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Date:         Fri, 20 Nov 1992 09:15:08 -0700
From:         "William T. Powers" <POWERS_W%FLC@VAXF.COLORADO.EDU>
Subject:      S-R theory and Neural nets

[From Bill Powers (921120.0830)]

Avery Andrews (921120.1145) --

You're reinventing S-R theory. Your proposal is essentially how neural
network people are trying to use neural nets -- to connect inputs to
behavioral outputs. The assumption behind this approach is that for each
discriminable situation, there is an appropriate output that will take
care of it. This assumption is false, and is the basic reason for the
existence of PCT.

>The idea is that whenever the pain wire fires, something happens
>inside the black box such that, subsequently, the closer the sense-
>vector gets to what it was when the pain wire fired, the larger a
>signal comes out along the fear wire.  This signal then is then
>handled in such a way as to typically induce behavior that will
>alter the sense-vector.

The catch in this is "in such a way as to typically induce behavior that
will alter the sense-vector" [in the right direction and by the right
amount]. The whole problem is how to induce just that qualitative and
quantitative action which, when added to all the other influences that
are also acting on the sense-vector, will move the sense-vector toward a
particular state from any initial state.

It's true that what is needed is to move the sense vector in the right
direction. But that is an OUTCOME, not a MEANS. The means employed must
be variable, and generally is of a different nature from the outcome.
Suppose the desired outcome is to move "away" from a threatening (fear-
inducing) object. The means of doing this is to alter the way the legs
are thrusting against the ground. But doing that in the right way
requires

(1) knowing the orientation of the body (do you just run or do you have
to turn around first, or run backward?),

(2) knowing the composition of the footing (are you on slippery mud, in
sand, or standing on concrete, or on an upslope, a sideslope, or a
downslope?).

(3) knowing the proximity and location of obstacles (is there a tree in
the way? Will I run off of a cliff?),

(4) knowing the current velocity and direction of locomotion (am I
already running in the right direction, or at right angles, or toward
the threatening object?),

(5) knowing the present bodily configuration (am I lying down, sitting
down, kneeling, standing? Is my head pointed in the same direction as my
body? Is the object centered in vision or in the periphery?),

(6) Knowing the probable reaction of the object to my running away (will
that encourage it to attack? Can I run faster than it can? Would I do
better to run at it and scare it away?),
(7) Knowing my relative strength and weaponry (Should I just kick it
aside? Shoot it? Grab it and push it away?),

and so on.

Under the S-R concept, it's necessary for each possible factor in the
environment that might bear on the choice of appropriate output to be
sensed and taken into account in the computation of output. It's also
necessary that every factor capable of altering the sensory outcome be
known -- that is, that the causes of possible disturbances be sensed.
But the causes of many important disturbances are insensible -- the
first we know of them is that the sense vector in question starts to
change, apparently spontaneously. Such a spontaneous change implies that
the current action is incorrect -- but there is no previous cue to tell
the computer what action WOULD be correct.

So an S-R model that would actually account for behavior in the way you
suggest would be EXTREMELY complex. It would have to have precisely
calibrated senses which would maintain their accuracy over long periods
of time. It would need to infer things it could not sense. And it would
have to compute the inverse dynamics of all physical output effects,
taking into account (by sensing them) the masses of the moving parts,
the influences of external forces like gravity and inertial forces, the
friction in the joints, the state of metabolic energy supplies near the
muscles, and the interactions among all muscle forces being used at the
same time. It would have to be acquainted with all the relevant laws of
physics and chemistry and geometry.

A control system, on the other hand, needs to sense only the outcome
that is to be controlled. It must be connected so that for any change in
the monitored outcome, it can exert an output effect directly on the
outcome, opposing the change. There is no need to compute inverse
dynamics. There is no need to sense the causes of any disturbance of the
input. There is no need to predict the behavior of such causes. There is
no need to understand physical laws. A control system is, by a wide
margin, the simplest system for accomplishing the things we observe to
happen. It may still be complex enough to puzzle us, but the problems we
face in modeling behavior with control systems are NOTHING compared to
what we would face in building a working model of an S-R system -- that
had to work in a real environment.

Grandmother neurons are not a problem. After all, we already know about
"line tilted 22 degrees from the vertical" neurons, and "this is a
particular face" neurons (in sheep, yet). Of course beneath those
neurons where we measure signals corresponding to such entities there is
a lot of computation -- the neuron is just an output neuron. But there's
nothing in principle or in experimental fact to rule out the idea that
we might measure a signal at a particular neuron when and only when
grandmother is present. The "grandmother neuron" objection is one of
those facile concepts that is famous because it's so well known.

------------------------------------------

The disk will go in the mail today.
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Wolfgang Zocher --

I have tried to reply directly to the return address on your posts and I
get "illegal host address."

I am VERY interested in your program for assembling control systems
using modules. If you will send me the source code I'll see if I can get
it running, and offer any suggestions that seem useful. If we can work
out something with the required properties, I'll switch to using it in
my Primer series.
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