I think the human brain answer is close to “Flower = instance of a recurring pattern in the data, defined by clustering” with an extra footnote that we also have easy access to patterns that are compositions of other known patterns. For example, a recurring pattern of “rubber” and recurring pattern of “wine glass” can be glued into a new pattern of “rubber wine glass”, such that we would immediately recognize one if we saw it. (There may be other footnotes too.)
Given that I believe that’s the human brain answer, I’m immediately skeptical that a totally different approach could reliably give the same answer. I feel like either there’s gotta be lots of cases where your approach gives results that we humans find unintuitive, or else you’re somehow sneaking human intuition / recurring patterns into your scheme without realizing it. Having said that, everything you wrote sounds reasonable, I can’t point to any particular problem. I dunno.
I didn’t really talk about it in the OP, but I think the OP’s approach naturally pops out if we’re doing roughly-Bayesian-clustering (especially in a lazy way).
The key question is: if we don’t directly observe the low-level structure of the flower, why do we believe that it’s consistent over time (to some extent) in the first place? The answer to that question is that there’s some clustering/Bayesian model comparison going on. We deduce the existence of some hidden variables which cause those predictable patterns in our observations, and those hidden variables are exactly the low-level structure which the OP talks about.
The neat thing about the approach in the OP is that we’re talking about seeing the underlying structure behind the cluster more directly; we’ve removed the human observer from the picture, and grounded things at a lower level, so it’s less dependent on the exact data which the observer receives.
Another way to put it: the OP’s approach is to look for the sort of structures which roughly-Bayesian-clustering could, in principle, be able to identify.
If you’re saying that “consistent low-level structure” is a frequent cause of “recurring patterns”, then sure, that seems reasonable.
Do they always go together?
If there are recurring patterns that are not related to consistent low-level structure, then I’d expect an intuitive concept that’s not an OP-type abstraction. I think that happens: for example any word that doesn’t refer to a physical object: “emotion”, “grammar”, “running”, “cold”, …
If there are consistent low-level structures that are not related to recurring patterns, then I’d expect an OP-type abstraction that’s not an intuitive concept. I can’t think of any examples. Maybe consistent low-level structures are automatically a recurring pattern. Like, if you make a visualization in which the low-level structure(s) is highlighted, you will immediately recognize that as a recurring pattern, I guess.
I think the human brain answer is close to “Flower = instance of a recurring pattern in the data, defined by clustering” with an extra footnote that we also have easy access to patterns that are compositions of other known patterns. For example, a recurring pattern of “rubber” and recurring pattern of “wine glass” can be glued into a new pattern of “rubber wine glass”, such that we would immediately recognize one if we saw it. (There may be other footnotes too.)
Given that I believe that’s the human brain answer, I’m immediately skeptical that a totally different approach could reliably give the same answer. I feel like either there’s gotta be lots of cases where your approach gives results that we humans find unintuitive, or else you’re somehow sneaking human intuition / recurring patterns into your scheme without realizing it. Having said that, everything you wrote sounds reasonable, I can’t point to any particular problem. I dunno.
I didn’t really talk about it in the OP, but I think the OP’s approach naturally pops out if we’re doing roughly-Bayesian-clustering (especially in a lazy way).
The key question is: if we don’t directly observe the low-level structure of the flower, why do we believe that it’s consistent over time (to some extent) in the first place? The answer to that question is that there’s some clustering/Bayesian model comparison going on. We deduce the existence of some hidden variables which cause those predictable patterns in our observations, and those hidden variables are exactly the low-level structure which the OP talks about.
The neat thing about the approach in the OP is that we’re talking about seeing the underlying structure behind the cluster more directly; we’ve removed the human observer from the picture, and grounded things at a lower level, so it’s less dependent on the exact data which the observer receives.
Another way to put it: the OP’s approach is to look for the sort of structures which roughly-Bayesian-clustering could, in principle, be able to identify.
If you’re saying that “consistent low-level structure” is a frequent cause of “recurring patterns”, then sure, that seems reasonable.
Do they always go together?
If there are recurring patterns that are not related to consistent low-level structure, then I’d expect an intuitive concept that’s not an OP-type abstraction. I think that happens: for example any word that doesn’t refer to a physical object: “emotion”, “grammar”, “running”, “cold”, …
If there are consistent low-level structures that are not related to recurring patterns, then I’d expect an OP-type abstraction that’s not an intuitive concept. I can’t think of any examples. Maybe consistent low-level structures are automatically a recurring pattern. Like, if you make a visualization in which the low-level structure(s) is highlighted, you will immediately recognize that as a recurring pattern, I guess.
Yeah, these seem right.