What is computational neuroscience? (II) What is theory good for?

To answer this question, I need to write about basic notions of epistemology (the philosophy of knowledge). Epistemology is concerned in particular with what knowledge is and how it is acquired.

What is knowledge? Essentially, knowledge is statements about the world. There are two types of statements. First there are specific statements or “observations”, for example, “James has two legs”. But “All men have two legs” is a universal statement: it applies to an infinite number of observations, about men I have seen but also about men I might see in the future. We also call universal statements “theories”.

How is knowledge acquired? The naive view, classical inductivism, consists in collecting a large number of observations and generalizing from them. For example, one notes that all men he has seen so far have two legs, and concludes that all men have two legs. Unfortunately, inductivism cannot produce universal statements with certainty. It is well possible that one day you might see a man with only one leg. The problem is there are always an infinite number of universal statements that are consistent with any finite set of observations. For example, you can continue a sequence of finite numbers with any numbers you want, and it will still give you a possible a sequence of numbers.

Therefore, inductivism cannot guide the development of knowledge. Karl Popper, probably the most influential philosopher of science of the twentieth century, proposed to solve this problem with the notion of falsifiability. What distinguishes a scientific statement from a metaphysical statement is that it can be disproved by an experiment. For example, “all men have two legs” is a scientific statement, because the theory could be disproved by observing a man with one leg. But “there is a God” is not a scientific statement. This is not to say that these statements are true or not true, but that they have a scientific nature or not (but note that, by definition, a metaphysical statement can have no predictable impact on any of our experience, otherwise this would produce a test of that statement).

Popper’s concept of falsifiability has had a huge influence on modern science, and it essentially determines what we call “experimental work” and “theoretical work”. In Popper’s view, an experiment is an empirical test designed to falsify a theory. More generally, it is a situation for which different theories predict different outcomes. Note how this concept is different from the naive idea of “observing the laws of nature”. Laws of nature cannot be “observed” because an experiment is a single observation, whereas a law is a universal statement. Therefore, from a logical standpoint, the role of an experiment is rather to distinguish between otherwise consistent theories.

The structure of a typical experimental paper follows this logic: 1) Introduction, in which the theoretical issues are presented (the different hypotheses about some specific subject), 2) Methods, in which the experiment is described in details, so as to be reproducible, 3) Results, in which the outcomes are presented, 4) Discussion, in which the outcomes are shown to corroborate or invalidate various theories. Thus, an experimental paper is about formulating and performing a critical test of one, or usually several, theories.

Popper’s line of thinking seems to imply that knowledge can only progress through experimental work. Indeed theories can either be logically consistent or inconsistent, so there is no way to distinguish between logically consistent theories. Only empirical tests can corroborate or invalidate theories, and therefore produce knowledge. Hence the occasional demeaning comments that any theoretician has heard, around the idea that theories are mind games for a bunch of smart math-oriented people. That is, theory is useless since only empirical work can produce scientific knowledge.

This is a really paradoxical remark, for theory is the goal of scientific progress. Science is not about accumulating data, it is about finding the laws of nature, a.k.a. theories. It is precisely the predictive nature of science that makes it useful. How can it be that science is about making theories, but that science can only progress through empirical work?

Maybe this is a misunderstanding of Popper’s reasoning. Falsifiability is about how to distinguish between theories. It clarifies what empirical work is about, and what distinguishes science from metaphysics. But it says nothing about how theories are formulated in the first place. Falsifiability is about empirical validation of theories, not about the mysterious process of making theories, which we might say is the “hard problem” of philosophy of science. Yet making theories is a central part of the development of science. Without theory, there is simply no experiment to be done. But more importantly, science is made of theories.

So I can now answer the question I started with. Theories constitute the core of any science. Theoretical work is about the development of theories. Experimental work is about the testing of theories. Accordingly, theoretical papers are organized quite differently from experimental papers, because the methodology is very different, but also because there is no normalized methodology (“how it should be”). A number of computational journals insist on enforcing the structure of experimental papers (introduction / methods / results / discussion), but I believe this is due to the view that simulations are experiments (Winsberg, Philosophy of Science 2001), which I will discuss in another post.

Theory is often depicted as speculative. This is quite right. Theory is, in essence, speculative, since it is about making universal statements. But this does not mean that theory is nonsense. Theories are usually developed so as to be consistent with a body of experimental data, i.e., they have an empirical basis. Biological theories also often include a teleonomic element, i.e., they “make sense”. These two elements impose hard constraints on theories. In fact, they are so constraining that I do not know of any theory that is consistent with all (or even most) experimental data and that makes sense in a plausible ecological context. So theory making is about finding principled ways to explain existing data, and at the same time to explain biological function. Because this is such a difficult task, theoretical work can have some autonomy, in the sense that it can produce knowledge in the absence of new empirical work.

This last point is worth stressing, because it departs significantly from the standard Popperian view of scientific progress, which makes it a source of misunderstandings between theoreticians and experimenters. I am referring to the complexity of biological organisms, shaped by millions of years of evolution. Biological organisms are made of physical things that we understand at some level (molecules), but at the same time they serve a project (the global project being reproductive invariance, in the words of Jacques Monod). That they serve a project is not the simple result of the interaction of these physical elements, rather they are the result of evolutionary pressure. This means that even though on one hand we understand physics, or biophysics, to a high degree of sophistication, and on the other hand there are well established theories of biological function, there still is a huge explanatory gap between the two. This gap is largely theoretical, in the sense that we are looking for a way to make these two aspects logically consistent. This is why I believe theoretical work is so important in biology. It also has two consequences that can be hard to digest for experimenters: 1) theory can be autonomous to some extent (i.e., there can be “good” and “bad” theories, independently of new empirical evidence), 2) theoretical work is not necessarily aimed at making experimental predictions.

This discussion raises many questions that I will try to answer in the next posts:

- Why are theoretical and experimental journals separate?

- Should theories make predictions?

- Should theories be consistent with data?

- What is a “biologically plausible” model? And by the way, what is a model?

- Is simulation a kind of experiment?

Laisser un commentaire

Votre adresse de messagerie ne sera pas publiée. Les champs obligatoires sont indiqués avec *