While it doesn't fully describe it, his category theory diagram reference seems relevant to me.

The stricter of the squares seem to be a homomorphism. But the "looser" ones which don't "preserve structure" after the transformation but "find a new structure" are some of the more interesting ones.

Semantic Bayesian hyper-graphs where each of the percepts have strong correlation between each other.

I’d argue you could bind them tighter by giving the corners strong relationships to each other as well.

We find these sorts of dense correlations pleasing because it’s the natural way we discover meaning. Even though in this case the meaning is fairly superficial.