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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