Mind tricks

One of my goals is to uncover the geometry in the computations. Most people see visualizations as cute, but unnecessary  additions.  Maybe with some very limited pedagogical value. Not the real thing.

The really funny thing is that, on average, people tend to take too seriously a visualization. Some animations trigger all sorts of reflexes which mislead the viewers into seeing too much.

The animal is there, skin deep. Eye deep.


A recent example of using visualizations for research  is how I arrived to build a kinesin like molecule by looking at the Y combinator and permutations.

This is a phenomenon which appeared previously in the artificial chemistry chemlambda. Recall how the analysis of the predecessor lambda term,  led to the introduction of chemlambda quines?

Same here.

Chemical computation is a sort of combinatorial movement, if this makes any sense. Lambda calculus or other means towards rigorous notions of computation clash with the reality: chemical computations, in living organisms, say, are horrendously complex movements of atoms and rearrangements of bonds. There is no input, nor output written with numbers. That’s all there is: movements and rearrangements.

Chemlambda marks some points by showing how to take lambda calculus as inspiration, then how we can see some interesting, movements and rearrangements related thing in the behaviour of the lambda term, then how we can exploit this for designing some pure (artificial) chemistry tour de force of unsupervised cascades of reactions which achieve some goal. Unsupervised, random!

OK, so here is a kinesin built in chemlambda. I see it works and I want to play a bit with it and also to show it.

The following animation has stirred some attention on 4chan, and less attention on google+, of course compared with others from the amazing chemlambda collection 🙂


It makes sense, you can relate with the two kinesins which meet together, they salute each other, then they go their way. One of them detaches from the microtubule (a particularly designed one, which allows kinesins to go in both directions, hm, because I can). The other roams a bit, quick, concerned.

It’s the result of randomness, but it conveys the right info, without introducing too much unrelated stuff.

The next one is about 4 kinesins on a circular microtubule.


This is a bit to much. They look like suspiciously quick moving spiders… Not something to relate to.

But still, there is no false suggestion in it.

People love more the following one, where there are 8 kinesins.


It looks like a creature which tries to feel the boundary of the frame. Cool, but misleading, because:

  • the coordinates of nodes of the graph in this representation are irrelevant
  • the boundary of the frame is not part of the model, it means nothing for the molecule.

In chemlambda there is a choice made: chemistry is separated from physics. The chemistry (so to say) part, i.e. the graph rewrites and the algorithm of application, is independent from the d3.js rendering of the evolution of the graph.

But people love to see graphs in space, they love to see boundaries and they relate with things which have an appearance of life (or some meaning).

That’s how we are made, no problem, but it plays mind tricks on us.

A clever influencer would play these tricks in favor of the model…

The viewers, if asked to support the research, would be less willing to do it after seeing the fast moving spiders…

I find this very entertaining!

For the record, here is another direction of thinking, inspired by the same permutations which led me to kinesins.