But that’s not how Richard responded. He literally restated the problem in different terminology, replacing the problems with black boxes that have the solution inside
I was being flippant. I mean, what were you expecting? Imagine that the person who first had the idea that thinking is done by neurons has just published it, and you ask them what you asked me. What can he tell you about finding a girlfriend? Only that it’s done by neurons. The leg work to discover just how those neurons are organised to do it is the problem, and finding a mate isn’t the place to start, experiments like Hubel and Wiesel’s on cat vision are the place to start, or mapping the nervous system of C. elegans.
Likewise, I’m not passing off “it’s done by control systems” as the solution of a problem, but as a basic insight that gives the beginning of a way to study living organisms. The place to begin that study and establish exactly what control systems are present and how they work is in studies like the one that you dismissed as a trivial game.
That’s what real work looks like. Have a spade. A start has been made at the various PCT links I’ve posted. Maybe in 50 years you’ll get an answer. But don’t be downhearted—it’s been more than a century so far for “it’s made of neurons”.
The place to begin that study and establish exactly what control systems are present and how they work is in studies like the one that you dismissed as a trivial game.
Telling a person “Perform this task, which involves acting like a control system” and discovering that people can, indeed, act like a control system doesn’t seem to demonstrate that people are physically made out of control systems. My desktop computer isn’t a control system, as such, but I can emulate a crude thermostat with a few lines of pseudocode...
The person performing that task is not “acting like” a control system, they actually are controlling the prescribed variable. The hypothesis is that living organisms are, in fact, constituted in this manner, with many control systems in a particular hierarchical arrangement. That every action they perform is an output action of a control system that is endeavouring to keep some perception at some reference level.
But I’ve belaboured this enough in this thread. Any more would just be a repetition of the materials I’ve pointed to.
The person performing that task is not “acting like” a control system, they actually are controlling the prescribed variable. The hypothesis is that living organisms are, in fact, constituted in this manner, with many control systems in a particular hierarchical arrangement. That every action they perform is an output action of a control system that is endeavouring to keep some perception at some reference level.
Indeed. I don’t disagree with anything here.
What I’m trying to say is that the ability to control one variable doesn’t provide much evidence for “it’s control systems all the way down”. One might as well claim “The brain is a finite state machine” because we can simulate them using pencil and paper.
Such modesty! It’s actually worse than that. You could write a program for a feedforward thermostat (i.e. which tries to predict how much to heat or cool based on factors other than the room temperature, like the sunshine, temp outside, insulation, etc.) on your computer, but Powers et al. would scream bloody murder if you tried to use that as evidence that living systems are feedforward control loops!
You could write a program for a feedforward thermostat
Actually, you couldn’t. At least, it wouldn’t work very well, not nearly as well as a system that simply measures the actual temperature and raises or lowers it as necessary.
Try it and see.
“Feedforward control loop” is pretty much a contradiction in terms. Look at anything described as feedforward control, and you’ll find that it’s wrapped inside a feedback loop, even if only a human operator keeping the feedforward system properly tuned. There are some demonstrable feedforward links in the nervous system, such as the vestibulo-ocular reflex, but as expected, the VOR is wrapped inside a feedback system that tunes its parameters. It wouldn’t work without that.
Actually, you couldn’t. At least, it wouldn’t work very well, not nearly as well as a system that simply measures the actual temperature and raises or lowers it as necessary.
Ah, but if I deliberately created an artificial scenario designed to make FF control work, then FF control would look rockin’.
You know, like the programs you linked do, except that they pimp feedback instead ;-)
Yes, feedback control is usually better; my point was the excessive extrapolation from that program.
“Feedforward control loop” is pretty much a contradiction in terms. Look at anything described as feedforward control, and you’ll find that it’s wrapped inside a feedback loop, even if only a human operator keeping the feedforward system properly tuned.
Yes, very true, which reminds me: I saw a point in the demo1 program (link when I get a chance) on the site pjeby linked where they have you try to control a system using either a) your knowledge of the disturbance (feedforward), or b) your knowledge of the error (feedback), and you inevitably do better with b).
Here’s the thing though: it noted that you can get really good at a) if you practice it and get a good feel for how the disturbance relates to how you should move the mouse. BUT it didn’t use this excellent opportunity to point out that even then, such improvement is itself due to another feedback loop! Specifically, one that takes past performace as the feedback, and desired performance as the reference.
my point was the excessive extrapolation from that program.
PCT is not derived from the demos; the demos are derived from PCT.
even then, such improvement is itself due to another feedback loop
So you see, wherever you look in the behaviour of living organisms, you find feedback control!
If that seems trivial to you, then it is probably because you are not an experimental psychologist, which is the area in most need of the insight that living organisms control their perception. You probably also do not work in AI, most of whose practitioners (of strong or weak AI) are using such things as reinforcement learning, planning, modelling, and so on. Robotics engineers—some of them—are about the only exception, and they have a better track record of making things that work.
BTW, I’m not touting PCT or anything else as the secret of real AI. Any better understanding of how real brains operate, whether it comes from PCT or anything else, will presumably facilitate making artificial ones, and I have used it as the basis of a fairly good (but only simulated) walking robot, but strong AI is not my mission.
In that case, why do you keep insinuating that control theory is useful as a high-level model of intelligence? That seems analogous to deriding computational neuroscientists for not using neurons in their models.
ETA: By comparison, evolutionary psychologists can’t do the math yet on the selective advantage of genes coding for variations in mental traits, but their derived models of human psychology allow them to make significant predictions that weren’t thought of without the model, and which often check out in experiments. Does PCT have any novel experimental consequences that have been verified?
I was being flippant. I mean, what were you expecting?
I was expecting you to show me how the controls paradigm does a better job of explaining behaviors than the prevailing, but messy, model of evolutionary psychology. (Not that they would contradict, but your model would have to be simpler and/or more precise.)
That was the challenge I had presented to you before: show how “humans are control systems” is better at compressing a description of our observations.
If it seems hard to do, it’s probably because it is hard to, because it’s not a better model, because the behavior is so hard to express in controls terminology. Finding a mate is simply not like making sure one line is under another.
Imagine that the person who first had the idea that thinking is done by neurons has just published it, and you ask them what you asked me.
If I were the first person to publish the neuron theory, it would include an actual model with actual moving parts and therefore have actual explanatory power over actual observations that actual other people can make. It would not say, as you have essentially done, “people think with neurons, so like, when you think, it’s all … neurony. Are you thinking about sex? Yeah, the neuronal model has that. Neurons cause that thinking too. See, once I know what you’re thinking about, I can predict, using the neuronal model, what you’re thinking.”
The leg work to discover just how those neurons are organised to do it is the problem, and finding a mate isn’t the place to start,
But human behavior is where you’ve started, hence my confusion of what exactly the “humans as controllers” model accomplishes.
Likewise, I’m not passing off “it’s done by control systems” as the solution of a problem, but as a basic insight that gives the beginning of a way to study living organisms.
But it’s not an insight unless it makes the problem easier. Everything you’ve presented here simply shows how you could rephrase the solution to predicting organism behavior into a controls model once it’s been solved. So where does the ease come in? What problem becomes easier when I approach it your way?
The place to begin that study and establish exactly what control systems are present and how they work is in studies like the one that you dismissed as a trivial game.
I dismissed it as a trivial game because it is a trivial game. From the fact that I use proportional feedback control to keep two lines together, it does not follow that this is a useful general model of all organism activity.
Believe it or not, I have put a lot of work (in terms of fraction of my spare time) into exploring PCT. I’ve completed demo1 on the site pjeby linked, and have run it and four others in DOS Box. I’ve read some of the pdfs expalining feedback systems at the cellular level. I am going far out of my way to see if there’s anything to PCT, so please do not write me off as if I’m making you hold my hand.
I am making every effort to make things easy for you. All that’s left for your is to have a real model that you actually understand.
That, in turn, would rebut my strongly justified suspicion that the model’s “predictions” are ad hoc and the parallels with control systems superficial.
The reason why expressing the connection between not having a mate and seeking a mate in terms of PCT is so difficult is because “not having a mate” is not a perception, and because “seeking a mate” is not a behavior. Rather, these are an abstract world state with multiple perceptual correlates, and a broad class of complex behaviors that no known model explains fully. Given such a confusing problem statement, what did you expect if not a confused response?
The second problem, I think, is that you may have gotten a somewhat confused idea of what (non-perceptual) control systems look like. There was a series of articles about them on LW, but unfortunately, it stopped just short of the key insight, which is the PID controller model. A PID controller looks at not just the current value of its sensor (position, P), but also its recent history (integral, I) and rate of change (derivative, D).
If you want to test PCT, you need to step back and look at something simpler. The most obvious example is motor control. Most basic motor control tasks, like balancing, are a matter of generating some representation of body and object position, figuring out which neurons trigger muscles to push it in certain ways, and holding position constant; and to do that, any organism, whether it’s a human or a simple invertebrate, needs some neural mechanism that acts very much like a PID controller. That establishes that controllers are handled in neurology somehow, but not their scope. There’s another example, however, which shows that it’s considerably broader than just motor control.
Humans and animals have various neurons which respond to aspects of their biochemistry, such as concentrations of certain nutrients and proteins in the blood. If these start changing suddenly, we feel sick and the body takes appropriate action. But the interesting thing is that small displacements which indicate dietary deficiencies somehow trigger cravings for foods with the appropriate nutrient. The only plausible mechanism I can think of for this is that the brain remembers the effect that foods had, and looks for foods which displaced sensors in the direction opposite the current displacement. The alternative would be a separate chemical pathway for monitoring each and every nutrient, which would break every time the organism became dependent on a new nutrient or lost access to an old one.
Moving up to higher levels of consciousness, things get significantly more muddled. Psychology and clear explanations have always been mutually exclusive, and no single mechanism can possibly cover everything, but then it doesn’t need to, since the brain has many obviously-different specialized structures within it, each of which presumably requires its own theory. But I think control theory does a good job explaining a broad enough range of psychological phenomena that it should be kept in mind when approaching new phenomena.
Moving up to higher levels of consciousness, things get significantly more muddled.
I disagree, but that’s probably because I’ve seized on PCT as a compressed version of things that were already in my models, as disconnected observations. (Like time-delayed “giving up” or “symptom substitution”.) I don’t really see many gaps in PCT because those gaps are already filled (for me at least), by Ainslie’s “conditioned appetites” and Hawkins’ HTM model.
Ainslie’s “interests” model is a very strong fit with PCT, as are the hierarchy, sequence, memory, and imagination aspects of HTM. Interests/appetites and HTM look just like more fleshed-out versions of what PCT says about those things.
Is it a complete model of intelligence and humans? Heck no. Does it go a long way towards reverse-engineering and mapping the probable implementation of huge chunks of our behavior? You bet.
What’s still mostly missing, IMO, after you put Ainslie, PCT, and HTM together, is dealing with “System 2” thinking in humans: i.e. dealing with logic, reasoning, complex verbalizations, and some other things like that. From my POV, though, these are the least interesting parts of modeling a human, because these are the parts that generally have the least actual impact on their behavior. ;-)
So, there is little indication as to whether System 2 thinking can be modeled as a controller hierarchy in itself, but it’s also pretty plain that it is subject to the System 1 control hierarchy, that lets us know (for example) whether it’s time for us to speak, how loud we’re speaking, what it would be polite to say, whether someone is attacking our point of view, etc. etc.
It’s also likely that the reason we intuitively see the world in terms of actions and events rather than controlled variables is simply because it’s easier to model discrete sequences in a control hierarchy, than it is to directly model a control hierarchy in another control hierarchy! Discrete symbolic processing on invariants lets us reuse the controllers representing “events”, without having to devote duplicated circuitry to model other creatures’ controller hierarchies. (The HTM model has a better detailed explanation of this symbolic/pattern/sequence processing, IMO, than PCT, even though in the broad strokes, they’re basically the same.)
(And although you could argue that the fact we use symbols means they’re more “compressed” than control networks, it’s important to note that this is a deliberately lossy compression; discrete modeling of continuous actions makes thinking simpler, but increases prediction errors.)
The reason why expressing the connection between not having a mate and seeking a mate in terms of PCT is so difficult is because “not having a mate” is not a perception, and because “seeking a mate” is not a behavior. Rather, these are an abstract world state with multiple perceptual correlates, and a broad class of complex behaviors that no known model explains fully. Given such a confusing problem statement, what did you expect if not a confused response?
What a pitiful excuse.
Let’s get some perspective here: the model I’m trying to understand is so vague in the first place (in terms of what insight it has to offer), despite all of my efforts to understand it with basic questions about what it does to replace existing models. Of course my questions about such an ill-supported paradigm are going to look confused, but then again, it’s not my responsibility to make the paradigm clear. That burden, currently unmet, lies on the person presenting it.
If you’re familiar with the tools of rationality, it is a trivial task to handle “confused” questions of exactly the kind I just asked—but that pre-supposes you have a clue what you’re talking about. All you have to do is identify the error it makes, find the nearest meaningful problem, and show how your model handles that.
A confused response is neither appropriate, nor deserved, and only reflects poorly on the responder.
Let me show you how it works. Let’s say I’m some noble defender of the novel Galilean model, trying to enlighten the stubborn Ptolemaic system supporters. Then, some idiot comes along and asks me, “Okay, smarty, how does the Galilean model plot the epicycle for Jupiter?”
In response, do I roll my eyes at how he didn’t use my model’s terminology and hasn’t yet appreciated my models beauty? Do I resign myself to giving a “confused response”? No.
And do you know why I don’t? Because I have an actual scientific model, that I actually understand.
So in response to such a “hopeless” dilemma, I marshal my rationalist skills and give a non-confused response.
Ready to have your mind blown? Here goes:
“When you ask me about Jupiter’s epicycle, what you’re really looking for is how to plot its position relative to earth. But my point is, you don’t need to take this step of calculating or looking up epicycles. Rather, just model Jupiter as going around the sun in this well-defined eilliptical path, and the earth in this other one. We know where they will be relative to the sun as a function of time, so finding Jupiter relative to the earth is just matter of adding the earth-to-sun vector to the sun-to-jupiter vector.”
There, that wasn’t so hard, was it? But, I had it easy in that I’m defending an actual model that actually compresses actual observations. Richard, OTOH, isn’t so lucky.
Notice what I did not say: “You find Jupiter in the sky and then you draw an epicycle consistent with its position, but with the earth going around the sun”, which is about what I got from Richard.
Moving up to higher levels of consciousness, things get significantly more muddled.
Yeah, that’s the point. Those higher levels are exactly what pjeby attempts to use PCT for, which is where I think any usefulness (of the kind seen in biochemical feedback loops) loses its compression abilities, and any apparent similarity to simple feedback control systems is superficial and ad-hoc, which is exactly why no one seems to be able to even describe the form of the relationship between the higher and lower levels in a way that gives insight. That is, break down “finding a mate” into related controllable values and identify related outputs. Some specification is certainly possible here, no?
Given such a confusing problem statement, what did you expect if not a confused response?
What a pitiful excuse.
[...]
A confused response is neither appropriate, nor deserved, and only reflects poorly on the responder.
Neither the problem statement, nor any of the confused responses were mine. My post was meant to clarify, not to excuse anything.
If you’re familiar with the tools of rationality, it is a trivial task to handle “confused” questions of exactly the kind I just asked—but that pre-supposes you have a clue what you’re talking about. All you have to do is identify the error it makes, find the nearest meaningful problem, and show how your model handles that.
No, that is not the correct way to handle confused questions. The correct way to handle them is to back up, and explain the issues that lead to the confusion. In this case, there are many different directions the question could have been rounded in, each of which would take a fairly lengthy amount of text to handle, and people aren’t willing to do that when you could just say that wasn’t what you meant. I should also observe that pjeby gave you a citation and ducked out of the conversation, specifically citing length as the problem.
At some point, you seem to have switched from conducting a discussion to conducting a battle. Most of the parent post is not talking about the supposed topic of discussion, but about the people who participated in it before. Unfortunately, the history of this thread is far too long for me to read through, so I cannot respond to those parts. However, I am strongly tempted to disregard your arguments solely on the basis of your tone; it leads me to believe that you’re in an affective death spiral.
Neither the problem statement, nor any of the confused responses were mine.
I know. Still a pitiful excuse, and yes, it was an excuse; you insinuated that my confused question deserved the flippant response. It didn’t. It required a simple, clear answer, which of course can only be given when the other party actually has a model he understands.
No, that is not the correct way to handle confused questions. The correct way to handle them is to back up, and explain the issues that lead to the confusion.
We’re bickering over semantics. The point is, there are more helpful answers, which one can reasonably be expected to give, than the “confused reply” you referred to. Richard knows what “finding a mate” means. So, if he actually understands his own model, he can break down “finding a mate” into its constituent references and outputs.
Or say how finding a mate should really be viewed as a set of other, specific references being tracked.
Or somehow give a hint that he understands his own model and can apply it to standard problems.
Was my epicycle example not the kind of response I could reasonably expect from someone who understands his own model?
I was being flippant. I mean, what were you expecting? Imagine that the person who first had the idea that thinking is done by neurons has just published it, and you ask them what you asked me. What can he tell you about finding a girlfriend? Only that it’s done by neurons. The leg work to discover just how those neurons are organised to do it is the problem, and finding a mate isn’t the place to start, experiments like Hubel and Wiesel’s on cat vision are the place to start, or mapping the nervous system of C. elegans.
Likewise, I’m not passing off “it’s done by control systems” as the solution of a problem, but as a basic insight that gives the beginning of a way to study living organisms. The place to begin that study and establish exactly what control systems are present and how they work is in studies like the one that you dismissed as a trivial game.
That’s what real work looks like. Have a spade. A start has been made at the various PCT links I’ve posted. Maybe in 50 years you’ll get an answer. But don’t be downhearted—it’s been more than a century so far for “it’s made of neurons”.
Telling a person “Perform this task, which involves acting like a control system” and discovering that people can, indeed, act like a control system doesn’t seem to demonstrate that people are physically made out of control systems. My desktop computer isn’t a control system, as such, but I can emulate a crude thermostat with a few lines of pseudocode...
while(1) {
while(DesiredTemp > ActualTemp) {runAirConditioner(); }
while(DesiredTemp < ActualTemp) {runFurnace(); }
}
The person performing that task is not “acting like” a control system, they actually are controlling the prescribed variable. The hypothesis is that living organisms are, in fact, constituted in this manner, with many control systems in a particular hierarchical arrangement. That every action they perform is an output action of a control system that is endeavouring to keep some perception at some reference level.
But I’ve belaboured this enough in this thread. Any more would just be a repetition of the materials I’ve pointed to.
Indeed. I don’t disagree with anything here.
What I’m trying to say is that the ability to control one variable doesn’t provide much evidence for “it’s control systems all the way down”. One might as well claim “The brain is a finite state machine” because we can simulate them using pencil and paper.
Such modesty! It’s actually worse than that. You could write a program for a feedforward thermostat (i.e. which tries to predict how much to heat or cool based on factors other than the room temperature, like the sunshine, temp outside, insulation, etc.) on your computer, but Powers et al. would scream bloody murder if you tried to use that as evidence that living systems are feedforward control loops!
Actually, you couldn’t. At least, it wouldn’t work very well, not nearly as well as a system that simply measures the actual temperature and raises or lowers it as necessary.
Try it and see.
“Feedforward control loop” is pretty much a contradiction in terms. Look at anything described as feedforward control, and you’ll find that it’s wrapped inside a feedback loop, even if only a human operator keeping the feedforward system properly tuned. There are some demonstrable feedforward links in the nervous system, such as the vestibulo-ocular reflex, but as expected, the VOR is wrapped inside a feedback system that tunes its parameters. It wouldn’t work without that.
Ah, but if I deliberately created an artificial scenario designed to make FF control work, then FF control would look rockin’.
You know, like the programs you linked do, except that they pimp feedback instead ;-)
Yes, feedback control is usually better; my point was the excessive extrapolation from that program.
Yes, very true, which reminds me: I saw a point in the demo1 program (link when I get a chance) on the site pjeby linked where they have you try to control a system using either a) your knowledge of the disturbance (feedforward), or b) your knowledge of the error (feedback), and you inevitably do better with b).
Here’s the thing though: it noted that you can get really good at a) if you practice it and get a good feel for how the disturbance relates to how you should move the mouse. BUT it didn’t use this excellent opportunity to point out that even then, such improvement is itself due to another feedback loop! Specifically, one that takes past performace as the feedback, and desired performance as the reference.
PCT is not derived from the demos; the demos are derived from PCT.
So you see, wherever you look in the behaviour of living organisms, you find feedback control!
If that seems trivial to you, then it is probably because you are not an experimental psychologist, which is the area in most need of the insight that living organisms control their perception. You probably also do not work in AI, most of whose practitioners (of strong or weak AI) are using such things as reinforcement learning, planning, modelling, and so on. Robotics engineers—some of them—are about the only exception, and they have a better track record of making things that work.
BTW, I’m not touting PCT or anything else as the secret of real AI. Any better understanding of how real brains operate, whether it comes from PCT or anything else, will presumably facilitate making artificial ones, and I have used it as the basis of a fairly good (but only simulated) walking robot, but strong AI is not my mission.
In that case, why do you keep insinuating that control theory is useful as a high-level model of intelligence? That seems analogous to deriding computational neuroscientists for not using neurons in their models.
ETA: By comparison, evolutionary psychologists can’t do the math yet on the selective advantage of genes coding for variations in mental traits, but their derived models of human psychology allow them to make significant predictions that weren’t thought of without the model, and which often check out in experiments. Does PCT have any novel experimental consequences that have been verified?
I was expecting you to show me how the controls paradigm does a better job of explaining behaviors than the prevailing, but messy, model of evolutionary psychology. (Not that they would contradict, but your model would have to be simpler and/or more precise.)
That was the challenge I had presented to you before: show how “humans are control systems” is better at compressing a description of our observations.
If it seems hard to do, it’s probably because it is hard to, because it’s not a better model, because the behavior is so hard to express in controls terminology. Finding a mate is simply not like making sure one line is under another.
If I were the first person to publish the neuron theory, it would include an actual model with actual moving parts and therefore have actual explanatory power over actual observations that actual other people can make. It would not say, as you have essentially done, “people think with neurons, so like, when you think, it’s all … neurony. Are you thinking about sex? Yeah, the neuronal model has that. Neurons cause that thinking too. See, once I know what you’re thinking about, I can predict, using the neuronal model, what you’re thinking.”
But human behavior is where you’ve started, hence my confusion of what exactly the “humans as controllers” model accomplishes.
But it’s not an insight unless it makes the problem easier. Everything you’ve presented here simply shows how you could rephrase the solution to predicting organism behavior into a controls model once it’s been solved. So where does the ease come in? What problem becomes easier when I approach it your way?
I dismissed it as a trivial game because it is a trivial game. From the fact that I use proportional feedback control to keep two lines together, it does not follow that this is a useful general model of all organism activity.
Believe it or not, I have put a lot of work (in terms of fraction of my spare time) into exploring PCT. I’ve completed demo1 on the site pjeby linked, and have run it and four others in DOS Box. I’ve read some of the pdfs expalining feedback systems at the cellular level. I am going far out of my way to see if there’s anything to PCT, so please do not write me off as if I’m making you hold my hand.
I am making every effort to make things easy for you. All that’s left for your is to have a real model that you actually understand.
That, in turn, would rebut my strongly justified suspicion that the model’s “predictions” are ad hoc and the parallels with control systems superficial.
The reason why expressing the connection between not having a mate and seeking a mate in terms of PCT is so difficult is because “not having a mate” is not a perception, and because “seeking a mate” is not a behavior. Rather, these are an abstract world state with multiple perceptual correlates, and a broad class of complex behaviors that no known model explains fully. Given such a confusing problem statement, what did you expect if not a confused response?
The second problem, I think, is that you may have gotten a somewhat confused idea of what (non-perceptual) control systems look like. There was a series of articles about them on LW, but unfortunately, it stopped just short of the key insight, which is the PID controller model. A PID controller looks at not just the current value of its sensor (position, P), but also its recent history (integral, I) and rate of change (derivative, D).
If you want to test PCT, you need to step back and look at something simpler. The most obvious example is motor control. Most basic motor control tasks, like balancing, are a matter of generating some representation of body and object position, figuring out which neurons trigger muscles to push it in certain ways, and holding position constant; and to do that, any organism, whether it’s a human or a simple invertebrate, needs some neural mechanism that acts very much like a PID controller. That establishes that controllers are handled in neurology somehow, but not their scope. There’s another example, however, which shows that it’s considerably broader than just motor control.
Humans and animals have various neurons which respond to aspects of their biochemistry, such as concentrations of certain nutrients and proteins in the blood. If these start changing suddenly, we feel sick and the body takes appropriate action. But the interesting thing is that small displacements which indicate dietary deficiencies somehow trigger cravings for foods with the appropriate nutrient. The only plausible mechanism I can think of for this is that the brain remembers the effect that foods had, and looks for foods which displaced sensors in the direction opposite the current displacement. The alternative would be a separate chemical pathway for monitoring each and every nutrient, which would break every time the organism became dependent on a new nutrient or lost access to an old one.
Moving up to higher levels of consciousness, things get significantly more muddled. Psychology and clear explanations have always been mutually exclusive, and no single mechanism can possibly cover everything, but then it doesn’t need to, since the brain has many obviously-different specialized structures within it, each of which presumably requires its own theory. But I think control theory does a good job explaining a broad enough range of psychological phenomena that it should be kept in mind when approaching new phenomena.
I disagree, but that’s probably because I’ve seized on PCT as a compressed version of things that were already in my models, as disconnected observations. (Like time-delayed “giving up” or “symptom substitution”.) I don’t really see many gaps in PCT because those gaps are already filled (for me at least), by Ainslie’s “conditioned appetites” and Hawkins’ HTM model.
Ainslie’s “interests” model is a very strong fit with PCT, as are the hierarchy, sequence, memory, and imagination aspects of HTM. Interests/appetites and HTM look just like more fleshed-out versions of what PCT says about those things.
Is it a complete model of intelligence and humans? Heck no. Does it go a long way towards reverse-engineering and mapping the probable implementation of huge chunks of our behavior? You bet.
What’s still mostly missing, IMO, after you put Ainslie, PCT, and HTM together, is dealing with “System 2” thinking in humans: i.e. dealing with logic, reasoning, complex verbalizations, and some other things like that. From my POV, though, these are the least interesting parts of modeling a human, because these are the parts that generally have the least actual impact on their behavior. ;-)
So, there is little indication as to whether System 2 thinking can be modeled as a controller hierarchy in itself, but it’s also pretty plain that it is subject to the System 1 control hierarchy, that lets us know (for example) whether it’s time for us to speak, how loud we’re speaking, what it would be polite to say, whether someone is attacking our point of view, etc. etc.
It’s also likely that the reason we intuitively see the world in terms of actions and events rather than controlled variables is simply because it’s easier to model discrete sequences in a control hierarchy, than it is to directly model a control hierarchy in another control hierarchy! Discrete symbolic processing on invariants lets us reuse the controllers representing “events”, without having to devote duplicated circuitry to model other creatures’ controller hierarchies. (The HTM model has a better detailed explanation of this symbolic/pattern/sequence processing, IMO, than PCT, even though in the broad strokes, they’re basically the same.)
(And although you could argue that the fact we use symbols means they’re more “compressed” than control networks, it’s important to note that this is a deliberately lossy compression; discrete modeling of continuous actions makes thinking simpler, but increases prediction errors.)
What a pitiful excuse.
Let’s get some perspective here: the model I’m trying to understand is so vague in the first place (in terms of what insight it has to offer), despite all of my efforts to understand it with basic questions about what it does to replace existing models. Of course my questions about such an ill-supported paradigm are going to look confused, but then again, it’s not my responsibility to make the paradigm clear. That burden, currently unmet, lies on the person presenting it.
If you’re familiar with the tools of rationality, it is a trivial task to handle “confused” questions of exactly the kind I just asked—but that pre-supposes you have a clue what you’re talking about. All you have to do is identify the error it makes, find the nearest meaningful problem, and show how your model handles that.
A confused response is neither appropriate, nor deserved, and only reflects poorly on the responder.
Let me show you how it works. Let’s say I’m some noble defender of the novel Galilean model, trying to enlighten the stubborn Ptolemaic system supporters. Then, some idiot comes along and asks me, “Okay, smarty, how does the Galilean model plot the epicycle for Jupiter?”
In response, do I roll my eyes at how he didn’t use my model’s terminology and hasn’t yet appreciated my models beauty? Do I resign myself to giving a “confused response”? No.
And do you know why I don’t? Because I have an actual scientific model, that I actually understand.
So in response to such a “hopeless” dilemma, I marshal my rationalist skills and give a non-confused response.
Ready to have your mind blown? Here goes:
“When you ask me about Jupiter’s epicycle, what you’re really looking for is how to plot its position relative to earth. But my point is, you don’t need to take this step of calculating or looking up epicycles. Rather, just model Jupiter as going around the sun in this well-defined eilliptical path, and the earth in this other one. We know where they will be relative to the sun as a function of time, so finding Jupiter relative to the earth is just matter of adding the earth-to-sun vector to the sun-to-jupiter vector.”
There, that wasn’t so hard, was it? But, I had it easy in that I’m defending an actual model that actually compresses actual observations. Richard, OTOH, isn’t so lucky.
Notice what I did not say: “You find Jupiter in the sky and then you draw an epicycle consistent with its position, but with the earth going around the sun”, which is about what I got from Richard.
Yeah, that’s the point. Those higher levels are exactly what pjeby attempts to use PCT for, which is where I think any usefulness (of the kind seen in biochemical feedback loops) loses its compression abilities, and any apparent similarity to simple feedback control systems is superficial and ad-hoc, which is exactly why no one seems to be able to even describe the form of the relationship between the higher and lower levels in a way that gives insight. That is, break down “finding a mate” into related controllable values and identify related outputs. Some specification is certainly possible here, no?
Neither the problem statement, nor any of the confused responses were mine. My post was meant to clarify, not to excuse anything.
No, that is not the correct way to handle confused questions. The correct way to handle them is to back up, and explain the issues that lead to the confusion. In this case, there are many different directions the question could have been rounded in, each of which would take a fairly lengthy amount of text to handle, and people aren’t willing to do that when you could just say that wasn’t what you meant. I should also observe that pjeby gave you a citation and ducked out of the conversation, specifically citing length as the problem.
At some point, you seem to have switched from conducting a discussion to conducting a battle. Most of the parent post is not talking about the supposed topic of discussion, but about the people who participated in it before. Unfortunately, the history of this thread is far too long for me to read through, so I cannot respond to those parts. However, I am strongly tempted to disregard your arguments solely on the basis of your tone; it leads me to believe that you’re in an affective death spiral.
I know. Still a pitiful excuse, and yes, it was an excuse; you insinuated that my confused question deserved the flippant response. It didn’t. It required a simple, clear answer, which of course can only be given when the other party actually has a model he understands.
We’re bickering over semantics. The point is, there are more helpful answers, which one can reasonably be expected to give, than the “confused reply” you referred to. Richard knows what “finding a mate” means. So, if he actually understands his own model, he can break down “finding a mate” into its constituent references and outputs.
Or say how finding a mate should really be viewed as a set of other, specific references being tracked.
Or somehow give a hint that he understands his own model and can apply it to standard problems.
Was my epicycle example not the kind of response I could reasonably expect from someone who understands his own model?