Bite-Size Semiotics

The Boring series

What?

Starting August 2025, on Tuesdays I try to post analyses of boring signs. They will have a Boring Tuesdays: prefix to separate them from the rest.

Why?

Some time ago I was exchanging messages with an actual linguist, Dr. Schimkowsky, after I read one of his articles that had been recommended for me by other actual linguist acquaintances. At some point he off-handedly mentioned that there is a sort of built-in bias of etnographic studies where the researchers tend to study things that, well, interest them. This kind of bias is very hard to battle, and probably cannot be avoided per se. And I don't claim that my approach here is even a reasonable solution. But in an experimental approach to at least offset or disturb the bias, I've decided to also analyze some signs that I don't find interesting.

Unsurprisingly it's actually a bit boring to try and analyze things I don't find interesting. Hence I'm calling these posts the Boring series. I'm trying to post these on Tuesdays if I've had the time and energy for boring tasks.

How do I define boring?

That's the question, isn't it. For me boring has connotations similar to mundane, unexciting or predictable. And these are not always bad things. In my daily work I often describe that enterprise AI-solutions should be massive, boring and gray. You don't want to get excited or surprised about your morning porridge, bus route or email system, you want it to function as expected.

Relatedly, I've often thought about the idea that in order to appreciate e.g. trope subverting stories, or movies that avoid cliches, you first need to ingest a lot of culture to know what the (boring) cliches and tropes are. So boring sets the baseline.

From these points of view, I then try to look at signs that do not catch my attention due to some interesting aspect of them. With a risk of going overly poetic, writing this down brings to my mind ideas somewhat related to Shoshin. The way this was once described to me goes something like:

When you walk down a street and see a tree, your brain will automatically fill in a label like "a tree" and you move on. This is sometimes beneficial, as it dumps processing to System 1 instead of invoking System 2. But when a small child walks down the same street and sees the tree they might see a million leaves, a dozen branches, sunlight filtering through the tree and forming shadows, birds and squirrels jumping around, and all of this in constant motion with the blowing of the wind. It will take years of exposure to lots of trees to produce the labeling system that will just project all of the magnificantly different variants of biological systems they see to just "a tree".

Sometimes you want to try and see the tree with such a "beginner's mind" to appreaciate it more.

So I'm not saying that I will start meditating on standard street signs. But I will try to look for examples which I would normally just automatically skip over.

A living journal of insights

Starting off, I've already written up a few queued boring posts and have several more drafts. This has already given me a few insights, which means that the project is already a success. I'm hoping for more insights, so I've set aside this section as a living journal of any insights I find worthy of recording.

22.7.2025 insights on "what would I count as a sign".

What should I include in my list of "allowed targets" of boring signs? If you go and read Peirce and/or Saussure you quickly realize that essentially everything can technically be a sign. Some definitions of a sign excludes pure self-reference from a classical "a sign is a thing that stands for something else", i.e. a sign has to signify something else than just itself.1 But this only stops you for a bit depending on how contrived significations you allow. For example, a random rock on the ground indexes the existence of a physical underlying reality that makes stable matter possible. (Let's not bring Descartes or Matrix to the debate, you get my point.)

But I want to analyze signs that afford at least a bit of analysis. So for now I think I will (mostly) focus on signs that have been purposefully built to convey information as one of their primary functions. (You'll know that I've run out of ideas if I start posting pictures of random blades of grass as "signs that index the existence of water and sunlight in the area".) I guess I'm approaching the literally classical idea of natural vs conventional signs, with my interests being mostly on the conventional side of things.

28.7.2025 insights on the amount of signs

There are so many boring signs. Walking through a supermarket, each price tag is a specifically constructed to convey a message to the viewer. This means that they fit easily in my category of signs that I will want to analyze. But I will not analyze each of them, or even several of them. I will probably analyze a few different looking (boring) price tags. And this will create a sort of massive sampling bias that we can't avoid without having the blog contain 50 posts about interesting signs and 10 000 posts about the price tags I saw in my local supermarket.

My brain keeps bringing up a connection to the concept of typicality in information theory, but I can't get it to properly click.2 But I somehow feel that there is some quantifiable concept here on how much you need repetition or copies for something to be boring. Anything unique tends to be not boring.


  1. I'm not sure how I would compare a random rock being denoted as a sign of itself to a signpost with the text "This sign" on it. 

  2. There the rough idea is that in a set of possible messages, coming from e.g. some communication channel, we often have the set of "typical messages". These are not the most probable messages, but they are "a/the" set with the property that if you sample messages randomly, you tend to get something from the typical set, and the distribution within the typical examples is quite uniform. Usually there is a numerically relatively very small set of typical messages which nevertheless cover most of the probability mass of the full message distribution.