[From Rick Marken (961023.1320)]
Bill Powers (961023.0745 MDT)
I think those efferents in the optic nerve must be doing something to the
retinal perceptual systems. What, I can't imagine.
Bill Benzon --
But it probably has something to do with controlling perception.
Not necessarily. I think it is far more likely that the efferents in the
optic nerve are components of "image enhancement" operations (like lateral
inhibition). They might not even be components of control loops; for example,
the efferents may affect the firing rate of afferent neurons that have no
have no effect on the firing rate of these same efferents. I believe that
efferents worked this way in F. Ratliff's model of lateral inhibition in
Bruce Gregory (961023.1245 EDT) --
I don't find it [Martin's approach] fundamentally different from what you
are saying. The attractor notion _describes_ how systems behave, but it
does not _explain_ why they behave that way. That explanation is provided
by the dynamical equations (and the constraints).
Have you seen Bill Powers' "Gatherings" program in action? I think it's
available on Dag's PCT Demos disk. Anyway, in one scenario (called "guru" I
think) a bunch of dots (representing individuals) converge toward and
eventually encircle a stationary dot -- the "guru". I am thinking that a plot
of the dynamical pattern of movements of these dots resembles an "attractor".
I presume that are a set of dynamical equations -- the attractor "theory" or
equations -- which (with the appropriate parameter settings) can generate an
approximation to the dynamical behavior of the set of dots. These equations
do not (to my mind) provide an explanation of the behavior of the dots; they
simply describe the behavior of the dots.
I believe that these dynamical equations _do_ provide an explanation for
behavior in certain circumstances: in particular, I believe that the
"attractor" equations are an explanation of the behavior of physical
phenomena like, say the behavior of marbles converging toward and ending up
in a circle at the bottom of a bowl. In this case, the parameters and
variables of the attractor equations would correspond to the functional
relationships assumed to exist between actual and theoretical entities in
this situation. Some parameters in the model would correspond to the assumed
downward acceleration of gravity; others would correspond to the actual
initial positions and masses of the "dots", etc. In this case, the dynamical
"attractor" equations are simply a physical model of a particular situation:
one where a bunch of marbles all fall into a bowl at the same time.
To say that these dynamical attractor equations _explain_ the behavior of
the dots in the gatherings program (which I beleive _is_ what Martin is
saying) is a huge mistake. It implies that the behavior of the dots in the
"Gatherings" program - - social behavior -- results from the same kind of
dynamical interaction of forces and masses that causes the dynamical behavior
of the marbles. It is a way of sneaking (by methaphor) the cause-effect model
of physics in as an explanation of what is likely to be a phenomenon based on
Even if one is careful to remember that the description of the social
behavior using the attractor equations is just metaphorical, it is hard to
see what the value of such a metaphor might be. At best, it is just a
colorful and fun way of describing an observation; the dots in the
"Gatherings" program _do_ seem to be pulled toward an attractor -- the
guru. But at worst, it is misleading and directs attention away from the
basic explanation of all social behavior: control of perception.
I think Kent McClellend's approach to the study of social behavior shows
how much can be done by looking at this phenomenon in terms of PCT. Kent's
approach is based on observing how control systems _actually_ interact.
Rather than looking for metaphors for social behavior in the trendy science
literature, Kent actually sat down and started studying what happened when
control systems controlled the same or similar environmental variables. What
Kent has found (so far) is that different kinds of observed social
interactions, from cooperation to conflict, result when control systems
simply "do there thing" in a common environment.
Kent's is purely theoretical work (so far) -- just like Martin's. The
difference, to me, is that Kent is contributing worthwhile and new discoveries
about how control theory might actually relate to social behavior; and he's
doing it by studying how control systems actually interact. Martin is
contributing new discoveries about superficial similarities between control
theory and other theories that have nothing to do with control; I'm just not