I would describe my steps in baking a cake (and of course this abstracts away from lower level detail)
You could describe them that way, yes, and that would nominally describe the behaviors you emit. However, it’s trivial to prove that this does not describe the implementation in your head that emits those behaviors!
For example, you might forget to preheat the oven, in which case the order of your steps is going to change. There are any number of disruptions that can occur in your sequence of “steps”, that will cause you to change your actions to work around the disruptions, with varying degrees of automaticity, depending on how high in your control hierarchy the disruptions reach.
A simple disruption like a spill on the floor you need to walk around will be handled with barely a conscious notice, while a complex disruption like the power being off (and the oven therefore not working) will induce more complex behavior requiring conscious attention.
If a sequence of steps could actually describe human behavior, we could feed your list of steps to a computer and get it done. It’s actually omitting some of the most important information: the goals of the steps, and how to measure whether they’ve been obtained.
And that’s information our brains have to be able to know and use in order to actually carry out behavior. We tend to assume we do this by “thinking”, but we usually only “think” in order to handle high-level disturbances that require rearrangement of goals, rather than just using existing control systems to work around the disturbance.
Your claimed improvement over this framing of these events is: [list I wouldn’t use to describe it]
When you get to higher levels of modeling, you can certainly deal with sequences of subgoals that are matching of perceptual patterns like “cake is in the oven”. Did you read the TOTE loops reference I gave? TOTE loops act on something until it reaches a certain state, then activate another TOTE loop. PCT incorporates the previously-proposed cog psych notion of TOTE loops, and proposes some ways to build TOTE loops out of simpler controllers.
Part of the modeling elegance of using controllers and TOTE loops to implement overall behavioral programs is that they allow you to notice, for example, that your assistant chef has already set the timer for you as you placed the cake in the oven… and thereby skipping the need to perform that step.
Among other things, you’re forced to solve the object recognition problem and identify a format for comparison. But if I’ve solved the (biological) object recognition problem, my model can simply invoke the actual neural mechanism being used, without the added complexity of reformatting the causal flow into feedback loops.
This I don’t get. We already know that (visual) object recognition can be implemented by time-varying hierarchical feature sequences tied to the so-called “grandmother neurons”. Both HTM and PCT include this concept, except that PCT proposes you get an analog “grandmotherness” signal, whereas IIRC the HTM model assumes it’s a digital “grandmother present” signal. But HTM at least has pattern recognition demos that handle automatic learning and pattern extraction from noisy inputs, and it uses exactly the same sort of recognition hierarchy that the full PCT model calls for.
That’s why I keep saying that if you want to see the entire PCT model, you need to read the book. Most of the primers either talk about low-level stuff or high-level stuff. Object recognition, action sequences, and that sort of thing are all in the middle layers that make up the bulk of the book.
You defend this model by its elegance, but you only get the elegance after you solve the problem some other way. That is, I only have an elegant hierarchical feedback loop if I can, somehow, solve the object recognition problem that allows me to actually specify a reference and feedback signal. A model isn’t any good if it presupposes the solution of the problem it’s being used to solve.
Note, by the way, that this is backwards, when applied to listing steps and calling it a model. The steps cannot be used to actually predict behavior, because they list only the nominal case, where everything goes according to plan. The extra information that PCT forces you to include results in a more accurate model—one that does not simply elide or handwave away the parts of behavior that we intuitively ignore.
That is, the parts we don’t usually bother communicating to other human beings, because we assume they’ll fill in the gaps.
PCT is useful because it shows where those gaps are and what is needed to fill them in, in much the same way that the initial description of evolution identified gaps in our knowledge about biology, and what information was needed to fill them in, in place of the asumptive handwaving that “God did it”. In the same way, we currently handwave most of what we don’t understand about behavior as, “X did it”, where X is some blurry entity or other such as learning, environment, intelligence, habit, genetics, conditioning, etc.
(And no, PCT doesn’t merely replace X with “control systems”, because it shows HOW control systems can “do it”, whereas other values of X are simply stopping the explanation at that point.)
You could describe them that way, yes, and that would nominally describe the behaviors you emit. However, it’s trivial to prove that this does not describe the implementation in your head that emits those behaviors!
For example, you might forget to preheat the oven, in which case the order of your steps is going to change. There are any number of disruptions that can occur in your sequence of “steps”, that will cause you to change your actions to work around the disruptions, with varying degrees of automaticity, depending on how high in your control hierarchy the disruptions reach.
A simple disruption like a spill on the floor you need to walk around will be handled with barely a conscious notice, while a complex disruption like the power being off (and the oven therefore not working) will induce more complex behavior requiring conscious attention.
If a sequence of steps could actually describe human behavior, we could feed your list of steps to a computer and get it done. It’s actually omitting some of the most important information: the goals of the steps, and how to measure whether they’ve been obtained.
And that’s information our brains have to be able to know and use in order to actually carry out behavior. We tend to assume we do this by “thinking”, but we usually only “think” in order to handle high-level disturbances that require rearrangement of goals, rather than just using existing control systems to work around the disturbance.
When you get to higher levels of modeling, you can certainly deal with sequences of subgoals that are matching of perceptual patterns like “cake is in the oven”. Did you read the TOTE loops reference I gave? TOTE loops act on something until it reaches a certain state, then activate another TOTE loop. PCT incorporates the previously-proposed cog psych notion of TOTE loops, and proposes some ways to build TOTE loops out of simpler controllers.
Part of the modeling elegance of using controllers and TOTE loops to implement overall behavioral programs is that they allow you to notice, for example, that your assistant chef has already set the timer for you as you placed the cake in the oven… and thereby skipping the need to perform that step.
This I don’t get. We already know that (visual) object recognition can be implemented by time-varying hierarchical feature sequences tied to the so-called “grandmother neurons”. Both HTM and PCT include this concept, except that PCT proposes you get an analog “grandmotherness” signal, whereas IIRC the HTM model assumes it’s a digital “grandmother present” signal. But HTM at least has pattern recognition demos that handle automatic learning and pattern extraction from noisy inputs, and it uses exactly the same sort of recognition hierarchy that the full PCT model calls for.
That’s why I keep saying that if you want to see the entire PCT model, you need to read the book. Most of the primers either talk about low-level stuff or high-level stuff. Object recognition, action sequences, and that sort of thing are all in the middle layers that make up the bulk of the book.
Note, by the way, that this is backwards, when applied to listing steps and calling it a model. The steps cannot be used to actually predict behavior, because they list only the nominal case, where everything goes according to plan. The extra information that PCT forces you to include results in a more accurate model—one that does not simply elide or handwave away the parts of behavior that we intuitively ignore.
That is, the parts we don’t usually bother communicating to other human beings, because we assume they’ll fill in the gaps.
PCT is useful because it shows where those gaps are and what is needed to fill them in, in much the same way that the initial description of evolution identified gaps in our knowledge about biology, and what information was needed to fill them in, in place of the asumptive handwaving that “God did it”. In the same way, we currently handwave most of what we don’t understand about behavior as, “X did it”, where X is some blurry entity or other such as learning, environment, intelligence, habit, genetics, conditioning, etc.
(And no, PCT doesn’t merely replace X with “control systems”, because it shows HOW control systems can “do it”, whereas other values of X are simply stopping the explanation at that point.)