I don’t significantly disagree, but I feel uneasy about a few points.
Theories of the sort I take you to be gesturing at often emphasize this nice aspect of their theory, that bottom-up attention (ie attention due to interesting stimulus) can be more or less captured by surprise, IE, local facts about the shifts in probabilities.
I agree that this seems to be a very good correlate of attention. However, the surprise itself wouldn’t seem to be the attention.
Surprise points merit extra computation. In terms of belief prop, it’s useful to prioritize the messages which are creating the biggest belief shifts. The brain is parallel, so you might think all messages get propagated regardless, but of course, the brain also likes to conserve resources. So, it makes sense that there’d be a mechanism for prioritizing messages.
Yet, message prioritization (I believe) does not account adequately for our experience.
There seems to be an additional mechanism which places surprising content into the global workspace (at least, if we want to phrase this in global workspace theory).
What if we don’t like global workspace theory?
Another idea that I think about here is: the brain’s “natural grammar” might be a head grammar. This is the fancy linguistics thing which sort of corresponds to the intuitive concept of “the key word in that sentence”. Parsing consists not only of grouping words together hierarchically into trees, but furthermore, whenever words are grouped, promoting one of them to be the “head” of that phrase.
In terms of a visual hierarchy, this would mean “some low level details float to the top”.
This would potentially explain why we can “see low-level detail” even if we think the rest of the brain primarily consumes the upper layers of the visual hierarchy. We can focus on individual leafs, even while seeing the whole tree as a tree, because we re-parse the tree to make that leaf the “head”. We see a leaf with a tree attached.
Maybe.
Without a mechanism like this, we could end up somewhat trapped into the high-level descriptions of what we see, leaving artists unable to invent perspective drawings, and so on.
bottom-up attention (ie attention due to interesting stimulus) can be more or less captured by surprise
Hmm. That’s not something I would have said.
I guess I think of two ways that sensory inputs can impact top-level processing.
First, I think sensory inputs impact top-level processing when top-level processing tries to make a prediction that is (directly or indirectly) falsified by the sensory input, and that prediction gets rejected, and top-level processing is forced to think a different thought instead.
If top-level processing is “paying close attention to some aspect X of sensory input”, then that involves “making very specific predictions about aspect X of sensory input”, and therefore the predictions are going to keep getting falsified unless they’re almost exactly tracking the moment-to-moment status of X.
On the opposite extreme, if top-level processing is “totally zoning out”, then that involves “not making any predictions whatsoever about sensory input”, and therefore no matter what the sensory input is, top-level processing can carry on doing what it’s doing.
In between those two extremes, we get the situation where top-level processing is making a pretty generic high-level prediction about sensory input, like “there’s confetti on the stage”. If the confetti suddenly disappeared altogether, it would falsify the top-level hypothesis, triggering a search for a new model, and being “noticed”. But if the detailed configuration of the confetti changes—and it certainly will—it’s still compatible with the top-level prediction “there’s confetti on the stage” being true, and so top-level processing can carry on doing what it’s doing without interruption.
So just to be explicit, I think you can have a lot of low-level surprise without it impacting top-level processing. In the confetti example, down in low-level V1, the cortical columns are constantly being surprised by the detailed way that each piece of confetti jiggles around as it falls, I think, but we don’t notice if we’re not paying top-down attention.
The second way that I think sensory inputs can impact top-level processing is by a very different route, something like sensory input → amygdala → hypothalamus → top-level processing. (I’m not sure of all the details and I’m leaving some things out; more HERE.) I think this route is kinda an autonomous subsystem, in the sense that top-down processing can’t just tell it what to do, and it’s not trained on the same reward signal as top-level processing is, and the information can flow in a way that totally bypasses top-level processing. The amygdala is trained (by supervised learning) to activate when detecting things that have immediately preceded feelings of excitement / scared / etc. previously in life, and the hypothalamus is running some hardcoded innate algorithm, I think. (Again, more HERE.) When this route activates, there’s a chain of events that results in the forcing of top-level processing to start paying attention to the corresponding sensory input (i.e. start issuing very specific predictions about the corresponding sensory input).
I guess it’s possible that there are other mechanisms besides these two, but I can’t immediately think of anything that these two mechanisms (or something like them) can’t explain.
What if we don’t like global workspace theory?
I dunno, I for one like global workspace theory. I called it “top-level processing” in this comment to be inclusive to other possibilities :)
I don’t significantly disagree, but I feel uneasy about a few points.
Theories of the sort I take you to be gesturing at often emphasize this nice aspect of their theory, that bottom-up attention (ie attention due to interesting stimulus) can be more or less captured by surprise, IE, local facts about the shifts in probabilities.
I agree that this seems to be a very good correlate of attention. However, the surprise itself wouldn’t seem to be the attention.
Surprise points merit extra computation. In terms of belief prop, it’s useful to prioritize the messages which are creating the biggest belief shifts. The brain is parallel, so you might think all messages get propagated regardless, but of course, the brain also likes to conserve resources. So, it makes sense that there’d be a mechanism for prioritizing messages.
Yet, message prioritization (I believe) does not account adequately for our experience.
There seems to be an additional mechanism which places surprising content into the global workspace (at least, if we want to phrase this in global workspace theory).
What if we don’t like global workspace theory?
Another idea that I think about here is: the brain’s “natural grammar” might be a head grammar. This is the fancy linguistics thing which sort of corresponds to the intuitive concept of “the key word in that sentence”. Parsing consists not only of grouping words together hierarchically into trees, but furthermore, whenever words are grouped, promoting one of them to be the “head” of that phrase.
In terms of a visual hierarchy, this would mean “some low level details float to the top”.
This would potentially explain why we can “see low-level detail” even if we think the rest of the brain primarily consumes the upper layers of the visual hierarchy. We can focus on individual leafs, even while seeing the whole tree as a tree, because we re-parse the tree to make that leaf the “head”. We see a leaf with a tree attached.
Maybe.
Without a mechanism like this, we could end up somewhat trapped into the high-level descriptions of what we see, leaving artists unable to invent perspective drawings, and so on.
Hmm. That’s not something I would have said.
I guess I think of two ways that sensory inputs can impact top-level processing.
First, I think sensory inputs impact top-level processing when top-level processing tries to make a prediction that is (directly or indirectly) falsified by the sensory input, and that prediction gets rejected, and top-level processing is forced to think a different thought instead.
If top-level processing is “paying close attention to some aspect X of sensory input”, then that involves “making very specific predictions about aspect X of sensory input”, and therefore the predictions are going to keep getting falsified unless they’re almost exactly tracking the moment-to-moment status of X.
On the opposite extreme, if top-level processing is “totally zoning out”, then that involves “not making any predictions whatsoever about sensory input”, and therefore no matter what the sensory input is, top-level processing can carry on doing what it’s doing.
In between those two extremes, we get the situation where top-level processing is making a pretty generic high-level prediction about sensory input, like “there’s confetti on the stage”. If the confetti suddenly disappeared altogether, it would falsify the top-level hypothesis, triggering a search for a new model, and being “noticed”. But if the detailed configuration of the confetti changes—and it certainly will—it’s still compatible with the top-level prediction “there’s confetti on the stage” being true, and so top-level processing can carry on doing what it’s doing without interruption.
So just to be explicit, I think you can have a lot of low-level surprise without it impacting top-level processing. In the confetti example, down in low-level V1, the cortical columns are constantly being surprised by the detailed way that each piece of confetti jiggles around as it falls, I think, but we don’t notice if we’re not paying top-down attention.
The second way that I think sensory inputs can impact top-level processing is by a very different route, something like sensory input → amygdala → hypothalamus → top-level processing. (I’m not sure of all the details and I’m leaving some things out; more HERE.) I think this route is kinda an autonomous subsystem, in the sense that top-down processing can’t just tell it what to do, and it’s not trained on the same reward signal as top-level processing is, and the information can flow in a way that totally bypasses top-level processing. The amygdala is trained (by supervised learning) to activate when detecting things that have immediately preceded feelings of excitement / scared / etc. previously in life, and the hypothalamus is running some hardcoded innate algorithm, I think. (Again, more HERE.) When this route activates, there’s a chain of events that results in the forcing of top-level processing to start paying attention to the corresponding sensory input (i.e. start issuing very specific predictions about the corresponding sensory input).
I guess it’s possible that there are other mechanisms besides these two, but I can’t immediately think of anything that these two mechanisms (or something like them) can’t explain.
I dunno, I for one like global workspace theory. I called it “top-level processing” in this comment to be inclusive to other possibilities :)