Scientific Racism

The site’s been quiet in 2017, with little time to blog on top of my regular professional responsibilities, and of course watching the fascist smoke rising from the garbage fire of our 45th presidential administration with horrified disbelief. At work, my two new classes are keeping me plenty busy, and their content is quite distinct – one is on the archaeological record of Central Asia, the other centers around Homo naledi to teach about fossils. But by complete accident, examples of scientific racism came up in the readings for each course last week.

scientific-racism

Scientific racism refers to using data or evidence from the biological and social sciences to support racist arguments, that one racial group is better or worse than another group; the groups of course, are culturally determined rather than empirically discrete biological entities. This evidence is often cherry-picked, misinterpreted, and/or outright weak. Nicolas’ Wade’s 2014 A Troublesome Inheritance is a recent example of such a work. The book’s racial claims amount to nothing more than handwaving, and so egregious is the misrepresentation of genetic evidence that nearly 150 of the world’s top geneticists signed a letter to the editor rebuking Wade for “misappropriation of research from our field to support arguments about differences among human societies.” Wade’s book has no place in scientific discourse, but then almost anyone can write a book as long as a publisher thinks it will sell.

In addition to the outright misrepresentation of scientific evidence to support racist arguments, another manifestation of scientific racism is the influence of cultural biases in the interpretation of empirical observations. This may be less malicious than the first example, but is equally dangerous as it more tacitly supports systemic and pervasive racism. And this brings us to my classes’ recent readings.

First was a reference to the “Movius Line” in a review of the Paleolithic record of Central Asia (Vishnyatsky 1999) for my prehistory class. Back in the 1940s Hallum Movius, archaeologist and amazing-name-haver, noticed a distinct geographic pattern in the distribution of early stone tool technology across the Old World: “hand-axes” could be found at sites across Africa and western Eurasia, while they were largely absent from East Asian sites, which were dominated by more basic stone tools.

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Movius’ illustration of the distribution of Early Paleolithic technologies. From Fig. 1 in Dennell (2015).

Robin Dennell (2016) provides a nice review of how Movius’ personal, culturally influenced perception of China colored his interpretation of this pattern. Movius read this archaeological evidence to mean that early East Asian humans were unable to create the more advanced technology of the west, a biological and cognitive deficiency resulting from cultural separation: “East Asia gives the impression of having acted (just as historical China and in sharp contrast with the Mediterranean world) as an isolated and self-sufficient area, closed to any major human migratory wave” (Movius 1941: 86, cited in Dennell 2015). Racial and cultural stereotypes about East Asia directly translated to his interpretation of an archaeological pattern.

This type of old school scientific racism also arose in a review of endocasts (Falk, 2014) for my Homo naledi class. Endocasts are negative impressions or casts of a space or cavity, and comprise the only direct evidence of what extinct animals’ brains looked like. So to see how the structure of the brain has changed over the course of human evolution, scientists can search for the impressions of important brain structures in fossil human endocasts. Falk (2014) reviews one of the most famous of these structures – the “lunate sulcus” – which was used as evidence for reorganization of the hominin brain for nearly 100 years. In the early 20th century, anatomist and anthropologist GE Smith (not GE Smith from the Saturday Night Live Band)  thought he’d identified the human homologue of a groove that in apes separates the parietal lobe from the visual cortex. In humans, however, this groove was positioned more toward the back of the brain, which Smith interpreted as an expansion of an area relating to advanced cognition.

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The back of the brain, viewed from the left, of a chimpanzee (left) and two humans, the red line illustrating the Affenspalte or lunate sulcus (Fig. 1 from Falk 2014, which was modified from Smith 1903). The middle one also might be a grumpy fish.

It turns out that the lunate sulcus does not actually exist in humans, as the grooves identified as such are not structurally or functionally the same as the lunate sulcus in apes (Allen et al., 2006). Nevertheless, given what Smith thought the lunate sulcus was, it’s tragic to read his interpretations of human variation: “resemblance to the Simian [ape] pattern… is not quite so obvious…. in European types of brain….” (Smith 1904: 437, quoted in Falk 2014). The human condition for this trait was for it to be located in the back, reflecting an expansion of the cognitive area in front of it, and this pattern was less pronounced, according to Smith, in non-European people’s brains. This interpretation reflects two traditions at the time: 1) to refer to racial ‘types,’ ignoring variation within and overlap between groups, as well as 2) the prevailing wisdom that Europeans were more intelligent or advanced than other geographical groups.

ResearchBlogging.orgAnecdotes such as these may seem like mere scientific and historical curios, but they should serve as important reminders both that science can be accidentally guided by cultural values, or intentionally used for malevolent ends. Misconceptions and errors of the past shouldn’t be erased, but rather touted so that we don’t repeat mistakes that can have major consequences in our not-so-post-racial society.

References

Allen JS, Bruss J, & Damasio H (2006). Looking for the lunate sulcus: a magnetic resonance imaging study in modern humans. The anatomical record. Part A, Discoveries in molecular, cellular, and evolutionary biology, 288 (8), 867-76 PMID: 16835937

Dennell, R. (2016). Life without the Movius Line: The structure of the East and Southeast Asian Early Palaeolithic Quaternary International, 400, 14-22 DOI: 10.1016/j.quaint.2015.09.001

Falk D (2014). Interpreting sulci on hominin endocasts: old hypotheses and new findings. Frontiers in human neuroscience, 8 PMID: 24822043

Vishnyatsky L (1999). The Paleolithic of Central Asia. Journal of World Prehistory, 13, 69-122.

Bioanthro lab activity: Hominin brain size

Last week in my Human Evolution class we looked at whether we could estimate hominin brain sizes, or endocranial volumes (ECV), based on just the length and width of the bony brain case. Students took these measurements on 3D surface scans…

Maximum cranial length in Australopithecus boisei specimen KNM-ER 406.

Maximum cranial length in Australopithecus boisei specimen KNM-ER 406.

… and then plugged their data into equations relating these measurements to brain size in chimpanzees (Neubauer et al., 2012) and humans (Coqueugniot and Hublin, 2012).

The relationship between cranial length (x axis) and ECV (y axis).

The relationship between cranial length (x axis) and ECV (y axis). Left shows the chimpanzee regression (modified from Fig. 2 in Neubauer et al., 2012), while the right plot is humans (from the Supplementary Materials of Coqueugniot and Hublin, 2012).

So in addition to spending time with fossils, students also learned about osteometric landmarks with fun names like “glabella” and “opisthocranion.” More importantly, students compared their estimates with published endocranial volumes for these specimens, based on endocast measurements:

Human and chimpanzee regression equations don't do great at estimating hominin brain sizes.

Human and chimpanzee regression equations don’t do great at predicting hominin brain sizes. Each point is a hominin fossil, the x value depicting its directly-measured endocranial volume and the y value its estimated volume based on different regression equations. Black and red points are estimates based on chimpanzee cranial width and length, respectively, while green and blue points are based on human width and length, respectively. The dashed line shows y=x, or a correct estimate.

This comparison highlights the point that regression equations might not be appropriate outside of the samples on which they are developed. Here, estimates based on the relationship between cranial dimensions and brain size in chimpanzees tend to underestimate fossils’ actual values (black and red in the plot above), while the human regressions tend to overestimate hominins’ brain sizes. Students must think about why these equations perform poorly on fossil hominins.

Most of the fossil scans come from AfricanFossils.org, but a few are from Artec’s sample gallery. One of the cool, fairly recent humans at African Fossils (KNM ER 5306) will give students something else to think about:

"Why doesn't this look like the rest of the human crania we've seen this semester?"

“Why doesn’t this look like the rest of the human crania we’ve seen this semester?”

Here are the lab materials so you can use and adapt this for your own class:

Lab 4-Brain size (Instructions & questions)

Lab 4 data table (with equations)

ResearchBlogging.orgReferences
Coqueugniot, H., & Hublin, J. (2012). Age-related changes of digital endocranial volume during human ontogeny: Results from an osteological reference collection American Journal of Physical Anthropology, 147 (2), 312-318 DOI: 10.1002/ajpa.21655

Neubauer, S., Gunz, P., Schwarz, U., Hublin, J., & Boesch, C. (2012). Brief communication: Endocranial volumes in an ontogenetic sample of chimpanzees from the taï forest national park, ivory coast American Journal of Physical Anthropology, 147 (2), 319-325 DOI: 10.1002/ajpa.21641

Bioanthro lab activity: Sexual dimorphism

A few weeks ago we examined sexual dimorphism – characteristic differences between males and females – in my Intro to Bioanthro class. Sexual dimorphism roughly correlates with aspects of social behavior in animals, and so we compared dimorphism in our class with what is seen in other primates. For the lab, we collected our body masses, heights, and lengths of our 2nd and 4th fingers, then I plotted the data and we went over it together.

When collecting data on your students, make sure to get permission from your institution and let students know they can opt out of sharing their personal data. I’ve also assigned students randomized ID numbers to help keep their data private and as anonymous as possible.

This activity builds on the first lab we did this year, measuring our head circumferences to estimate brain size and examining how this varies within the classroom. We saw then that our class’s males have  larger brain (well, head) sizes than females. We hypothesized that this was simply due to body size differences – all else being equal, larger people should have larger brains. Now that we collected body mass data, we could test this hypothesis – in fact, when body mass is taken into account, our class’s females have larger brains than males:

Sexual dimorphism in brain size (left), body size (center), and brain/body size.

Sexual dimorphism in brain size (left), body size (center), and brain size relative to body size (right).

These are sex differences based on raw numbers. Another way to look at dimorphism is to se the extent to which sexes deviate from a scaling relationship (“allometry”). Looking to the left plot below, there is a positive linear relationship between body and brain size: as body size increases, so does brain size. As we saw above, male values are elevated above females’ but there is overlap. Importantly, the right plot shows that deviations from this linear trend, quantified as residuals, are not significantly different for the two sexes. So even though females have large brains relative to their body size in absolute terms, this is not exceptional given how brain size scales with body size.

Brain-body allometry in our classroom. Males and females in our classroom do not seem to deviate appreciably from a common pattern of allometry.

Brain-body allometry in our classroom. Males and females in our classroom do not seem to deviate appreciably from a common pattern of allometry.

While lab activities help students to understand patterns in data, this lab also shows students the importance of comparing patterns of variation.  Students learn from readings and lectures that humans show relatively low levels of dimorphism, and this activity helps them see why we say that. Situating our data within the context of primate dimorphism and mating systems, they can ask if there is an adaptive or evolutionary significance behind our level of dimorphism.

Sexual dimorphism in our classroom compared with what is seen in primates with different mating systems and levels male-male competition. Our class values are the stars, and in the right plot blue is males and green is females. Figures from Plavcan (2012) and Nelson & Schultz (2010).

Sexual dimorphism in our classroom compared with what is seen in primates with different mating systems and levels male-male competition. Our class values are the stars, and in the right plot blue is males and green is females. Figures from Plavcan (2012) and Nelson & Schultz (2010).

In this broader comparative context, students tackle what it means for human dimorphism, and ratios of the 2nd digit/4th digit, to be intermediate between what we see in monogamous vs. non-monogamous primates. This can lead some interesting class discussion.

Handout: Lab 3-Sexual dimorphism (Instructions and questions)

ResearchBlogging.orgReferences
 Nelson E, & Shultz S (2010). Finger length ratios (2D:4D) in anthropoids implicate reduced prenatal androgens in social bonding. American Journal of Physical Anthropology, 141 (3), 395-405. PMID: 19862809

Plavcan JM (2012). Sexual size dimorphism, canine dimorphism, and male-male competition in primates: where do humans fit in? Human Nature, 23 (1), 45-67. PMID: 22388772

Bioanthro lab activity: What species is it?

We’re learning about the divergence between robust Australopithecus and early Homo 2.5-ish million years ago in my Human Evolution class this week. Because of this multiplicity of contemporaneous species, when scientists find new hominin fossils in Early Pleistocene sites, a preliminary question is, “What species is it?”

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Scrutinizing the fossil record, asking the difficult questions. (Science credit)

To help my students learn how we know whether certain fossils belong to the same species, and to which group new fossils might belong, in this week’s lab we compared tooth sizes of Australopithecus boisei and early Homo. After seeing how tooth sizes differed between these groups, students then tested whether they could determine whether two “mystery” fossils (KNM-ER 60000 and 62000; Leakey et al. 2012) belonged either group.

Early Pleistocene hominin fossils from Kenya. Left to right: KNM-ER 406, ER 62000 and ER 1470.

Early Pleistocene hominin fossils from Kenya. Left to right: KNM-ER 406, ER 62000 and ER 1470. At the center is one f the lab’s “mystery jaws.”

Students downloaded 3D scans of hominin fossils from AfricanFossils.org, and measured buccolingual/labiolingual tooth crown diameters using MeshLab.

Early Pleistocene hominin mandibles. Left to right: KNM-ER 3230, ER 60000 ("mystery" jaw) and ER 1802.

Early Pleistocene hominin mandibles. Left to right: KNM-ER 3230, ER 60000 (“mystery” jaw) and ER 1802.

The first purpose of this lab was to help familiarize students with skull and tooth anatomy of early Pleistocene humans. Although lectures and readings are full of images, a lab activity forces students to spend time visually examining fossils. Plus, they’re in 3D which is a whole D greater than 2D – the visual equivalent of going to eleven! The second goal of the lab was to help prepare students for their term projects, in which they must pose a research question about human evolution, generate predictions, and find and use data to test hypotheses.

If you’re interested in using or adapting this activity for your class, here are the handout and data sheet into which students enter their measurements. The data sheet specifies the fossils that can be downloaded from africanfossils.org.  Some relevant fossils (i.e., KNM WT 15000 and ER 992) were not included because the 3D scans yield larger measurements than in reality.

Lab 3-Mystery Jaws (instructions and questions)

Lab 3-Mystery jaws data sheet

ResearchBlogging.orgReference
Leakey MG, Spoor F, Dean MC, Feibel CS, Antón SC, Kiarie C, & Leakey LN (2012). New fossils from Koobi Fora in northern Kenya confirm taxonomic diversity in early Homo. Nature, 488 (7410), 201-4 PMID: 22874966

A new year of bioanthro lab activities

One of my goals in teaching is to introduce students to how we come to know things in biological anthropology, and lab activities give students hands-on experience in using scientific approaches to address research questions. Biological anthropology (really, all biology) is about understanding variation, and I’ve created some labs for students to scrutinize biological variation within the classroom.

In my Introduction class, the first aspect of human uniqueness we will focus on is the brain. To complement readings and lectures, we’ll also investigate variation in brain size among students in class. Of course, measuring their actual brain sizes is impossible without either murdering them (unethical and messy) or subjecting them to CT or MRI scanning (costly and time-consuming). Instead, it’s fast and easy to measure head circumference, so we’ll estimate just how brainy they are in a way that will also introduce them to data collection, measurement error, and the regression analysis.

The lab activity is based on a paper by Bartholomeusz and colleagues (2002), who used CT scanning to measure the external head circumferences and brain volumes of males ranging from 1-40 years. Focusing on the adults of this sample, there are several possible regression equations that students could use to estimate their brain size from their head circumference:

The relationship between head circumference and brain volume in adult humans. Note each regression line is based on different age groups.

The relationship between head circumference and brain volume in adult humans. Note each regression line is based on different age groups. Data from Bartholomeusz et al. (2002).

Bartholomeusz et al. divided their sample into age groups, and students will learn that the relationship between the two variables differs subtly depending on the age group. Students will therefore have to decide (and justify) which equation they will use – should they pick the one based on their own age group, or the one with the lowest prediction error?

Once students have estimated their brain sizes, I’ll enter the data into R and we’ll look at how (estimated) brain size varies within the classroom, looking also at possible covariates including sex and region of birth. After discussing our data in class, students have to write up a brief report describing our research question and proposing additional hypotheses about brain size variation.

So that’s this week’s lab in Introduction to Biological Anthropology. There will be four more this semester, in three of which students will collect data on themselves, as well as four other labs for my Human Evolution course. In case you’re interested in using this activity for your class, I’m including the lab handout here. I’ll also try to post lab assignments to the blog (as I’ve done here) as the semester progresses.

Activity handout: Lab 1 Instructions and report

ResearchBlogging.orgReference

Bartholomeusz, H., Courchesne, E., & Karns, C. (2002). Relationship Between Head Circumference and Brain Volume in Healthy Normal Toddlers, Children, and Adults Neuropediatrics, 33 (5), 239-241 DOI: 10.1055/s-2002-36735

Shockingly alarming pedagogical discovery

You heard it here first: class attendance is correlated with test performance. The discovery was made in two undergraduate anthropology courses in Astana, Kazakhstan, though the findings can probably be replicated elsewhere. This result runs counter to the widely held consensus among undergraduate students, that it is not important to attend lectures.

Midterm exam scores (out of 32 points) plotted against class attendance (left) and participation grades (right). Participation is based on in-class quizzes over readings, and so measures students exposure to both lecture and reading.

Figure 1. Midterm exam scores (out of 32 points) plotted against class attendance (left) and participation grades (right), for one biological anthropology class. Correlations and regressions slopes are significantly higher than zero.

Highly paid scientists collected data on students’ midterm exam scores, the number of sessions students were physically present at a lecture (“attendance”), and how they performed on in-class quizzes (“participation”). As quizzes are based on course readings, participation measures active investment beyond simply attendance.

Figure 2. Same variables plotted as in the previous figure, but for a second class.

Figure 2. Same variables plotted as in the previous figure, but for a second class (exam out of 25 points). In addition to linear regression lines (solid black), polynomial regressions (dashed red) were also fit for this class. Polynomial regressions have slightly lower standard errors and slightly higher coefficients of determination. Linear regressions have slopes significantly different from zero while polynomial coefficients are not statistically significant. Either way, more investment generally translate into higher grades.

The researchers were shocked to find positive relationships between students’ exam performance and measures of course participation and active participation. “With the rise of unsourced information on the internet, we assumed students didn’t need to go to class – what could a professor possibly say in lecture that can’t hasn’t already been said on ‘the Net’,” said an out of touch analyst who wasn’t involved in the analysis. The lead investigator of the study remarked, “All college students are hard-working and motivated, so we figured they would read and come to lectures if they knew they’d benefit. Our findings hint that maybe they don’t know everything after all.”

Scientists think these findings have important implications for students everywhere. An empirical link between active participation in class and grades mean that a student’s chances of doing passing or even excelling in a class can improve dramatically with increased attendance. So take note, students: read and go to class! Who knows, you might even learn something from it.

* These are my students’ actual grades and attendance this semester. No undergraduates were harmed in this study.