[From Bill Powers (930923.1930 MDT)]
Chris Wickens (9309xx) --
I'm gradually learning from experience: before we get into a big
discussion about PCT vs. the manual control literature, we're
going to have to get our terminology straightened out. We use
terms differently in PCT from the way they're used in standard
control engineering. When we say "input" we don't mean the
reference signal, but the variable being sensed by the system at
the input boundary between system and environment; when we say
"output" we don't mean the external variable that's under
control, but the immediate output of the system's effector(s)
acting on the local environment. The reason I suspect that we
have a terminology difference is this:
I dont think any of us in the serious manual control literature
believe that error (as defined explicitly by the difference
between the state of the output..its position and derivative --
and the input -- its position and derivatives) MUST
automatically be the signal that drives control (with an
opposite sign, to reduce the error).
If you mean "controlled variable" by output and "reference
signal" by input, then that paragraph makes sense. But if you
mean "effector output" and "sensory input" respectively, you're
not even describing a control system as we understand and model
it. We clearly have to get this sorted out.
One thing we do in PCT that's not explicitly done in standard
control engineering is to clearly delineate the input and output
boundaries between the active system and its environment, and
specifically identify the sensors and effectors involved, plus
their relationships to environmental variables. This is important
in modeling organisms in order to distinguish what is a property
of the organism from what is a property of its environment. The
environment part of a control loop can be defined in a way common
to all organisms, but the organism part may be different with
every different organism. In particular, a given condition of the
same environment may constitute an error for one control system,
and no error or the opposite error for another one.
Hans Blom has suggested that in talking with control engineers we
use the topology of their customary diagrams instead of our usual
"canonical" PCT diagram. So here's what I'm talking about in the
standard engineering form:
>>
ref ---- e ----- ||
signal->|comp|--->|outpt| || ----------
[INPUT] ---- |funct|--->---qo -->|f.b. funct|->
> ------ || ---------- |
percep>tual ORGAN- || |
sig>nal ISM ENVIRONMENT |
> ----- || |
---<---|input|<-------qi--------<--------
>funct> >> (controlled var)
----- || | [OUTPUT]
>> \
---<-- disturbance
The vertical double line is the organism-environment boundary,
the organism being on the left. The output function includes the
effector, which turns the error signal (e) into an output
quantity that is the physical effect on the immediate
environment. The state of the output quantity acts, through
properties of the environment represented as the environmental
"feedback function", on the variable that is to be controlled,
here labelled qi. The controlled variable is also subject, in
general, to independent disturbances, so its state is the sum of
two (or more) influences, one being the behaving system's own
action. Note that what we call qi, the input quantity or
controlled variable, is called in standard engineering
terminology the _output_. Also, in the standard engineering
diagrams, the output is shown coming directly out of the control
system without explicit mention of the effector or intervening
functions in the environment, and the input path generally
contains no explicit representation of the sensor.
In PCT, the input function senses the state of the input quantity
and converts it to a signal representing its state. This is the
perceptual signal, which enters the comparator. The other input
to the comparator is a reference signal (called _input_ in
standard engineering diagrams).
The perceptual signal represents the controlling system's only
knowledge of the controlled quantity. What the system controls,
therefore, is the state of the perceptual signal, not necessarily
the state of the external (observable to others) controlled
variable qi. If the sensor calibration drifts, the perceptual
signal will still be maintained in a match with the reference
signal, while the visible controlled quantity's value changes.
The variable most reliably controlled by this system is the
perceptual signal. Thus the name of my first book: _Behavior: the
control of perception_.
Notice where the error signal is in this system: inside the
organism. There is no error in the environment. The input
function does not detect any error conditions: it simply reports
the state of the controlled variable in the form of a perceptual
signal. Because the reference signal could be set to any value,
there is no "natural" error condition in the environment. What
states of the input quantity constitute an error depends entirely
on the setting of the reference signal inside the organism.
This is a "compensatory" configuration. In a "pursuit"
configuration the situation inside the organism is exactly the
same. However, the perception is now derived from two
environmental variables, one that is under direct feedback
control, and a second that is independent. The input function
senses the difference between the controlled environmental
variable and the uncontrolled one, reporting the difference
between them (in visual tracking, the distance between the target
and the controlled cursor). The perceptual signal now stands for
the magnitude of this difference. There is still no error in the
environment; the perceived difference is simply whatever it is.
It is the reference signal that specifies the desired difference,
which may or may not be zero. A person can just as easily keep
the cursor one inch to the right of the target as on it, by
setting the reference signal to a nonzero value corresponding to
one inch of separation.
So in PCT there is never an error in the environment, under any
conditions. The variable under control is defined by the nature
of the input function. The reference signal determines what state
of the resulting perception is the zero-error state. In the
hierarchical PCT (HPCT) model, the reference signal is the output
of a higher-level control system which acts to control its own
more abstract variable by varying the reference signal of the
lower-order system(s). There are many levels in the HPCT model,
related in this way.
You can see that the organization of the PCT model is identical
to that of normal control systems. The main difference is that we
don't define control in terms of objective consequences outside
the organism, but in terms of perceptions and their associated
reference signals. Reference signals are generated entirely
inside the organism; the only inputs from the outside world are
sensory inputs representing the states of environmental variables
which are defined by the nature of the input function involved.
Our "test for intentions" is really a general test to determine
what external variables the organism appears to be controlling.
Presumably, these are represented by perceptual signals. The Test
requires applying disturbances to the supposed controlled
variable, and seeing whether the system's output action varies so
as to have an equal and opposite effect on it. When such a
relationship is found, we presume that there is a perception
inside the system corresponding to the observable variable being
controlled relative to an internal reference signal. We can infer
the setting of the reference signal by finding the condition of
the controlled variable toward which the system's actions always
urge it in the presence of disturbances. The intention of the
system is the setting of its reference signal. The qualitative
aspect of the intention is defined by the nature of the
controlled variable found with the Test. The quantitative aspect
is the particular state in which the system appears to be
stabilizing that variable. The intentions or goals of the system
relate to perceptions, not objective aspects of the environment.
To intend is to intend to perceive.
This very brief summary doesn't get into hierarchical control, or
into "reorganization" which is the primary learning mechanism of
this model. But perhaps you can see enough of the PCT approach in
this to make some comparisons with the manual control approach.
I'd be very interested in seeing them.
ยทยทยท
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Best,
Bill P.