Normal Science: Chapter 2 of Kuhn

I've been going through Thomas Kuhn's book/essay The Structure of Scientific Revolutions, and I'm sharing my thoughts as I go (Chapter 1). Chapter 2 is about what Normal Science is and how it comes about from the general confusion that precedes the first settled scientific paradigm in a given field. The chapter feels a bit like it's set up chronologically backwards working from what being inside of a paradigm is like, then going back to how the paradigm forms, and finally talking a bit about what doing science pre-paradigm is like.

I think I'll go in chronological order instead, using an example like the early study of electricity. Now by "early" I don't mean going back before Newtonian mechanics became a thing, but early enough that we really only know that a few strange things are happening but no clue why. So actually let's back up one more step and say that you don't want to study electricity, but instead you want to study why static shocks happen. After all, "studying electricity" implies that you know a whole bunch of events and data are connected. In your study of static shocks, you learn that rubbing certain materials causes shocks more than others, sometimes. You learn that certain combinations work better. You also might realize that shocks come off of batteries (or rudimentary things like them). But now you have to try to put all of this together. Here are some of Kuhn's thoughts:

In the absence of a paradigm or some candidate for paradigm, all of the facts that could possibly pertain to the development of a given science are likely to seem equally relevant. As a result, early fact-gathering is a far more nearly random activity than the one that subsequent scientific development makes familiar. Furthermore, in the absence of a reason for seeking some particular form of more recondite information, early fact-gathering is usually restricted to the wealth of data that lie ready to hand.

So before you get started, you have no idea if anything could affect your measurements of static. Time of day, how dusty your floor is, what shoes you wear, and whether you hold your pinky out or not are all variables that could somehow matter. In the case of static, air moisture actually does matter and was probably really hard to control or even measure. I can imagine a number of scientists trying to discern esoteric patterns out of the day to day (or yearlong) fluctuations caused by moisture in the air. Suppose, after years and years of data collection and analysis, they finally did figure it out without a more general theory of electricity. All that analysis would tell them is that water makes shocks more conductive. I suppose there could be some benefit there, but from a modern perspective it seems like a big distraction.

Also, without a paradigm, you have no way of agreeing with any data collected by someone else studying the same thing. If any variable could matter, then it's impossible to report your results. Did you collect your data over multiple days? Did you include the moisture level? If I'm someone who thinks that's incredibly important, and all you want to talk about is the materials you rub together to make static shocks, you may not have taken the time and energy to collect the moisture data (or the data that some third person really cares about). If one hundred people all think something different matters to the creation of the static shocks, then collecting the right data to discuss becomes practically impossible.

So now we see we need some way to agree on what's relevant and what isn't. To do that we need a paradigm. More Kuhn:

Men whose research is based on shared paradigms are committed to the same rules
and standards for scientific practice. That commitment and the apparent consensus it produces are prerequisites for normal science, i.e., for the genesis and continuation of a particular research tradition.

...

No wonder, then, that in the early stages of the development of any science different men confronting the same range of phenomena, but not usually all the same particular phenomena, describe and interpret them in different ways. What is surprising, and perhaps also unique in its degree to the fields we call science, is that such initial divergences should ever largely disappear.

So once you've gathered enough data early on, and you convince a few fellow researchers that some set of parameters are what matter, you're on your way to studying static. Another aspect of the paradigm (mentioned above) is the way you interpret your data. I actually think that the parameter paradigm and the interpretation paradigm are separate things. Both make it easier to communicate to likeminded researchers, but the data is still the data. A highly religions mystic and a hard materialist could agree on a parameter paradigm but then one would interpret electricity as the wrath of spirits while the other might interpret it as the emission of stored energy. Those disagreements will make it harder to agree on what follow-up experiments should be, and peer review will likely be tricky, but the data itself would still be acceptable.

After all of this is agreed upon, we get to do what Kuhn calls normal science. Kuhn sometimes talks as though normal science happens most of the time, but at other times, when he's talking about paradigms within a very specialized field, it makes me wonder if paradigms are almost always shifting. In psychological research there is constant discussion and disagreement about which human behaviors, optical illusions, or EEG signals should be lumped together and why. My only experience with something like normal science was when I was on a team designing a sonar array (set of microphones on a string behind the boat). Even in that case, where the behavior of sound in water was almost entirely agreed upon, when I came up with some unintuitively good results (a computer model of the array could detect torpedoes in the water better than we expected), and someone else replicated them, everyone was still incredibly skeptical. In spite of having a shared paradigm, it felt as though pushing people out of their comfort zone still required a shift in how they thought, eventually using non-simulated data. In the end, though, this process of convincing the Navy using real data probably does fall within normal science because there was a final agreed-upon test of the real system that would prove I was right.

One interesting note from this chapter is that Kuhn spends a few pages talking about book-making. It took me a while to figure out what he meant, but I guess in his day, writing a book would be done if you're addressing the public while just writing journal articles or reports would be for one's peers.

Only in the earlier, pre-paradigm, stages of the development of the various sciences did the book ordinarily possess the same relation to professional achievement that it still retains in other creative fields. And only in those fields that still retain the book, with or without the article, as a vehicle for research communication are the lines of professionalization still so loosely drawn that the layman may hope to follow progress by reading the practitioners’ original reports.
...

Although it has become customary, and is surely proper, to deplore the widening gulf that separates the professional scientist from his colleagues in other fields, too little attention is paid to the essential relationship between that gulf and the mechanisms intrinsic to scientific advance.

So Kuhn is arguing that because of the community that is created by a scientific paradigm, it separates from the lay public and develops its own subculture, which in turn makes it harder for the scientists in that subculture to communicate with others. People often complain that scientists are terrible public speakers, and this gives a reason why that may actually be necessary. It's an interesting thought, and it leaves open the possibility that scientific translation may become more valuable as science progresses. I'll just leave a link to the interesting idea of research debt that I've seen a few times this week. If Kuhn is right that the development of a paradigm automatically separates the scientists from the lay community, then I think the solution to bringing research to a wider audience is more nuanced than many people presume.

PS - I improved the layout a bit. I hope it looks decent.

Scientific Method: Chapter 1 of "The Structure of Scientific Revolutions" by Kuhn

Before I get started, here's the PDF I'm using.

I started thinking about this post a while ago because I wanted to talk about what the scientific method is like in reality as opposed to how it works according to textbooks and simple graphs like the one below (taken from here).

After looking around a little, I realized I could try to reinvent the wheel or I could start from what other people have said and go from there. I listened to a discussion on this book (Kuhn calls his over 100 page book an essay), and it sounded like the author has something interesting to say, though I will probably disagree enough that it will be interesting to write about my thoughts.

Kuhn starts off by talking about how the field of history has a role to play in upending the textbook story of how science progresses. According to Kuhn, textbook progression of science is just like the diagram above, and importantly the textbook progression also says that we are steadfastly marching towards the truth. Go Science! But, Kuhn says, that's not how science actually works. We often ignore data points as outliers. You may actually remember doing things like this in statistics class with box and whisker plots. Scientists also tend to get stuck on particular compelling theories, even when the data doesn't entire support them. Sometimes the holders of a previous theory have to die off before a new one is fully accepted. I can imagine many Newtonian physicists were too appalled by relativity to ever embrace it (hopefully covered in a later chapter). Lastly, Kuhn discusses how the order of discovery seems to affect which theories get adopted. He talks about how a new scientist entering a field with scientific skills but no knowledge of that field will likely arrive at a very different theory from another person doing the same thing, depending on which order experiments are done in.

Those are the phenomena that Kuhn is trying to explain. An interesting connection this sparked for me is that the rejection of outliers is also connected to why scientists don't often report negative results. If a project is going horribly, it may be because you aren't using your equipment right, or training an animal correctly, or any number of other completely scientifically boring reasons. When I meet with professors to discuss a work in progress, the first five or so iterations of the work are completely unpublishable because minor errors make the analysis (or the collected data) meaningless. Kuhn does raise an interesting point that properly identifying these sorts of errors is crucial to doing science well, and it is hard to tell (especially as an outsider) what should be categorized as an outlier and what should be categorized as an interesting anomaly. It is common practice for a scientist to tell another scientist that they must have done something wrong because the data "looks off". This is usually good advice.

Kuhn then goes on to introduce the idea of a scientific paradigm, which consists of a set of standards for what is normal, what amounts of error are acceptable in certain measurements, and what interpretations of data are valid. He says that normal science works basically as described in the diagram above, with the standards all in place. Here's a quote on it:

Normal science, the activity in which most scientists inevitably spend almost all their time, is predicated on the assumption that the scientific community knows what the world is like. Much of the success of the enterprise derives from the community’s willingness to defend that assumption, if necessary at considerable cost. Normal science, for example, often suppresses fundamental novelties because they are necessarily subversive of its basic commitments.

He's quite persuasive in his writing, but I have to admit I disagree with his reasoning about why scientists behave this way. The suppression of fundamental novelties is because the scientist is acting entirely reasonably. Taking the motion of the planets as an example, a scientists who believed in Newtonian mechanics would be quite justified, having seen both the discovery of Neptune from the orbits of other planets, and the regular prediction of every planet's motion except Mercury. So being that scientist, do you really expect that you need to completely rethink everything you know about time and space? Maybe instead, there is something strange about Mercury that we haven't discovered. Can it be explained by strange composition? Maybe tidal forces with the sun? An invisible moon? Who knows? And furthermore, I regularly read about physics professors having crackpots tell them they solved every physics problem with some mystical-sounding theory (neuroscience has it's share too). If the new theory sounds like one of those, it's not surprising that the professor might take extraordinary persuasion to overturn Newton. At the very least, I hope Kuhn will discuss this as a very reasonable possibility, and if he does reject it I hope it is with good reason.

Kuhn wants to draw a distinction between Normal science as described above and times of Paradigm shift, when the concepts in use are changing, the standards shift, and a new paradigm emerges, under which we continue to do normal science again. I'm not sure the two are so separate, but maybe that is because the field of neuroscience is relatively young. In neuroscience we know we are unable to measure most of the variables that affect what we care to study, so new theories are practically expected, though if someone claimed magic rays caused our thoughts and it had nothing to do with neurons I would certainly be dismissive.

As a final caveat to all of this, I have heard (though I don't think he mentioned in this chapter) that Kuhn believes that the data itself changes when a paradigm shift occurs. Whatever he means by that, I hope that he doesn't mean that repeating a procedure after the shift will produce a different outcome. That way lies madness.