways or cascades, or mechanisms, there's often a suggestion that they have additional features and that there's differences across those types of causal systems.

So I would say part of what we see here is the sense in which there are causal varieties that scientists care about. And we need a way to clearly capture them and refer to them because the differences across those systems matter for the behaviors they produce, for how we study them. And right now, the term mechanism is getting in the way of our ability to do that. I mean, mechanism just means so many different things to different people. Sometimes in the same paper, you see it being used in different ways. And so sometimes I wonder if just setting that term aside and talking about types of causal systems or types of explanatory factors is a more helpful way to move forward because ultimately that's what we care about.

I mean, we want explanations and we want to identify causes and causal systems if we're giving a causal explanation. So mechanism is often a kind of problematic term in the sense that it's ambiguous or it's not giving us the clarity we need because it's it's used in so many different ways. So yeah, that would be, I would say part of what your examples show is that there's uh causal distinctions that matter in science. And I think we currently lack the ability to clearly refer to those and to refer to the ways in which those differences matter for different scientific projects and maybe even theoretical projects as well. Yeah, that's great. Yeah, I mean, I think it's also a symptom of the distinction I'm making is a symptom

of a different implicit view of science. So I think for a lot of evolutionary behavioral scientists, our goal is to not document an experimental effect. It's to have a claim about here's a bit of abstract functions, sort of like physiology used to be. Like I think there's a system that does this, this, and this. With the caveat of these are rough takes, and it's probably more complicated when you get to the lower levels of instantiation, versus a view of science that is we're really interested in factors that are predictive, but maybe not causal. So we want to do an intervention or describe experimental effects or behavioral tendencies. And that's often from an applied perspective or a very experimental perspective, where the

goal is to document here's the way people behave. Here's the if you put them in this context, they'll behave in this way. And I think that evolutionary behavioral sciences grew up in a more, we're interested in the mechanisms, not necessarily predicting behavior. And we'd rather have a good model the causal systems like we have for vision or motor control for social things, for example. But we'd rather have a description, a schematic, a functional schematic of how it works, and maybe not one where you could predict the outcomes unless you experimentally control them, right? Like that's, I think, I think we're good at experimental control. Um, and I think we have really good predictive validity in our models, but maybe not real

world predictive validity, right? Um, in the sense of the real world's complicated. So, but I think that I think the the misuse of mechanism, I think, is a symptom of the goals being somewhat different. I don't know, David, if you would agree with that. Um Yeah, I I would agree. I mean, uh, it seems to me that what separates the evolutionary behavioral sciences from the non-evolutionary behavioral sciences is a desire to go to a deeper level of explanatory depth. And we can get to what it means to explain something and what it means to explain something at a deeper level of analysis. But it seems to me that a lot of social science uh rests at the approximate level of A causes B, B causes C, but we don't have a deeper explanation of where the causal

system come comes from, what its function or teleological purpose is, uh, how it came into existence, uh, how it is situated uh in the evolutionary history of the organism in question, and typically in the social sciences, it's it's humans we're talking about. Um and I think what we seek to do in the evolutionary behavioral sciences is to um offer a framework that is concilient with the natural sciences and ideally all the way down to the physical sciences, where we have natural selection as an antientropic process that builds functional order and complexity into biological systems. And we want to understand all living things, uh, including humans, including the human mind, including uh human social structures, uh, in terms of that overarching framework.

Um and so I'm I'm curious um if what you what you see as the as the pitfalls um of that project and and and whether there are ways to get lost in that attempt to uh integrate the human sciences with the natural sciences and to offer a deeper level of of explanation. So just out of curiosity, by deeper level, what do you mean here? Deeper level of explanation? Asking why uh a second time or a third time. Yeah. And I think also, and just to interrupt too, I think also to add to that, there's often a um heuristic where, again, to just appeal to Dennett, uh Dan Dave and I often talk about this. I think uh it's also typically in psychology, there's often a lot of intentional level description. And often in certain areas of psychology, including evolutionary, it's

a more design stance uh level description. So we kind of want to eschew intentional level in the sense of uh the intentional level is the thing to be explained, but it's not the level at which mechanisms or predictions lie. We prefer to say there's a system that's sensitive to these cues. It produces these pro it generates this kind of, it's algorithmically does this process, produces this output. So even among the in the proximate system that has nothing to do with evolutionary time, it's more of a focus on software as opposed to theory of mindy intentional level. And then what David was alluding to is also then that even when we have that software level functionalist description, we also want a causal story out

in the world that is historical, that's constrained by the deductive logic of natural selection. And to say any of these functions we're postulating need to be deductively consistent with what would be selected for, given certain auxiliary assumptions about selection pressures, features of the world, what outcomes would be selected for and not. And that is missing. Um, David is correct. I mean, that that last part especially is missing in in most of psychology, yeah. Perfect. Very clear. In so in philosophy of science, there has been interest in capturing what it means to give a scientific explanation. And then there's also interest in what makes explanations deeper or better or more complete.

And earlier on in my field, I mean, perhaps actually, you know, um in the 1960s at least, you still saw ideas that deeper explanations or better explanations often involved including lower level details. There was also a sort of default assumption that explanations in physics were were better automatically than explanations at higher scales. And this was often combined with the suggestion that explanations are better when you include more detail. Now there has been movement away from those types of assumptions where if you look, I mean, part of what we care about doing in philosophy of science is capturing actual scientific work. And if you look at the explanations that social scientists provide, and biologists and

neuroscientists, they don't cite fundamental physics and they do quite well with respect to various goals. And you might think this is because they don't need to. And so part of what you see now in work in philosophy of science, and I think work that's helpful, is starting to show that when we're interested in this question of what how do you how do you give an explanation? And what makes explanations better? A first thing you have to be very clear about is what do you want to explain? What's the explanatory target? And part of what your discussion is pointing to is there's very different types of things we might want to explain. There's very different types of explanatory why questions that scientists ask.

And so some are going to be more evolutionary type questions, and others just aren't. And part of what that helpfully captures is a view where the only way you can capture a scientist giving an explanation is when they have a very clear explanatory target, in my opinion. And it needs to be precise. There are other views that I think are less helpful in philosophy of science that suggest that whole or better explanations have explanatory targets that are like complete and multifaceted, and you want to explain everything about a system. It's sort of a theory of everything type of explanation. Now, if you think of the explanatory target being everything about a human, how on earth could you ever give an explanation?

I mean, it doesn't even right. So part of what I think it's helpful for philosophers of science or theoreticians to do in this space is we have to capture what the standards are for first the explanatory why question or the explanatory target. You can't give an explanation until you know what it is you want to explain, right? You have to have a very clear explanatory target to do that. And we sometimes forget that that step is difficult for scientists. We're more focused on how do you give the explanation once you have the why question? We forget there's standards for the kinds of why questions you can ask in science. You don't get to just ask any kind of question for it to count as a well defined explanatory target.

So there's an example, the philosopher Nagel has this example that nicely fits with this discussion, which is why do humans have lungs? And he says it's ambiguous. Like that's actually not well defined enough yet, because it might be asking a more evolutionary type question or a physiological one. That's a small example that just shows, and I think it's helpful in scientific context because sometimes you have scientists who are talking past each other. They want to explain consciousness, right? Or something else. And the word they use, it actually can refer to and can be interpreted in terms of many different properties. And sometimes even the contrast you need to specify. You often need to say as opposed to what.

So if you want to explain an eye color in a fruit fly, you know, you need to say the as opposed to what, or else it just isn't clear enough what you want to explain. So part of what I think your discussion suggests is look, there's different types of explanatory why questions that we need to capture in this project of understanding how scientists give explanations. And part of what that reveals is that there isn't one way scientists give explanations because there's not one kind of explanatory target. So now in philosophy of science, we're more pluralist. We think there are many different types of explanations that scientists give that are legitimate, the challenge is explanation isn't an anything goes type project.

So you have to put kind of standards and boundaries on what counts because you don't want your pluralism to just be anything goes. And you want to be able to say when someone is giving a bad explanation. And I mean, in contexts where we're communicating to the public, we want to be able to say how science is giving us our best understanding of the world and why COVID isn't explained by 5G wireless networks. So you need to be able to say when an explanation is bad, as opposed to just if it's satisfying it counts. That would be a standard that's too low. Um so yeah, we're more pluralist, or I think we should be, but with that pluralism comes precision. You need a very precise explanatory target.

And you can just ask different questions about the same like system or like thing in the world. And so that's a different view of explanation that's more piecemeal as opposed to like a theory of everything, explanation, it also suggests that the lower you go, you don't always get a better explanation. Um, sometimes I refer to this in terms of explanation isn't a game of how low can you go, but what gives you control or what gives you information about control. But yeah, very interesting deeper levels of explanation, and that's um something philosophers latch onto as well. And I think even in science, we we're very attracted to maybe going to lower and lower levels, or just if you ask more follow-up why questions, you sort

of change the explanatory target as well. Yeah, that's that that's amazing because I think like as you were talking, I was just thinking a lot of I think the even my experience of science is the explanations are just this ad hoc aggregate. So in social psychology, there's just certain kinds of explanations. The explanatory target is always implicit. And then as a graduate student, you sort of, well, there's the neuroscientists and they have these kinds of explanations. There's social psychologists and they have these kinds of explanations. There's evolutionary psychologists, there's cognitive scientists, there's developmental psychologists. Nobody is being explicit about their explanatory targets, but it's a pile of

explanations. And then I think what students do, and I know I did this, and I still do this, it's just this implicit individual difference of like what kind of explanation do I find appealing? And I think many of us go through our entire careers without even having a discussion about what are the different kinds of explanations? How do they all fit together? And I think what we, what David and I, and I think many evolutionary psychologists were were worried about this because we feel like we're victims of misunderstandings, of where people misunderstand what we're trying to do, and they think sometimes we're in conflict with them when we're not. And then it's also the opposite where there are times where I am in conflict

with somebody, but they don't think I am, right? Um, and I think it's this, it's a consequence of uh, and I just make this concrete. Uh, so I study social groups. I'm really interested in sort of like, well, how does the mind define a social group at a level that you can engineer into a system? And I'll get people saying, you well, why do you need that? It's obvious what a social group is. And that's an example of the person, it wants to use their own evolved psychology, which does produce and is useful in everyday, like in a Dan Dennett sense, like you know, you understand what a group is, but they're failing to see why there's another explanation of, well, how does your intuition work? Right.

Um, and so that's something like I've personally dealt with, where I've literally gotten a paper rejected because people don't even accept the premise that my opening question is valid. Um, and so I think that's just a clear example of a consequence of this ad hoc accumulation of explanations, as opposed to this like it's very therapeutic to hear you talk because I feel like you're articulating, you're like you're like a counselor for the behavioral sciences to explaining why we're not coordinating. And I feel like we need a better coordination of explanatory targets. It's funny that you say that because there's a famous philosopher of science, Bill Wimsat, who suggests that philosophers can or should be like therapists to

scientists. I'm not sure I'm not sure I'm totally on board with that. It's a very I mean I I know it's very it's quite I mean I think philosophers should work to be helpful to scientists and they actually what's interesting is WIMSAT and others also refer to philosophy of science as conceptual engineering or cognitive engineering, where there's a functional element involved in how we're looking at scientists because we think they're doing things on purpose with a function or a goal in mind, and we're trying to figure out what that is. So yeah, it's a little bit of a function coming in in a different way. But part of our job, I mean, part of it is coming into a space and trying to figure out what you all care about.

Because I think, and most of us think, you have the, you know, you have the, like you know what you're doing, but then sometimes there are these disagreements where this, you know, super, you know, technical work and philosophy that's been happening for decades can be helpful because we went down a rabbit hole and something didn't work out. Or hopefully we can help kind of clarify what is going on. But that's a high that's a high bar for people in my field. And uh they don't always care about science as much as they should. But um but yeah, we're also kind of studying what you all do, although I your field is more theoretical too. So um there's a kind of a similar angle because our work certainly has that aspect as well.

Yeah, I think for me and David I feel probably I David I'll let speak for himself, but I feel like I've become like I love philosophy, but I become uh I've been pushed into it as well because I wanted to just do engineering. Uh, I was just like, I want to think about people mechanistically. It's fun to think about social things from like uh what's the doohickey behind this, you know, like what's the software? Um, and uh in an evolutionary approach was really helpful for that. But then it was just it with the sociology of the social sciences were not already understanding these different kinds of explanations. And so people just got really angry at me as a graduate student. And I had to figure out, well, why that people are getting angry when I'm talking about

there's like a system that does this, it has this function. People, it wasn't what people were used to doing. and I I can see now why that is and where we are at historically. But it was for me, it was just like it was it was born out of just a confusion about why isn't what seems pretty straightforward being accepted sociologically. But I'll let David kind of speak for himself too. Yeah, no, I I I would agree with that account and and second it. There's definitely um different philosophies within the social and behavioral sciences, uh, that often collide with each other, uh, in rather unproductive ways. Um, and I for one think that uh the social sciences would greatly benefit from more philosophy or from more uh philosophical rigor about the concepts that are being used, uh,

and about, you know, what those concepts mean and about what goals we're actually pursuing. I don't think there is enough of that. And and I think there ought to be more. I think, I mean, a big part of it is just the theory crisis, which we've talked about on many episodes of the podcast. I don't think there is an overarching theory that the social sciences have gotten behind in the same way that other sciences have had overarching theoretical frameworks. And that is enormously helpful for scientific progress, I think. Um, and I think I I think of philosophy as in large part a kind of theoretical science, or or at least the non-empirical aspects of scientific work, where we're talking about we're

making deductive and inductive and abductive arguments, uh, and we're making inferences to the best explanation and working out what the implications are of particular empirical results. That that to me is essentially what philosophy is: working out the implications of various premises. Um, and I don't think we're doing enough of that. Um so I I would agree that, yeah, w with with Dave that there's not enough coordination, there's not enough theoretical integration, uh, and I think greater philosophical rigor can help with that. But I'm curious if you if you share that opinion, uh being from you know outside of our discipline. Absolutely. It's I mean, I'm biased, of course, because I was trained from philosophers who take science very seriously and who

expect it to accommodate descriptively what scientists are up to, but also have a normative element where it's searching for the justification and the rationale behind the work. And so I I got very nice examples of philosophers doing that when I was in grad school. Jim Woodward is one of them. Uh I mentioned Bill Wimsat. He's another fantastic example. And so I mean, I think it's not easy for philosophers to do that for all sorts of reasons that have to be well versed enough in a scientific domain. And you're, you know, if you're if you're talking with scientists, which you should be, they're just so expert in their field that you need to learn a bit of the language that they use. I mean, all of our fields have, and I have to also not use philosophical jargon because

it's not always going to be helpful. So it is partly figuring out when you're talking with scientists or if you're engaged with scientific work, which you should be if you're interested in writing about how explanations work in, you know, the social sciences or evolutionary psychology or physics, you know, you gotta know a little bit about what's going on in those domains. But it can be non-trivial. I mean, many philosophers of science start studying in a scientific domain and then they get drawn into philosophy of science. That's a common path for us. But even still, we have to avoid science reporting, where we're just kind of saying what scientists are up to. If you're searching for that justification, that normative element, that's where you can

really, I think, do helpful work. And that's where I think you can work with scientists so that they can pitch their ideas in a way that shows, like, look, here's the question I'm focused on. Your question is a great one too, and you're working on that. But here's this specific thing I'm interested in. Here's how it's different. And here's the approach I'm taking to do this. It's interesting how it's a bit of a social, I mean, getting reviewers to see what you're up to or grants accepted. I mean, there's a very interesting just social scene where you're trying to persuade someone and use words to indicate, you know, that you're doing uh what you're up to and to kind of convince them it's a good project.

So it's it's interesting to see scientists manage that, but it's very, it's very fun, and it can be very rewarding as a philosopher to look at that justification for this work and being able to. I mean, part of what we're doing is there's just these distinctions. You're trying to distinguish explanation is one thing scientists are interested in, description is another project, and those are different. And within explanation, maybe there's different types. How do we capture what those types are? And um yeah, I think we want to be useful to scientists, and then we also want to help with science communication sometimes. If scientists are communicating their work to the public, it's very useful to have the

justification because then you can say, hey, look, here's why we view this as a helpful way to understand, you know, what explains this outcome and also what doesn't. Um, but that's yeah, can it can be challenging because it's sort of different audiences. Um, but yeah, I definitely favor of you who are philosophers. And philosophy of science can be helpful and can be continuous with scientific work. I think Jerry Fodor, I think this is a joke, but I think he was meaning it sarcastically. But I like most things, I admire Jerry Foder, but I think he was wrong to be sarcastic about it. He has a line that said, The reason why um social sciences or psychology isn't working is because n or even philosophy of mind isn't working is because not enough distinctions are

being made. And he was, I think, joking about that, but I think that's right. I think uh for me at least, like we I have a we have a paper about modularity, anyways, just saying there's two sides, there's a debate about modularity, but the argument is that um the distinction about what we one side means by it and another means by it, they're just missing each other. So there's this debate that's completely unnecessary. Um and so I like this idea that um part of coordinating everybody is to just see if the scientists are hitting each other for a for a bad reason or a good reason. And if it's for a good reason, well then let them do that within the rules of the game. But if for it's a bad reason, then please fix that so that it's the unnecessary debate.

Yeah. Um, what do you concretely, if if evolutionary behavioral scientists want help from a philosopher or philosopher of science to uh look at what we're doing, characterize it, almost be an ethnographer or translator of what we're doing, Is it something that those of us who are doing it are in a best position? Or just concretely, what can we do as a community to communicate with outsiders about what our explanatory goals are? Are there particular conferences or people? Um, you know like how do we how do we hire a philosopher of science to help us uh to consult in in sort of for PR or say here's the here's the way you um can explain what you're doing um and maybe that's an ill posed question because it's very

uh discipline specific but do you have any practical suggestions for us um well one would be that you all probably are philosophers a little bit already, and that I mean you you brought up Dennett a number of times, but in some cases, finding the places where the kind of theoretical elements of your work, whether it's assumptions or differences in methods, um or kind of topics are coming up, I guess, is kind of seeing how that's very similar to and already involves a bit of philosophy of science. Philosophy of science is a pretty small field, and so it's probably um helpful to um talk to people that seem people that work in philosophy of science who seem useful. There's also the Stanford Encyclopedia of Philosophy, which is a great resource that

often reviews different topics in philosophy of science and gives a lot of different citations. I think probably figuring out what you what you think philosophy could be helpful for in the context of your work, and then seeking out either philosophers or philosophical work would be good ways to start. And um yeah, I think we've brought up a number of places where philosophy can be useful. Um Topics that came up are like mechanism or giving accounts of different types of explanations that are showing up, perhaps also standards in science. Um, but yeah, maybe kind of starting first with Concrete challenges in your field that you think philosophers can be helpful for, or if there are particular things that you think are particularly difficult to communicate

to different audiences, like the public, kind of figuring out what those are and why it's um why it's difficult. I mean, I work on causation, and so even just telling people outside of academia, I mean, if you tell them you weren't you're a philosopher of science, that doesn't usually get a lot of yeah, yeah. And but even causation, it's a it's a little non-trivial to think of. And of course, like the the shark and the ice cream sales examples come up quickly. But it's good to just continuously be open to new ways of clearly capturing what's going on in a scientific domain or an academic one when you're communicating to different audiences. I think when you become expert in an area, you sort of lose your theory of mind or your

vantage point from somewhere else. And so you forget that your comp your the complicated words in your field don't, you know, mean something to someone else. So you sort of forget when people are outside your field what they understand quickly and what they don't. So maybe interdisciplinary discussions can help with that a bit. But yeah, I do think we need to focus more on communication to the public, science communication to the public. And maybe part of this pluralism angle would be one helpful way forward, right? I mean, there's lots of interesting biases about how science works in everyday life and in science, the sort of picture of the lower you go, the better the explanation is, is one of them.

Also, mechanism sometimes is used in a machine-like way, where the human body is just a machine and you got to find the lower level engine that does the stuff that produces. If you have that picture of the world, then what about social factors at a higher level or environmental factors at a high level? Yeah, you just can't capture things that causally influence and modern medicine is criticized for for not being able to capture social causes as much. So maybe, maybe working to figure out what are these more simple, cartoonish default pictures we have. And we have them in science too. It can be attractive to let go, or it can be attractive to have that lower level picture and hard to kind of let go of it.

And so, yeah, maybe when it comes to communication to the public, figuring out what the sticky points are and the challenging concepts and um yeah, how to how to communicate them clearly and how to show that there isn't this like one way it all works where there's this more pluralistic picture, and that science is an ongoing process. You're getting more and more information in your explanation, but maybe they're all a little partial too, more schematic, like you mentioned earlier. Yeah, I really like um, I think from my two sense bet is um dualism. So implicit dualism, where sort of low-level stuff is resigned for low level body stuff. And then mental stuff is resigned for the little homunculus in the head.

So the idea that something like breastfeeding is gonna make like a kid uh have a better mental existence is sort of unintuitive, right? Like it's just, you know, people don't really think that way, or like the food you eat is gonna affect like your well being your mental well being, you know. Like that's a little bit it's even it's hard hard for me to even think that way, right? It's more just sort of like, well, you should decide to be happy or you know think about being happy or the idea that um something like cues low-level cues in the ecology are affecting your risk taking or um things like that. I think that's very unintuitive to people. And I think like part of training in cognitive science is this idea that

The stuff that you're conscious of is only a subset of all the things that are causing your mental um experiences. And I think that's a lot of a lot of what we run up against is sort of dualism. So I think that would be an example of this kind of sticky way of thinking that really is almost like a scientific bias that the public and frankly many of us, I mean, I would include myself, have uh as scientists. Do you think that that has to do with an expectation that causal relationships need to be physical or physically interact, like billiard ball cases? I have this uh um thing about the evolutionary psychology of psychology. So I think like it's it's very denet. It's very much like I think we have this folk physics, and so it's very intuitive for that

system to sort of there's certain things in the world that are into that are intuitively applicable to the that system. So if we think about like brains, oh yeah, I can imagine billiard ball interactions. But that system doesn't really help me for like day-to-day human behavior, right? Because it's it's not matter that I can see moving around and hitting each other. And then I think we have this folk artifact system. And I think a lot of what I what we do as scientists, so kind of like software engineering or reverse engineering functionalist systems, like I study things about groups, and I think there's a system that is attentive to this class of cues, and it produces these outputs, and it's all very uh abstract, functional design stance.

And I think that's an output of the artifact system. And then there's our theory of mind system, the intentional level. Um, and that's the beliefs, desires. There's a little person in my head, there's the stuff that's me, there's the stuff that's not me, and there's interactionism between those. Um, I think those three ontologies are produced by these evolved systems. And I think when when a scientific enterprise involves more than one, it gets complicated. I think psychology is really hard because maybe uniquely it's three uh evolved ontologies. Like physics is is the weird empirical results and our intuitive physics, you know. Um uh chemistry uh maybe you know maybe two systems I don't know but I think like psychology is the intentional the design

stance well I mean engineering would be like the design stance the physical stance and the empirical results but I think psychology involves this intentional thing too so we have these three ontologies on top of the empirical data And I think for my my take on psychology is it's just a mess because those three ontologies are still not integrated conceptually. And uh we are still generations of scientists who were trying to, those of us who are trying to be clear about which ontology we're contributing to, are interacting with a world that those ontologies aren't clear among reviewers or funding agencies. I think we'll get there. But I think the evolutionary sciences, not uniquely, but I think the evolutionary sciences

are rare in that we really are design stancy about things that are typically intentional right now. Um, we're more designy than intentional. And I think we we get a lot of blowback for that reason. I think that's where we're at. Yeah, I would have thought that the design stance would have various advantages and even allow it to the field or one of the one of the kind of theoretical modules to be viewed as advantageous. I think it I think it has to do with explanatory. I think it's because intentional level uh descriptions are so adequate because we have this theory mind system. If I tell you that David loves France, you know, it's like a Jerry Fodor point. You know, it's like everyday common sense, belief, desire psychology is so effective.

And I'm not like an eliminativist where I think, like, you know, like I don't think lower levels of reduction are better. I just think you have to supplement, right? And so I don't want to get rid of an intentional level description, but I want to explain it in terms of mechanism. And I think where there's institutional selection for the most succinct description, um, for some reason the design-mindedness is more maybe too precise. It's not super helpful yet. Like I don't know if from a predictive perspective it's always um accurate outside the context of a very delineated study. And so I think people are in the habit of intentional level descriptions. That's my that's my guess. And I don't know, maybe David has a thought about the sociology.

But but I definitely think that that's I agree with you, Lauren, that like it seems puzzling that the design stance um is the uh upstart um underdog. Um but I but I kind of wonder if historically that's always true. And it's just that eventually, because it can be a bit more precise, even if it's not because if it can be more precise and while it's accompanied by the higher level description, both do better than just the high-level description on its own. But I think until you start having enough data or people figuring out how to do those design level descriptions, it seems very speculative. Like just like I think a physiology is a good example. So, like the idea that um, like when I I um coming across like in

like um physiology for a long time, the the concepts or the entities were things like moisture, darkness, warmth, which are the things that you like find when you cut into a body, there was no, and those were the kinds of intuitive apparent phenomena. Something like a filtering nephritia, you know, like nobody was going to come up with that because that's not a substance, that's not a feature that you kind of find when you cut in. It's a theoretical concept based on an idea about function. And if you postulate, I think there's a system that does filtering, somebody will be like, Well, what's your data for that? And right now, early days of physiology, there was no data. So people would be like, I don't have any data, right?

So so in other words, like there's a there's a million ways to measure the moisture of different parts of the body. There's a million ways to measure the warmth of different parts of the body. There's a million, so if your currency is data, I think that there's sort of like a local minima that uh is can be defended, which is sort of if you're postulating an abstract function that we don't even know how to look for yet, we don't have any data for, it seems very speculative. That can be um argued against because there's no data for it, if the currency is data. And so I uh that's actually my my full uh theory of why we're we're at this intentional level description for many areas, not all areas, and it's changing, but that's that's my

thesis, Yeah. Fascinating. And it reminds me of cases in medical sciences and neighboring fields where different types of disease phenotypes can have signs and symptoms that are easier or harder to basically measure. And so there's sometimes a view that if a disease has so symptoms are typically viewed as something that a patient experiences, but a third party can't easily measure, like pain or dizziness. If I'm dizzy or I'm in pain, you sort of have to rely on my report. I might be acting a certain way, but you can't measure it the way you could measure my temperature or if I have a rash. That would be a sign. So we often distinguish between signs and symptoms and symptoms are a little more viewed as questionable, because you just can't easily measure

them. And then if you get various diseases like in psychiatry or other fields, where the cluster of signs and symptoms is variable across patients that we think have the same condition, again, you run into this like, how do we measure how many people have schizophrenia when the list of characteristics that a patient needs to have to get that diagnosis, we allow wiggle room in terms of what how they need to present. Compare that to something like, you know, hypertension if you have high blood pressure, you have like a single property and it's super easy to measure. And that's the so there's there's different things that we're interested in, maybe in the natural world. Um, and some of them are are easier or

harder to measure. I wonder if that's one way to put it, or there's biases about how real something is based on it being more physical, more material, and sort of easier to measure. But of course, it's very important to appreciate that a patient's in pain or they're experiencing some symptoms when we have no ability to get the same kind of measurement of it as something else. So, yeah, interesting where there's cases of more complex phenomena of interest where we are really working conceptually to build the category that matters and maybe showing people examples of cases where we do that and where they wouldn't want to deny it's a real thing, but where it is just different from that intuitive physics picture of, well, there's a

baseball and it hit the window. You know, not everything in science is going to be as like simple as those cases. And when you get a human involved, there's tons of examples that show up, of course. um disease examples and many others, just where things get get so complicated. Yeah. And I think that's like I would David mentioned the theory crisis. And one of the symptoms of the theory crisis, especially in the behavioral sciences, is that constructs are re-descriptions of the data. And I think that's a consequence of these things that are not just the bait window and the baseball, it requires institutional tolerance of ambiguity and patience. Because if a grant reviewer is like, well, where's the where's the data?

You know, it's it's indirect, or it's it's or it's a more complicated, abstract, latent thing we're trying to make an inference about. But if you have a measure and it's you can measure it, and this is the construct, you know, social dominance orientation. It's these 13 questions, it's the number I assign. I can give you Lauren and you, David, a number for it. It's concrete. Nobody can argue with that concreteness. They can argue with the validity of it, but not the concreteness. And I think um I think the patience for understanding. I think it comes back to the explanatory goals. If you're trying to make an inference about an inferred entity, like a system in the body or the mind, especially, that isn't just a baseball and a

window, it requires tolerance for um it doesn't look sciencey. It doesn't have, you know, like a neuro, a picture of neurons looks sciencey. The numbers look sciencey. But if I say I think there's a system that tracks alliances, and there's 75 ways to measure it, and they all get at it indirectly, that's words on a page. Um, and I might be right, but if somebody's measuring a single neuron and has beautiful figures, um, or if somebody can take a picture of it, for people who are not experts, it just seems more concrete and more real. Um so yeah, I don't know. I yeah. Yeah, there's famous examples from the history of medicine and bacteriology, where Robert Koch, who was postulating that germs were the cause of certain

diseases, couldn't early on take pictures of like anthrax bacteria because photography wasn't advanced enough at that point for him to do that. So early on, he was drawing pictures of these bacteria that he saw in this microscope, trying to convince his research community. And um yeah, but once photography was more advanced, that was more motivating for the community to get on board with the fact that there were these tiny bacteria that were single species. But even then it took a very long time to change the medical community. I mean, or the scientific community. And part of what's interesting is to think of how we do that. The how because it in in grant calls and the idea of scientific work is that you are doing

something novel. And in grants, there is often the suggestion that you need to do something transformative, but like you're sure it'll work out, but you're not totally sure because it's so novel and transformative. So it's it's sort of like you have to play in the you have to play within the rules enough so that you can kind of break them a little bit down the road. And so it is interesting. We have this in philosophy too where you sort of have to work in some ways, or you feel like you're constrained a bit by the current views, and you have to show you're working within them enough to then make progress in another direction. But then there are examples from the history of science where someone is coming, you know,

just from a completely different field and they're maybe ignored for a long time, or they can make a big change. But it it is interesting kind of how to think about the steps to take to advance programs that are different. Um yeah. And what kinds of evidence, as you're suggesting, does look more sciencey to the researchers who were essentially evaluating your work. I want to um pitch you on a way that I've been thinking about explanations. Uh and uh and you know, I I recognize that this is my own intuitive way of understanding it and and looking for what counts as a good explanation or a bad explanation. I'm curious to see if if you can tear it apart and tell me why I'm wrong. But the way I'm thinking about how to evaluate a good explanation is in terms of the ratio

of what it assumes to what it successfully predicts or explains. And that it makes very few assumptions. You know, few assumptions about variation, heritability, uh, non uh sorry, differential reproduction. And these are really uh plausible, rock solid assumptions that would be hard to deny. And yet by making those very uh that very small number of very plausible assumptions, um, you get this enormous explanatory and predictive boon, right? Where you can uh essentially explain the adaptedness of organisms around the world to their environment. And you get a whole array of predictions you can make about how organisms will be adapted to their environment, how those traits will change over time.

And it's essentially the centerpiece of biology in terms of an underlying explanatory and predictive framework. And so the ratio of what it assumes to what it successfully predicts and explains is enormous. It's mind-boggling to think about the difference between that numerator and that denominator, right? And so that's what I strive for in terms of providing explanations for things in my work and what I how I try to evaluate theoretical frameworks is to think about, well, what are the assumptions it's making? Um, how many of those assumptions are there? How many of those assumptions are implicit and unstated? How plausible are those assumptions based on everything we know from the empirics? And then what is it buying us in terms of successful predictions and explanations?

And I'm basically just looking at the ratio of those two things, and that's how I'm thinking. I'm curious if you think that uh is is the right way to think about explanation, or if I'm missing something, or if there's uh something else to the story. I like that. There's a couple of follow-up questions I would ask, which would be in terms of assumptions, what if you just toggled the plausibility, not how many you have? But suppose you just have a lot of assumptions, but they're all super plausible. Yeah. Versus uh just having few. Suppose I only have one assumption, but it's really not so medium. So one question would be what do you think about Plausibility more than how many assumptions you bring on board, but I like that.

And then the other thing is we get into this topic and question about prediction because sometimes we can get predictions from stuff we think isn't explanatory. So would you want to beef that up more and say This is giving me more than just prediction. It's giving me information about hypothetical changes or hypothetical control. That would be a question I would have about the prediction piece only because mirror correlations or sometimes things we think are not explanatory can be predictive. So, yeah, what would you think about that? I I totally buy both of those amendments. Yeah, plausibility is the better way to think about it. Um yeah, as you as you say, a single assumption could be very implausible, and you could

have ten assumptions that are all extremely plausible. Um it really what matters is the the plausibility of your assumptions. Uh granted there will be a correlation, but if you're making lots of assumptions, chances are, you know, not all of them can be true. I mean, just probabilistically speaking, but in general, plausibility is the more important thing to look at. And yeah, um, and not mere prediction, but I think successful prediction, predictions that turn out to be vindicated by the data. Uh and and I think of an explan uh, you know, uh explanatory power as a prediction of something that we already know is true. Right. Like uh you can work out an implication from this model that it predicts the state of

affairs, and turns out that we we live in those state of affairs. And I think of that as an explanation rather than a prediction. But I that's that's how I'm thinking of it. And I I I buy your uh amendments. Good. I mean one one question too is is if you think breadth is something that gives you a better explanation, or is it fine for it to explain a kind of narrow set of cases or systems or circumstances? Um yeah, um, I would think that breadth is better, all else equal. Um, the more stuff that you can successfully predict or explain, the better. Granted, there are going to be limitations, and that if you're if you're trying to explain a small piece of the world, there's going to be limits to how much you can successfully

predict or explain. But in general, yeah, I would say intuitively, the more you can explain and predict, the better. But a trade-off would probably be the realistic susiga. I mean, in practice. In practice, yeah. Nothing's going to explain everything or predict everything, but um you want to get as you know as much bang for your uh buck as possible, I think. Yeah. Sorry, Lauren, go ahead and you're gonna say something. Um I wanted to ask both of you, Lauren especially, um, is so the the one thing we do a lot, I'm gonna again sort of this is my therapy session of like we evolutionary behavioral scientists, um, we speculate about mechanisms prior to coming up with tests of them. And so I'm curious from a like a history of science or philosophical perspective, where

we're talking about David's talking about we want an explanation that accounts for the data, But we're also in this weird situation where when we're doing it real time, we're making a claim that we don't even know how what data to collect yet, right? Like in some sense. This is the physiology analogy where if you think there's something like a filter, that is a speculation prior to figuring out if there is such a filter, this is what it should look like if it's doing this job. Therefore, we should collect these data. And if we're right, this is what the data will look like. But there's a delay between what you think exists and what data you collect. As Marvin Minsky, who is one of the founders of AI, put it, you can't look for something

till you have the idea of it, which I think is a good but funny, funnily apt point that people sometimes forget. And so I wonder if historically those things where it's a claim, but it's a prediction where the data aren't there yet, if those are sort of don't win out as well, or they get more um beaten down than those where the prediction can be pretty quickly turned around, um, such that we already have the data, for example, or we we know very quickly how to measure the prediction. Um, if that question makes sense. So it's this idea that some ideas, there's almost uh an extra step of auxiliary assumptions to even know how to generate the data versus those where the data, if it doesn't already exist, is pretty reasonably gettable with very

minimal work, time, effort, auxiliary assumptions, etc. I wonder if so to me that sounds like a very reasonable approach for um tackling various types of systems and problems. I wonder if it's characteristic of a more function-based, goal-oriented system where you can you can play that game actually quite well or better than in other cases where you don't have the the goal or the function piece as much. But It reminds me of some situations in in COG-SI where there's studies of causal cognition where you're first sort of seeing what humans are up to and what they're doing and how they're reasoning. And then you're sort of postulating like what's going on in their cognitive process for doing that thing.

And you're you're very much focused on like what's the function of their reasoning in this situation. And then you are sort of reverse engineering, well, what's the reasoning process that allows them to do that? There's a book called Causation with a human face, where this is discussed in a more theoretical way by the philosopher Jim Woodward. But um, but yeah, I wonder if leaning into the sense in which that's something you can do in a very principled way with the function piece and with the goal or the goal oriented piece, where that's, I mean, we sometimes talk about explanations that are functional explanations, where those look a bit different from standard causal explanations. Some of the examples here look a little cartoonish where it's, you know,

you're interested in explaining um the existence of something on the basis of the fact that it serves a function. Like a doorstop would be a good concrete example. Like a doorstop, yeah. Yeah, like a doorstop. So there's this artifact or this system in a living organism that's present, and you're sort of like, why is this present? And the way you give the explanation for why it's present or why it exists is the sense in which it serves a function or it serves a goal, and it evolved to come into being, or a uh someone designed it or created it on the basis of the fact that it so that's an explanation where the parts are set up differently than here's the disease, what's the cause that explains this disease?

There's no, I mean, there's no functional. You can do away with functional considerations more in that case. And so I I wonder if there are certain strategies or heuristics you can use if you have that functional piece and so much of our the way we're thinking about living systems, it needs to have that functional piece. I mean, genes aren't just, you know, these things aren't just doing random, you know, actions or engaged in random processes without very much being tuned to functions and goals. So no, I quite like that. It would be interesting to look at cases where you get pushback with that kind of model. When a reviewer is looking at it, or someone who's maybe coming from a paradigm that is the

more I found a gene, let me wiggle it, or I found a single molecule, let me intervene on it. It'd be interesting to see scientists from those other domains. What are their main thoughts when they first see that picture? And is there a way to present it that keeps the hackles down? Or I don't, yeah, it maybe it depends too on who the main um yeah reviewers are or different audiences. Yeah, I really like that. The idea of function, um, yeah, it especially this is where I mean that's why I'm an evolutionary person too. And I think it's no accident in my experience, and David, you can tell me what yours is, is that it's people who already are thinking functionally about the mind that find evolution

helpful. And the people who either, and I don't want to be too, I mean many of these are my dear friends, and I don't want to be too dismissive, but I think individual differences and how much they like thinking about the mind as a mechanical functional entity track how useful they find evolution. Um, in the sense of if you have a belief, desire, intentional level description, like people like social groups. And I I can run through my theory of mind of is this the kind of social group I would like? I can do a lot of studies just based on that model. Um, but for me, uh I I wanted a more mechanical explanation, a functional explanation of well, what does it mean to um why why do people like joining groups?

And you just there's no um how would you engineer a system? What rules would it have to say this is a good group or this is a bad group? And then you start to say, well, I don't know how to do that from an intentional perspective. I could give you examples, but I don't know what to program into the robot. But from an evolutionary perspective, you could say, well, there's these abstractions in the world, like there's costs. There's a class of outcomes where somebody could hurt you or take your stuff. And so people providing entity agents providing like loyalty and assistance to you. So you're starting to come up with these abstractions that are grounded in classes of outcomes. But it's it's you're talking about new constructs, like it's still loyalty, but it's like a

system that's looking for loyalty. And well, how would that work? And what cues would that use? And so um I think an evolutionary approach um populates the a design level or this mechanical level um account um in a way that the intentional level um account doesn't. And uh and yeah, I think the pushback sometimes people give for you don't need a mechanism is is missing that that transition. Yeah. Yeah, I will give you um a link to the name of this book, this Woodward book I'm mentioning, because there it does really sound like what would be interesting to look at is whether this is a different sequence of steps in explanatory practice. And I think there are other cases where there's attempts to capture what's going on in

some of these situations where maybe you have more clearly identified an agreement on the assumption of what the goals are for a system. And then you're sort of like, well, these are the goals. You can see how it's useful for the system. And then you're like, if we find something that serves those goals, then this gives us a way of um, yeah, I forget how to kind of recreate this explanatory, but it's very different from this kind of standard garden variety picture of you have the explanatory target over here, you have the explanatory stuff on the other side, but doesn't involve this functional piece. Um so yeah, I wonder if the distinction of types of explanation and types of explanatory practice you can do with a functional

orientation. You can do something more with that. Of course, you have to get them to agree with the goals. That's an assumption, but I don't think that will be the difficult part. What might be nice is to just show the sequence of steps where look, once you agree with these goals, now you can um show the kind of utility of understanding a system that is like hooked up to them or um has various mechanisms that are serving those goals. But yeah it is I mean partly if mechanism does mean something very particular to A researcher who's who you're talking to, um, they're you know, it could it could be difficult or they they might just resist it based on the word. We see this partly in neuroscience where you have neuroscientists talking about network

mechanisms or cognitive mechanisms. And the more reductive molecular biology researchers are like, you guys don't have mechanisms. We have mechanisms, they involve lower level molecular cellular entities, but they're like a network mechanism just doesn't so it's more of a rhetorical game there where it's it's not always very helpful to use that word if it if it just means different things. but maybe explanation and prediction or causation, and highlighting the utility of function and what it allows you to do in cases, and maybe philosophical work where other people have already said, hey, look, this is an explanatory pattern. This you see it in cognitive science in this crazy area. You see it over here in Uri Allen's work in systems biology, where he's reverse

engineering, you know, circuits that function to do all sorts of things, oscillate and whatever. So if you if you have other people referring to this as an explanatory pattern and and you're sort of able to say it's different from the one you're used to or that like maybe more traditional one, I wonder if that would wonder if that would help. Yeah, I think I think function is hitting the nail on the head. And I think that's both our bet our what we bring to the table, but it's also our um our vulnerability. And I think like you see this where people will say, oh, evolutionary psychology or evolutionary behavioral scientists, they just think everything's adaptive. So it's this Panglossian thing where, you know, like, oh, noses are for holding up

eyeglasses. Um, and I love that example because it's a great example of it, it uh it eliminates its own argument because it's obviously absurd. Because if you think about a nose, it's not well designed to hold up eyeglasses at all. Like the design features of a nose are not at all well designed. And that's so obvious that it's absurd. But then you think, well, people are saying you could just postulate anything as has any function, and there's no pre functional predictions about what it should look like. When in fact the the example relies on that you can do the very thing they're claiming you can't do, which is say something doesn't look like it's well designed to do that. I don't know if I'm explaining that well, but it's basically the nose is not well designed.

Obviously that functional account is wrong. So therefore you could just say any functional account will go. Like that's the logic of the yeah. I mean maybe what would be nice is to have a very clear um discussion on the like just so story criticism. Yeah. And sh because that does come up so often. And so to actually bring it up and to show what this is used to suggest and how it's actually um not a criticism of a type of this work that does this particular thing. But yeah, we are sometimes stuck with these very cartoonish suggestions or pictures of what's wrong with something or what science is really like. And so maybe tackling it head on and just being like, you know, here's exactly. And sometimes it's difficult too to say exactly what's wrong with this

very just common view that's out there. You see it with mechanism, the view that everything is caused by stuff at lower levels. It's sort of like, how else could it be? How could you how could you have any other way of giving an explanation or any other way of causality? And so being able to tackle those assumptions head on, I think is a nice focus, both probably for scientific contexts and then in in everyday life. Yeah. So like an example of that would be something like if you go to the doctor and your leg circulation is bad, most people would want a physiological explanation and saying something structural like you don't have public transportation, you're not walking enough. Like those kinds of explanations just fall off the radar and people want just the

physiological, like what's happening in my veins for why I'm not having sufficient circulation. Oh sure. Or suppose you have two different patients of two different races. One of them is more um has higher blood pressure and is more stressed than the other, and one of them experiences racism more than the other. And you want to know why is it the case that you know the patients I'm seeing that differ in terms of race? Some of them have higher blood pressure, higher cortisol, they're more stressed out than others. And talking about their physiology isn't helpful because the main difference maker across those white patients or the, you know, black or Mexican patients is the fact that they're in these very different social situations, or they're experiencing racism differently,

or socioeconomic status. So, yeah, it is partly finding those cases and being able to show the justification and the rationale. And it's still, it still requires walking someone through carefully. In some cases, they'll say it looks like downward causation, which is viewed as mysterious. So yeah, there's all sorts of ways in which higher level causes producing lower level outcomes is viewed as obscure or mysterious. But yeah, that's exactly the kind of cases I'm thinking of, where you know, a patient isn't buying their medication and they're said to be non-compliant, and you realize they don't have the money to kind of buy the medication in the first place. So why are they not taking their medication, their their choice?

So you blame the patient as opposed to, you know, the fact that they can't afford the medication in the first place. But yeah, it's worth these are sometimes called examples of cases where physicians need to be have structural competency. And so there's um there's been a push to include this type of work in medical education. Um, but you know, there's there's so many things that physicians need to study and that are part of their education. And the system is so big. There's yeah, so there's lots of changes that it would be good to make. But one of them, kind of related to what you're saying, one of them is to first show that it even makes sense to think of these as causes that are explanatory, right?

Before you go intervene on them or before you go get data, you need to show that conceptually this makes sense. So maybe that's another similarity with your project where you're first saying, like, look, for this question and for this explanatory target, this makes sense. This is conceptually principled and justified. And having social things as causes is perfectly fine. Here's why. It's similar to these other cases. Yeah. So maybe places where that conceptual and theoretical work is important and where you're giving that justification, and where you can sometimes use it, you can find other examples in other sciences that they're okay with the same justification. You kind of use it to support your view.

Where it's like, look, over here in physics, you're perfectly fine with these like structural causes or constraints. You should be fine with them over here too in the social sciences, where you got the same kind of thing. Yeah. But yeah, maybe a little more of a I kind of want to um push back on a on an earlier point you made about the um the how low can you go perspective uh that um people often uh espouse in in philosophy of science. Um and I I want to defend a more limited version of the how low you can go uh thesis, which is, you know, I agree that we don't want to be explaining uh jealousy or anger in terms of quarks and electrons. Um, but I do think that there is a defensible version of that hypothesis where you don't

want to necessarily go all the way down to the floor, but you do want to go at least one level uh below you. And you want to be at the place where you can pass the baton to the to the level below you, um, such that you're not positing theories and mechanisms and explanatory factors that are inconsistent with or incompatible with the lower the level below you. So if I'm a microbiologist, I don't want to be positing uh microbiological structures that are incompatible with what we know about chemistry and with what and with what molecules are stable, right? Similarly, if I'm a chemist, I don't want to be positing molecular structures that are incompatible with what we know about how atoms work and interact with each other,

right? And in the same way, if I'm positing psychological mechanisms, I don't want to be positing mechanisms that are incompatible with what we know about how natural selection works or with how information processing structures work, right? And so I think w what our goals are, at least from our perspective in terms of evolutionary psychology, is to get psychology and cognitive science in a place where we can be putting forward ideas and theories and mechanisms that fit with and are consistent with everything we know about evolutionary biology, about how biological systems evolve. And what Dave and I sometimes get frustrated with is theories in psychology that are actually pretty incompatible with and inconsistent with things we

know about how evolution works. Right. A lot of the times we see people positing um theories of how the mind works that really don't make a lot of evolutionary sense or that could not have plausibly evolved by natural selection if you if you go through and do the do the game theory, like the strategy would be defeated by this other strategy. It's implausible that we have this. Or a lot of the thinking in psychology views our psychological mechanisms uh in a kind of intentional or in a kind of design stance. But the design is wrong. The function or the telos of the system is wrong. The telos is framed as achieving happiness or uh mental well-being, as if there's a little homunculus in the head that's pursuing a mental substance called happiness.

Uh, and that is the design criteria, where how how well does this uh system produce uh the mental substance called happiness? Is it maximizing feelings of well-being and good vibes? And that's how we evaluate whether it's well designed or not. When from an evolutionary perspective, that makes almost no sense at all. To have a biological system that's engineered to seek out a substance within itself, it makes more sense that the system would be uh designed to seek out something in the world that uh facilitates its biological fitness. Um so I want to get your take on whether you find that view plausible, that we don't want to go down to the floor necessarily, but I think we should be working to go uh

uh the level below us so that we can meet up with the level below us and at least have a kind of handshake or baton passing to the level below us. Oh, that's so cool to think about. And part of what it it captures for me is the sense in which there are sometimes cases where we're interested in an explanation or a theory where it needs to be compatible with something else. Or it's a kind of game over situation, and you can use the fact that it's not compatible with something else to say, look, guys, we have a problem here. The question is, what counts as the thing it needs to be compatible with? And I would say, why or does everything in biology and social science need to be compatible with physics?

Does it need to be? What would it why don't they need to be compatible with us? Or so I think the question is we don't want to assume that understanding things in biology or neuroscience or cognitive science or evolutionary psychology, we wouldn't want to just assume that successful explanations in those domains need to be compatible with lower level physics unless there's a good reason for them to be. I'm not sure we have that exactly. I think we have an assumption that the world is layered and that lower things are more fundamental or more um foundational and more explanatory, but I think we need to question that. We don't want to question that there's stuff down there in a just a physicalist picture.

No one should question that. The question is whether it's explanatory for the higher level stuff, or do we need to be compatible with fundamental physics? We need a good reason for that without just assuming it. I think your examples show cases where there is something that is a standard that the theory needs to be compatible with. I like your cases of the evolutionary considerations a lot. And I wonder if there's other cases where you play the same kind of game in science, where it's like, here's your theory, but if it's not compatible with this thing about chemical reactions or with some other kind of like feature that we know is present, then you have a problem. I would just say I wouldn't assume that physics gets to play that

role for everything in the life sciences. Um why not, you know, why not the other way around? But then, yeah, so but I I think it I think there are cases where lack of compatibility with another theory or another principle is a way to say, like, look, there's something wrong here, or this isn't quite right. I'm just more non-reductive in the sense that I don't assume physics gets to be the rule that biologists and social scientists and evolutionary psychologists need to always care about or follow. And there's a lot more to say about that. And that might even be resisting a kind of common picture or a default assumption. The one thing I'll add is that I'm not denying that there are things at lower scales.

The question is does information about that stuff at lower scales constrain what you get to say in the social sciences? I wouldn't I don't want to assume it does, and I think there's lots of successful work in all sorts of higher level sciences that's totally ignorant of fundamental physics and is very successful. So I wouldn't want to assume always we would need to be beholden to physicists, but this kind of relates to debates about it. But I don't need I don't think that psychologists necessarily need to be beholden to physicists. I think they do in the sense that we don't want to posit theories that defy the laws of physics. Like I don't want to say that, you know, uh Superman is the cause of this thing and he can

fly without means of propulsion, or that there's some kind of telekinesis or whatever. We don't want to act, you know, defy the laws of physics. But I don't think we need to be constantly thinking about the laws of physics when we construct our psychological theories. But I think we do need to be thinking about um biology and evolution as a way of constraining our theories in the same way that chemists, chemists need to be thinking about physics and uh microbiologists need to be thinking about chemistry. Yeah, I wonder if there's things at certain scales that you see and that you can capture, but if you go down, you lose it. Like some emergent phenomena are said to be like this: viscosity, or

there's some types of features that you can only kind of see or capture, supposedly, at one sort of scale. And once you go, when you go down, you actually don't get a better handle on things. At least that's the suggestion. But I wonder if it relates to the target, the explanatory target. Are there some targets in chemistry where, I mean, you don't want to, yeah, where you don't need to be beholden to physics. But yeah, it's certainly, it's certainly worth considering more. I think I do want to highlight the sense in which your the examples you give with evolutionary considerations, I think those are those are quite nice. It'd be interesting to look at other cases where you can play the same kind of game.

Um it's also helpful, right? Because you can use that to show here, this theory is actually better than this other one because it's compatible with our best understanding of evolutionary systems and whatnot. Yeah, I think like it motivates, like maybe there is a sense in which there the account could be reasonable with respect to what we know about natural selection, but that researcher hasn't articulated it. Like that would be a first kind of pass take on it. I'm very partial to David's take, but I will be devil's advocate too. Like I think there, I'm not an expert in this, but there are examples in the history of science where, for example, like DNA, the people DNA was a function first because I'm I

like function. I'm a you know I'm a proselytizer of functional thinking. Uh the functional think functional thinking started before there was evidence of what DNA was. And somebody was like, well, there has to be a molecule that can reproduce aspects of itself with like near perfect fidelity. And chemists were like, There's no such thing. And they laughed at the person who said that. So that was a good example of like at the time the chemists were saying that's not consistent with what we know about how chemical substances work, but it was because they hadn't discovered that one chemical substance that had that property, that functional property. So I do think like there's examples where I think this also happened in chemistry and

physics, where chemists were saying there were certain things that happened, and the physics were like, Well, that's not consistent. And actually, what changed was physics and not the chemistry. So, yeah. So, I think like I completely agree with David. I think it's good to have those fights. Um, and sometimes you can steel man, it's good to sort of steel man the non evolutionary account into a the best case adaptationist account. But I agree with David that there's just some things that they're almost imp they're not even coherent, I guess. They don't even like start, they don't even go out the get out of the gate. You know, that's that's one way to put it. Yeah. Uh I I would love for psychology to be at the at the place where we could have a strong

enough body of evidence where we could contradict something in biology. Where you could say, No, we have the better data. You need to trust us and not the other way around. Um, but I don't think we're we're anywhere close to that point. Daryl Bem almost had us being psychic. That's true. Oh my gosh. You almost turned everything around. Yes. Um, Lauren, is there anything? Uh we're we're kind of but this has been so wonderful. I feel like we should we should have you on again, but um, we're coming up on our usual timing here. Um, is there anything about your own work? Because you know, we really uh we selfishly sort of um again, this was our therapy session with you, but um, is there anything about your own work?

Um, both in terms of like your work or your books that are coming out, or things for people who are interested in philosophy of science or causation, in particular what you're doing, that you might want to point people toward, or is there something we should have talked to you about or you'd like to mention? Yeah. Oh, um nothing that we should have discussed but didn't get to, lots of great topics and a nice discussion that starts to kind of unpack also what philosophers of science are up to and what they're interested in. The um yeah, so maybe I'll just briefly mention Um a couple things. So I have a book that was recently published, Cambridge Elements book. It's called Explanation in Biology.

It discusses different types of causal explanation and non-causal explanation. If people are interested in mechanism, there's a paper that Danny Bassett and I wrote in Nature Reviews Neuroscience called Causation in Neuroscience Keeping Mechanism Meaningful. So there we talk about different notions of mechanism in the field of neuroscience. It's similar to notions in other scientific domains. So there's that paper if there's interest. And then yeah, just to highlight part of what we discussed, um, philosophers of science in this space are interested in capturing real types of explanations that scientists provide, but also the standards that they need to meet. And we now, in current work in philosophy of science, are more pluralist about what

counts. It used to be the case that philosophers were looking for the one single way that explanations worked. And now, fortunately, we're more under the view that there's different types of legitimate explanations. Our discussion has nicely kind of supported that kind of view where there's different explanatory targets or why questions, and then just different steps you take to answer that why question and to give an explanation. And then, yeah, just final things to note for why that matters that came up in our discussion. We want to know when explanations are good or bad. And so you need standards to be able to say when they're good explanations, when they don't count. We need that for the public too, because there's lots of conspiracy theory types

of topics and explanations that are inconsistent with scientific work. And so scientists need to be able to tell the public why we think that X explains Y and what the rationale is. So it's important for those discussions, but also as we mentioned, for the standards in science for pay for the standards that determine the papers that are published, the research that's funded. And when you're making progress in science, like we're talking about, being able to say, look, here is this explanatory program that I'm operating within, and it's different from this other one. It's important for making progress in science. So, yeah, a lot of interest here in capturing distinct types of explanation, what their standards are, and then the justification and rationale for viewing them as

explanatory as opposed to merely descriptive or predictive or just classifying and showing how it um they're they may be serving different goals and how they do have justification to kind of capture how science is giving us our best understanding of the world. So yeah, just a little bit of recap. Very happy to talk more at future points and bring in more philosophy of science if it's ever of interest. Yeah, that'd be great. I mean, I think this conversation has been a beautiful, nice microcosm of I think it's really a productive thing. It's it's fun for us to think about that there are other people who have tackled these issues and we don't have to do it on our own. But then it's also helpful in clarifying um uh to hear what

those precedents are. And um, and I hope it was helpful for you to see what mess we're in ourselves. Um, but yeah, no, um, we as we mentioned before we started, we're all local, and um there's a lot of evolutionary behavioral scientists in the Southern California area. And I think it would be lovely to have a philosophy of science and evolutionary sort of um um uh some get together or something like that. Um, I think that would be that would be fantastic. So that sounds great. Yeah. So uh Lauren, thank you so much. Um uh it was a pleasure and um yeah, I I look forward to talking more um uh with you and other philosophers. It's it's a real treat and um we always appreciate having I think David put it, um I love

talking to philosophers, they're so clear-minded. I think is that what you said, David? Yes. Yes. Um well yeah, it was a total pleasure for me. So thank you so much for having me on. And yeah, loved learning more about the work and uh debates in your field. So yeah, thank you for that. Yeah, thank you, Lauren. It was fantastic. David, that just flew by like usual. Yep, it always does. Yeah. Time flies. So there's probably work on time perception. Um, I mean, I know there's work on time perception and how it gets, you know, expanded or contracted. Yeah. I I haven't really read much about it. It seems like a really interesting uh uh topic for an evolutionary psychologist to dive into. I got contacted by a grad student.

Um shout out to you. Um I'm not gonna mention names but but somebody's thinking about it um uh who's uh who's at Fullerton um oh cool with Patrick Durkey and it's a wonderful uh idea and um um she reached out to contact me about if she knew if anybody was doing it i'm not aware but yeah the an adaptationist approach to time perception is fantastic so please keep I would love I would love to read that paper. So please write it so I can read it. So yes you're please keep going. Uh you're you're on the right track for for what my two cents is worth. So yes it would be about time. Oh my goodness. You are a professional comedian. I sure am. Uh as George Carlin once put it. Um that was one of his bits, right?

I'm a professional comedian and like the people in your around the water cooler, which I don't think it's true anymore. But yeah. Yeah. Um okay. Um Patreon, uh, go check out the show notes. Lots of cool up the there should be a lot of good show notes for for this episode. Lauren talked about a bunch of uh her work, a book, and other other such things. I don't know, do you have been anything else? Uh no. I mean leave us a kind review. Say nice things about us on social media. Tell a friend about this uh wonderful podcast. Uh anything you can do to support us so that you can keep hearing our wonderful voices in your ear. I think we really should get t shirts. Yeah? Yeah. But really just I would, especially if it was a black t-shirt with like subtle font

um where it was just the podcast. Just the podcast? Yeah, just in parentheses. Although we'd have to make sure that there isn't a podcast that's just called the podcast in parentheses. Um, but yeah. Maybe that's too subtle. I don't know. What do you think? I think that might be a bit subtle. It's weird though, because if it was too big and in your face, I'd probably be too be embarrassed to wear it. Yeah, I know. I kind of don't want to wear an evolutionary psychology shirt because I'm gonna walk by and be like, oh, that guy's a jerk because he's an evolutionary psychologist. So yeah, I don't want to get eggs thrown at me and tomatoes. In the in the pro in the professional literature of stigma, it's a remote, it's a um it's a

concealable stigma of being an evolutionary psychologist. Unlike, you know, being a member of a racial minority or a physical deformity, for example, where you can't remove it. Being a kind of scientist is not something people can readily perceive to the extent that it's a stigmatized thing among some people. That is true. And so we gotta, you know, be patient about revealing it in the right setting. And this is a safe space for us to um to reveal our stigmatized identity. Maybe this is just a long walk to face tattoos, but I don't know. Maybe maybe we shouldn't. Yeah, that'd be pretty cool. Okay, yeah. Uh that's an adjective I wouldn't use, but it's it's an idea. Yeah. I mean it would be a very costly signal.

We'd only get the most loyal, devoted evolutionary psychologists. Somebody does face tattoo uh work. Is it Louis? I think is it Louie? I think there was a Dan Fessler paper on commitment signals and tattoos was I don't know if there's more work on this, but I remember reading. If you see somebody with a face tattoo about something, you know that they're committed to that thing. I think there's stuff on like prisons where they have prison tattoos to like signal what gang you're a part of. Yeah. I don't know. But yeah, no, uh I think we should do face tattoos. I think um I think we'll we'll uh we'll filter out all the uncommitted evolutionary psychologists. Like myself. Yeah, exactly. Would you get a would you really get a face tattoo, David?

Are we joking or serious? Seriously no. Joking is uh so if somebody wants to start a club where you demand a face tattoo, you might be a member of that club yourself. So um yeah. Yeah. All right. Anyway. All right. I don't know how we got there. It was, it was, yeah. Enjoy your week. Um, enjoy the summer. And um, you know, uh, I think a good lesson from this conversation is if somebody if you disagree with somebody or think somebody is wrong, think about how they might be explaining something different than what you're trying to explain. Yep. Great, beautiful advice. Go forth and use it. Take care. See you next week. See you next week. Pontum Pon P, and Canton, Pontum, Pantum, Pont, Pontum, Pontum, Canton,