Literature studies for the scientist

Literature scholars are far better equipped to talk intelligently about science than scientists are to discuss the study of literature. So lamented a friend of mine on Facebook recently, based on his experience as a professor of English. It is sad, he went on, that the value of his field is as unappreciated among STEM colleagues as in society at large.

Galvanised by an ensuing stream of arguments and rebuttals, I decided to test the claim by polling fellow scientists on twitter: do you think the study of literature matters in general and to your work? Of the thirteen who replied, five said the study of literature is not useful for science, and two of these said it is generally not useful. It seems my friend has a point.

The poll result is all the more striking given the ambiguity of my question. As one Twitter user put it, did I mean contemplating the works of Shakespeare, Márquez, and Cervantes, or the scientific papers of one’s own discipline? Uncertainty should have prompted more positive responses. But here I will argue that it shouldn’t matter. Blinkered as I am by an exclusively STEM-focussed post-16 education, I can still see several ways in which wisdom gleaned from the study of literature in general — as practiced in university departments of English and their equivalents — might enrich my understanding of the world and help me become a better scientist.

Let’s start with the easiest case. As in any academic field, the literature of science is a messy ecosystem of arguments, counterarguments, modifications, dead ends, and attempts at synthesis. More is written on each topic than any of us could hope to read. We make sense of this textual jungle by being selective; by learning how to discern the strength of evidence; by spotting flawed logic or crucial omissions; and by forming opinions about particular theories, research programmes and researchers. Not all good work is popular, and not all that’s popular is good. Fashion, politics and celebrity matter. And culture matters. For example, in the information age it’s commonplace to make potentially misleading links between DNA and computer code, gene pathways and electrical circuits, or evolution and machine learning. Therefore smart interpretations must account for context. All of which is bread and butter for a literature specialist.

Then there’s how we go about writing the stuff. The standard form for a scientific report describes an orderly progression from question to hypothesis, test, and conclusion. This is of course an artificial narrative imposed on a jumble of events, ideas, and observations. Indeed, many of the finest scientific papers are structured like genre fiction. We shape our science stories according to the idiosyncratic conventions of generalist or specialist journals, conference posters, job talks, and seminar slideshows. Clear communication is notoriously difficult, yet English majors know how to do it better than most.

What about the core of the scientific enterprise: how we attempt to understand reality? Just as painters of the same subject might variously aim to convey light, form, psychology, or narrative, so scientists will draw different features from the same set of observations. Or pursuing the same question, each will design a different set of experiments. Our ways of seeing are informed by training, personality, and taste in problems. Writing on theoretical biology in particular is often akin to philosophy. The Price equation and inclusive fitness theory offer either deep insights or worthless tautologies, depending on who you ask. Humanity scholars can help us recognise and understand unavoidable subjectivity.

My particular way of understanding nature is through mathematical models. A useful model describes an imaginary, internally-consistent system that behaves at least a little like some aspect of reality. Modellers prize simplicity. So do playwrights. If you put a gun in your model then it had better go off. Amalgamate your bit players into composite characters. And consider carefully what fundamental feature — the mathematical MacGuffin — you use to drive the action. I find much the same qualities to admire in an elegant mathematical model and a taut movie plot.

Whereas I’ve focussed here on my area of biology, the arguments extend to all of science. For sure, if your sole aim is to measure the mass of an electron then you needn’t worry so much about epistemology. But the mass of an electron, a star, or an elephant is only interesting inasmuch as it provides a parameter of a predictive theory. And theories — even those as successful as general relativity — are always fair game for debate.

I imagine that a literature scholar would find the above arguments woefully simplistic and unoriginal. And that’s exactly my point. My job as a scientist requires me to interpret more or less subjective literature; to weave narratives; and to identify meaningful patterns while accounting for my biasses and those of others; yet scientific training devotes scant time to any of these difficult skills. Rather than relying on checklists and templates, or trying to reinvent the wheel through trial and error, wouldn’t we do well to learn from those whose knowhow is honed by years of specialist study, founded on generations of scholarship addressing precisely this set of problems? Just as athletes gain from cross-training, we can strengthen our critical faculties by exploring alternative intellectual frameworks. At the very least, we might prepare ourselves to ask more informed questions when we next encounter a scholar who doesn’t work in STEM.

For a more nuanced take on the parallels and contrasts between science and literature, I suggest an interview with my PhD advisor Sunetra Gupta, discussing her dual roles of theoretical biologist and novelist.

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Author: Rob Noble

I use mathematical and computational models to investigate evolutionary and ecological systems. I am currently working, in close collaboration with laboratory scientists, on models of cancer evolution and the development of drug resistance. My methods include game theory, analysis of dynamical systems, spatially structured models, and Bayesian inference. During my PhD at the University of Oxford (2009-2013) I used mathematical models, informed by statistical analysis of laboratory data, to understand the immune evasion mechanisms of the malaria parasite Plasmodium falciparum.

4 thoughts on “Literature studies for the scientist”

  1. Good stuff, Rob. Another avenue to consider that might be interesting for scientists is the role of literature in the history of science. It’s common enough for today’s scientists to see themselves as heirs to the Enlightenment, and to see the Enlightenment in simplified terms as a time when scientists cast off the yoke of religious superstition, etc. But there was no such thing as a ‘scientist’ in the age of Bacon, Newton, Hooke, and Boyle, who understood themselves as ‘natural philosophers’ building upon the likes of Aristotle. And the Royal Society relied significantly on historians and poets to get off the ground in a time when 1) most considered experimentalism a form of quackery and 2) experimentalists hadn’t yet formed a common or reliable set of methodologies for what they were doing (this is why, e.g., the poet and Royal Society member Abraham Cowley’s History of the Royal Society was so important: it was a key public relations text). Boyle was famously cantankerous about prose style because he understood that writing would be key not just to communicating about but fundamentally to understanding the shape and form of the natural world. And I’d even go so far as to say that the ‘invention’ of the novel, such as it was, was a departure from fantastical romance fiction, and was a formal effort to represent the truth of the world more faithfully. So the early novelists (in the period of literature I study) both drew from and contributed to theories of empiricism and experimentalism that gave us ‘science’ in the modern sense. As my ongoing research suggests, the same is true of the concept of data, which entered the English language in the 17th c. and developed into roughly its modern meaning throughout the 18th c., with the rise of experimentalism and innovations in data visualization (Joseph Priestley, William Petty, William Playfair). Most people don’t know that the word ‘data’ (obviously imported from Latin) originally referred to evidence from scripture or scriptural data, ‘things given’ and not to be argued with because they were taken as given by Biblical authority.

    The scientists I know who are even aware of these things tend to treat them as curiosities and cool trivia about the history of science, but the fact is that literature and philosophy have always been inextricable from science, as well as formative in the development of science as we know it. If you’ve never bothered with the writing of Jonathan Swift, Charlotte Lennox, Margaret Cavendish, Aphra Behn, Henry Fielding, Tobias Smollett–to say nothing of texts like Mary Shelley’s Frankenstein, metaphysical poetry, or Abraham Cowley’s “Ode to the Royal Society” or “To Mr. Hobbes”–then it’s hard to get a complete picture of how science developed during the Enlightenment.

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  2. An astute and learned comment (as expected). My original plan included a bit about Darwin and others who brought about the biological enlightenment of the modern synthesis, but I quickly realised that I lack the expertise to say anything very insightful about even the giants of my field. So I settled for more basic utilitarian arguments. There’s a lot I want to learn.

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  3. As I mentioned on twitter, I really like this post.

    In particular, I like your application of Chekhov’s gun to model construction. I find it to be a much better analogy than the quote people (wrongly) attribute to Einstein on “make things as simple as possible but not simpler”. Mostly, because the Chekhov’s gun version it makes the aesthetic and contextual considerations more apparent.

    I also really like the next sentence that encourages us to “[a]malgamate your bit players into composite characters. ” I think that is the bigger issue underlying the ‘reproducibility crisis’ in psychology and bio-medicine. Our theories don’t produce compelling reusable characters, which makes every experiment disjoint from others instead of building on our understanding and expectations of a single role. A few years ago, I also chose a literary analogy to write about this as Sherlock Holmes and the Case of the Missing Replication.

    The only passing comment I took offense to was: ” if your sole aim is to measure the mass of an electron then you needn’t worry so much about epistemology.” I think the electron and other basic ontic units of modern physics provide some of the great examples of having written such compelling and well-fleshed out characters that we assigned them the status of “really existing”. A status that was controversial when they were just bit players.

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  4. I like that analogy for the reproducibility crisis. Perhaps the problem for psychology (and parts of biology) is that there really is an enormous cast of interacting minor characters that can’t be amalgamated. More like George R R Martin (or a soap opera) than Arthur Conan Doyle.

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