[From Bill Powers (960302.0730 MST)]
Martin Taylor 960301 1515 --
In Bill Powers' approach to reorganization, the top level
perceptual references are all zero.
There are several possibilities for any given highest-level reference
signal: it is zero; it is the average value (over some time) of the
perceptual signal; it is a value set at random through reorganization;
it is genetically fixed. I've never settled on one explanation. During
maturation, the level that is "highest" keeps changing.
Learning has nothing to do with reference levels, anyway. It has to
do with the functions and the linkages that produce negative loop
gains (that is to say: control)
Got to be careful about the way people will interpret "learning": this
is true of "reorganization" but not necessarily of the broader and
looser term, "learning." One of the first things I do when I meet a new
person is to learn his or her name. But that's not reorganization.
I agree generally with your comments to Shannon.
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Martin Taylor 960301 17:00 --
I have the same problem with Bill P's switch between real-time and
model-based control. Why either-or?
Well, just figure out what happens if you have both. You have two
perceptual signals, one from the model and one from the real world. How
do you combine them to yield "the" perceptual signal that is compared
with the reference signal? Do you add them together? Average them? And
what happens when the real perceptual signal is lost? If you're
averaging them, then the net perceptual signal suddenly is halved!
I think that what really happens is that you operate with the real
perceptual signal as long as it's available, because that gives you the
best control. When that signal disappears, your behavior immediately
starts to run away, but this is caught by a higher system which switches
(when possible) to a model that allows you to carry on for a bit longer.
If there is no good model, then you switch to controlling some other
perception that is related to the control task, hoping it is good enough
to keep things under control until the real perception comes back. When
you drive from sunlight into an unfamiliar dark tunnel, you switch to
keeping the steering wheel in a fixed position and hope that there's no
curve in the tunnel that will get you before your eyes adapt. Model-
based control is really useful only when the environment is familiar and
you can count on there being no surprises.
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Shannon Williams (960301.1815) --
The diagram that I sent earlier is just a PCT diagram with some
neural nets thrown in. Does that constitute a model? If not, then
tell me what I need to do to make that a model.
Pick some real task that can be implemented by a human being interacting
with a computer. Write a program organized as your diagram is organized
to carry out the task. Compare the behavior of your program with the
behavior of a real person doing the same task. Modify the program until
it behaves like the person. Then you have a usable model.
If you aren't at the stage of experimentation with real people, then you
can _propose_ a model. To propose a model, you write a program that is
organized like your diagram (as you understand it), run it with a
simulated task, and see what the program actually does (as opposed,
possibly, to what you thought it would do). This at least demonstrates
that what you say about your diagram is true. You have a working model.
It may not behave like a real person yet, but at least it behaves.
If you don't know how to write a program that is organized like your
diagram, then you may be working toward a model, but you don't have one
yet. Many parts of HPCT are still at that stage. Some have gone to the
stage of working models. A few have gone to the stage of usable models,
where the model produces behavior very close to that of a real person.
When I say "write a program" I mean "Demonstrate in some way that a
system organized as your diagram is organized would really do the things
you say it would do." Programming a computer is a very easy way to test
a model, but any real test that shows how the model would actually
behave would do as well. It's very easy to draw a diagram and make
claims about what a system organized like that would do. But showing
that it actually would is a horse of a different feather.
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Tell me what data has been explained by HPCT, and I will tell you
how it is explained by a PCT diagram with neural nets.
A person holds a control handle that moves a cursor on a computer
display screen. Also on the screen is a target that can move. Inside the
computer, two disturbance generators produce smoothed random patterns of
disturbing variables, obtained by filtering the output of a pseudo-
random number generator. One disturbance moves the target, the other is
added to the effect of the control handle on the cursor.
A control-system model recieves information about target and cursor
position, and outputs a number that affects the cursor, with the same
disturbance patterns acting. The parameters of this model are adjusted
until the simulated handle movements match (as well as possible) the
handle movements of a real person doing the same task.
Then the model is run again using a new randomly-generated pair of
disturbances, using the parameters obtained from the first experiment,
to create a predicted pattern of handle movements. The new disturbance
patterns correlate less than 0.2 with the old ones. After this is done,
the person does the same task with the same (new) pattern of
disturbances. The handle movements of the person are compared with the
stimulated handle movements of the model.
The predicted handle movements deviate by 5 to 10 percent RMS from the
real ones (comparing them point by point), and the correlation between
model and real handle movements is somewhere between 0.99 and 0.999.
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Best to all,
Bill P.