Category Archives: Philosophy of Science

Biological landscapes, surfaces, and morphospaces: what are they good for?


Metaphors are rampant in both everyday language and in science, and while they are inevitable, readers of this blog also know by now that I’m rather skeptical of their widespread use, both in professional publications and, especially, when addressing the general public. (See here, here, here, and here.) One such problematic metaphor is that of so-called adaptive landscapes, or surfaces, in evolutionary biology, something on which I did a fair amount of research when I was running a laboratory of ecology and evolutionary biology.

My detailed criticism of the way the landscape metaphor has sometimes warped biologists’ thinking is detailed in a chapter that was published back in 2012 as part of a very interesting collection entitled The Adaptive Landscape in Evolutionary Biology, edited by Erik Svensson and Ryan Calsbeek for Oxford University Press. As it often happens, mine was the lone contribution from the token skeptic…

Few metaphors in biology are more enduring than the idea of adaptive landscapes, originally proposed by Sewall Wright in 1932 as a way to visually present to an audience of typically non-mathematically savvy biologists his ideas about the relative role of natural selection and genetic drift in the course of evolution. The metaphor was born troubled, not the least reason for which is the fact that Wright presented different diagrams in his original paper that simply cannot refer to the same concept and are therefore hard to reconcile with each other. For instance, in some usages, the landscape’s non-fitness axes represent combinations of individual genotypes, while in other usages the points on the diagram represent gene or genotypic frequencies, and so are actually populations, not individuals.

typical (hypothetical) fitness landscape

Things got even more confusing after the landscape metaphor began to play an extended role within the Modern Synthesis in evolutionary biology and was appropriated by G.G. Simpson to further his project of reconciling macro- and micro-evolution, i.e. to reduce paleontology to population genetics. This time the non-fitness axes of the landscape were phenotypic traits, not genetic measures at all. How one would then translate from one landscape to another (i.e., genes to morphologies) is entirely unaddressed in the literature, except for vague motions to an ill-defined and very rarely calculated “genotype-phenotype mapping function.”

These are serious issues, if we wish to use the landscape metaphor as a unified key to an integrated treatment of genotypic and phenotypic evolution (as well as of micro- and macro-evolution). Without such unification evolutionary biology would be left in the awkward position of having two separate theories, one about genetic change, the other about phenotypic change, and no conceptual bridge to connect them.

To try to clarify things a bit, I went through the available literature and arrived at a typology of four different kinds of “landscapes” routinely used by biologists:

Fitness landscapes. These are the sort of entities originally introduced by Wright. The non-fitness dimensions are measures of genotypic diversity. The points on the landscape are typically population means, and the mathematical approach is rooted in population genetics. (see figure above)

Adaptive Landscapes. These are the non straightforward “generalizations” of fitness landscapes introduced by Simpson, where the non-fitness dimensions now are phenotypic traits. The points on the landscape are populations speciating in response to ecological pressures or even above-species level lineages (i.e., this is about macro-evolution). There is — with very special exceptions discussed in my paper — no known way to move from fitness to adaptive landscapes or vice versa, even though this is usually assumed by authors.

Fitness surfaces.These were introduced by Russell Lande and Steve Arnold back in the ‘80s to quantify the study of natural selection. Here phenotypic traits are plotted against a surrogate measure of fitness, and the landscapes are statistical estimates used in quantitative genetic modeling. The points on the landscape can be either individuals within a population or population means, in both cases belonging to a single species (i.e. this is about micro-evolution).

Morphospaces. These were first articulated by paleontologist David Raup in the mid-’60s, and differ dramatically from the other types for two reasons: (a) they do not have a fitness axis; and (b) their dimensions, while representing phenotypic (“morphological”) traits, are generated via a priori geometrical or mathematical models, i.e. they are not the result of observational measurements. They typically refer to across species (macro-evolutionary) differences, though they can be used for within-species work as well.

The first thing to note is that there are few actual biological examples of fitness landscapes (Wright-style) or Adaptive Landscapes (Simpson-style) available, while there is a good number of well understood examples of morphospaces (Raup-style) and particularly of adaptive surfaces (Lande–Arnold style). These differences are highly significant for my discussion of the metaphor. The paper summarizes examples — both conceptual and empirical — of each type of landscape and the complex, often barely sketched out, relationships among the different types.

When it comes to asking what the metaphor of landscapes in biology is for, we need to distinguish between the visual metaphor, which is necessarily low-dimensional, and the general idea that evolution takes place in some sort of hyper-dimensional space. Remember that Wright introduced the metaphor because his advisor suggested that a biological audience at a conference would be more receptive toward diagrams than toward a series of equations. But of course the diagrams are simply not necessary for the equations to do their work. More to the point, subsequent research by my former University of Tennessee colleague Sergey Gavrilets and his collaborators has shown in a rather dramatic fashion that the original (mathematical) models were far too simple and that the accompanying visual metaphor is therefore not just incomplete, but highly misleading. It turns out that hyper-dimensional dynamics are very much qualitatively different from the low-dimensional ones originally considered by Wright.

In a very important sense Wright’s metaphor of fitness landscapes was meant to have purely heuristic value, to aid biologists to think in general terms about how evolution takes place, not to actually provide a rigorous analysis of, or predictions about, the evolutionary process (it was left to the math to do that work). Seen from this perspective, fitness landscapes have been problematic for decades, generating research aimed at solving problems — like the so-called peak shift one (how do populations stuck on a local fitness peak “shift” to a higher one?) that do not actually exist as formulated, since high-dimensional landscapes don’t have “peaks” at all, as their topology is radically different.

There are problems also with the Lande-Arnold type landscapes (discussed in the paper), but here I want to shift to some good news: the actual usefulness of the fourth type of landscape: Raup-style morphospaces. One of the best examples was produced by Raup himself, with crucial follow-up by one of his graduate students, John Chamberlain. It is a study of potential ammonoid forms that puts the actual (i.e., not just heuristic) usefulness of morphospaces in stark contrast with the cases of fitness and adaptive landscapes. Ammonoids, of course, were beautiful shelled marine invertebrates that existed in a bewildering variety of forms for a good chunk of Earth’s biological history, and eventually went extinct 65 million years ago, together with the dinosaurs. This is going to be a bit technical, but stick with me, it will be worth it.

Raup explored a mathematical-geometrical space of ammonoid forms defined by two variables: W, the rate of expansion of the whorl of the shell; and D, the distance between the aperture of the shell and the coiling axis. Raup arrived at two simple equations that can be used to generate pretty much any shell morphology that could potentially count as “ammonoid-like,” including shells that — as far as we know — have never actually evolved in any ammonoid lineage. Raup then moved from theory to empirical data by plotting the frequency distribution of 405 actual ammonoid species in W/D space and immediately discovered two interesting things: first, the distribution had an obvious peak around 0.3 <D <0.4 and W near 2. Remember that this kind of peak is not a direct measure of fitness or adaptation, it is simply a reflection of the frequency of occurrence of certain forms rather than others. Second, the entire distribution of ammonoid forms was bounded by the W = 1/D hyperbola, meaning that few if any species crossed that boundary on the morphospace. The reason for this was immediately obvious: the 1/D line represents the limit in morphospace where whorls still overlap with one another. This means that for some reason very few ammonites ever evolved shells in which the whorls did not touch or overlap.

one-peak ammonoid morphospace

Raup’s initial findings were intriguing, but they were lacking a sustained functional analysis that would account for the actual distribution of forms in W/D space. Why one peak, and why located around those particular coordinates? Here is where things become interesting and the morphospace metaphor delivers much more than just heuristic value. John Chamberlain, a student of Raup, carried out experimental work to estimate the drag coefficient of the different types of ammonoid shells. His first result clarified why most actual species of ammonoids are found below the W=1/D hyperbola: shells with whorl overlap have a significantly lower drag coefficient, resulting in more efficiently swimming animals.

However, Chamberlain also found something more intriguing: the experimental data suggested that there should be two regions of the W/D morphospace corresponding to shells with maximum swimming efficiency, while Raup’s original frequency morphospace detected only one peak. It seemed that for some reason natural selection found one peak, but not the other. Four decades had to pass from Raup’s paper for the mystery of the second peak to be cleared up: the addition of 597 new species of ammonoids to the original database showed that indeed the second peak had also been occupied!, a rather spectacular case of confirmed prediction in evolutionary biology, not exactly a common occurrence, particularly in paleontology.

two-peak ammonoid morphospace, with representative shell forms

So, is the landscape metaphor in biology useful? It depends. The original versions, those introduced by Sewall Wright to make his math accessible to his colleagues, have been highly influential for decades, and yet have arguably channeled both empirical and theoretical research in unproductive directions, inventing problems (like the peak shift one) that arguably do not exist, at least not as formulated. The Lande-Arnold landscapes, which I have not discussed in this post, but do treat in the paper, have a mixed record. They have been heuristically useful for biologists interesting in quantifying natural selection in the field, but have also arguably brought about a degree of tunnel vision in both the theoretical and empirical study of that most important concept in modern evolutionary theory. Morphospaces, by contrast, have a very good record of being useful in terms of generating insight into the evolution of animal (and plant) form, and yet, they are actually the least commonly deployed version of the landscape idea in the technical literature. And because population genetics, with its mathematical approach, is considered more sophisticated than paleontology, things are unlikely to change in the near future. Unfortunately.


Book Club: Darwin’s Unfinished Symphony, 9, the arts

painting elephants“The logic of cultural evolution is identical to that of biological evolution, even if the details differ. New ideas, behaviors, or products are devised through diverse creative processes; these differ in their attractiveness, appeal, or utility, and as a result are differentially adopted, with newfangled variants superseding the obsolete,” says Kevin Laland at the beginning of the last chapter of his book, Darwin’s Unfinished Symphony: How Culture Made the Human Mind (p. 292). It is, therefore, with a brief commentary on this chapter, focusing on the arts, that I will end my series on Kevin’s fascinating view of the young field of cultural evolution.

That introductory gambit actually illustrates where Laland’s and my views begin to diverge, though perhaps not as sharply as each of our perspectives differs from standard evolutionary psychology. I see cultural evolution as linked to its biological counterpart in two ways: first, because it originated from it; and second, because there is a broad analogy between the two. But I fall far short of Kevin’s strong statement that the two are “identical” in logic. They are not, in my mind, fundamentally because biological evolution is propelled by the teolonomic process of natural selection. Cultural evolution, by contrast, is moved by the teleological process of human cognition. The two are not the same, and I maintain that no currently available theory of cultural evolution satisfactorily accounts for either the difference or the relationship between the two. (I hasten to say, which should not be necessary, that I see nothing magical or “mysterian” about this. At all. It is simply an open scientific question, like many others.)

The cultural evolution of art is, obviously, a huge topic, which would require a book of its own. So Laland takes a reasonable approach, focusing on aspects of the evolution of a particular art form: dance. As we shall see, he has lots of interesting things to say, but not much that would surprise a historian of dance, and definitely not much that originates specifically from a biological evolutionary perspective.

Before getting to dancing, Kevin briefly discusses another art form, acting, making the case that it crucially (though not solely, of course) depends on imitation, which he has argued previously, is an important evolved skill in the human lineage. Since dancing also fully deploys our ability to imitate others, and given that neither acting nor dancing presumably were direct targets of natural selection, he can then conclude that both art forms are in fact a byproduct of natural selection for the capacity to imitate.

“Imitation is no trivial matter. Few other animals are capable of motor imitation, and even those that do exhibit this form of learning cannot imitate with anything like the accuracy and precision of our species.” (p. 295)

Our ancestors at some point became able to solve what Laland calls the correspondence problem: imagine, for instance, that you are trying to learn how to use chopsticks. This is done by imitation, which requires translating the visual cues obtained by watching someone using chopsticks into the motor control that our own muscles have to exercise in order for us to be able to do the same. The sensory experiences involved in watching and doing are utterly different, and yet somehow our brain has to be capable to solve this correspondence problem.

Recent research has shown that human beings solve the correspondence problem by using neural networks similar to the so-called mirror neurons discovered in other primates. Kevin suggests that it is plausible that the mirror neuron or equivalent network has been selected precisely to facilitate imitation, that this particular skill has been much more refined by natural selection in humans, and that one of its most astounding and least recognized byproducts is our ability to do and appreciate art — not just movies and dancing, but also painting, sculpture, theater, music, and even computer gaming.

Kevin doesn’t think much of the alleged ability of other animals to produce art, and I think he is right:

“The motor control that allows humans to produce artistic works and performances spontaneously is a capability that no other animal shares. … The claim that chimpanzees [for instance] are artists, in any meaningful sense, is greeted with skepticism by animal behaviorists and art scholars alike.” (p. 299)

He also thoroughly debunks the idea that elephants in Thailand can paint, referring instead to evidence that the animals have been well trained to respond to subtle cues provided by their handlers, through the simple device of tugging at the elephant’s ears.

What about dancing? Here again the suggestion has been made that some animals do it, though as Laland points out, much of the answer depends on how one defines dancing, and what counts as instances of the art form. Regardless, and more importantly, he highlights the fact that the only good candidates for dancing animals are, not surprisingly, those species that are most capable of imitation. (The same considerations apply to singing animals, by the way.)

“The most transparent connection between dance and imitation … will be readily apparent to just about anyone who has ever taken or observed a dance lesson; that is, dance sequences are typically learned through imitation. … It is no coincidence that dance rehearsal studios around the world almost always have large mirrors along one wall. These allow the learner to flit rapidly between observing the movements of the instructor or choreographer and observing their own performance.” (p. 307-308)

The other thing that makes for a good dancer is the ability to learn a long sequence of actions, and Kevin has shown before in the book that this type of learning is very difficult in a non-social setting, because it pretty much requires teachers. So the evolution of teaching, which he has discussed previously as a crucial component of early cultural evolution in the human lineage, is also a prerequisite for the wonderful byproduct of our biology that we call dance.

Much of the remainder of the chapter concerns itself with the history of dancing, and it is there, I think, that the limits of insights from biological evolution are most painfully clear. Laland asks whether dance could be said to have evolved in any “rigorous” sense of the term, by which he means to ask whether dance as a “system” possesses the characteristics that any evolving system has to possess: variation, differential fitness, and inheritance. But it should be obvious that while the evolution of dance does display all three, we have essentially no account whatsoever of the second element, differential fitness. This deficiency, I argue, at the moment makes cultural evolution into a tautological theory of the kind that Karl Popper (mistakenly) thought the theory of biological evolution was. While Darwin and his successors solved that problem in the biological case, neither evolutionary psychologists nor the more sophisticated approach advocated by Kevin and colleagues has been able to solve it in the case of cultural evolution.

Kevin presents readers with a number of examples showing that there is much variation among the world’s dances, and that this variation is culturally inherited via imitation (though, crucially, the equivalent of biological “mutation” and “recombination” result from conscious or unconscious human decision making, which follows, and indeed also shapes, human aesthetic judgments).

We therefore learn about European sword dances, which apparently first appeared in ancient Greece and were brought to Britain by invading Danes and Vikings. Waltz is Kevin’s favorite example of cultural fitness, as he calls it. And yet, here the limits of his approach are stark, in his own words:

“Relative to other dances in the late eighteenth century, the waltz could be said to possess high ‘cultural fitness,’ which really means little more than it was unusually appealing and as a result increased readily in frequency.” (p. 311)

Right. And that, right there, is the problem. Strip the fancy wording and we are left with: “waltz (at that particular time, in that particular culture) had high fitness because it had high fitness.” That’s the sort of vicious circularity that rightly annoyed Popper. You don’t find it in evolutionary biology because a separate discipline comes to the rescue: functional ecology. It is the latter that allows us to make predictions about which organismal traits are going to be adaptive in one environment or another, given the organism’s anatomy, physiology, and ecology (and given the laws of physics and chemistry). We don’t just say that natural selection favors the fit, and then immediately turn around and define the fit as those that are favored by natural selection. But that’s pretty much what cultural evolutionary theory does, at the moment, and it shares this limitation with other approaches, such as evolutionary psychology and memetics, though for different reasons that are specific to each approach.

To be fair, Kevin does attempt to sketch an elementary functional ecology of dance. For instance we are told that waltz was attractive in late 18th century Europe, in part because of the “dance’s intoxicating swirling, and the dangerously intimate contact between male and female were a major draw.”

Okay, but presumably swirling and close male and female contact have always been intoxicating. So why late 18th century Europe? Moreover, I don’t know much about the history of dance as an academic field of study, but I doubt anything Laland says in this chapter will come as a surprise to historians of dance — and I mean everything, from the genealogical patterns of evolution by imitation to the “mutations” introduced by different cultures at different times, to ad hoc explanations (which may even be true) like the intoxicating effect of a particular dance. In other words, invoking Darwin here does no work at all, or almost.

I don’t have a better alternative. I chose Kevin’s book precisely because I think it is one of the best in the field of cultural evolution, reflecting the incredible vigor and ingenuity of Kevin as a principal investigator, not to mention the many collaborators he gives due credit throughout the book. It’s all tantalizing and very, very interesting. But it falls far short of a comprehensive theory of cultural evolution. It is good to learn about the importance of social learning, of teaching, and of imitation throughout the history of hominins. It is fascinating to think that such biological history has a lot to do with the subsequent shaping of cultural evolution. But we are still nowhere near giving a decent scientific account of sword dancing, waltz, flamenco, polka, jitterbug, or rock’n’roll. Not to mention Michelangelo, Picasso, and de Kooning; or Mozart, Beethoven and Tchaikovsky; or Homer, Dante, and Shakespeare. And so on and so forth, encompassing the bewildering variety of manifestations of what we call culture.


And now for something completely different: our next book will be Early Socratic Dialogues, edited by Trevor J. Saunders, Penguin 2005. I figured that this is a blog called Footnotes to Plato, and yet we have hardly talked about Plato. So, here we go…

Book Club: Darwin’s Unfinished Symphony, 8, foundations of cooperation

reciprocal altruismThink about the complexities involved in allowing you to do something that nowadays is fairly normal: getting on a plane and fly to another city, across an ocean. It’s not just the sophisticated machinery, ground transportation, the airports, and so forth. It’s the people. Accomplishing such a feat requires the coordinated cooperation of a large number of people who don’t know each other, and don’t know you or why you wish to get on that plane in the first place. This observation sets the stage for the next to the last chapter of Kevin Laland’s Darwin’s Unfinished Symphony: How Culture Made the Human Mind, which we have been discussing for a while now.

The first point Kevin makes in this chapter (n. 11 in the book) is that conventional evolutionary explanations, such as kin selection and other gene-based explanations are insufficient to account for the degree and sophistication of cooperative activities that have characterized human civilization ever since the agricultural revolution. A fully formed theory of cultural evolution is needed, to draw the outlines of which, of course, is Kevin’s goal. Obviously, the idea is not that cultural evolution is independent from its biological counterpart, but rather that it is a novel mode of evolutionary change that resulted from the particular path of biological evolution that hominins happen to have taken.

Two of the factors that make large-scale human cooperation possible are the ability to teach others, and language, which Laland has already argued itself evolved to facilitate teaching. A third factor was the origin of social norms. These specify how individuals are expected to behave within a group, including how to treat individuals who violate norms. Crucially, norms also make possible for people to identify with a particular group, as abiding by its norms carries privileges for in-group members.

Moreover, humans are pretty much the only animals capable of trading goods (there are a few alleged cases in other primates, but they are disputed), and certainly the only ones that arrived at that convenient abstraction we call money. This level of sophistication requires language, and it is both facilitated and made necessary by the existence of division of labor, something that evolved to a high degree of sophistication, again, after the agricultural revolution, which made possible the existence of large and stable groups of humans.

All of this coordination is beneficial thanks to the advantage provided to individuals by indirect reciprocity: I do something for you, you do something for someone else, and at some point down the line another person that has been benefiting from in-group membership does something for me. Like allowing me to safely cross the Atlantic to get from New York to Rome. Repeated bouts of indirect reciprocity require gossip, so that people have a sense of who they can trust and who to stay away from. Needless to say, gossiping, and hence the building and destroying of social reputations, is not possible, again, without language.

Language, in turn, also evolves, quickly generating local dialects. Dialects then rapidly become a mark of local membership, a quick heuristic to tell apart in- from out-group members. They increase within-group cooperation, and likely across-group conflict, which sets the stage for group selection at the cultural level:

“Cultural processes generate plenty of variation among human groups for natural selection to act upon. Extensive data now demonstrate that the differences between human societies result far more from cultural rather than genetic variation. … Symbolic group marker systems, such as rituals, dances, songs, languages, dress, and flags, make it considerably easier for cultures to maintain their identities and to resist imported cultural traits from immigrants, than it is for local gene pools to maintain their identity by resisting gene flow.” (p. 283)

This is something important to keep in mind, as it is intuitive to say that cultures change more rapidly than genes. While this is true if we are talking about mutations (which are, indeed, rare), it is not the case once we consider gene flow and genetic recombination, which happen far more frequently, as Kevin points out, than some types of cultural change.

Laland also remarks on the widespread existence of practices that synchronize the behavior of individuals, like group dancing, or military marches. These activities result in the simultaneous release of endorphins, which in turn promotes within-group bonding. The broader point is that humans evolved a psychology of group behavior that is entirely unknown in other animals, and that cannot be explained on the basis of standard genetic models of evolution. Pace the evolutionary psychologists, of course, for whom we have seen Laland has relatively little patience.

We are reaching the end of this series of posts on Darwin’s Unfinished Symphony. The next and last installment will focus on the cultural evolutionary origin and significance of art.

Why machine-information metaphors are bad for science education, part II: the search for new metaphors

metaphor vs simileWhile discussing some sections of a paper I wrote with Maarten Boudry, we have seen a number of reasons why using machine-information metaphors is bad for science education. As I pointed out before, the full paper also devotes quite a bit of space to arguing that those metaphors haven’t been particularly good in actual scientific research. One of the fascinating things to watch after I posted the first part of this commentary was the number of people who vehemently defended the “biological organisms are machines” take, both here on the blog and on my Twitter feed. It’s like here we are, in the second decade of the 21st century, and there are still a lot of Cartesians around, who have apparently never heard of David Hume. Oh well.

In the conclusion of this two-part series I am going to focus on the last section of my paper with Maarten, where we discuss the search for alternative metaphors, and in the end (spoiler alert!) suggest that the best thing to do at this point is just to describe things as they are, staying as clear as possible of metaphorical language. And when one really cannot avoid it, then use multiple metaphors and be very clear on the limits of their use. Let’s take a look.

In their classic work on metaphors, Lakoff and Johnson argue that the basic function of metaphorical concepts is to structure a new kind of experience in terms of a more familiar and delineated experience. In science as well as in everyday language, metaphors highlight particular aspects of whatever it is we are trying to grasp, but they will inevitably distort others. For example, the image of the “tree of life,” with new species branching off as budding twigs and extinct species as dead branches, is an instructive approximation of the relations of evolutionary descent. However, it can also foster misconceptions about “progress” in evolution, or lead to a simplistic conception of speciation events, or to a downplay of horizontal gene transfer and reticulate (i.e., by interspecies hybridization) speciation events. To give one more example, in physical chemistry the model of the atom as a miniature solar system, with electrons orbiting the nucleus as planets, though still having wide public appeal, is fundamentally inaccurate.

Of course, no metaphor will do its job perfectly, but it is crucial to realize, as Lakoff and Johnson have shown, that the widespread deployment of a particular metaphor can have a feedback effect on the way we perceive things, not just how we present them to others. In the examples discussed in my paper with Maarten, the lure of machine-information metaphors in the history of biology has invited scientists to think of genomes as “blueprints” for organisms, written in the four-letter alphabet of DNA and readable in a manner analogous to a computer code. But as we argue, the machine-information conception of living systems has led both the public and the scientific community astray.

In response to this problem, some scientists and science educators have proposed several alternative and improved metaphors to characterize the relationship between genotype and phenotype. Biologist Patrick Bateson, for instance, was probably the first to compare the DNA sequence of living organisms with a recipe for a cake. The idea of a genetic recipe has several advantages over the blueprint metaphor, the most important being that it takes into account pleiotropy (one gene affecting more than one trait) and epistasis (gene–gene interactions). As a consequence, the simple picture of a one-to-one (or close to) correspondence between particular genes and phenotypic traits is abandoned, which becomes clear when one considers that there is no way to locate particular ingredients in individual crumbs of a cake. Accordingly, there is no possibility of reverse-engineering the end product to the set of procedures (the “recipe”) that made the final product possible. This has important consequences not just for science education, but for research agendas, as the idea of ‘‘reverse engineering’’ is commonly invoked everywhere from genomic studies to the understanding of the brain.

Of course, if carried too far, the recipe metaphor can in turn be quite misleading. To get the desired result, a cook has to lump together different ingredients in the correct proportions and follow a set of instructions for handling the dough and preparing the oven. But actual developmental encoding in living organisms is an enormously more complex and very different sort of procedure, which is also highly dependent on epigenetic factors and unpredictable vagaries of the external environment. The expression of specific genes in the course of development resembles nothing like the way a cook handles the ingredients of a recipe. Living organisms are also highly differentiated in a number of functional parts or components (cell types, tissues, etc.), in contrast with the homogenous cake that comes out of the oven. Moreover, the genome is not written in anything like a ‘‘language,’’ as in the case of a recipe, and it certainly does not contain a description of the desired end product in any meaningful sense of the word ‘‘description.’’

Condit and colleagues have discussed the recipe metaphor as an alternative to talk of blueprints, pointing out that it was adopted ‘‘with surprising swiftness’’ by science popularizers and the media in the 1990s. However, they also remark that, as a new ‘‘master metaphor’’ to capture the relationship between genotype and phenotype, the image of a recipe for a cake has little to recommend either. For example, evoking recipes can invite people to think of the genome as a step-by-step manual that describes ‘‘how to make a human,’’ in that sense falling into the same trap as the idea of a blueprint.

That being said, if contrasted with the blueprint metaphor, the recipe metaphor conveys the point about lack of one-to-one correspondence between genes and phenotypes very well, and hence it highlights an important fact about development and what biologists call the Genotype => Phenotype map. If the recipe metaphor is used within this restricted context, for example in explicit contrast with the characteristics of a blueprint, it is immediately clear what are the salient points of connection with living systems, and people are less likely to be misled by stretching the metaphor beyond usefulness. If the recipe metaphor is presented as an alternative to the blueprint, however, it is bound to mislead people no less than its rival.

The same point applies to other interesting metaphors that have been proposed in this context, for example Lewis Wolpert’s comparison of early embryonic development with the Japanese art of origami. The analogy highlights the circuitous step-by-step development of the early embryo, but of course in a piece of origami art the structure is imposed top-down from an intelligent agent, whereas the functional differentiation in the embryo is regulated bottom-up by a complex interaction between genes and environment. Moreover, origami simply fold to yield the final product, which in a very real sense is already there from the beginning. This is definitely not the way embryos develop, with their ability to respond to local and external environmental fluctuations.

The general problem that we have been discussing seems to us to be not just that one kind of metaphor or another is woefully inadequate to conceptualize biological organisms and their evolution. It is that it simply does not seem to be possible to come up with a metaphor that is cogent and appropriate beyond a very limited conceptual space. Although some of the alternatives are more accurate than the blueprint metaphor (in some respects), Maarten and I certainly have not found one that we would recommend as a replacement. Should we therefore try to avoid the use of metaphors in biological teaching and research altogether? Or do we simply expect too much from metaphors in science and education?

Analogical and metaphorical thinking is widespread among human beings, although of course different cultures and historical moments inspire people to use different metaphors. After all, a metaphor is an attempt to make sense of novel concepts by pairing them with known ideas to increase our overall understanding. Metaphorical thinking is therefore part of our language, and language is inextricably connected to our thinking, but to put it as Wittgenstein did: ‘‘It is, in most cases, impossible to show an exact point where an analogy starts to mislead us.’’ Yet a great part of doing philosophy consists precisely in clarifying our language in an attempt to advance our thinking. To quote Wittgenstein again: ‘‘Philosophy is a battle against the bewitchment of our intelligence by means of our language.’’ To complicate matters further, there is emerging empirical evidence that the human brain processes metaphors in a specific fashion: research on Alzheimer’s patients, for instance (see ref. in the paper), found that impairment of the brain’s ‘‘executive’’ function, associated with the prefrontal cortex, leads to poor understanding of novel metaphors (while, interestingly, comprehension of familiar metaphors is unaffected). Metaphorical thinking seems to be a biologically entrenched functional mode of our brains, and may therefore be hard to avoid altogether.

Both science and philosophy have made ample use of metaphorical and analogical thinking, sometimes with spectacularly positive results, at other times more questionably so. Nonetheless, it seems that nowhere is metaphorical thinking so entrenched — and so potentially misleading — as in biology. Given the maturity of biology as a science, and considering that it deals with objects whose nature is not as alien to our daily experience as, say, those of quantum physics, Maarten and I do not actually see any good reason for clinging onto outdated metaphors in biological education and research for characterizing living organisms, their genomes and their means of development. Taking into account the fact that the machine information metaphors have been grist to the mill of ID creationism, fostering design intuitions and other misconceptions about living systems, we think it is time to dispense with them altogether. Still, we are also not as naive as to expect that this advice will be followed by scientists and science educators any time soon, precisely because the machine/information metaphor is so entrenched in biology education. What to do then? We propose two approaches, one for science educators, the other for practicing scientists.

In science education, talk of metaphorical thinking can be turned into a teaching moment. Students (and the public at large) would actually greatly benefit from explanations that contrast different metaphors with the express goal of highlighting the limitations intrinsic in metaphors and analogies. So, for instance, science educators and writers could talk about the human genome by introducing the blueprint metaphor, only to immediately point out why it does not capture much of what genomes and organisms are about; they could then proceed to familiarize their students and readers with alternative metaphors, say the recipe one, focusing on differences with the original metaphor while of course not neglecting to point out the (different) deficiencies of the new approach as well. The goal of this process would be to foster a cautious attitude about metaphorical thinking, as well as to develop a broader understanding of how unlike commonsense modern science really is. On the latter point, it is interesting to note, for instance, that a popular refrain among evolution or global warming deniers is that ‘‘simple commonsense’’ shows that the scientists are wrong, a position that ignores the proper weight of technical expertise in favor of a folk understanding of nature. It is therefore crucial that the public appreciates the limitations of common sense thinking about science.

There is an analogous teaching moment that can be brought to bear when research scientists engage in unbridled metaphorical thinking: we could refer to this as a philosophy appreciation moment. Scientists are notoriously insensitive to, or even downright dismissive of, considerations arising from the history and philosophy of their discipline, and often for good practical reasons: modern science is a highly specialized activity, where there is barely enough time to keep up with the overwhelming literature in one’s own narrow field of research, and certainly not enough incentive to indulge in historical readings or philosophical speculation. Nonetheless, historians and philosophers of science can easily show the pitfalls of metaphorical thinking (by using well-documented historical examples) and even get across to their colleagues some basic notions of philosophy (by analyzing the effects of particular metaphors on the development of specific lines of scientific inquiry). None of this will quickly amount to overcoming C.P. Snow’s infamous divide between ‘‘the two cultures,’’ but it may bring about better understanding and appreciation of philosophy by scientists, and perhaps even help science see new horizons that have been hitherto obscured by a superficially illuminating metaphor.

Why machine-information metaphors are bad for science education, part I: biological machines and intelligent design

bacterial flagellum

bacterial flagellum, as often represented in biology education

Genes are often described by biologists using metaphors derived from computational science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Predictably, modern proponents of Intelligent Design so-called theory, the latest incarnation of creationism, have exploited biologists’ use of the language of information and blueprints to make their spurious case, based on pseudoscientific concepts such as ‘‘irreducible complexity’’ and on flawed analogies between living cells and mechanical factories.

In reality, the living organism = machine analogy was criticized already by David Hume in his Dialogues Concerning Natural Religion. In line with Hume’s criticism, over the past several years a more nuanced and accurate understanding of what genes are and how they operate has emerged, ironically in part from the work of computational scientists who take biology, and in particular developmental biology, more seriously than some biologists seem to do.

My friend and collaborator Maarten Boudry and I have written an article several years ago in which we connect Hume’s original criticism of the living organism = machine analogy with the modern ID movement, and illustrate how the use of misleading and outdated metaphors in science can play into the hands of pseudoscientists. We argued that dropping the blueprint and similar metaphors will improve both the science of biology and its understanding by the general public.

We have discussed this topic twice in the last couple of years, once on the occasion of another paper with Maarten, on why machine metaphors in biology are misleading; more recently because of a paper I wrote about genes as blueprints; the current entry completes the trilogy, so to speak. In part I, here, I will present what Maarten and I had to say about the origin of machine-information metaphors in biology, as well as its questionable use in science education. In part II, next week, I’ll talk about the search for new and better metaphors in science and science education. Interested readers are referred to the original paper for references, as well as for a discussion of the misuse of machine-information metaphors in actual biological research (i.e., not just for educational purposes).

When delving into unknown territory, scientists have often naturally relied on their experiences in more familiar domains to make sense of what they encounter. In the early days of the scientific revolution, mechanical metaphors proved to be a powerful instrument to get a grip on new discoveries about the living world and the universe at large, and we can trace back the emergence of machine metaphors at least to the Middle Ages, when new achievements of technology had a profound cultural influence and captured the collective imagination. Against this background of technological innovation, it is not surprising that the pioneers of anatomy and physiology relied on the metaphor of the animal body as a complicated piece of machinery to make sense of their discoveries. The mechanical language provided a richness of meaning and allowed them to structure the new phenomena in terms of familiar experiences. For example, the image of the human heart as a pump with intricate mechanical components played an important role in William Harvey’s discoveries about blood circulation.

In the course of the 17th century, a new philosophy of nature became prominent that developed a conception of the universe in purely mechanical terms. According to this mechanical philosophy, which was developed by thinkers like Rene` Descartes, Pierre Gassendi and Robert Boyle, the phenomena of nature can be understood purely in terms of mechanical interactions of inert matter. This mechanization of nature proved an important driving force behind the Scientific Revolution, and at the end of the 17th century culminated in Newton’s theory of motion. Newton’s description of planetary orbits following the fixed laws of gravity conveyed an image of a clockwork universe set in motion by an intelligent First Cause. In fact, that was exactly how Newton conceived the universe and its relation to the Creator. For Newton and many of his contemporaries, the importance of the mechanical conception of nature was greater than the mere term ‘metaphor’ would suggest, as the development of mechanistic philosophy was itself largely inspired by religious motivations; indeed, the very employment of machine metaphors invited theological speculation.

In the second part of the 17th century, the mechanical pictures of living organisms and of the cosmos at large converged into an intellectual tradition where theology and science were intimately intertwined: natural theology. The most famous representative of this tradition was William Paley, whose work Natural Theology, of Evidence of Existence and Attributes of the Deity, Collected from the Appearances of Nature (1802) made a deep impression on the young Charles Darwin. As the title of the book makes clear, Paley and the natural theologians conceived of Nature as a complicated machinery of intricate wheels within wheels, in which every organism has its proper place and is adapted to its environment. According to Paley, the contrivance and usefulness of parts exhibited by living organisms attests to the intelligence and providence of a benevolent Creator. This so-called ‘design argument’ already had a long intellectual pedigree, dating back to Plato, Cicero and Thomas Aquinas, but its most famous formulation is found in the first chapter of Natural Theology, in which Paley famously relies on the analogy between living organisms and a pocket watch to support his design inference.

While Darwin was the one who gave the most decisive blow to the design argument by suggesting a natural explanation for adaptive complexity in the living world, many philosophers would agree that David Hume foreshadowed its demise, by exposing several problems with the central analogy. In his Dialogues Concerning Natural Religion (1779), which actually predates Paley’s magnum opus by more than 50 years, we find a discussion of the design argument among Philo, the skeptical character that voices Hume’s ideas, Demea, the orthodox religious believer, and Cleanthes, the advocate of natural theology.

After Cleanthes has set out the design argument in terms foreshadowing Paley’s analogy of the watch, Philo objects that it is dangerous to derive conclusions about the whole of the universe on the basis of a spurious analogy with one of its parts. Given that our experience with design is limited to human artifacts only, we have to proceed with great caution, and it would be presumptuous to take so minute and select a principle as the human mind as the model for the origin of the whole universe. Hume realized that, at least in some cases, appearances of intelligent design can be deceptive.

In contemplating that ‘‘many worlds might have been botched and bungled, throughout an eternity, ere this system was struck out’’, Hume even comes close to Darwin’s crucial insight about the power of natural selection. Although Hume does not deny that we can discern similarities between nature and human artifacts, he warns us that the analogy is also defective in several respects. And if the effects are not sufficiently similar, conclusions about similar causes are premature. To illustrate this, Philo proposes another possible cosmogony on the basis of the analogy between the world and an animal:

“A continual circulation of matter in [the universe] produces no disorder; a continual waste in every part is incessantly repaired: The closest sympathy is perceived throughout the entire system: And each part or member, in performing its proper offices, operates both to its own preservation and to that of the whole. The world, therefore, I infer, is an animal.” (Hume 1779, p. 39)

In The Origin of Species, Charles Darwin (1859) finally proposed a natural explanation for the phenomenon that inspired Paley but failed to convince Hume. Although the design argument is still of interest to philosophers and historians of science, it has been widely discarded in the scientific community. However, the analogy on which Paley based his inference seems to be alive and well, not only in the minds of creationists and ID proponents, but also in the writings of science popularizers and educators. Many scientists have actually argued that Paley at least offered an incisive formulation of the problem as there is indeed a hard-to-shake intuition of contrivance and intelligent design in nature. As one of the most ardent defenders and popularizers of evolutionary theory, Richard Dawkins, put it, ‘‘Biology is the study of complicated things that give the appearance of having been designed for a purpose.” Adaptive complexity, then, is still regarded as something that requires a special explanation.

In textbooks, science educators have presented the comparison of living organisms and man-made machines not just as a superficial analogy, but carrying it out to a considerable level of detail. For example, the cell has been described as a miniature factory, complete with assembly lines, messengers, transport vehicles, etc. Consider the following quote from Bruce Alberts, molecular biologist, and former president of the National Academy of Sciences:

“The entire cell can be viewed as a factory that contains an elaborate network of interlocking assembly lines, each of which is composed of a set of large protein machines. … Why do we call the large protein assemblies that underlie cell function protein machines? Precisely because, like machines invented by humans to deal efficiently with the macroscopic world, these protein assemblies contain highly coordinated moving parts. Given the ubiquity of protein machines in biology, we should be seriously attempting a comparative analysis of all of the known machines, with the aim of classifying them into types and deriving some general principles for future analyses. Some of the methodologies that have been derived by the engineers who analyze the machines of our common experience are likely to be relevant.” (Alberts 1998, p. 291)

Creationists and their modern heirs of the Intelligent Design movement have been eager to exploit mechanical metaphors for their own purposes. For example, Bruce Alberts’ description of the living cell as a factory has been approvingly quoted by both Michael Behe and William Dembski, two leading figures in the ID movement. For ID proponents, of course, these are not metaphors at all, but literal descriptions of the living world, arching back to Newton’s conception of the Universe as a clock-like device made by the Creator. The very fact that scientists rely on mechanical analogies to make sense of living systems, while disclaiming any literal interpretation, strengthens creationists in their misconception that scientists are ”blinded” by a naturalistic prejudice. In the creationist textbook Of Pandas and People, which has been proposed by ID advocates as an alternative to standard biology textbooks in high school, we read that ‘‘Intelligent design […] locates the origin of new organisms in an immaterial cause: in a blueprint, a plan, a pattern, devised by an intelligent agent’’ (Davis et al. 1993, p. 14).

The analogy between living organisms and man-made machines has proven a persuasive rhetorical tool of the ID movement. In fact, for all the technical lingo and mathematical “demonstrations,” in much of their public presentations it is clear that ID theorists actually expect the analogies to do the argumentative work for them. In Darwin’s Black Box, Behe takes Alberts’ machine analogy to its extreme, describing the living cell as a complicated factory containing cargo-delivery systems, scanner machines, transportation systems and a library full of blueprints. Here is a typical instance of Behe’s reasoning:

“In the main area [cytoplasm] are many machines and machine parts; nuts, bolts, and wires float freely about. In this section reside many copies of what are called master machines [ribosomes], whose job it is to make other machines. They do this by reading the punch holes in a blueprint [DNA], grabbing nuts, bolts, and other parts that are floating by, and mechanically assembling the machine piece by piece.” (Behe 2006, pp. 104–5)

Behe’s favorite model of biochemical systems is a mechanical mousetrap, the familiar variant consisting of a wooden platform, a metal hammer, a spring etc. According to Behe, if any one of these components is missing, the mousetrap is no longer able to catch mice. He has termed this interlocking of parts ‘‘irreducible complexity’’ and thinks it characterizes typical biochemical systems. n other words, the mousetrap is to Behe what the well-designed pocket watch was for Paley. But whereas Paley can be excused on the grounds of the state of scientific knowledge in the 18th century, for Behe the situation is a little different. Modern biochemistry, nota bene Behe’s own discipline, has revealed that biochemical systems are not like mechanical artifacts at all. Moreover, even biological systems that are irreducibly complex under Behe’s definition pose no problem for evolution by natural selection, as has been in detail by people like cell biologist Ken Miller.

ID proponents have buttressed their analogies between living systems and mechanical contraptions with a lot of visual rhetoric as well. The flagellum of the bacterium E. coli, the hallmark of the ID movement, has been represented as a full-fledged outboard rotary motor, with a stator, drive shaft, fuel supply, etc.. It features on the cover of Dembski’s book No Free Lunch, and has been used numerous times in presentations and online articles. The idea seems to be that if it looks designed, it has to be designed. But as Mark Perakh has documented in a paper published in 2008, ID supporters invariably use idealized and heavily stylized representations of the flagellum, in order to make it more resemble a man-made contraption. Another striking example of this visual rhetoric is a video by Discovery Institute president Stephen C. Meyer, which presents a computer-simulated — and again heavily stylized — journey inside the cell, and describes the biochemical processes in terms of ‘‘digital characters in a machine code,’’ ‘‘information-recognition devices,’’ and ‘‘mechanical assembly lines.’’ Meyer commented that evolutionists will have a hard time now dissuading the public from the fact that ‘‘the evidence for design literally unfolds before them.’’

Of course, the mere observation that creationists have seized on machine metaphors in biology does not suffice to demonstrate that these metaphors do not make scientific sense. However, the fact that they tend to do so systematically, using full-length quotes from respectable scientists, should make us weary of the possible dangers of misleading metaphors. If the rhetoric of the ID movement is demonstrably based on these mechanical analogies, it can be instructive to reexamine their scientific merits. In the paper, Maarten and I argue that the machine-information analogy has indeed influenced the way scientists themselves think about biological structure, function, and evolution. By analyzing the consequences of and reactions to this analogy in actual biological research, we show that its scientific merits are very weak, and that its place in modern biology has become questionable. What then? Stay tuned for part II, on the search for new and better metaphors…

Book Club: Darwin’s Unfinished Symphony, 7, the dawn of civilization

Egyptian agricultureHomo sapiens is the only species on planet Earth to have experienced three phases of evolution: the standard biological one, driven by mutation and natural selection; gene-culture coevolution; and now the period of evolution driven primarily by culture. This is how chapter 10 of Kevin Laland’s Darwin’s Unfinished Symphony: How Culture Made the Human Mind, begins the transition to the author’s discussion of that very last, novel, and crucial phase. (More entries in this ongoing series here.)

It’s an obviously crucial topic for a variety of reasons. First off, to help explain why on earth we evolved such large and metabolically expensive brains. Keep in mind that the human brain accounts for only 2% of our total body weight, and yet it consumes a whopping 20% of our daily caloric intake. (It’s unfortunate that thinking harder doesn’t lead to weight loss…). Second, as Kevin has documented in the previous chapters of the book, it is our capacity for social learning (and teaching) that accounts for the incredible success of our species, as the third mode of evolution is what has made possible for us to build giant cities, go to the Moon, and waste our existence on social media.

Kevin begins by addressing a related question: why did it take so long for our species to develop complex civilizations, while hunter-gatherer societies still today have very limited technology and simple cultures? The likely answer has to do with the severe limitations imposed by a hunter-gatherer lifestyle. To begin with, of course, hunter-gatherers have to be constantly on the move, changing base location once the local resources are depleted. This means that it is impossible to settle down long enough to develop a large population size and the division of labor that foster new technological developments. And even if some new technology were to be developed, it would have to be of limited size and complexity, again because the entire population has to pick up and move every few weeks or so.

Similarly, in hunter-gatherer societies the birthrate is typically low, with new pregnancies well separated in time, as a human female cannot carry and care for many small children when the group is constantly on the move. Small population size and temporary abodes also means no accumulation of wealth of the kind that makes division of labor possible, leading in turn to the origin of specialized classes of workers that can rapidly accumulate specific technical knowledge over few generations.

“This helps us understand why hunter-gather technology was only slowly changing for such a long time, and also why, even today, many small-scale societies possess limited technology. Hunter-gatherers are effectively trapped in a vicious cycle that severely constrains their rate of cultural evolution.” (p. 248)

That’s also why the invention of agriculture, which took place multiple times after the last Ice Age, is tightly linked with the origin of complex human technological cultures. The reason agriculture did not originate earlier is because the conditions following that Ice Age, about 11,500 years ago, have actually been the most favorable — climatically speaking — for such an event over the last two million years of hominid evolution. And before then our ancestors simply did not have the required brain power and ability to communicate through language.

Plant and animal domestication of some sort preceded the full blown agriculture revolution, and the first plants to be domesticated were annuals, characterized by a rapid life cycle and hence easy to select artificially. These included peas, wheat, rye, barley, and maize. A new form of wheat, for instance, appeared around 9,600 BCE in the eastern Mediterranean region. Maize was farmed in southern Mexico around 9,000 years ago. Millet appeared in China between 10,300 and 8,700 years ago, rice around 9,000 years ago.

The invention of agriculture was not without its own problems. The more stable source of food led to population explosions, which in turn caused periodical famines. Indeed, the archeological data show that Europeans became shorter by about 7 cm. between 2,300 and as little as 400 years ago, because of poor nutrition.

As Laland points out, agriculture is a great example of niche construction on the part of human beings. The old idea, in ecology, that niches are “out there,” waiting to be filled by new species of organisms, has been questioned for some time now. Rather, living beings actively alter their environment, co-evolving with it, if you will. By far the most spectacular example in the history of earth is the fact that we have high levels of oxygen in our atmosphere, a byproduct of photosynthesis, an organic process that has made animal life possible in the first place.

Since agriculture was not an unqualified good, it is reasonable to ask how come the new mode of life largely and rapidly replaced the old hunter-gathering. Kevin offers two main reasons: first, agriculturalists simply outbred hunter-gatherers, because of the larger population size made possible by a sedentary lifestyle. Before the advent of agriculture the world’s human population had stabilized at around one million people. By the time of the Roman empire it was up to 60 millions.

The second factor was a wave of innovations triggered by agriculture. For instance, the invention of the wheel, which appeared simultaneously in Mesopotamia, Russia and central Europe around 5,500 years ago. The first organized religions also sprang in agricultural societies, with different cultures, predictably, worshiping gods related to agriculture: Inti, the sun-god of the Inca; Renenutet, the Egyptian god of harvest; Ashnan, the goddess of grain in Mesopotamia; and Ceres, the Roman goddess (counterpart of the Greek Demeter) who was credited with the discovery of wheat, the invention of ploughing, the yoking of oxen, and similar.

Here is another way to appreciate the difference between pre- and post-agriculture humanity:

“Prior to the advent of agriculture, each population would have possessed at most a few hundred types of artifacts, while today the inhabitants of New York are able to choose between 100 billion bar-coded items. … One recent estimate of the amount of information now stored on the internet is 1,200,000 terabytes.” (p. 263, 269)

Kevin points out that all this innovation has had dark sides, including environmental destruction, not just today, but throughout the last 10,000 years or so, with humanity being responsible for countless extinctions of other species; as well as of course the scale of war that technology has made possible; and the increasing inequality (compared to hunter-gatherer societies) among human beings themselves. It seems like both natural and cultural selection don’t really care about ethical considerations, although of course we should. But that’s another story.

Book Club: Darwin’s Unfinished Symphony, 6, gene-culture co-evolution

lactose tolerance

map of lactose tolerance

Kevin Laland’s book, Darwin’s Unfinished Symphony, which I have been discussing for several posts now, is basically one long argument in favor of the thesis that human evolution has been shaped by a feedback process involving a cultural drive mechanism initiated by natural selection, a mechanism that favored the acquisition of accurate and efficient copying. Chapter 9, to be examined here, is devoted to the classic approach of gene-culture co-evolution, the fundamental notion that cultural changes affect genetic evolution, and indeed that the more time passes the more human evolution is increasingly driven by culture and less so by biology (though biology always remains a fundamental constraint to be reckoned with):

“Genetic propensities, expressed throughout development, influence the cultural traits that are learned, while cultural knowledge, expressed in behavior and artifacts, spreads through populations and modifies how natural selection affects human populations in repeated, richly interwoven interactions.” (p. 217)

While the chapter begins with an interesting treatment of the phenomenon of right-handedness, the standard example of gene-culture co-evolution is, of course, lactose tolerance. In most humans, the ability to metabolize milk disappears in adulthood, as it was not pre-historically needed. But some populations have large numbers of adult individuals that retain a functional version of the gene coding for lactase activity, resulting in the phenotype of lactose tolerance. We now know that lactose tolerance evolved independently at least six times, and that this happened after the switch to agriculture following the last glaciation, making it a strong candidate for culture-driven genetic change in humans. Interestingly, mathematical models show that the rapidity of spread of the genetic trait depends on the fidelity of transmission of the cultural one: the more likely children of milk drinkers are to become milk drinkers themselves, the stronger the selection coefficient favoring the continued expression of the lactase gene into adulthood.

Several other traits have been shown to have evolved in a similar fashion in recent human history, including genes involved in skin pigmentation, salt retention, and heat stress, all obviously related to the sorts of climate changes experienced by human populations during their migrations. Unfortunately for us today, some of these strongly selected genes facilitate a highly efficient usage of food sources, as well as storage of energy into fats. Hence the trouble that many moderns are experiencing with obesity, leading to diabetes and heart problems, among other negative effects. Another fascinating example is the sarcomeric myosin gene MYH16, expressed mostly in the jawbone. A sizable chunk of the gene has been deleted, leading hominins to lose a lot of jaw muscles. This genetic event occurred at about the time we invented cooking, which made strong jaw muscles unnecessary (and likely metabolically expensive). And of course, many genes involved with brain development, particularly the neocortex, are now known to have undergone very strong positive selection in recent time.

As Kevin is careful to point out, none of this means that natural selection stopped working in humans. So long as there will be differential survival and reproduction, selection will be active on our genomes. But its mode and tempo have been dramatically altered by the onset of cultural evolution, which has become a drive, rather than an outcome, of natural selection in our species. As Laland puts it:

“Theoretical models consistently find that gene-culture dynamics are typically faster, stronger, and operate over a broader range of conditions than conventional evolutionary dynamics. … This picture of the evolution of the human mind is radically different from the portrayal advanced by evolutionary psychologists and many popular science writers.” (p. 239)

I think Kevin is a bit too mild when he discusses the limitations of evolutionary psychology (whose initial central hypothesis, a massive modularity of the human mind, has now been definitively rejected empirically). He states that current research in gene-culture co-evolution shows that the degree of mismatch between our genetic endowment and our culturally created environment is “far more limited” than evopsych authors envisioned. I’d say that’s a dramatic understatement, but certainly still an observation that should lead serious evolutionary psychologists to revise a great deal of what they are doing, abandoning the increasingly silly idea that the Pleistocene was a crucial “environment of evolutionary adaptedness” (EEA), as if the genetic evolution of Homo sapiens had suddenly stopped at that point in time.

“Far from being trapped in the past by an outdated biological legacy, humans are characterized by a remarkable plasticity. Our adaptiveness is reinforced by both cultural and biological evolution.” (p. 240)

If people who write about evopsych were to take this conclusion a bit more seriously, especially when they write for a general public, there would be a lot less garbage floating around the pop science literature. But I ain’t holding my breath…