[Martin Taylor 2015.04.25.14.05]
Actually, Rick was right, but so was I. We both ignored what the
other saw. Bill was talking about both a model of the way the world
works AND a model of the way the world is perceived. I think the reason I didn’t see the bit about creating new
perceptual functions and modifying others to work better in the real
world was that for me it is a very old idea, that I learned first
from Jim Taylor (J. G. Taylor, “The Behavioral Basis of Perception”,
Yale UP, 1963, which I reviewed when it was first published). Jim,
by the way, is no relation, although I came to know him quite well
after he retired from Capetown University and came to work at the
same institute as me, largely because of my review. I contributed a
paper that I have sometimes mentioned on CSGnet to a Festschrift in
honour of the tenth anniversary of the book
.
JG’s theory was that we learn to perceive whatever functions of our
sensor inputs are required for behaving in the real world, through a
whole mess of feedback loops. If we form perceptual functions not
used in behaving, those functions would be lost. If we needed new
perceptual functions and the data were available, we would be
likely, but not certain, to build them. JG was a Hullian
reinforcement theorist from a clinical research background, but I
rather think that if he and Bill had got together they would have
converged views very quickly, JG to embrace PCT, and Bill to build
working models of the development and modification of perceptual
functions in the hierarchy. What Bill says at the beginning and near
the end of tie “Insight” message sound to me as though Bill would
have arrived at JG’s theory, properly embedded within PCT.
Here’s Bill’s message, complete.
Martin
···
I thought that since this posting has
been a topic of discussion, it might be worthwhile to repost it. I
will add it after my comments.
[Martin Taylor 2015.04.25.10.41]
http://www.mmtaylor.net/Academic/SouthAfricanJ_OCR_edit.doc
Insight into PCT models
From Bill Powers
(2010.12.22.2300 MDT)]
Something is coming together that is making sense of some ideas
I
have resisted for a long time. It has to do with the brain's
models
of the external world. From the way I have seen those models
proposed
by others such as Ashby and Modern Control Theory adherents, I
have
thought they were simply impractical, calling for far too much
knowledge, computing power, and precision of action -- as indeed
they
are and they do, as they have been presented.
But those ideas may nevertheless be right. Some of those other
blind
men standing around the elephant are perhaps only a little
nearsighted, and are seeing something going on that looks
fuzzily
like modeling, but there's something funny about it so it isn't
quite
how it seems from this angle or that. This particular blind or
nearsighted man writing these sentences has not seen models; he
has
seen a hierarchy of perceptions that somehow represents an
external
world, and a large collection of Complex Environmental Variables
(as
Martin Taylor calls them) that is mirrored inside the brain in
the
form of perceptions.
Briefly, then: what I call the hierarchy of perceptions is the
model.
When you open your eyes and look around, what you see -- and
feel,
smell, hear, and taste -- is the model. In fact we never
experience
ANYTHING BUT the model. The model is composed of perceptions of
all
kinds from intensities on up.
Warren Mansell asked some questions about feedback and
feedfoward
that stirred a few thoughts up. I think his ambition to
integrate
different ideas people have had about control theory suddenly
looked
more appealing than before. I've been working on and thinking
about
how to get a better fit of the current tracking model to the
real
behavior, and that has stirred up a lot more thoughts. I was
thinking
about how to add a two-level controller in which the upper level
controls position and the level below controls rate of change
(yes, I
know that's backward). I realized that I would need a sensor
that
senses rate of change of position, and that, in turn, called to
mind
the neat analog-computing techique that computes first
derivatives by
putting an integrator in the feedback path of a little control
system
-- it's actually described in LCS3, chapter 5.
I considered using that method to implement a new model for the
TrackAnalyze program and for some reason didn't like the idea of
doing it that way. Then the reason dawned on me: I was actually
proposing to put a model of the physical environment into my PCT
model, and I'm not supposed to be in favor of doing that. But it
happens that if you integrate the force applied to a mass, the
value
of the integral represents the velocity, which keeps changing in
proportion to the force. The velocity is the first derivative of
position. The factor applied to the force as it is being
integrated
represents the reciprocal of the mass of the object being pushed
upon. So I was proposing to put a model of the mass of an arm,
together with Newton's laws of motion, into my sacred PCT model.
So: I was thinking of sticking a model into my model, between
the
output and the input, as a convenient way of getting a signal
that
would represent velocity. It would be generated by applying a
force
to a simulated mass. So the arm controller would sense the force
its
muscles were producing and integrate the force to create a
synthesized perception of the velocity, and then it would have a
controller for controlling that integrated perception and we
would
have one level of control.
But wait. Where did that model come from? Don't we need to
control
through the real world outside? It came from applying perceived
forces to perceived things and -- for one example -- seeing them
move. A kinesthetically detected output force becomes a
perceptual
signal representing force; the force signal is integrated to
produce
a visual perception of changing velocity; a visual perception of
velocity is integrated to produce a visual perception of
position,
and a changing velocity produces a perception of acceleration.
And
this is all happening inside the nervous system. In a model.
The modern control theorists came closest to seeing how this
works.
They said that the internal model was carefully constructed to
have
the same properties as the external "plant" that was to be
controlled. Then the brain could work out, internally, what
signal it
had to send into the model to make it behave in a certain way,
and
when it had that working, it could send the same signal to the
external "plant" and it would behave the same way. They admit
that to
make this work the model of the plant has to be rather
dauntingly
accurate, and every disturbance has to be accurately anticipated
as
to size, direction, and time of occurrance.
So the picture I got was that the brain supposedly had the
ability to
examine the plant and measure its properties, and then
constructed a
computed model inside itself based on the data thus obtained.
But of
course I knew that the brain can do no such thing: all it knows
are
the perceptions it gets, and it has no way to compare them with
the
real plant Out There to see if it got the measurements right.
Everything it does has to be done with the perceptions, not with
the
real plant.
That is where I had always stopped before, just prior to
discarding
the model-based control idea once again. But for some reason,
this
time I kept going.
We can sense output force because the tendons have sensors that
report how hard the muscles are pulling, and we have pressure
sensors
all over that detect how hard a hand or foot is pressing against
something else. We have sensors to tell us if a joint angle is
changing as a result of the force, and of course we have vision
to
give a different spatial view of the result. So by experimenting
with
output forces, we can build up a set of control systems for
controlling the immediate consequences of applying forces. We
can get
to know how much consequence a given amount of force produces.
Years
later we will learn that the ratio of force to consequence is
called
"mass." But if we integrate the force to produce a velocity, we
can
discover empirically what the value of this ratio is for
different
objects, without calling it anything.
That is all we need to do to build up a model of the external
world.
It's not even that; it's just a model of the world. The idea
that
there's also an external world that we don't experience takes a
while
to develop. At first it's just the only world there is.
So that is the model that Ashby and the Modern Control Theorists
are
talking about. It's the world we experience. When we examine
that
external plant in order to model it, we're already looking at
the
brain's model. It lacks detail, but as we probe and push and
peer
and twiddle and otherwise act on these rudimentary perceptions,
new
perceptions form that begin to add features and properties --
like
mass -- to the model. We say we are analyzing the plant. What we
are
doing is building up perceptions of properties and features that
can
be affected by sending signals outward, learning how to control
the
perceptions. Why we have to act one way instead of another to
get a
particular effect is unknown, but we learn the rules. When we
don’t
get the effect we want, we alter what we are doing until we do
get it.
We never do actually, knowingly, interact with the plant itself.
It seems very risky to be operating entirely on an internal
model
without any ability to know what is really going on that we
can’t
see, but really, it's not. Before you step into the bathtub you
feel
the water, so if you've made a mistake you're not going to scald
your
whole body. We detect errors very quickly and make adjustments
almost
as quickly to limit the errors, and eventually to keep them from
ever
getting very large. We're always interacting with whatever is
Out
There, and we learn fast. Most of us. most of the time, don't
even
think about the invisible universe Out There. The visible one is
sufficient to keep us busy and interested. The idea that there's
another bigger one that actually determines what the rules are
doesn't usually arise.
I'm beginning to get an idea now about how to model perceptions,
at
least at the lower levels. All we have to do is make a model of
the
environment, just like that analog-computing trick for
calculating
rates of change by using integrators, which turns out to embody
Newton's laws of motion. This whole idea is still very new and I
don't see very far along the path ahead, but I have a feeling
that
what looked very difficult before may start getting a little
less difficult.
I'd better get to bed; it's very strange to look around at this
room
and think "This is my model. I, or something in me, constructed
every
detail in it, all the things I recognize and know about it and
can do
to it. Help, is this solipsism?"
But no, it's not. Solipsism says there really isn't anything
else. We
can freely assume that there is a huge lawful universe full of
regularities, as long as we realize that all we will ever
experience
of it is the model that we build in our brains. When it does
what we
call raining we get what we call wet, but we can only assume
that
those experiences occurring in our models correspond in some
unknowable way to whatever else there is.
I hope all of this doesn't evaporate overnight.
Best,
Bill P.
On 2015/04/25 12:23 AM, Richard
Marken ( via
csgnet Mailing List) wrote:
[From Rick Marken (2015.04.24.2120)]
Martin Taylor(2015.04.24.17.22)–
RM: I don't think theimagination connection has
anything to do with it.
There is certainly no
mention of it in Bill’s
quote. The “model’” Bill
was talking about (as he
said) IS the hierarchy of
perception so it starts at
the lowest level.
MT: No, I disagree. Read itagain – the whole thing, if you want.
Bill was talking about a model of “The
way the world works”.