Maybe our concept of a sock or an apple somehow (structurally) resembles a sock or an apple.
I could start writing pairs of sentences like:
REAL WORLD: feet often have socks on them MY INTUITIVE MODELS: “feet” “often” “have” “socks” “on” “them”
REAL WORLD: socks are usually stretchy MY INTUITIVE MODELS: “socks” “are” “usually” “stretchy”
(… 7000 more things like that …)
If you take all those things, AND the information that all these things wound up in my intuitive models via the process of my brain doing predictive learning from observations of real-world socks over the course of my life, AND the information that my intuitive models of socks tend to activate when I’m looking at actual real-world socks, and to contribute to me successfully predicting what I see … and you mix all that together … then I think we wind up in a place where saying “my intuitive model of socks has by-and-large pretty good veridical correspondence to actual socks” is perfectly obvious common sense. :)
(This is all the same kinds of things I would say if you ask me what makes something a map of London. If it has features that straightforwardly correspond to features of London, and if it was made by someone trying to map London, and if it is actually useful for navigating London in the same kind of way that maps are normally useful, then yeah, that’s definitely a map of London. If there’s a weird edge case where some of those apply but not others, then OK, it’s a weird edge case, and I don’t see any point in drawing a sharp line through the thicket of weird edge cases. Just call them edge cases!)
But what if I’m thinking of the content of your suitcase, and I don’t know whether it is a sock or an apple or something else? Surely the part of the model (my brain) which represents/refers to the content of your suitcase does not in any way (structurally or otherwise) resemble a sock, even if the content of your suitcase is indeed identical to a sock.
Right, if I don’t know what’s in your suitcase, then there will be rather little veridical correspondence between my intuitive model of the inside of your suitcase, and the actual inside of your suitcase! :)
(The statement “my intuitive model of socks has by-and-large pretty good veridical correspondence to actual socks” does not mean I have omniscient knowledge of every sock on Earth, or that nothing about socks will ever surprise me, etc.!)
But Scott and [Yvain] are an object in the territory, not parts of a model, so the parts of the model which do represent Scott and Yvain require the existence of some sort of representation relation.
Oh sorry, I thought that was clear from context … when I say “Scott is one part of M”, obviously I mean something more like “[the part of my intuitive world-model that I would describe as Scott] is one part of M”. M is a model, i.e. data structure, stored in the cortex. So everything in M is a part of a model by definition.
But what if I’m thinking of the content of your suitcase, and I don’t know whether it is a sock or an apple or something else? Surely the part of the model (my brain) which represents/refers to the content of your suitcase does not in any way (structurally or otherwise) resemble a sock, even if the content of your suitcase is indeed identical to a sock.
Right, if I don’t know what’s in your suitcase, then there will be rather little veridical correspondence between my intuitive model of the inside of your suitcase, and the actual inside of your suitcase! :)
(The statement “my intuitive model of socks has by-and-large pretty good veridical correspondence to actual socks” does not mean I have omniscient knowledge of every sock on Earth, or that nothing about socks will ever surprise me, etc.!)
Okay, but then this theory doesn’t explain how we (or a hypothetical ML model) can in fact successfully refer to / think about things which aren’t known more or less directly. Like the contents of the suitcase, the person ringing at the door, the cause of the car failing to start, the reason for birth rate decline, the birthday present, what I had for dinner a week ago, what I will have for dinner tomorrow, the surprise guest, the solution to some equation, the unknown proof of some conjecture, the things I forgot about etc.
What you’re saying is basically: sometimes we know some aspects of a thing, but don’t know other aspects of it. There’s a thing in a suitcase. Well, I know where it is (in the suitcase), and a bit about how big it is (smaller than a bulldozer), and whether it’s tangible versus abstract (tangible). Then there are other things about it that I don’t know, like its color and shape. OK, cool. That’s not unusual—absolutely everything is like that. Even things I’m looking straight at are like that. I don’t know their weight, internal composition, etc.
I don’t need a “theory” to explain how a “hypothetical” learning algorithm can build a generative model that can represent this kind of information in its latent variables, and draw appropriate inferences. It’s not a hypothetical! Any generative model built by a predictive learning algorithm will actually do this—it will pick up on local patterns and extrapolate them, even in the absence of omniscient knowledge of every aspect of the thing / situation. It will draw inferences from the limited information it does have. Trained LLMs do this, and an adult cortex does it too.
I think you’re going wrong by taking “aboutness” to be a bedrock principle of how you’re thinking about things. These predictive learning algorithms and trained models actually exist. If, when you run these algorithms, you wind up with all kinds of edge cases where it’s unclear what is “about” what, (and you do), then that’s a sign that you should not be treating “aboutness” as a bedrock principle in the first place. “Aboutness” is like any other word / category—there are cases where it’s clearly a useful notion, and cases where it’s clearly not, and lots of edge cases in between. The sensible way to deal with edge cases is to use more words to elaborate what’s going on. (“Is chess a sport?” “Well, it’s like a sport in such-and-such respects but it also has so-and-so properties which are not very sport-like.” That’s a good response! No need for philosophizing.)
That’s how I’m using “veridicality” (≈ aboutness) in this series. I defined the term in Post 1 and am using it regularly, because I think there are lots of central cases where it’s clearly useful. There are also plenty of edge cases, and when I hit an edge case, I just use more words to elaborate exactly what’s going on. [Copying from Post 1:] For example, suppose intuitive concept X faithfully captures the behavior of algorithm Y, but X is intuitively conceptualized as a spirit floating in the room, rather than as an algorithm within the Platonic, ethereal realm of algorithms. Well then, I would just say something like: “X has good veridical correspondence to the behavior of algorithm Y, but the spirit- and location-related aspects of X do not veridically correspond to anything at all.” (This is basically a real example—it’s how some “awakened” (Post 6) people talk about what I call conscious awareness in this post.) I think you want “aboutness” to be something more fundamental than that, and I think that you’re wrong to want that.
I could start writing pairs of sentences like:
REAL WORLD: feet often have socks on them
MY INTUITIVE MODELS: “feet” “often” “have” “socks” “on” “them”
REAL WORLD: socks are usually stretchy
MY INTUITIVE MODELS: “socks” “are” “usually” “stretchy”
(… 7000 more things like that …)
If you take all those things, AND the information that all these things wound up in my intuitive models via the process of my brain doing predictive learning from observations of real-world socks over the course of my life, AND the information that my intuitive models of socks tend to activate when I’m looking at actual real-world socks, and to contribute to me successfully predicting what I see … and you mix all that together … then I think we wind up in a place where saying “my intuitive model of socks has by-and-large pretty good veridical correspondence to actual socks” is perfectly obvious common sense. :)
(This is all the same kinds of things I would say if you ask me what makes something a map of London. If it has features that straightforwardly correspond to features of London, and if it was made by someone trying to map London, and if it is actually useful for navigating London in the same kind of way that maps are normally useful, then yeah, that’s definitely a map of London. If there’s a weird edge case where some of those apply but not others, then OK, it’s a weird edge case, and I don’t see any point in drawing a sharp line through the thicket of weird edge cases. Just call them edge cases!)
Right, if I don’t know what’s in your suitcase, then there will be rather little veridical correspondence between my intuitive model of the inside of your suitcase, and the actual inside of your suitcase! :)
(The statement “my intuitive model of socks has by-and-large pretty good veridical correspondence to actual socks” does not mean I have omniscient knowledge of every sock on Earth, or that nothing about socks will ever surprise me, etc.!)
Oh sorry, I thought that was clear from context … when I say “Scott is one part of M”, obviously I mean something more like “[the part of my intuitive world-model that I would describe as Scott] is one part of M”. M is a model, i.e. data structure, stored in the cortex. So everything in M is a part of a model by definition.
Okay, but then this theory doesn’t explain how we (or a hypothetical ML model) can in fact successfully refer to / think about things which aren’t known more or less directly. Like the contents of the suitcase, the person ringing at the door, the cause of the car failing to start, the reason for birth rate decline, the birthday present, what I had for dinner a week ago, what I will have for dinner tomorrow, the surprise guest, the solution to some equation, the unknown proof of some conjecture, the things I forgot about etc.
What you’re saying is basically: sometimes we know some aspects of a thing, but don’t know other aspects of it. There’s a thing in a suitcase. Well, I know where it is (in the suitcase), and a bit about how big it is (smaller than a bulldozer), and whether it’s tangible versus abstract (tangible). Then there are other things about it that I don’t know, like its color and shape. OK, cool. That’s not unusual—absolutely everything is like that. Even things I’m looking straight at are like that. I don’t know their weight, internal composition, etc.
I don’t need a “theory” to explain how a “hypothetical” learning algorithm can build a generative model that can represent this kind of information in its latent variables, and draw appropriate inferences. It’s not a hypothetical! Any generative model built by a predictive learning algorithm will actually do this—it will pick up on local patterns and extrapolate them, even in the absence of omniscient knowledge of every aspect of the thing / situation. It will draw inferences from the limited information it does have. Trained LLMs do this, and an adult cortex does it too.
I think you’re going wrong by taking “aboutness” to be a bedrock principle of how you’re thinking about things. These predictive learning algorithms and trained models actually exist. If, when you run these algorithms, you wind up with all kinds of edge cases where it’s unclear what is “about” what, (and you do), then that’s a sign that you should not be treating “aboutness” as a bedrock principle in the first place. “Aboutness” is like any other word / category—there are cases where it’s clearly a useful notion, and cases where it’s clearly not, and lots of edge cases in between. The sensible way to deal with edge cases is to use more words to elaborate what’s going on. (“Is chess a sport?” “Well, it’s like a sport in such-and-such respects but it also has so-and-so properties which are not very sport-like.” That’s a good response! No need for philosophizing.)
That’s how I’m using “veridicality” (≈ aboutness) in this series. I defined the term in Post 1 and am using it regularly, because I think there are lots of central cases where it’s clearly useful. There are also plenty of edge cases, and when I hit an edge case, I just use more words to elaborate exactly what’s going on. [Copying from Post 1:] For example, suppose intuitive concept X faithfully captures the behavior of algorithm Y, but X is intuitively conceptualized as a spirit floating in the room, rather than as an algorithm within the Platonic, ethereal realm of algorithms. Well then, I would just say something like: “X has good veridical correspondence to the behavior of algorithm Y, but the spirit- and location-related aspects of X do not veridically correspond to anything at all.” (This is basically a real example—it’s how some “awakened” (Post 6) people talk about what I call conscious awareness in this post.) I think you want “aboutness” to be something more fundamental than that, and I think that you’re wrong to want that.