Instrumentalism in Linguistics
(Note: Unlike my previous posts, this one is not aimed at a general audience. this one’s for linguists)
As a generative linguist, I like to think of myself as a scientist. Certainly, my field is not as mature and developed as physics, chemistry, and biology, but my fellow linguists and I approach language and its relation to human psychology scientifically. This is crucial to our identity. Sure our universities consider linguistics a member of the humanities, and we often share departments with literary theorists, but we’re scientists!
Because it’s so central to our identity, we’re horribly insecure about our status as scientists. As a result of our desire to be seen as a scientific field, we’ve adopted a particular philosophy of science without even realizing it: Instrumentalism.
But, what is instrumentalism? It’s the belief that the sole, or at least primary, purpose of a scientific theory is its ability to generate and predict the outcome of empirical tests. So, one theory is preferable to another if and only if the former better predicts the data than the latter. A theory’s simplicity, intelligibility, or consistency is at best a secondary consideration. Two theories that have the same empirical value can then be compared according to these standards. Generative linguistics seems to have adopted this philosophy, to its detriment.
What’s wrong with instrumentalism? Nothing per se. It definitely has its place in science. It’s perfectly reasonable for a chemist in a lab to view quantum mechanics as an experiment-generating machine. In fact, it might be an impediment to their work to worry about how intelligible QM is. They would be happy to leave that kind of thinking to the theorists and philosophers while they, the experimenter, used the sanitized mathematical expressions of QM to design and carry out their work.
“Linguistics is a science,” the linguist thinks to themself. “ So, linguists ought to behave like scientists.” Then with a glance at the experimental chemist, the linguist adopts instrumentalism. But, there’s a fallacy in that line of thinking: Instrumentalism being an appropriate attitude for some people in a mature science, like chemistry, does not mean it should be the default attitude for people in a nascent science, like linguistics. In fact, there are good reasons for instrumentalism to be only a marginally acceptable attitude in linguistics. Rather, we should judge our theories on the more humanistic measures of intelligibility, simplicity, and self-consistency in addition to consistency with experience.
What’s wrong with instrumentalism in linguistics?
So why can’t linguists be like the chemist in the lab? Why can’t we read the theory, develop the tests of the theory, and run them? There are a number of reasons. First, as some philosophers of science have argued, It is never the case that a theoretical statement is put to the test by an empirical statement, but rather the former is tested by the latter in light of a suite of background assumptions. So, chemists can count the number of molecules in a sample of gas if they know its pressure, volume, and temperature. How do they know, say, the temperature of the gas sample? They use a thermometer, of course, an instrument they trust by virtue of their background assumptions regarding the how matter, in general, and mercury, in particular, are affected by temperature changes. Lucky for chemists, those assumptions have centuries worth of testing and thinking behind them. No such luck for generative linguists, we’ve only got a few decades of testing and thinking behind our assumptions, which is reflected by how few empirical tools we have and how unreliable they are. Our tests for syntactic constituency are pretty good in a few cases — good enough to provide evidence that syntax traffics in constituency — but they give way too many false positives and negatives. Their unreliability means real syntactic work must develop diagnostics which are more intricate and which carry much more theoretical baggage. If a theory is merely a hypothesis-machine, and the tools for testing those hypotheses depend on the theory, how can we avoid rigging the game in our favour?
Suppose we have two theories, T1 and T2, which are sets of statements regarding an empirical domain D. T1 has been rigorously vetted and found to be internally consistent, simple, and intelligible, and predicts 80% of the facts in D. T2 is rife with inconsistencies, hidden complexities, and opaque concepts, but covers 90% of the facts in D. Which is the better theory? Instrumentalism would suggest T2 is the superior theory due to its empirical coverage. Non-dogmatic people might disagree, but I suspect would all be uncomfortable with instrumentalism as the sole arbiter in this case.
The second problem, which exacerbates the first, is that there’s too much data, and it’s too easy to get even more. This has resulted in subdisciplines being further divided into several niches each devoted to a particular phenomenon or group of languages. Such a narrowing of the empirical domain, coupled with an instrumentalist view of theorizing, has frequently led to the development of competing theories of that domain, theories which are largely impenetrable to those conversant with the general theory but uninitiated with the niche in question. This is a different situation from the one described above. In this situation T1 and T2 might each cover 60% of a subdomain D’, but those 60% are overlapping. Each has a core set of facts that the other cannot, as yet, touch, so the two sides take turns claiming parts of the overlap as their sole territory, and no progress is made.
Often it’s the case that one of the competing specific theories is inconsistent with the general theory, but proponents of the other theory don’t use that fact in their arguments. In their estimation the data always trumps theory, regardless of how inherently theory-laden the description of the data is. It’s as if two factions were fighting each other with swords despite the fact that one side had a cache of rifles and ammunition that they decided not to use.
The third problem, one that has been noted by other theory-minded linguists here and here, is that the line between theoretical and empirical linguistics is blurry. To put it a bit more strongly, what is called “theoretical linguistics” is often empirical linguistics masquerading as theoretical. This assertion becomes clear when we look at the usual structure of a “theoretical syntax” paper in the abstract. First, a grammatical phenomenon is identified and demonstrate. After some discussion of previous work, the author demonstrates the results of some diagnostics and from those results gives a formal analysis of the phenomenon. If we translated this into the language of a mature science it would be indistinguishable from an experimental report. A phenomenon is identified and discussed, the results of some empirical techniques are reported, and an analysis is given.
You might ask “So what? Who cares what empirical syntacticians call themselves?” Well, if you’re a “theoretical syntactician,” then you might propose a modification of syntactic theory to make your empirical analysis work, and other “theoretical syntacticians” will accept those modifications and propose some modifications of their own. It doesn’t take too long in this cycle before the standard theory is rife with inconsistencies, hidden complexities, and opaque concepts. None of that matters, however, if your goal is just to cover the data.
Or, to take another common “theoretical” move, suppose we find an empirical generalization, G (e.g., All languages that allow X also allow Y), the difficult task of the theoretician is to show that G follows from independently motivated theoretical principles. The “theoretician,” on the other hand, has another path available, which is to restate G in “theoretical” terms (e.g., Functional head, H, is responsible for both X and Y), and then (maybe) go looking for some corroboration. Never mind that restating G in different terms does nothing to expand our understanding of why G holds, but understanding is always secondary for instrumentalism.
So, what’s to be done?
Reading this, you might think I don’t value empirical work in linguistics, which is simply not the case. Quite frankly, I am constantly in awe of linguists who can take a horrible mess of data and make even a modicum sense out of it. Empirical work has value, but linguistics has somehow managed to both over- and under-value it. We over-value it by tacitly embracing instrumentalism as our guiding philosophy. We under-value it by giving the title “theoretical linguist” a certain level of prestige. We think empirical work is easier and less-than. This has led us to under-value theoretical work, and view theoretical arguments as just gravy when they’re in our favour, and irrelevancies when they’re against us.
What we should strive for, is an appropriate balance between empirical and theoretical work. To get to that balance we must do the unthinkable and look to the humanities. To develop as a science, we ought to look at mature sciences, not as they are now, but as they developed. Put another way, we need to think historically. If we truly want our theory to explain the human language faculty, we need to accept that we will be explaining it to humans and designing a theory that another human can understand requires us to embrace our non-rational qualities like intuition and imagination.
In sum, we could all use a little humility. Maybe we’ll reach a point when instrumentalism will work for empirical linguistics, but we’re not there yet, and pretending we are won’t make it so.