I think what you're saying might be a stretch. I strongly believe the brain holds some information in a sequential pattern -- not necessarily all of it or even the majority of it. There's a book called Moonwalking with Einstein that digs into this precise fact -- you can remember sequences much much better if you associate a visual image with each item in the sequence. Sequential association covers a lot of human knowledge but certainly not the spatial aspects. I think it's a big reason why I think LLM's might not be as smart as we think they are. The complex spatial representations are only encoded implicitly via projection on to textual descriptions.

But it does cover the state described in the top comment. A<-B is never going to be easily retrieved if you only experienced A->B during training, regardless if you are a human neural network or an artificial one. Also, you need to define "spatial" better. This is about logic after all and not geometry. Or topology? It's unclear which context you refer to. It's certainly not the topic of this thread.