Classic Theory In Evolution: The Big Four Questions

Explanations for things appear to be a truly vexing issue for people in many instances. Admittedly, that might sound a little strange; after all, we seem to explain things all the times without much apparent effort. We could consider a number of examples for explanations of behavior: people punch walls because they’re angry; people have sex because it feels good; people eat certain foods because they prefer those flavors, and so on. Explanations like these seem to come automatically to us; one might even say naturally. The trouble that people appear to have with explanations is with respect to the following issue: there are multiple, distinct, and complimentary ways of explaining the same thing. Now by that I don’t mean that, for instance, someone punched a wall because they were angry and drunk, but rather that there are qualitatively different ways to explain the same thing. For instance, if you ask me what an object is, I could tell you it’s a metallic box that appears to run on electricity and contains a heating element that can be adjusted via knobs; I could also tell you it’s a toaster. The former explanation tells you about various features of the object, while the latter tells you (roughly) what it functions to do (or, at least, what it was initially designed to do).

…And might have saved you that trip to the ER.

More precisely, the two issues people seem to run into when it comes to these different kinds of explanations is that they (a) don’t view these explanations as complimentary, but rather as mutually-exclusive, or (b) don’t realize that there are distinct classes of explanations that require different considerations from one another. It is on the second point that I want to focus today. Let’s start by considering the questions found in the first paragraph in what is perhaps their most basic form: “what causes that behavior?” or, alternatively, “what preceding events contributed to the occurrence of the behavior?” We could use as our example the man punching the wall to guide us through the different classes of explanations, of which there are 4 generally-agreed upon categories (Tinbergen, 1963).

The first two of these classes of explanations can be considered proximate – or immediate – causes of the behavior. The standard explanation many people might give for why the man punched the wall would be to reference the aforementioned anger. This would correspond to Tinbergen’s (1963) category of causation which, roughly, can be captured by considerations of how the cognitive systems which are responsible for generating the emotional outputs of anger and corresponding wall-punching work on a mechanical level: what inputs do they use, how are these inputs operated upon to generate outputs, what outputs are generated, what structures in the brain become activated, and so on. It is on this proximate level of causation that most psychological research focuses, and with good reason: the hypothesized proximate causes for behaviors are generally the most open to direct observation. Now that’s certainly not to say that they are easy to observe and distinguish in practice (as we need to determine what cognitive or behavioral units we’re talking about, and how they might be distinct from others), but the potential is there.

The second type of explanation one might offer is also a proximate-type of explanation: an ontological explanation. Ontology refers to changes to the underlying proximate mechanisms that takes place during the course of development, growth, and aging of an organism. Tinbergen (1963) is explicit in what this does not refer to: behavioral changes that correspond to environmental changes. For instance, a predator might evidence feeding behavior in the presence of prey, but not evidence that behavior in absence of prey. This is not good evidence that anything has changed in the underlying mechanisms that generate the behavior in question; it’s more likely that they exist in the form they did moments prior, but now have been provided with novel inputs. More specifically, then, ontology refers, roughly, to considerations of what internal or external inputs are responsible for shaping the underlying mechanisms as they develop (i.e. how is the mechanism shaped as you grow from a single cell into an adult organism). For instance, if you raise certain organisms in total darkness, parts of their eyes may fail to process visual information later in life; light, then, is a necessary developmental input for portions of the visual system. To continue on with the wall-punching example, ontological explanations for why the man punched the wall would reference what inputs are responsible for the development of the underlying mechanisms that would produce the eventual behavior.

Like their father’s fear of commitment…

The next two classes of explanations refer to ultimate – or distal – causal explanations. The first of these is what Tinbergen calls evolution, though it could be more accurately referred to as a phylogenetic explanation. Species tend to resemble each other to varying degrees because of shared ancestry. Accordingly, the presence of certain traits and mechanisms can be explained by homology (common descent). The more recently two species diverged from one another in their evolutionary history, the more traits we might expect the two to share in common. In other words, all the great apes might have eyes because they all share a common ancestor who had eyes, rather than because they all independently evolved the trait. Continuing on with our example, the act of wall-punching might be explained phylogenetically by noting that the cognitive mechanisms we possess related to, say, aggression, are to some degree shared with a variety of species.

Finally, this brings us to my personal favorite: survival value. Survival value explanations for traits involve (necessarily-speculative, but perfectly testable) considerations about what evolutionary function a given trait might have (i.e. what reproductively-relevant problem, if any, is “solved” by the mechanism in question). Considerations of function help inform some of the “why” questions of the proximate levels, such as “why are these particular inputs used by the mechanism?”, “why do these mechanisms generate the output they do?”, or “why does this trait develop in the manner that it does?”. To return to the punching example, we might say that the man punched the wall because aggressive responses to particular frustrations might have solved some adaptive problem (like convincing others to give you a needed resource rather than face the costs of your aggression). Considerations of function also manage to inform the evolution, or phylogeny, level, allowing us to answer questions along the lines of, “why was this trait maintained in certain species but not others?”. As another for instance, even if cave-dwelling and non-cave dwelling species share a common ancestor that had working eyes, that’s no guarantee that functional eyes will persist in both populations. Homology might explain why the cave-dweller develops non-functional eyes, but it would not itself explain why those eyes don’t work. Similarly, noting that people punch walls when they are angry alone does not explain why we do so.

All four types of explanations answer the question “what causes this behavior?”, but in distinct ways. This distinction between questions of function and questions of causation, ontogeny, and phylogeny, for instance, can be summed up quite well by a quote from Tinbergen (1963):

No physiologist applying the term “eye” to a vertebrate lens eye as well as a compound Arthropod eye is in danger of assuming that the mechanism of the two is the same; he just knows that the word “eye” characterizes achievement, and nothing more.

Using the word “eye” to refer to a functional outcome of a mechanism (processing particular classes of light-related information) allows us to speak of the “eyes” of different species, despite them making use of different proximate mechanisms and cues, developing in unique fashions over the span of an organism’s life, and having distinct evolutionary histories. If the functional level of analysis was not distinct, in some sense, from analyzes concerning development, proximate functioning, and evolutionary history, then we would not be able to even discuss these different types of “eyes” as being types of the same underlying thing; we would fail to recognize a rather useful similarity.

“I’m going to need about 10,000 contact lens”

To get a complete (for lack of a better word) understanding of a trait, all four of these questions need to be considered jointly. Thankfully, each level of analysis can, in some ways, help inform the other levels: understanding the ultimate function of a trait can help inform research into how that trait functions proximately; homologous traits might well serve similar functions in different species; what variables a trait is sensitive towards during development might inform us as to its function, and so on. That said, each of these levels of analysis remains distinct, and one can potentially speculate about the function of a trait without knowing much about how it develops, just as one could research the proximate mechanisms of a trait without knowing much about its evolutionary history.

Unfortunately, there has been and, sadly, continues to be, some hostility and misunderstandings with respect to certain levels of analyzes. Tinbergen (1963) had this to say:

It was a reaction against the habit of making uncritical guesses about the survival value, the function, of life processes and structures. This reaction, of course healthy in itself, did not (as one might expect) result in an attempt to improve methods of studying survival value; rather it deteriorated into lack of interest in the problem – one of the most deplorable things that can happen in science. Worse, it even developed into an attitude of intolerance: even wondering about survival value was consider unscientific

That these same kinds of criticisms continue to exist over 50 years later (and they weren’t novel when Tinbergen was writing either) might suggest that some deeper, psychological issue exists surrounding our understanding of explanations. Ironically enough, the proximate functioning of the mechanisms that generate these criticisms might even give us some insight into their ultimate function. Then again, we don’t want to just end up telling stories and making assumptions about why traits work, do we?

References: Tinbergen, N. (1963). On aims and methods of Ethology. Zeitschrift für Tierpsychologie, 20, 410-433.

Should Evolutionary Psychology Be A History Course?

Imagine for a moment that you happen to live in a Dr. Seuss-style world. Having just graduated from your local institute of educational hobnobbery, you find yourself hired into a lucrative new position: you’re a whatsitdoer. The job description is self-explanatory: it’s your job to examine a series of curious-looking objects and figure out what it is they were designed to do; what their function happens to be. On your first day at work, a shiny metal box comes down the conveyer belt. You begin to examine the object for evidence of special design. That is, does the object appear to be improbably well-designed for a solving various aspects of a particular task with efficiency? You note that the black cord ending in metal prongs running out of the box might suggest that it runs on electricity, the two slots at the top of the box appear to be shaped appropriately so as it fit bread rather well, and there appears to be a heating apparatus within the box. You test each design feature according: the device only functions when plugged it, bread does indeed fit well in the box, and is evenly toasted by the heating element. Importantly, larger items don’t seem to fit in well, smaller items fall in, becoming unreachable, and non-bread items, like CDs tend to melt or catch fire. In other words, the function of this tool appears as if it were designed to use a relatively narrow set of inputs to produce a useful output – toast.

Alternative hypothesis 34: Bath warmer

When you report the results of your tests to your boss, however, he’s not at all pleased with you analysis: “How can you possibly say that this object is designed to make toast when you haven’t recreated the steps of its manufacturing process? Until you have examined the history of how this object has come to be, how the material it is made out of was gathered and shaped, as well as what earlier prototypes of the model might have looked like extending back thousands of years, I can’t accept your suggestion, as you haven’t tested your functional explanation at all!” Now this all strikes you as very strange: you haven’t made any claims about how the object was developed or what earlier versions looked like; you made a claim about how the contemporary object you were given likely functions. As such, understanding the history of the object, while perhaps a neat piece of information that might inform later research on function, is not by any means a requirement for understanding an object’s function. In other words, you should be able to persuade your boss that the toaster is pretty good at making toast without having to give him a complete history of the thing.

Sure; it’s possible that the toaster-like object actually wasn’t designed to make toast at all; toast just happens to be a pretty convenient byproduct of another function it was designed to carry out. However, if we were deriving predictions about what that alternative function was, we still shouldn’t need the history lesson to do that. It’s not that the history information would be necessarily useless: for instance, if you knew the device existed before bread was a thing, then toasting bread certainly couldn’t have been its initial function (though it may well have been co-opted for that function when bread – or pop tarts – became a thing). However, if toasters are well-suited for other functions, you should be able to demonstrate those other functions with design evidence.  History is useful insomuch as it helps inform us about what design evidence to look for, certainly, but does not itself inform us as to functionality.

That said, there have been suggestions that phylogenetic analyzes (examinations of the branching of evolutionary tree) can help inform us as to whether a trait is functional (i.e. an adaptation) or not. Specifically, Fraley, Brumbaugh, & Marks (2005) wrote that, “In order to evaluate the adaptive nature of the relationship between traits, it is necessary to account for phylogenetic relationships among species” (p.733, emphasis mine). The authors go on to note, correctly, that species may be similar to one another because of a shared evolutionary history: rather than all the ape species evolving eyes independently, a common ancestor to all of them might well have had eyes and, because we share that ancestor, we all have eyes as well. Now, as you should be careful to note, this is a claim about the evolutionary history of the trait: whether it was independently evolved multiple times in different lineages, or whether it was evolved once and then maintained into subsequent generations. You should note, however, that this is not a functional claim: it doesn’t tell us what eyes do, what inputs they use, what outputs they generate, and so on. Some examples should make this distinction quite clear.

Figure 1: This ugly bird

Let’s consider, as an example, two species of birds: the ostrich and any variety of parrot you’d prefer. In the interests of full disclosure, I don’t know how recently the two species shared a common ancestor, but at some point we can say they did. Further, for the sake of argument, let’s say that this common ancestor between ostriches and parrots had feathers. The fact that both ostriches and parrots have feathers, then, can be said to be the result of homology (i.e. shared ancestry). However, this does not tell us anything about what the function(s) of these feathers are in their respective species, nor what selective pressures are responsible for their initial appear or maintenance across time. For instance, parrots are capable of flight while ostriches are not. We might expect that parrot feathers show some adaptations for that function, whereas such adaptive designs might have been degraded or lost entirely in ostriches (presuming the common ancestor flew, that is). However, the feathers might also serve other, identical functions in both species: perhaps feathers are also used to keep both species warm, or are advertised during sexual displays. Whatever the respective functions (or lack thereof) of these feathers in different species, we are unable to deduce that function from a phylogenetic analysis alone. What we need are considerations of what adaptive tasks the feathers might serve, and what design evidence we might expect to find because of that.

Fraley et al (2005) go on to suggest that if two traits repeatedly evolve together across species, these, “…correlation[s] between the traits…strongly suggests a functional relationship” (p. 734, emphasis mine). Again, the claim being made is that we can go from history – the two traits tend to show up together – to function. On top of the problems outlined above, such a statement runs into another large obstacle: non-functional or maladaptive byproducts of traits should be expected to correlate as well as adaptive ones. Let’s start with a non-functional example: imagine that placental mammals evolved multiple times over the course of history. If you were examining many different species, you’d probably see a good correlation between the evolution of placentas and presence of belly-buttons. However, the correlation between these two traits doesn’t tell you anything at all about the function (or lack thereof) of either placentas or belly-buttons. In another case, you might notice that there’s a pretty decent correlation between animals with a taste for sugar and the development of cavities in their teeth, but this in no way implies that a preference for sugar has any functional relationship with cavities; bacteria dissolving your teeth generally has poor fitness outcomes, and wouldn’t be selected for.

One final example might help make the point crystal clear: human male investment in children. Let’s say you have two males displaying the same behavior: buying food for their child. That’s awfully charitable of them. However, in one case, the man is the genetic father of the child, whereas the other male is only the child’s stepfather. These behaviors, while ostensible similar, might actually be serving distinct functions. In the case of the genetic father, the investment in that child might involve mechanisms designed for kin-directed altruism (i.e. investing in the child because the child carries some of his genes) and/or mechanisms designed for mating effort (i.e. investing in the child as a means of increasing sexual access to the mother). In contrast, we might expect the stepfather to be investing for the latter reason, but not the former. In other words, we have the same behavior – investing in children – being driven by two very different functional mechanisms. The predictions that can be drawn from these alternative functions are testable even without any reference at all to history or phylogeny: as it turns out, genetic fathers living with the mother invest more than genetic fathers not living with the mother, but genetic fathers continue to invest in offspring even lacking the relationship. On the other hand, stepfathers will invest in children when living with the mother, but when the relationship ends their investment tends to all but dry up entirely. Further, when both are living with the mother, genetic fathers invest more than stepfathers (Anderson, Kaplan, & Lancaster, 1999). This evidence is consistent with a role for both mechanisms designed for investing in children for inclusive fitness and mating reasons. Despite the surface level similarities, then, the investment of these two males might actually be being driven by different functional considerations.

And we didn’t even need a fossil record to figure that out.

So, when it comes to testing claims of biological function, there is no necessity for information about phylogeny: you don’t need to know where traits originated or what earlier versions of the trait were like in order to test competing hypotheses. That’s not to say that such information might not be useful: if you didn’t know about bread, you might have a more difficult time understanding some of the toaster’s design, just as if you didn’t know about toasters you might be hard pressed to explain the shape of pop-tarts (or their name, for that matter). Similarly, correlations between traits do not “strongly suggest” any evidence of a functional relationship either; some correlations might be consistent with a particular functional relationship, sure, but the correlation itself tells you comparatively little when compared with evidence of function (just like correlations between ice cream sales and murder do not strongly suggest any causal relationship, though an experiment examining whether feeding people ice cream made them more violent might). Claims about function should be kept distinct from claims about history. Why the two seem to get conflated from time to time is certainly an interesting matter.

References: Anderson, K., Kaplan, H., & Lancaster, J. (1999). Parental care by genetic fathers and stepfathers I: Reports from Albuquerque men. Evolution and Human Behavior, 20, 405-431.

Fraley, R., Brumbaugh, C., & Marks, M. (2005). The evolution and function of adult attachment: A comparative and phylogenetic analysis. Journal of Personality and Social Psychology, 89, 731-746.

What Percent Of Professors Are Bad Teachers?

Let’s make a few assumptions about teaching ability. The first of these is that the ability to be an effective teacher (a broad trait, to be sure, compromised of many different sub-traits) – as measured by your ability to, roughly, put knowledge into people’s head in such a manner as so they can recall it later – is not an ability that is evenly distributed throughout the human population. Put simply, some people will make better teachers than others, all else being equal.The second assumption is that teaching ability is approximately normally distributed: a few people are outstanding teachers, a few people are horrible, and most are a little above or below average. This may or may not be true, but let’s just assume that it is to make things easy for us. Given these two assumptions, we might wonder how many of those truly outstanding-tail-end teachers end up being instructors at the college level. The answer to that question depends, of course; on what basis are teachers being hired?

Glasses AND a sweater vest? Seems legitimate enough for me.

Now, having never served on any hiring committees myself, I can offer little data or direct insight on that matter. Thankfully, I can offer anecdotes. From what I’ve been told, many colleges seem to look at two things when considering how to make their initial cut of the dozens or hundreds of resumes they receive for the single job they are offering: publications in academic journals (more publications in “better” journals is a good thing) and grant funding (the more money you have, the better you look, for obvious reasons). Of course, those two factors aren’t everything when it comes to who gets hired, but they at least get your foot in the door for consideration or an interview. The importance of those two factors doesn’t end post-hiring either, as far as I’ve been told, later becoming relevant for such minor issues like “promotions” and “tenure”. Again, this is all gossip, so take it with a grain of salt.

However, to the extent that this resembles the truth of the matter, it would seem to game the incentive system away from investing time and effort into becoming a “good” teacher, as such investments in teaching (as well as the teaching itself) would be more of a “distraction” from other, more-important matters. How does this bear on our initial question? Well, if college professors are being hired primarily on their ability to do things other than teach, we ought to expect that the proportion of professors being drawn from the upper-tail of that distribution in teaching ability might end up being lower than we would prefer (that is, unless teaching ability correlates pretty well with one’s ability to do research and get grants, which is certainly an empirical matter). I’m sure many of you can relate to that issue, having both had teachers who inspired you to pursue an entirely new path in life, as well as teachers who inspired you to get an extra hour of sleep instead of showing up to their class.The difference between a good teacher (and you’ll know them when you see them, just like porn) and a mediocre or poor one can be massive.

So why ask this questions about teaching ability? It has to do with a recent meta-analysis by Freeman et al (2014) examining what the empirical research has to say about the improvements in education outcomes that active learning classes have over traditional lecture teaching in STEM fields. For those of you not in the know, “active learning” is a rather broad, umbrella term for a variety of classroom setups and teaching styles that go beyond strictly lecturing. As the authors put it, the term, “...included approaches as diverse as occasional group problem-solving, worksheets or tutorials completed during class, use of personal response systems with or without peer instruction, and studio or workshop course designs“. Freeman et al (2014) wanted to see which instruction style had better outcomes for both (1) standardized tests and (2) failure/withdrawal rates from the classes.

“Don’t lecture him, dear; just let the active learning happen”

The results found that, despite this exceedingly-broad definition for active learning, the method seemed to have a marked increase in learning outcomes, relative to lecture classes. With respect to the standardized test scores, the average effect size was 0.47, meaning that, on the whole, students in active learning classes tended to score about half a standard deviation higher than students in lecture based classes. In simpler terms, this means that students in the active learning classes should be expected to earn about a B on that standardized test, relative to the lecture student’s B-. While that might seem neat, if not terribly dramatic, the effect of the failure rate was substantially more noteworthy: specifically, students in lecture-only classes were 1.5 times more likely to fail than a student in an active learning class (roughly 22% failure rate in active learning classes, relative to lecture’s 34%). These effects were larger in small classes, relative to large ones, but held regardless of class size or subject matter. Active learning seemed to be better.

The question of why active learning seems to have these benefits is certainly an interesting one, especially given the diversity of methods that fall under the term. As the authors note, “active learning” could refer both to a class that spent 10% of its time on “clicker” questions (real-time multiple choice questions) or a class that was entirely lecture-free. One potential explanation is that active learning per se doesn’t actually have too much of a benefit; instead, the results might be due to the “good” professors being more likely to volunteer for research on the topic of teaching or likely to adopt the method. This explanation, while it might have some truth to it, seems to be contradicted by the fact that the data reported by Freeman et al (2014) suggests that the active learning effect isn’t diminished even when it’s the same professor doing the teaching in both kinds of courses.

We might also consider that there’s a lot to be said for learning by doing. When students have practice answering similar kinds of questions (along with feedback) to those which might appear on tests – either of the professor’s making or the standardized varieties – we might also expect that they do better on the tasks when they counts. After all, there’s a big difference between reading a lot of books about how to paint and actually being able to create a painting that bears a resemblance to what you hoped it would look like. Similarly, answering questions about your subject matter before a test might be good at getting you to answer questions better. Simple enough. While an exceedingly-plausible sounding explanation, the extent to which active learning facilitates learning in this manner is an unknown. In the current study, as previously mentioned, active learning could involve something as brief as a few quick questions or an entire class without lecture; the duration or type of active learning wasn’t controlled for. Learning by doing seems to help, but past a certain point it might simply be overkill.

Which is good news for all you metalhead professors out there

Another potential explanation that occurs to me returns to our initial question. If we assume that many professors do not receive their jobs on the basis of their teaching ability – at least not primarily – and if increasing one’s skill at teaching isn’t often or thoroughly incentivized, then it’s quite possible that many people placed in teaching positions are not particularly outstanding when it comes to their teaching ability. If student learning is in some way tied to teaching ability (likely), then we shouldn’t necessarily expect the best learning outcomes if the teacher is the only source of information. What that might mean is that students could learn better when they are able to rely on something that isn’t their teacher to achieve that end. As the current study might hint towards, what that “something” is might not even need to be very specific; almost anything might be preferable to a teacher reading powerpoint slides which they didn’t make and are just restatements of the textbook verbatim, as seems to be popular among many instructors who use lectures currently. If some professors view teaching as more of a chore than a pleasure, we might see similar issues. Before calling the lecture itself a worse format, I would like to see more discussion of how it might be improved and whether there are specific variables that separate “good” lectures from “bad” ones. Perhaps all lectures will turn out to be equally poor, and teaching ability has nothing at all to do with student’s performance in those classes. I would just like to see that evidence before coming to any strong conclusions about their effectiveness.

References: Freeman, S., Eddy, S., McDonough, M., Smith, M., Okoroafor, N., Jordt, H., & Wenderoth, M. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1319030111.

What’s Counterintuitive About Discrimination?

One of the main concerns I have with some research in psychology (and while I have no data on the matter, I don’t think I’m alone in it) is that some portion of it is, explicitly or otherwise, agenda-driven. Specifically, the researchers have a particular social goal in mind that they seek to call attention to with their work. Now there’s nothing necessarily wrong with that, especially if there actually happens to be some troubling social issue that needs to be addressed. Nothing necessarily wrong, however, does not imply that it doesn’t frequently lead to problems with the research, either in its design or its interpretation. In other words, when people want to see a particular problem, or a particular interpretation of their results, they’re often pretty good at finding it. As it turns out, people in the social sciences are also pretty keen on trying to find racism and sexism.

“The clouds must be discriminating; they’re all white”

Now I want to be absolutely clear on one point at the outset: people do discriminate. They do so all the time for a variety of reasons, whether we’re talking about sexual partners or hiring people for jobs. Some of these reasons for discrimination happen to be more socially acceptable than others, so one ought to be caution when making accusations that someone – or some group – is discriminating the basis of them. The implications of being call a racist or a sexist, for instance, can be rather large. So, with that said, let’s consider some research that suggests the whole of academia in the US is both of those things. Not explicitly, of course: it just suggests that certain types of people are “plagued” by “barriers” on the basis of their sex and/or race caused by “biases” and are “underrepresented” in certain professions, violating claims to “fairness”. Certainly, this is a much different type of claim than an outright accusation of racism or sexism, and could not possibly be interpreted in any damning manner. Certainly…

Anyway, in the paper – by Milkman et al (2014) – the researchers have a very particular meaning for the word “underrepresented”: women and minority groups are not represented in academic positions in equal percentages to their representation in the population. In that sense, one might consider such groups underrepresented. In another sense – perhaps a more meaningful one – we might consider underrepresentation instead in terms of the various talents, preferences, and willingness of different groups. That is to say 99% or so of plumbers are men, but women aren’t underrepresented in that field largely because most women seem to express no interest in entering the field, at least relative to their alternatives. This sense of underrepresentation is more difficult to determine however, and might undercut any points about racism or sexism, so, needless to say, most researchers examining racism or sexism don’t ever seem to use it; at least not as far as I’ve seen.

In any case, Milkman et al (2014) wanted to examine how discrimination on the basis of sex or race might pose barriers to women or minorities entering academia. Towards examining this issue, the researchers sent out around 6,500 stock emails to professors at around 250 universities across 109 different fields of study. This email reads as follows:

Subject Line: Prospective Doctoral Student (On Campus Today/[Next Monday])
Dear Professor [Surname of Professor Inserted Here],
I am writing you because I am a prospective doctoral student with considerable interest in your research. My plan is to apply to doctoral programs this coming fall, and I am eager to learn as much as I can about research opportunities in the meantime.
I will be on campus today/[next Monday], and although I know it is short notice, I was wondering if you might have 10 minutes when you would be willing to meet with me to briefly talk about your work and any possible opportunities for me to get involved in your research. Any time that would be convenient for you would be fine with me, as meeting with you is my first priority during this campus visit.
Thank you in advance for your consideration.
[Student’s Full Name Inserted Here]

The student’s name was varied to either be typical of man or a woman, and/or typical of a White, Black, Hispanic, Chinese, or Indian person. The names were intentionally made to be as stereotypical as possible so as to avoid any confusion on the recipient’s part. They also kept track of which professors were receiving the emails, both in terms of their sex, race, and field of study.

With a message like that, I’m surprised they got a single response.

Some of the results are unlikely to be terribly surprising to most people: in general, the fake emails from minority groups and women tended to receive fewer responses than those from white males. There were some exceptions, as the size of this effect fluctuated markedly and which group it favored fluctuated moderately (the main exception to the general rule seemed to be fine arts programs, which discriminated more against the white male emails). Also, the higher-paid fields tended to respond to women and minorities less (the authors speculate more than once that this might be due to those in high-paying jobs having different values but, remember, this isn’t about calling anyone a racist or a sexist). Don’t worry, though; your moral outrage about these results might be tempered somewhat by the following additional finding: no matter how the researchers tried to slice it, this discrimination was independent of the professor’s race or sex. A black female professor did the same thing as a white male professor no matter the sex or race of the ostensible sender. Fancy that.

Now what I find particularly interesting about this paper is what the authors say about that last set of results: it’s “counterintuitive”. The result would only be counterintuitive, it seems, if you had a particular model as to who might discriminate and why already in your head. Specifically, the authors seem to write as if the only reason people might discriminate is on the basis of biases that have no bearing in reality; it must be people’s “values” or discrimination of the basis of a stereotype (which isn’t true, of course). To give credit where it’s do, the authors note that, sure, their study can’t actually tell if any racial/gender bias is responsible for these results or whether these patterns of discrimination were based on some other factors. Unfortunately, this point is placed at the end of the paper as more of an afterthought. If this point was placed at the beginning of the paper and expanded upon in almost any detail whatsoever, I imagine this paper would be a much different read. As it is, however, the point feels added in at the end as a halfhearted acknowledge that their research doesn’t actually tell us anything meaningful about the points they spent the entire introduction discussing. So allow me to expand on that point a bit more.

I want you to consider the following hypothetical: you’re a doctor, and a patient has come to you with a list of symptoms. These symptoms are consistent with one of two life-threatening conditions and there’s no time to test them to find out which condition it is. You have a drug for each condition, but you can only administer one (let’s say because both together would be fatal). Which drug should you give your patient? Well, that depends: which condition is more common? If both are equally as common, both drugs should receive an approximately equal chance of being administered; if one disease happens to be more common, then that’s the one you should treat for. The point is basic enough: you don’t want to ignore base-rates To make this less of a metaphor, if you’re a professor with limited time and energy, you can’t respond to every unsolicited message you get unless you want other parts of your life to suffer (work/life balance, and all that). This message is about as vague as can be: it might be coming from a student that cares and would be valuable, or might be coming from a student that sent the same bland email out to dozens of people and is wasting your time. You only have two choices: respond or ignore. What do you do?

It all depends on who you think is on the other side…

Well, that’s contingent on what information you have: a name. The name tells you gender and race. Now here comes the part that most people don’t want to acknowledge: do those two things tell you anything of value? The answer to that question that you’d receive from people would, I imagine, depend in part on one’s preferred definition for “underrepresented”. While I won’t pretend that I can tell you what information might or might not be present in a name (i.e. what factors tend to correlate with sex and/or race that predict one’s ability to be a worthwhile graduate student), what I will tell you is that research on the subject of stereotypes has a very bad habit of never bothering to test for the stereotype’s accuracy. There’s a lot of work on trying to demonstrate discrimination without much work trying to understand it. That professors of all races and sexes seemed to show the same bias might suggest that there is something there worth paying attention to in a name when one lacks any other useful source of information. Perhaps such a point should be the topic of research, rather than an end note to it.

References: Milkman, K., Akinola, M., & Chugh, D. (2014). What Happens Before? A Field Experiment Exploring How Pay and Representation Differentially Shape Bias on the Pathway into Organizations.

In The World Of The Blind, The Woman With A Low WHR Is Queen

If you happen to have memories of watching TV in the 90s, chances are you might remember the old advertisements they used to run for the Cinnamon Toast Crunch cereal. The general premise of the ads followed the same formula: “Person X is really good at seeing Y, but can they see why kids love the taste of Cinnamon Toast Crunch?”. Inevitably, the answer was always “no”, as adults are apparently so square that they couldn’t wrap their minds around the idea that children happen to like sugar. Difficult concept, I know. Now, obviously, adults aren’t nearly so clueless in reality. In fact, as you’re about to see, even adults who are really rather poor at seeing things can still “see”, so to speak, why men tend to find certain features in women attractive.

Sauron majored in sociology, so he guessed “cultural conditioning”

The first paper up for consideration is a 2010 piece by Karremans et al. The researchers begin by noting that men appear to demonstrate a preference for women with relatively-low waist-to-hip ratios (WHRs). Women with low WHRs tend to have figures that resemble the classic hourglass shape. Low WHRs are thought to be found attractive by men because they are cues to a woman’s fertility status: specifically, women with lower WHRs – around a 0.7 – tend to be more fertile than their more tubular-shaped peers. That said, this preference – just like any of our preferences – does not magically appear in our minds; every preference needs to develop over our lives, and development requires particular input conditions. If these developmental input conditions aren’t met, then the preference should not be expected to form. Simple enough. The question of interest, then, is what precisely these conditions are; what factors are responsible for men finding low WHRs attractive?

One ostensibly obvious condition for the development of a preference for low WHRs might be visual input. After all, if men couldn’t see women’s WHRs – and all those unrealistic expectations of female body type set by the nefarious media – it might seem awfully difficult to develop a taste for them. This poses something of an empirical hurdle to test, as most men have the ability to see, Thankfully for psychological research – though not so thankfully for the subjects of that research – some men, for whatever reasons, happen to have been born blind. If visual input was a key condition for the development of preferences for low WHRs in women, then these blind men should not be expected to show it. While large samples of congenitally blind men are not the easiest to come by, Karremans et al (2010) managed to recruit around 20 of them.

These blind men were presented with two female mannequins wearing tight-fitting dresses. One of these mannequins had a WHR of 0.7 – around what most people rate as the most attractive – and the other had a slightly-higher 0.84. The blind men were asked to feel and rate each mannequins on attractiveness from 1 to 10. Additionally, the researchers recruited about 40 sighted men to complete the task as well: 20 completing it while blindfolded and 20 without the blindfold. Of note is that all data collection was carried out in a van (read: “mobile laboratory room”) because sometimes psychological research is just fun like that.

“How about coming into my van to feel my mannequins?”

The first set of results to consider come from the sighted men, who completed the task with the full use of their eyes: they gave the mannequin with the low WHR a rating of around an 8, whereas the mannequin with the higher WHR received only around a 6.5, as one might expect. In the blindfold condition, this difference was reduced somewhat (with ratings of 7.5 and around 7, respectively), suggesting that visual input might play some role in determining this preference. However, visual input was clearly not necessary: the blind men rated the low WHR mannequin at around a 7, but the high WHR mannequin at about a 6. In the words of the fine people over at Cinnamon Toast Crunch: “even blind men who can’t see much of anything can still see why men love the figures of women with low WHRs”.

Further evidence from earlier research points towards a similar conclusion (that these preferences are unlikely to be the result of portrayals of women in the media). A paper by Singh (1993) analyzed a trove of data on the female bodies that appeared in Playboy as centerfolds (from 1955-1965 and 1976-1990) and that won Miss America pageants (1923-1987). One might imagine that depictions of women in the media or found to be attractive might change somewhat over six decades if the type of women being portrayed were favored for some arbitrary set of reasons. Indeed, there was one noticeable trend: the centerfolds and pageant winners tended to be getting a little bit skinnier over that time period. Despite these changes in overall BMI, however, the WHR of the groups didn’t vary. Both grounds hovered around a consistent 0.7. Presumably, if blind men were consuming pornography, they would prefer the women depicted in Playboy just as much as non-blind men do.

“We’ll be needing more “databases” for the mobile laboratory room…”

Given the correlation between WHR and fertility, this consistency in men’s preferences should be expected. That’s not to say, of course, that these preferences for low WHR aren’t modifiable. As I mentioned before, every preference needs to develop, and to the extent that certain modifications of that preference would be adaptive in different contexts, we should expect it to fluctuate accordingly. Now that matter of precisely what input conditions are responsible for the development of this preference remain shrouded: while visual inputs don’t seem to be necessary, the matter of which cues are – as well as why they are – are questions that have yet to be answered. For what it’s worth, I would recommend turning research away from the idea that the media is responsible for just about everything, but that’s just me.

References: Karremans, J., Frankenhuis, W., & Arons S. (2010). Blind men prefer a low waist-to-hip ratio. Evolution and Human Behavior, 31, 182-186.

Singh, D. (1993). Adaptive significance of female physical attractiveness: Role of waist to hip ratio. Journal of Personality and Social Psychology, 65, 293-307.