Bill's Gate IV

(This reply has also been posted from another email address,
it didn't show up within 12 hours, so I'm posting it again)

From Mervyn van Kuyen (971013 15:30 CET)

[Mervyn van Kuyen (971006 16:45 CET)]

My system does not reorganize itself whenever the input changes. It
organizes itself in such a matter that it can have the reference match
the sensory input to the greatest possible extent at all times (using
a single structure).

[Bill Powers (971008.0900 MDT)]

... if the system changed its
reference signal to match the perceptual signal, it would take no action,
because the reference signal would always match the perceptual signal...

I'm _not_ proposing a system that is always trying to make reference and
sensory input match by instantly changing its reference, that is just a
slow, more complex way of increasing the match between the two. (It will
completely change its reference when it detects that is has moved to a
different control 'context' of course - cf. the paper at my homepage:
www.xs4all.nl/~mervyn). The other, more powerful one is _control_, as I
explained at the end of the same paragraph:

[Mervyn van Kuyen (971006 16:45 CET)]

a single structure). But apart from this notion, my network is
'guilty as charged': it would not 'need' to act on its environment,
if (and only if) it had somehow acquired a perfect world model.
In practice however, it is very unlikely that it would manage to do so
without acquiring any _control_ skills. _Control_ is such a powerful way
to limit the required complexity of the reference (which is, in my
model, a complex transformation of the mismatch patterns).
Therefore, I see the development of a human being as a shift from
acquiring references to acquiring _control_ skills (for our developing
body and associated increase of our physical freedom).

So, let us not focus on this feature of 'changing references', but
on the issue of control and how:
(1) two-way control is exerted by the proposed comparator
(2) 'discrete signalling' can create 'sensory input controlling' behavior
(that our theories both seem to recognize as being essential, and model)

ยทยทยท

=======

Concerning issue (1), the 'two-way control' issue:

[Bill Powers (971008.0900 MDT)]

How does it know which way to adjust the reference signal when there is a
mismatch?

It does not adjust the reference signal, it will try to adjust the
perceived physical state of the world by means of physical actions. These
actions result in shifts of patterns over many inputs (and comparators),
in other words: maps. Since the system knows what its references are, it
knows whether it was creating a reference while there was no input signal
or the other way around (no other information is required for deciding eg.
which way to shift the maps for a better match - as I will explain below).

Concerning issue (2), the 'discrete vs analog signalling' issue:

So, yes, I assume signals to be ideally discrete, while (by using variable
thresholds) the amount of mismatch still has analog properties: an input
pattern (map) that is shifted 2 'pixels' to the left in relation to a
reference pattern results in mismatch that can be interpreted by neurons
that test for some amount of _misalignment_ - at their turn triggering
appropiate acts of physical control.

We seem to disagree, fundamentally, on the importance of analog signalling
in living systems and therefore you question the effectiveness of my
model, when applied to living control systems. I, on the other hand, would
question the biological plausibility of the parameters that a perceptual
control system (as proposed by PCT) can indeed control:

[Bill Powers (971008.0900 MDT)]

Try the problem of keeping the car on the road.

[Mervyn van Kuyen (971006 16:45 CET)]

What kind of sensors did you have in mind for this imaginary organism?

[Bill Powers (971008.0900 MDT)]

How about controlling the distance of the map from your eyes, or its
orientation with north at the top, or its state of being folded up? How
about the perceived shape of a piece of clay you're using to sculpt a face?
In short, how about perceptions of the world around you? You have millions
of sensors in many modalities, all of which present you with a smoothly
changing picture of the world, and many of which you can bring to specific
states through continuous action on the world (as in steering a car).

People don't have sensors that explicitly provide us with a distance. Yes,
those millions of sensors present us changing pictures (maps) of the
world, but as I mentioned in the same reply: the errors that would have to
be corrected for these mismatch detections involve _transformations_ (eg.
shifts) of these _maps_, not the _adjustment_ of continuous parameters in
_single neurons_.

[Bill Powers (971008.0900 MDT)]

Yes, there are discrete variables that we control by logical means. But
they are a minority: most controlled variables involved in ordinary life
are continuously variable, and can be maintained anywhere in a continuum

of

values.

Please show me why sticking with discrete maps is such a restriction for a
real world control example: I can't think of one in which the essential
control parameter is directly picked up (eg. distance) by a _single_
sensor!

Regards, Mervyn