[From Rick Marken (960624.1600)]
Me:
If we know the form of f(), we can determine the value of d(t) from our
measurements of p(t) and o(t). We can then say (as Martin says) that we are
able to do this because there is "information about d(t) in p(t)". This is
obviously just a weird way of saying that, if we know that p(t) = f(d(t)+
o(t)) and we know o(t), p(t) and f(), then we can solve for d(t).
Bruce Gregory (960624.1555 EDT) --
I'm not sure why you find this wording "wierd"
You're right. I guess it's not really weird to say it that way. If I know
that 2X = Y then when you tell me the value of X you are giving me
information about Y. I can handle that.
I don't think I would have flown off the handle about this "information"
stuff if Martin had said, right off, that the perceptual signal (analogous
to X in the above equation) contains information that a control engineer can
use to determine the effective disturbance (Y in the equation). I went
bananas because it seemed like Martin was saying that the perceptual signal
contains (or carries) information that the control system itself uses to
determine the effective disturbance acting on the controlled variable.
Me:
The only reason I can imagine why people might want to believe that control
systems work by "extracting" or "using" or "consuming" information in p(t)
is if they want to maintain the idea that input _drives_ or _guides_
behavior.
Bruce:
I don't perceive this motive in Martin's writings, but he can plead guilty
or innocent without my help.
I don't think it's a conscious motive (purpose); and that might not be
Martin's purpose at all. Another, more likely, possibility is that Martin's
purpose is to perceive the relevance of a beloved theory (information theory)
to another beloved theory (PCT). Many people do this -- try to find a place
for one or another of their favorite theories in PCT (or vise versa). I have
never seen a case where this can be done without violating some fundamental
fact about how perceptual control systems work. But I sympathize with efforts
to do this. I tried it myself for the first couple of years I was learning
PCT.
The problem with trying to reconcile PCT with theories that _seem_
consistent with PCT (from my point of view) is that it keeps one from
striking off in the new direction that PCT requires. I think it limits the
contribution one can make to developing PCT as a science. Not that Martin
hasn't made some wonderful contributions to PCT; I just have a feeling
that such well-meaning attempts at reconciling irreconcilable theories
prevents one from diving into PCT "all the way".
I think there is evidence that attempts to reconcile PCT with non-PCT
(typically input-output) appoaches to understanding behavior have stifled the
development of PCT science. For example, Jeff Vancouver's Psych Bulletin
paper describes research by a _huge_ collection of people who are presumably
studying purposive ("goal-oriented") behavior. But all of these people assume
(unconsciously) that the study of purposive behavior can be done in the
context of the beloved cause-effect model research. Of course, it can't (for
reasons Jeff [Jeff Vancouver (960624.1600)] apparently finds insufferably
boring -- and I have repeated these reasons enough that they surely are
boring by now) so tons of time and money are being wasted by people who think
that they are contributing to the PCT database when they are not. I think
this is most unfortunate.
I myself didn't start doing worthwhile PCT research until I _stopped_ trying
to reconcile PCT with all the conventional data and theories that I thought
were important. I don't know if Tom Bourbon has connected to this Net again
or not but I know that he would echo this sentiment. I knew Tom (a fellow
"ex" conventional psychologist) while he went through the same process I went
through; the painful process of realizing that PCT is a whole new ballgame
that is completely inconsistent with everything we had learned to take for
granted in psychology. But once we got thru the pain, we started doing some
pretty good work, I think;-)
Best
Rick