Sure you can punch in a chemical fingerprint for say the smell of a specific type of rose. Maybe it doesn't matter for the learning process that in an equivalent human experience it was preceeded by someone making you a compliment a couple minutes earlier, or that it was combined with all the other chemical fingerprints present at the moment, like maybe it just rained shortly before and there's a slight smell of wet earth in the air or someone smoking a cigarette walked by and there's minimal leftovers of that, or the window wasn't opened for a couple hours and everything has a slight tint of "used air" to it, which might add a factor of dampened learning, which might be necessary for the specific learning process to happen slow enough to sink in properly etc...

Don't get me wrong I would be curious to see such research done to see whether it would improve anything above the stochastic parrot level - it's just going to take a while to figure out what is even relevant

I think those factors you mentioned are important but ultimately additional context to the main data, i.e. "rose smell". They certainly can add additional meanings and alter how the main data is processed, but they are just "context" added on, just like how the word "lie" is very context dependent and by itself it is nigh impossible to know what it means. Is it a verb? noun? Which verb, lying to someone or lie on a couch?

But an LLM has no problem at all deciphering and processing and most importantly, responding meaningfully to all the ways we can use or encounter the word "lie". I contend that if a model large enough is trained on enough data, the concepts will automatically blend and explain each other sufficiently, or at least enough to cover your example and those similar to it.