Someone shared with me a link to a genetics study. The paper is “Behavioural individuality in clonal fish arises despite near-identical rearing conditions” by David Bierbach, Kate L. Laskowski, and Max Wolf. From the abstract:
“Behavioural individuality is thought to be caused by differences in genes and/or environmental conditions. Therefore, if these sources of variation are removed, individuals are predicted to develop similar phenotypes lacking repeatable individual variation. Moreover, even among genetically identical individuals, direct social interactions are predicted to be a powerful factor shaping the development of individuality. We use tightly controlled ontogenetic experiments with clonal fish, the Amazon molly (Poecilia formosa), to test whether near-identical rearing conditions and lack of social contact dampen individuality. In sharp contrast to our predictions, we find that (i) substantial individual variation in behaviour emerges among genetically identical individuals isolated directly after birth into highly standardized environments and (ii) increasing levels of social experience during ontogeny do not affect levels of individual behavioural variation. In contrast to the current research paradigm, which focuses on genes and/or environmental drivers, our findings suggest that individuality might be an inevitable and potentially unpredictable outcome of development.”
Here is what this seems to imply. We don’t as of yet understand (much less are able to identify, isolate, and control) all of the genetic, epigenetic, environmental, etc factors that causally affect and contribute to individual development. Not only that but we don’t understand the complex interaction of those factors, known and unknown. To put it simply, our ignorance is much more vast than our knowledge. We don’t even have enough knowledge to know what we don’t know. But we are beginning to realize that we need to rethink what we thought we knew.
It reminds me of the mouse research where genetically identical mice in environmentally identical conditions led to diverse behavioral results. I’ve mentioned it many times before here in my blog, including a post specifically about it: Of Mice and Men and Environments (also see Heritability & Inheritance, Genetics & Epigenetics, Etc). In the mice post, along with quoting an article, I pointed to a fascinating passage from David Shenk’s book, The Genius in All of Us. Although I was previously aware of the influence of environmental conditions, the research discussed there makes it starkly clear. I was reminded of this because of another discussion about mice research, from Richard Harris’ Rigor Mortis with the subtitle of “How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions” (pp. 79-81):
“Garner said that mice have great potential for biological studies, but at the moment, he believes, researchers are going about it all wrong. For the past several decades, they have pursued a common strategy in animal studies: eliminate as many variables as you can, so you can more clearly see an effect when it’s real. It sounds quite sensible, but Garner believes it has backfired in mouse research. To illustrate this point, he pointed to two cages of genetically identical mice. One cage was at the top of the rack near the ceiling, the other near the floor. Garner said cage position is enough of a difference to affect the outcome of an experiment. Mice are leery of bright lights and open spaces, but here they live in those conditions all the time. “As you move from the bottom of the rack to the top of the rack, the animals are more anxious, more stressed-out, and more immune suppressed,” he said.
“Garner was part of an experiment involving six different mouse labs in Europe to see whether behavioral tests with genetically identical mice would vary depending on the location. The mice were all exactly the same age and all female. Even so, these “identical” tests produced widely different results, depending on whether they were conducted in Giessen, Muenster, Zurich, Mannheim, Munich, or Utrecht. The scientists tried to catalog all possible differences: mouse handlers in Zurich didn’t wear gloves, for example, and the lab in Utrecht had the radio on in the background. Bedding, food, and lighting also varied. Scientists have only recently come to realize that the sex of the person who handles the mice can also make a dramatic difference. “Mice are so afraid of males that it actually induces analgesia,” a pain-numbing reaction that screws up all sorts of studies, Garner said. Even a man’s sweaty T-shirt in the same room can trigger this response.
“Behavioral tests are used extensively in research with mice (after all, rodents can’t tell handlers how an experimental drug is affecting them), so it was sobering to realize how much those results vary from lab to lab. But here’s the hopeful twist in this experiment: when the researchers relaxed some of their strict requirements and tested a more heterogeneous group of mice, they paradoxically got more consistent results. Garner is trying to convince his colleagues that it’s much better to embrace variation than to tie yourself in knots trying to eliminate it.
““Imagine that I was testing a new drug to help control nausea in pregnancy, and I suggested to the [Food and Drug Administration (FDA)] that I tested it purely in thirty-five-year-old white women all in one small town in Wisconsin with identical husbands, identical homes, identical diets which I formulate, identical thermostats that I’ve set, and identical IQs. And incidentally they all have the same grandfather.” That would instantly be recognized as a terrible experiment, “but that’s exactly how we do mouse work. And fundamentally that’s why I think we have this enormous failure rate.”
“Garner goes even further in his thinking, arguing that studies should consider mice not simply as physiological machines but as organisms with social interactions and responses to their environment that can significantly affect their health and strongly affect the experiment results. Scientists have lost sight of that. “I fundamentally believe that animals are good models of human disease,” Garner said. “I just don’t think the way we’re doing the research right now is.”
“Malcolm Macleod has offered a suggestion that would address some of the issues Garner raises: when a drug looks promising in mice, scale up the mouse experiments before trying it in people. “I simply don’t understand the logic that says I can take a drug to clinical trial on the basis of information from 500 animals, but I’m going to need 5,000 human animals to tell me whether it will work or not. That simply doesn’t compute.” Researchers have occasionally run large mouse experiments at multiple research centers, just as many human clinical trials are conducted at several medical centers. The challenge is funding. Someone else can propose the same study involving a lot fewer animals, and that looks like a bargain. “Actually, the guy promising to do it for a third of the price isn’t going to do it properly, but it’s hard to get that across,” Macleod said.”
This is the problem with the framing debate as nature vs nurture (or similar framings such as biology vs culture and organism vs environment). Even when people are aware of the limitations of this frame, the powerful sway it holds over people’s minds causes them to continually fall back on them. Even when I have no interest in such dualistic thinking, some people feel it necessary to categorize the sides of a debate accordingly, where apparently I’m supposed to play the role of ‘nurturist’ in opposition to their ‘biology’ advocacy: “feel your life-force, Benjamin. Come with me to the biology side!” Well, I have no desire to take sides in a false dichotomy. Oddly, this guy trying to win me over to the “biology side” in debate (about human violence and war) is the same person who shared the clonal fish study that demonstrated how genetics couldn’t explain the differences observed. So, I’m not entirely sure what he thinks ‘biology’ means, what ideological commitments it represents in his personal worldview.
(As he has mentioned in our various discussions, his studies about all of this are tied up with his experience as a father who has struggled with parenting and a husband who is recently separated, partly over parenting concerns. The sense of conflict and blame he is struggling with sounds quite serious and I’m sympathetic. But I suspect he is looking for some kind of life meaning that maybe can’t be found where he is looking for it. Obviously, it is a highly personal issue for him, not a disinterested debate of abstract philosophy or scientific hypotheses. I’m starting to think that we aren’t even involved in the same discussion, just talking past one another. It’s doubtful that I can meet him on the level he finds himself, and so I don’t see how I can join him in the debate that seems to matter so much to him. I won’t even try. I’m not in that headspace. We’ve commented on each other’s blogs for quite a while now, but for whatever reason we simply can’t quite fully connect. Apparently, we are unable to agree enough about what is the debate to even meaningfully disagree about a debate. Although he is a nice guy and we are on friendly terms, I don’t see further dialogue going anywhere. *shrug*)
When we are speaking of so-called ‘nature’, this doesn’t only refer to human biology of genetics and physiology of development but also includes supposed junk DNA and epigenetics, brain plasticity and gut-brain connection, viruses and bacteria, parasites and parasite load, allergies and inflammation, microbiome and cultured foods, diet and nutrition, undernourishment and malnutrition, hunger and starvation, food deserts and scarcity, addiction and alcoholism, pharmaceuticals and medicines, farm chemicals and food additives, hormone mimics and heavy metal toxicity, environmental stress and physical trauma, abuse and violence, diseases of affluence and nature-deficit disorder, in utero conditions and maternal bond, etc. All of these alter the expression of genetics, both within a single lifetime of individuals and across the generations of entire populations.
There are numerous varieties of confounding factors. I could also point to sociocultural, structural, and institutional aspects of humanity: linguistic relativity and WEIRD research subjects, culture of trust and culture of honor, lifeways and mazeways, habitus and neighborhood effect, parenting and peers, inequality and segregation, placebos and nocebos, Pygmalion effect and Hawthorne effect, and on and on. As humans are social creatures, one could write a lengthy book simply listing all the larger influences of society.
Many of these problems have become most apparent in social science, but it is far from limited to that area of knowledge. Very similar problems are found in the biological and medical sciences, with the hard sciences having clear overlap with the soft sciences considering social constructions get fed back into scientific research. With mostly WEIRD scientists studying mostly WEIRD subjects, it’s the same WEIRD culture that has dominated nearly all of science and so it is WEIRD biases that have been the greatest stumbling blocks. Plus, with what has been proven from linguistic relativity, we would expect that how we talk about science will shape the research done, the results gained, the conclusions made, and the theories proposed. It’s all of one piece.
The point is that there are no easy answers and certain conclusions. In many ways, science is still in its infancy. We have barely scratched the surface of what potentially could be known. And much of what we think we know is being challenged, which is leading to a paradigm change that we can barely imagine. There is a lot at stake. It goes far beyond abstract theory, hypothetical debate, and idle speculation.
Most importantly, we must never forget that no theory is value-neutral or consequence-free. The ideological worldview we commit to doesn’t merely frame debate and narrow our search for knowledge. There is a real world impact on public policy and human lives, such as when medial research and practice becomes racialized (with a dark past connecting race realism and genetic determinism, racial hygiene and eugenics, medical testing on minorities and the continuing impact on healthcare). All of this raises questions about whether germs are to be treated as invading enemies, whether war is an evolutionary trait, whether addiction is biological, whether intelligence is genetic, whether language is a module in the brain, and whether the ideology of individualism is human nature.
We have come to look to the body for answers to everything. And so we have come to project almost every issue onto the body. It’s too easy to shape scientific theory in such a way that confirms what we already believe and what is self-serving or simply what conforms to the social order. There is a long history of the intentional abuse and unintentional misuse of science. It’s impossible to separate biology from biopolitics.
Worse still, our imaginations are hobbled, making it all that more difficult to face the problems before us. And cultural biases have limited the search for greater knowledge. More than anything, we need to seriously develop our capacity to radically imagine new possibilities. That would require entirely shifting the context and approach of our thinking, maybe to the extent of altering our consciousness and our perception of the world. A paradigm change that mattered at all would be one that went far beyond abstract theory and was able to touch the core of our being. Our failure on this level may explain why so much scientific research has fallen into a rut.
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I’ve been thinking about this for a long time. My thoughts here aren’t exactly new, but I wanted to share some new finds. It’s a topic worth returning to on occasion, as further research rolls in and the experts continue to debate. I’ll conclude with some more from Richard Harris’ Rigor Mortis. Below that are several earlier posts, a few relevant articles, and a bunch of interesting books (just because I love making long lists of books).
How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions
by Richard Harris
There has been no systematic attempt to measure the quality of biomedical science as a whole, but Leonard Freedman, who started a nonprofit called the Global Biological Standards Institute, teamed up with two economists to put a dollar figure on the problem in the United States. Extrapolating results from the few small studies that have attempted to quantify it, they estimated that 20 percent of studies have untrustworthy designs; about 25 percent use dubious ingredients, such as contaminated cells or antibodies that aren’t nearly as selective and accurate as scientists assume them to be; 8 percent involve poor lab technique; and 18 percent of the time, scientists mishandle their data analysis. In sum, Freedman figured that about half of all preclinical research isn’t trustworthy. He went on to calculate that untrustworthy papers are produced at the cost of $28 billion a year. This eye-popping estimate has raised more than a few skeptical eyebrows—and Freedman is the first to admit that the figure is soft, representing “a reasonable starting point for further debate.”
“To be clear, this does not imply that there was no return on that investment,” Freedman and his colleagues wrote. A lot of what they define as “not reproducible” really means that scientists who pick up a scientific paper won’t find enough information in it to run the experiment themselves. That’s a problem, to be sure, but hardly a disaster. The bigger problem is that the errors and missteps that Freedman highlights are, as Begley found, exceptionally common. And while scientists readily acknowledge that failure is part of the fabric of science, they are less likely to recognize just how often preventable errors taint studies.
“I don’t think anyone gets up in the morning and goes to work with the intention to do bad science or sloppy science,” said Malcolm Macleod at the University of Edinburgh. He has been writing and thinking about this problem for more than a decade. He started off wondering why almost no treatment for stroke has succeeded (with the exception of the drug tPA, which dissolves blood clots but doesn’t act on damaged nerve cells), despite many seemingly promising leads from animal studies. As he dug into this question, he came to a sobering conclusion. Unconscious bias among scientists arises every step of the way: in selecting the correct number of animals for a study, in deciding which results to include and which to simply toss aside, and in analyzing the final results. Each step of that process introduces considerable uncertainty. Macleod said that when you compound those sources of bias and error, only around 15 percent of published studies may be correct. In many cases, the reported effect may be real but considerably weaker than the study concludes.
Mostly these estimated failure rates are educated guesses. Only a few studies have tried to measure the magnitude of this problem directly. Scientists at the MD Anderson Cancer Center asked their colleagues whether they’d ever had trouble reproducing a study. Two-thirds of the senior investigators answered yes. Asked whether the differences were ever resolved, only about a third said they had been. “This finding is very alarming as scientific knowledge and advancement are based upon peer-reviewed publications, the cornerstone of access to ‘presumed’ knowledge,” the authors wrote when they published the survey findings.
The American Society for Cell Biology (ASCB) surveyed its members in 2014 and found that 71 percent of those who responded had at some point been unable to replicate a published result. Again, 40 percent of the time, the conflict was never resolved. Two-thirds of the time, the scientists suspected that the original finding had been a false positive or had been tainted by “a lack of expertise or rigor.” ASCB adds an important caveat: of the 8,000 members it surveyed, it heard back from 11 percent, so its numbers aren’t convincing. That said, Nature surveyed more than 1,500 scientists in the spring of 2016 and saw very similar results: more than 70 percent of those scientists had tried and failed to reproduce an experiment, and about half of those who responded agreed that there’s a “significant crisis” of reproducibility.
The batch effect is a stark reminder that, as biomedicine becomes more heavily reliant on massive data analysis, there are ever more ways to go astray. Analytical errors alone account for almost one in four irreproducible results in biomedicine, according to Leonard Freedman’s estimate. A large part of the problem is that biomedical researchers are often not well trained in statistics. Worse, researchers often follow the traditional practices of their fields, even when those practices are deeply problematic. For example, biomedical research has embraced a dubious method of determining whether results are likely to be true by relying far too heavily on a gauge of significance called the p-value (more about that soon). Potential help is often not far away: major universities have biostatisticians on staff who are usually aware of the common pitfalls in experiment design and subsequent analysis, but they are not enlisted as often as they could be. […]
A few years ago, he placed an informal wager of sorts with a few of his colleagues at other universities. He challenged them to come up with the most egregious examples of the batch effect. The “winning” examples would be published in a journal article. It was a first stab at determining how widespread this error is in the world of biomedicine. The batch effect turns out to be common.
Baggerly had a head start in this contest because he’d already exposed the problems with the OvaCheck test. But colleagues at Johns Hopkins were not to be outdone. Their entry involved a research paper that appeared to get at the very heart of a controversial issue: one purporting to show genetic differences between Asians and Caucasians. There’s a long, painful, failure-plagued history of people using biology to support prejudice, so modern studies of race and genetics meet with suspicion. The paper in question had been coauthored by a white man and an Asian woman (a married couple, as it happens), lowering the index of suspicion. Still, the evidence would need to be substantial. […]
The University of Washington team tracked down the details about the microarrays used in the experiment at Penn. They discovered that the data taken from the Caucasians had mostly been produced in 2003 and 2004, while the microarrays studying Asians had been produced in 2005 and 2006. That’s a red flag because microarrays vary from one manufacturing lot to the next, so results can differ from one day to the next, let alone from year to year. They then asked a basic question of all the genes on the chips (not just the ones that differed between Asians and Caucasians): Were they behaving the same in 2003–2004 as they were in 2005–2006? The answer was an emphatic no. In fact, the difference between years overwhelmed the apparent difference between races. The researchers wrote up a short analysis and sent it to Nature Genetics, concluding that the original findings were another instance of the batch effect.
These case studies became central examples in the research paper that Baggerly, Leek, and colleagues published in 2010, pointing out the perils of the batch effect. In that Nature Reviews Genetics paper, they conclude that these problems “are widespread and critical to address.”
“Every single assay we looked at, we could find examples where this problem was not only large but it could lead to clinically incorrect findings,” Baggerly told me. That means in many instances a patient’s health could be on the line if scientists rely on findings of this sort. “And these are not avoidable problems.” If you start out with data from different batches you can’t correct for that in the analysis. In biology today, researchers are inevitably trying to tease out a faint message from the cacophony of data, so the tests themselves must be tuned to pick up tiny changes. That also leaves them exquisitely sensitive to small perturbations—like the small differences between microarray chips or the air temperature and humidity when a mass spectrometer is running. Baggerly now routinely checks the dates when data are collected—and if cases and controls have been processed at different times, his suspicions quickly rise. It’s a simple and surprisingly powerful method for rooting out spurious results.
Over the years breathless headlines have celebrated scientists claiming to have found a gene linked to schizophrenia, obesity, depression, heart disease—you name it. These represent thousands of small-scale efforts in which labs went hunting for genes and thought they’d caught the big one. Most were dead wrong. John Ioannidis at Stanford set out in 2011 to review the vast sea of genomics papers. He and his colleagues looked at reported genetic links for obesity, depression, osteoporosis, coronary artery disease, high blood pressure, asthma, and other common conditions. He analyzed the flood of papers from the early days of genomics. “We’re talking tens of thousands of papers, and almost nothing survived” closer inspection. He says only 1.2 percent of the studies actually stood the test of time as truly positive results. The rest are what’s known in the business as false positives.
The field has come a long way since then. Ioannidis was among the scientists who pushed for more rigorous analytical approaches to genomics research. The formula for success was to insist on big studies, to make careful measurements, to use stringent statistics, and to have scientists in various labs collaborate with one another—“you know, doing things right, the way they should be done,” Ioannidis said. Under the best of these circumstances, several scientists go after exactly the same question in different labs. If they get the same results, that provides high confidence that they’re not chasing statistical ghosts. These improved standards for genomics research have largely taken hold, Ioannidis told me. “We went from an unreliable field to a highly reliable field.” He counts this as one of the great success stories in improving the reproducibility of biomedical science. Mostly. “There’s still tons of research being done the old fashioned way,” he lamented. He’s found that 70 percent of this substandard genomics work is taking place in China. The studies are being published in English-language journals, he said, “and almost all of them are wrong.”
Published retractions tend to be bland statements that some particular experiment was not reliable, but those notices often obscure the underlying reason. Arturo Casadevall at Johns Hopkins University and colleague Ferric Fang at the University of Washington dug into retractions and discovered a more disturbing truth: 70 percent of the retractions they studied resulted from bad behavior, not simply error. They also concluded that retractions are more common in high-profile journals—where scientists are most eager to publish in order to advance their careers. “We’re dealing with a real deep problem in the culture,” Casadevall said, “which is leading to significant degradation of the literature.” And even though retractions are on the rise, they are still rarities—only 0.02 percent of papers are retracted, Oransky estimates.
David Allison at the University of Alabama, Birmingham, and colleagues discovered just how hard it can be to get journals to set the record straight. Some scientists outright refuse to retract obviously wrong information, and journals may not insist. Allison and his colleagues sent letters to journals pointing out mistakes and asking for corrections. They were flabbergasted to find that some journals demanded payment—up to $2,100—just to publish their letter pointing out someone else’s error.
“Most people who work in science are working as hard as they can. They are working as long as they can in terms of the hours they are putting in,” said social scientist Brian Martinson. “They are often going beyond their own physical limits. And they are working as smart as they can. And so if you are doing all those things, what else can you do to get an edge, to get ahead, to be the person who crosses the finish line first? All you can do is cut corners. That’s the only option left you.” Martinson works at HealthPartners Institute, a nonprofit research agency in Minnesota. He has documented some of this behavior in anonymous surveys. Scientists rarely admit to outright misbehavior, but nearly a third of those he has surveyed admit to questionable practices such as dropping data that weakens a result, based on a “gut feeling,” or changing the design, methodology, or results of a study in response to pressures from a funding source. (Daniele Fanelli, now at Stanford University, came to a similar conclusion in a separate study.)
One of Martinson’s surveys found that 14 percent of scientists have observed serious misconduct such as fabrication or falsification, and 72 percent of scientists who responded said they were aware of less egregious behavior that falls into a category that universities label “questionable” and Martinson calls “detrimental.” In fact, almost half of the scientists acknowledged that they personally had used one or more of these practices in the past three years. And though he didn’t call these practices “questionable” or “detrimental” in his surveys, “I think people understand that they are admitting to something that they probably shouldn’t have done.” Martinson can’t directly link those reports to poor reproducibility in biomedicine. Nobody has funded a study exactly on that point. “But at the same time I think there’s plenty of social science theory, particularly coming out of social psychology, that tells us that if you set up a structure this way… it’s going to lead to bad behavior.”
Part of the problem boils down to an element of human nature that we develop as children and never let go of. Our notion of what’s “right” and “fair” doesn’t form in a vacuum. People look around and see how other people are behaving as a cue to their own behavior. If you perceive you have a fair shot, you’re less likely to bend the rules. “But if you feel the principles of distributive justice have been violated, you’ll say, ‘Screw it. Everybody cheats; I’m going to cheat too,’” Martinson said. If scientists perceive they are being treated unfairly, “they themselves are more likely to engage in less-than-ideal behavior. It’s that simple.” Scientists are smart, but that doesn’t exempt them from the rules that govern human behavior.
And once scientists start cutting corners, that practice has a natural tendency to spread throughout science. Martinson pointed to a paper arguing that sloppy labs actually outcompete good labs and gain an advantage. Paul Smaldino at the University of California, Merced, and Richard McElreath at the Max Planck Institute for Evolutionary Anthropology ran a model showing that labs that use quick-and-dirty practices will propagate more quickly than careful labs. The pressures of natural selection and evolution actually favor these labs because the volume of articles is rewarded over the quality of what gets published. Scientists who adopt these rapid-fire practices are more likely to succeed and to start new “progeny” labs that adopt the same dubious practices. “We term this process the natural selection of bad science to indicate that it requires no conscious strategizing nor cheating on the part of researchers,” Smaldino and McElreath wrote. This isn’t evolution in the strict biological sense, but they argue the same general principles apply as the culture of science evolves.
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What do we inherit? And from whom?
Identically Different: A Scientist Changes His Mind
Race Realism, Social Constructs, and Genetics
Race Realism and Racialized Medicine
The Bouncing Basketball of Race Realism
To Control or Be Controlled
Flawed Scientific Research
Human Nature: Categories & Biases
Bias About Bias
“Beyond that, there is only awe.”
Animal studies paint misleading picture by Janelle Weaver
Misleading mouse studies waste medical resources by Erika Check Hayden
A mouse’s house may ruin experiments by Sara Reardon
Curious mice need room to run by Laura Nelson
Male researchers stress out rodents by Alla Katsnelson
Bacteria bonanza found in remote Amazon village by Boer Deng
Case Closed: Apes Got Culture by Corey Binns
Study: Cat Parasite Affects Human Culture by Ker Than
Mind Control by Parasites by Bill Christensen
Human Biodiversity by Jonathan Marks
The Alternative Introduction to Biological Anthropology by Jonathan Marks
What it Means to be 98% Chimpanzee by Jonathan Marks
Tales of the Ex-Apes by Jonathan Marks
Why I Am Not a Scientist by Jonathan Marks
Is Science Racist? by Jonathan Marks
Biology Under the Influence by Lewontin & Levins
Biology as Ideology by Richard C. Lewontin
The Triple Helix by Richard Lewontin
Not In Our Genes by Lewontin & Rose
The Biopolitics of Race by Sokthan Yeng
The Brain’s Body by Victoria Pitts-Taylor
Misbehaving Science by Aaron Panofsky
The Flexible Phenotype by Piersma & Gils
Herding Hemingway’s Cats by Kat Arney
The Genome Factor by Conley & Fletcher
The Deeper Genome by John Parrington
Postgenomics by Richardson & Stevens
The Developing Genome by David S. Moore
The Epigenetics Revolution by Nessa Carey
Epigenetics by Richard C. Francis
Not In Your Genes by Oliver James
No Two Alike by Judith Rich Harris
Identically Different by Tim Spector
The Cultural Nature of Human Development by Barbara Rogoff
The Hidden Half of Nature by Montgomery & Biklé
10% Human by Alanna Collen
I Contain Multitudes by Ed Yong
The Mind-Gut Connection by Emeran Mayer
Bugs, Bowels, and Behavior by Arranga, Viadro, & Underwood
This Is Your Brain on Parasites by Kathleen McAuliffe
Infectious Behavior by Paul H. Patterson
Infectious Madness by Harriet A. Washington
Strange Contagion by Lee Daniel Kravetz
Childhood Interrupted by Beth Alison Maloney
Only One Chance by Philippe Grandjean
Why Zebras Don’t Get Ulcers by Robert M. Sapolsky
Resisting Reality by Sally Haslanger
Nature, Human Nature, and Human Difference by Justin E. H. Smith
Race, Monogamy, and Other Lies They Told You by Agustín Fuentes
The Invisible History of the Human Race by Christine Kenneally
Genetics and the Unsettled Past by Wailoo, Nelson, & Lee
The Mismeasure of Man by Stephen Jay Gould
Identity Politics and the New Genetics by Schramm, Skinner, & Rottenburg
The Material Gene by Kelly E. Happe
Fatal Invention by Dorothy Roberts
Inclusion by Steven Epstein
Black and Blue by John Hoberman
Race Decoded by Catherine Bliss
Breathing Race into the Machine by Lundy Braun
Race and the Genetic Revolution by Krimsky & Sloan
Race? by Tattersall & DeSalle
The Social Life of DNA by Alondra Nelson
Native American DNA by Kim TallBear
Making the Mexican Diabetic by Michael Montoya
Race in a Bottle by Jonathan Kahn
Uncertain Suffering by Carolyn Rouse
Sex Itself by Sarah S. Richardson
Building a Better Race by Wendy Kline
Choice and Coercion by Johanna Schoen
Sterilized by the State by Hansen & King
American Eugenics by Nancy Ordover
Eugenic Nation by Alexandra Minna Stern
A Century of Eugenics in America by Paul A. Lombardo
In the Name of Eugenics by Daniel J. Kevles
War Against the Weak by Edwin Black
Illiberal Reformers by Thomas C. Leonard
Defectives in the Land by Douglas C. Baynton
Framing the moron by Gerald V O’Brien
Imbeciles by Adam Cohen
Three Generations, No Imbeciles by Paul A. Lombardo
Defending the Master Race by Jonathan Peter Spiro
Hitler’s American Model by James Q. Whitman
Beyond Human Nature by Jesse J. Prinz
Beyond Nature and Culture by Philippe Descola
The Mirage of a Space between Nature and Nurture by Evelyn Fox Keller
Biocultural Creatures by Samantha Frost
Dynamics of Human Biocultural Diversity by Elisa J Sobo
Monoculture by F.S. Michaels
A Body Worth Defending by Ed Cohen
The Origin of Consciousness in the Breakdown of the Bicameral Mind by Julian Jaynes
A Psychohistory of Metaphors by Brian J. McVeigh
The Master and His Emissary by Iain McGilchrist
From Bacteria to Bach and Back by Daniel C. Dennett
Consciousness by Susan Blackmore
The Meme Machine by Blackmore & Dawkins
Chasing the Scream by Johann Hari
Don’t Sleep, There Are Snakes by Daniel L. Everett
Dark Matter of the Mind by Daniel L. Everett
Language by Daniel L. Everett
Linguistic Relativity by Caleb Everett
Numbers and the Making of Us by Caleb Everett
Linguistic Relativities by John Leavitt
The Language Myth by Vyvyan Evans
The Language Parallax by Paul Friedrich
Louder Than Words by Benjamin K. Bergen
Out of Our Heads by Alva Noe
Strange Tools by Alva Noë
From Bacteria to Bach and Back by Daniel C. Dennett
The Embodied Mind by Varela, Thompson, & Rosch
Immaterial Bodies by Lisa Blackman
Radical Embodied Cognitive Science by Anthony Chemero
How Things Shape the Mind by Lambros Malafouris
Vibrant Matter by Jane Bennett
Entangled by Ian Hodder
How Forests Think by Eduardo Kohn
The New Science of the Mind by Mark Rowlands
Supersizing the Mind by Andy Clark
Living Systems by Jane Cull
The Systems View of Life by Capra & Luisi
Evolution in Four Dimensions by Jablonka & Lamb
Hyperobjects by Timothy Morton
Sync by Steven H. Strogatz
How Nature Works by Per Bak
Warless Societies and the Origin of War by Raymond C. Kelly
War, Peace, and Human Nature by Douglas P. Fry
Darwinism, War and History by Paul Crook